Abstract and Figures

eScience demands large-scale computing clusters to support the efficient execution of resource-intensive scientific applications. Virtual Machines (VMs) have introduced the ability to provide customizable execution environments, at the expense of performance loss for applications. However, in recent years, containers have emerged as a light-weight virtualization technology compared to VMs. Indeed, the usage of containers for virtual clusters allows better performance for the applications and fast deployment of additional working nodes, for enhanced elasticity. This paper focuses on the deployment, configuration and management of Virtual Elastic computer Clusters (VEC) dedicated to processs scientific workloads. The nodes of the scientific cluster are hosted in containers running on bare-metal machines. The open-source tool Elastic Cluster for Docker (EC4Docker) is introduced, integrated with Docker Swarm to create auto-scaled virtual computer clusters of containers across distributed deployments. We also discuss the benefits and limitations of this solution and analyse the performance of the developed tools under a real scenario by means of a scientific use case that demonstrates the feasibility of the proposed approach.
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Container-based Virtual Elastic Clusters
Carlos de Alfonsoa,
, Amanda Calatravaa, Germ´an Molt´oa
aInstituto de Instrumentaci´on para Imagen Molecular (I3M)
Centro mixto CSIC - Universidad Polit´ecnica de Valencia - CIEMAT
Camino de Vera s/n, 46022, Valencia
Corresponding author: Tel. +34963877356
Email address: caralla@upv.es (Carlos de Alfonso)
Preprint submitted to Journal of Systems and Software December 12, 2016
eScience demands large-scale computing clusters to support the efficient execu-
tion of resource-intensive scientific applications. Virtual Machines (VMs) have
introduced the ability to provide customizable execution environments, at the
expense of performance loss for applications. However, in recent years, con-
tainers have emerged as a light-weight virtualization technology compared to
VMs. Indeed, the usage of containers for virtual clusters allows better perfor-
mance for the applications and fast deployment of additional working nodes, for
enhanced elasticity. This paper focuses on the deployment, configuration and
management of Virtual Elastic computer Clusters (VEC) dedicated to processs
scientific workloads. The nodes of the scientific cluster are hosted in contain-
ers running on bare-metal machines. The open-source tool Elastic Cluster for
Docker (EC4Docker) is introduced, integrated with Docker Swarm to create
auto-scaled virtual computer clusters of containers across distributed deploy-
ments. We also discuss the benefits and limitations of this solution and analyse
the performance of the developed tools under a real scenario by means of a
scientific use case that demonstrates the feasibility of the proposed approach.
Keywords: Computing, Containers, Cluster Computing, Elasticity
1. Introduction
eScience involves the execution of complex HTC (High Throughput Com-
puting), HPC (High Performance Computing) applications and long-running
workflows. This requires a significant amount of computing power and memory
capacity that can be only obtained via distributed computing. Indeed, large-5
scale Distributed Computing Infrastructures (DCIs), such as the European Grid
Infrastructure (EGI)1have been tremendously successful in supporting the com-
putational requirements of many scientific communities across Europe [1, 2].
However, one of the main limitations of Grid infrastructures is that applica-
tions have to be ported to the execution environments provided by the ma-10
chines involved, what results in a rigid structure composed by several Virtual
Organizations (VOs) that support a set of applications. This inability to pro-
vide customized execution environments for applications is addressed by Cloud
Computing by means of Virtual Machines (VMs) that encapsulate the Operat-
ing System (OS) together with the user application and its dependences in a15
Virtual Machine Image (VMI) that can be run on a physical machine by means
of a hypervisor.
Indeed, the ability to provide ubiquitous, on-demand network access to a
set of configurable computing resources, according to the NIST definition [3]
of Cloud Computing, has paved the way for the rise of many public Cloud20
providers (such as Amazon Web Services (AWS)2, Microsoft Azure3or Google
Cloud Platform4), different Cloud Management Frameworks (such as OpenNeb-
ula or OpenStack) and even initiatives to create large-scale community Clouds
(e.g. EGI Federated Cloud5). Cloud computing has provided researchers with
access to unprecedented customizable computing resources, either on-premises25
or on public Clouds. However, these computing resources still require a coor-
1European Grid Infrastructure: http://www.egi.eu
2Amazon Web Services: https://aws.amazon.com
3Microsoft Azure: https://azure.microsoft.com
4Google Cloud Platform: https://cloud.google.com
5EGI Federated Cloud: https://www.egi.eu/federation/egi-federated- cloud/
dinated use for applications to efficiently use them. For that, Local Resource
Management Systems (LRMS) such as Torque [4], SLURM [5] or HTCondor [6]
are job schedulers that are commonly used to dispatch jobs across nodes [7].
Indeed, computing clusters are still widely-used computing facilities to support30
the execution of many types of applications.
A scientific computing cluster is a type of parallel or distributed processing
system, which consists of a collection of interconnected stand-alone computers
working together as a single integrated computing resource [13]. The access to a
scientific cluster is usually made by means of a SSH connection to a “front-end”35
computer, and the users submit tasks to a middleware that will coordinate the
working nodes to run these tasks. All the computers usually share filesystem to
ease the distribution of the applications and the data that they need.
Virtual Elastic scientific computer Clusters (VEC) deployed on Cloud infras-
tructures have introduced many benefits when compared to physical clusters,40
as we addressed in our previous work [8], avoiding upfront investments and the
ability to adapt the execution environment to the applications (and not vicev-
ersa). This work was later extended to create EC36[9] an open-source tool to
create self-managed cost-efficient virtual hybrid elastic clusters across Clouds
that is currently offered as a free online service, being used for scientists to45
provision their own clusters on public, on-premises and federated Clouds.
In the quest for increased performance with respect to virtualisation tech-
niques, Linux containers appeared as a lightweight alternative to VMs. Linux
containers enable to run multiple isolated processes in a host without the over-
head caused by the hypervisor layer introduced by VMs. While hypervisors50
provide hardware abstraction, container-based virtualization is characterised by
multiple isolated user spaces running at the operating system level (see Figure
1). This provides process isolation at a fraction of the overhead introduced by
the hypervisor. Container-based virtualization proved to be an alternative to
traditional hypervisor-based systems, as it reduces the overhead caused by VMs55
6EC3 (Elastic Cloud Computing Cluster): http://www.grycap.upv.es/ec3
App App
Container Container
App App
Apps hosted in VMs Apps hosted in containers sharing
the same VM Apps hosted in containers
Libs Libs
Libs Libs
Container Container
App App
Libs Libs
Host OS Host OS Host OS
Figure 1: Virtual Machines and Containers possible architectural configurations.
in CPU, memory and storage, as described in [10] and [11]. Linux containers
can be run on top of VMs to achieve multi-tenant isolation using the VM as
the boundary of security and containers as the boundary of resource allocation
to applications. However, the main benefits of containers arise when used on
bare metal, in order to obtain increased performance compared to VMs. Among60
the different existing container platforms, Docker7stands out as a software con-
tainerization platform that can encapsulate an application in a complete filesys-
tem that contains all the dependences required to be executed (code, runtime,
system tools and libraries, etc.). This guarantees portability across multiple
platforms, regardless of the execution environment.65
Our hypothesis is that container-based technology can be effectively in-
tegrated with cluster-based computing to create virtual computer clusters of
Docker containers with the very same functionality as virtual clusters of VMs,
and physical clusters of PCs, but with enhanced capabilities that include: i)
7Docker: https://www.docker.com
improving the performance of resource-intensive applications that will run iso-70
lated on bare metal; ii) improving the elasticity of the cluster, by reducing the
time required to spawn and terminate additional containers and iii) supporting
customised execution environments via low-footprint images.
Therefore, this paper introduces an architecture to deploy container-based
virtual scientific computer clusters that feature automated elasticity and the75
ability to provide customised virtual execution environments across a bare-metal
backend on which containers managed by a Container Orchestration Platform
(COP) are executed. Several computer clusters customised for the execution
of different scientific applications can be provisioned to share the same physi-
cal computing backend. This provides increased resource utilisation and per-80
formance while maintaining isolation across workloads coming from different
To this aim, this paper describes EC4Docker8, an open-source tool to deploy,
configure and manage container-based virtual computer clusters that can be run
on bare-metal nodes (as well as on VMs). These virtual computer clusters expose85
the very same user interfaces expected by users (accessed via SSH, supporting
a LRMS, etc.) but they are completely backed by Docker containers that are
dynamically deployed, depending on the workload, across a distributed Docker
Swarm [12] backend that can be deployed either on bare metal or on public and
on-premises Clouds.90
After the introduction, the remainder of the paper is structured as follows.
First, section 2 introduces background information and covers the state of the
art related to containers, revising existing tools, performance studies and clus-
tering solutions of containers. Next, section 3 exposes and analyses the proposed
architecture to deploy these container-based virtual computer clusters. Then,95
section 4 addresses different scenarios in which the proposed solution is evalu-
ated and analyses the significant benefits of these approach. Finally, section 5
summarises the paper and points to future work.
8EC4Docker is available in https://github.com/grycap/ec4docker
2. Background and Related Work
According to Buyya [13], a computer cluster is a type of parallel or dis-100
tributed processing system, which consists of a collection of interconnected
stand-alone computers working together as a single integrated computing re-
source. The key components of a cluster include:
(i) Multiple Computers. Typically one of them (named the “front-end node”)
acts as an entry point to the computer cluster and the others execute the105
jobs (named the “working nodes”).
(ii) Operating Systems (OS). In scientific computing, the most common oper-
ating systems are Linux or Unix-based.
(iii) Interconnection network. The computers interact among them through a
local network. There may exist different networks specialised for different110
tasks (e.g. data, parallel processing, etc.) based on different technologies
for computers to communicate (e.g. Myrinet, GbE, 10GbE, etc.).
(iv) Cluster middleware. The cluster middleware, also known as LRMS, is a
set of tools to use the cluster as a single computing entity. These tools
carry out the whole lifecycle of executing a job in the cluster (e.g. staging115
the files in the working nodes, starting the applications, retrieving the
resulting files, etc.). Some examples of well known LRMS are Torque,
(v) Parallel programming environments. Applications typically use well-known
libraries to communicate between processes. Some examples are Open-120
MPI, LAM/MPI or MPICH, which support the Message Passing Interface
(MPI) standard. These libraries are usually optimised for the specific net-
work interfaces (e.g. SCI, Myrinet, etc.).
(vi) Applications. These are the user applications executed in the computer
The main interface employed by the users of the cluster is an interactive ses-
sion to the front-end node in order to submit jobs to be executed on the working
nodes [14]. Indeed, computer clusters used to be huge physical infrastructures,
but advances in virtualization technologies and Cloud computing paved the way
for Virtual Clusters (VCs) to appear. A VC is comprised of VMs and a virtual130
networking environment. The other components in a VC are the very same that
those used in the physical cluster. These VCs can be deployed in on-premises
infrastructures or in commercial public Clouds.
A VC relies on VMs even if they are not used (i.e. they are idle). These
idle virtual working nodes are a problem in a Cloud environment because (a) in135
case the cluster is deployed in an on-premises infrastructure, other users cannot
take advantage from the unused resources allocated to the VC, or (b) in case
the cluster is deployed in a public Cloud, the unused resources result in an
economic cost for the user. An Elastic Virtual Cluster (EVC) avoids wasting
either resources or money, by destroying the idle working nodes and deploying140
them again when they are needed. In order to implement an EVC, an elasticity
manager is required to take care of creating or destroying the working nodes,
depending on the workload.
The work described in this paper is a step forward on computer cluster
virtualization, that builds on container-based virtualization to reduce the per-145
formance penalty introduced by VMs. The goal for a container-based EVC is
to provide the users with computer clusters to be used as if they were physical
computing clusters, with the added value of using containers instead of VMs.
Therefore, the requirements for the container-based EVC is to preserve the very
same environment and usage patters that are commonly used in this computing150
platforms, i.e. the software stack: the OS, the cluster middleware, the parallel
environments and the applications, as shown in Figure 2.
The next section includes a review of related works about the different tech-
nologies that lie within the scope of this work.
Container technology
Container Orchestration Platform
Working node
LRMS Agent
Working node
LRMS Agent
Working node
LRMS Agent
Front-end node
Manager LRMS
Container Container
Working node
LRMS Agent
Physical Infrastructure
SSH/Web Client
Launch jobs
Figure 2: Generic architecture to deliver container-based virtual elastic computer clusters
deployed on a computing infrastructure managed by a Container Orchestration Platform.
2.1. Related work155
2.1.1. Containers
Container technologies have gained significant momentum in the last years,
introducing changes in the way applications are built, shipped, deployed, and
instantiated [15], [16]. There exists different software available to create Linux
containers, as is the case of Linux Containers (LXC) [17] and LXD [18], rkt160
[19], OpenVZ [20], Linux-VServer [21] and Docker [22]. In particular, Docker
turned containers into a mainstream technology, contributing: i) Docker Hub, a
global shared repository of Docker containers; ii) a procedure to create Docker
images out of Dockerfiles and iii) the usage of a layered file system that reduces
the footprint of Docker images. Docker containers use cgroups, a feature in the165
Linux kernel that allows to constrain the resources (e.g. CPU, memory and
network) consumed by a process together with namespaces to provide processes
with their own view of the system. In our case, the containers will correspond
to the working nodes that compose the VC.
2.1.2. Container Orchestration Platforms170
The ecosystem of applications around Docker has exploded in the last years
[23], with contributions in many areas such as Continuous Integration/Continuous
Delivery (CI/CD), application packaging and Container Orchestration Plat-
forms (COPs). Indeed, there are many applications to manage the execution of
containers across multiple hosts. For example, Kubernetes [24] is an open source175
orchestration system for Docker containers. It handles scheduling onto nodes
in a compute cluster and actively manages workloads to ensure that their state
matches the user’s declared intentions. The scheduling in Kubernetes is based in
Pods. These are groups of containers that are deployed and scheduled together.
Pods form the atomic unit of scheduling in Kubernetes, as opposed to single180
containers in other systems. Containers within a pod share an IP address, and
labels can be used to identify each group of containers. Apache Mesos [25] can
be used to deploy and manage applications inside containers in large-scale clus-
tered environments. The architecture of Mesos is designed to be high-available
and for that uses ZooKeeper. Mesos, in combination with a job system like185
Marathon [26] or Chronos [27], takes care of scheduling and running jobs and
tasks, that can be run in containers or directly in the nodes of the cluster. Fi-
nally, Docker Swarm [12] represents the native clustering approach proposed by
Docker, which “provides native clustering capabilities to turn a group of Docker
engines into a single, virtual Docker Engine”. This way, a container-based VC190
can be easily created on top of virtual or physical resources. The architecture
of Docker Swarm consists of hosts running a Swarm agent (working nodes) and
one host running a Swarm manager. The manager is responsible for the orches-
tration and scheduling of containers on the hosts. Moreover, Docker Swarm can
be run in a high-availability mode where either etcd, Consul or ZooKeeper is195
used to handle fail-over to a back-up manager. We opted for Docker Swarm due
to its easy integration with the Docker CLI (Command-Line Interface).
Notice that COPs are used to manage the execution of containers in a cluster.
The user describes the container, and the COP selects which of the physical
host is going to perform the execution of the container. Therefore, these tools200
represent for containers a similar concept than a LRMS (e.g. Torque, SLURM,
etc.) is for jobs in a computer cluster.
Notice that one could use the interfaces provided by a COP to directly deploy
containers that run their jobs on a set of computing resources. However, this
approach would be disruptive for traditional users of computer clusters since205
their usage patterns would significantly change. They would have to use client-
side tools to interact with such COPs and deal with data staging in the COP,
instead of performing an interactive session via SSH to the cluster. Instead, the
clusters deployed via EC4Docker maintain the very same user experience and
interfaces exposed by traditional computer clusters (e.g. SSH-based access to210
the front-end node).
2.1.3. Reducing overhead of VMs using Containers
There are studies in the literature that analyse the overhead of containers
for the execution of applications. In [10], the authors explore the performance of
traditional VM deployments and contrast them with the use of Linux containers215
(using Docker). Several benchmarks are used to demonstrate that containers
result in equal or better performance than VMs in terms of CPU, memory and
storage. The study covered in [28], analyzed the performance of three well known
open-source tools (KVM, OpenVZ, and Xen) in the context of HPC. The results
showed that the solution that offers near native CPU and I/O performance was220
OpenVZ. Other works in the literature have also analyzed the performance of
containers to execute scientific applications and workflows, such as [29] and [30].
Skyport [31] utilizes Docker containers to execute scientific workflows instead
of VMs, reducing the overhead caused by VM virtualization. Also, analysis
of the requirements of the applications to be executed in containers have been225
performed [32]. Because container-based virtualization works at the operating
system level, all instances (containers) share the same operational system ker-
nel. That is why container-based virtualization has a weaker isolation when
compared to hypervisor-based virtualization [33]. In order to guarantee the re-
source isolation between the host system and the containers running on, such a230
system implements kernel namespaces. However, using containers for security
isolation might not be a good idea [34]. The only way to have real isolation
with Docker is to either run one container per host, or one container per VM,
at the expense of a performance overhead. Nevertheless, for security reasons, it
might be worth sacrificing the performance of a pure-container deployment by235
introducing a VM to obtain true isolation.
Containers can run on VMs too, although such double virtualization imposes
performance overheads. In [35] authors investigate container-based technology
as an efficient virtualization technology for running high performance scientific
applications. They used Docker containers and VMs created using OpenStack to240
execute a molecular modeling simulation software. Results show that container-
based systems are more efficient in reducing the overall execution times for HPC
applications, because they can be deployed in a remarkable minor time and have
better memory management for multiple containers running in parallel.
2.1.4. Virtual computer Clusters245
Concerning the use of VC, several well-known tools already exist in the lit-
erature to deploy them, such as StarCluster [36], Elasticluster [37] and EC3 [9],
but all of them are based on the deployment of VMs. Concerning the creation
of VCs based on containers, studies like [38] analyzed and compared some of
the container technologies available to the community (Linux-VServer, OpenVZ250
and LXC) from the point of view of MapReduce workloads, executing several
benchmarks to test their performance and manageability. The results show
that container-based systems reached near-native performance though LXC of-
fers the best relationship of performance and isolation. The study covered in
[39] present the results of deploying Docker containers in a cluster environment255
when compared to the KVM hypervisor and an evaluation of its suitability as a
runtime for high performance parallel execution. The results showed that con-
tainers can be used to tailor the runtime environment for an MPI application
without compromising performance, and provide better Quality of Service for
users of scientific computing. The developers of a Linux-VServer address in [40]260
a container-based cluster management platform in which Docker and HTCon-
dor work together to execute scientific workflows. The results obtained from
executions of a Monte-Carlo simulation showed that Docker had a near native
performance comparing with a hypervisor-based virtualization solution.
To our knowledge, there are no works in the literature that feature the265
adoption of Docker containers to create VCs that provide users with the very
same execution environment (e.g. LRMS, client tools) typically available in
both physical clusters and virtual clusters of VMs. This pioneer approach allows
users to access well-known computing facilities, i.e. clusters of PCs, on top of
the lightweight virtualisation provided by containers in order to take profit from270
enhanced performance and fast elasticity.
3. Elastic Cluster for Docker (EC4Docker)
EC4Docker is an open-source tool that deploys Docker container-based Vir-
tual Elastic computer Clusters (CVEC). The cluster delivered by EC4Docker
consists of a Docker container that acts as the front-end node of the cluster,275
and a set of containers that act as the working nodes. The front-end container
behaves as a regular front-end in a cluster: it is accesible by SSH, has installed
a LRMS such as Torque or SLURM, and it shares its file system to the working
nodes using NFS (Network File System). The working nodes of the EC4Docker
cluster are also containers that behave like regular working nodes in a clus-280
ter: they are accesible from the front-end using password-less SSH, they are
integrated in the LRMS, and they mount the shared file system.
The novelty of EC4Docker is that the front-end of the cluster is able to cre-
ate and to destroy the internal nodes depending on the workload. This ability
is possible due to: i) the integration of the CLUES9[41] elasticity manager that285
decides when to power on or off the internal nodes and ii) a plugin for CLUES
that has been developed for EC4Docker, that makes it possible to translate the
commands to power on and off of the internal nodes into the proper Docker
instructions that create and destroy Docker containers. This plugin takes ad-
vantage from the ability of Docker to be used remotely by exposing its API290
9CLUES: https://github.com/grycap/clues
through a standard TCP/IP socket.
The concept of a container-based cluster as-is may be useful for prototyping,
as the containers are conceived to be ran in a specific host. However, in the
case of EC4Docker, it is integrated with Docker Swarm, which behaves as a
scheduler that manages a set of Docker hosts as a single entity. It works together295
with a discovery service, such as Consul10, that provides high availability to
the underlying Docker Swarm cluster. Using Docker Swarm, when a Docker
container is created, it is deployed in any of the Docker hosts managed by
the Swarm. Using this combination of EC4Docker and Docker Swarm, it is
possible to deploy the containers that build the CVEC across multiple hosts300
which, by the way, can be either physical or VMs. It is important to point that
other COPs could be employed instead of Docker Swarm, such as Kubernetes
or Apache Mesos as well as managed services for the deployment of containers
such as Amazon EC2 Container Service11.
3.1. Features of the Container-based Virtual Elastic Computer Cluster305
As stated earlier, using EC4Docker, the users are delivered a computer clus-
ter with the tools that they typically use, and they do not need to change the
way of interacting with the cluster. They access the cluster using SSH, where
they find the LRMS to which jobs can be submitted as usual. The LRMS is not
aware of any container and the applications require no modifications.310
However, even experienced users in traditional computing clusters can ben-
efit from the CVEC, because these are useful to create the specific execution
environment for their applications. Docker containers are commonly employed
to ease the distribution of applications: using Dockerfiles in Docker, users can
create the container images that include their application along with the re-315
quired libraries, the most appropriate OS distribution, etc. Starting from that
Docker image, the administrator will include the EC4Docker Dockerfiles that
10Consul: https://www.consul.io
11Amazon EC2 Container Service: https://aws.amazon.com/es/ecs/
will create the EC4Docker CVEC that will be delivered to the user.
Using this approach, although the underlying infrastructure is shared by all
the CVEC, different configurations can be employed. For example, a cluster320
based on Ubuntu 16.04 and the Torque LRMS can coexist and share the same
underlying computational resources with a Scientific Linux cluster whose jobs
are scheduled by SLURM. It is important to point out that this feature can be
very beneficial for the execution of software applications that are incompatible
with each other, without needing to physically isolate the resources. Therefore,325
the bare-metal physical nodes are shared by all the clusters deployed in the
infrastructure, where the container-based working nodes will be deployed to
execute the jobs of each cluster.
EC4Docker is not only useful for CPU-oriented applications. In case the
applications require access to specific devices, such as GPGPUs, it is possi-330
ble to instruct EC4Docker to allow the Docker containers to access these de-
vices. On the one hand, in the case of homogeneous configurations where all the
physical nodes have a GPGPU, EC4Docker can be instructed to automatically
mount that device inside the container to expose it to applications. In this case,
EC4Docker will use the Docker mechanisms to enable the applications to use335
the GPGPUs available in the physical hosts. For this, the container has to sup-
port the specific libraries and drivers required to use the GPGPU. On the other
hand, in the case where only a subset of the physical nodes have a GPGPU,
rCUDA [42] can be used in order to turn those nodes into servers that provide
GPU services to the container nodes that actually execute the applications. The340
applications do not require source code modification since the rCUDA runtime
takes care of the details of routing requests to the specific hardware device.
3.2. Behaviour of a container-based virtual elastic cluster
Figure 3 describes the designed architecture employed to deploy CVECs on
top of a physical infrastructure, as an instantiation of the general architecture345
shown in 2. Therefore, the workflow to create the CVEC follows the next steps:
1. Preparation of the Docker images. The preparation of a CVEC starts with
the creation of the Docker images that will be used to create the front-
end and the working nodes, and its instrumentation using the EC4Docker
Dockerfile fragments.350
2. Creation of a network for the container. The CVEC needs a network for
containers to communicate. In case of using Docker Swarm, an overlay
network that spans across the different sites is required. This overlay
network enables different hosts to become part of a swarm and assign
non-overlapping IP addresses to their containers to enable communication355
among them. There is the option of using a single overlay network shared
among all the containers from all the CVECs, or to create per-cluster
networks in order to isolate the different CVEC. The overlay network
is used to virtualize the interconnection network for the creation of the
3. Creation of the CVEC. The creation of the cluster consists of deploying the
container that will act as the front-end of the CVEC. Since we want the
computer clusters to span across multiple hosts, the request to create the
container will be submitted to the Docker swarm front-end. A container
will be instantiated out of a Docker image created from the EC4Docker365
Dockerfiles, which include an installation of CLUES and the LRMS chosen
by the end-user (SLURM or Torque). The containers are used to virtualize
the working nodes for the creation of the CVEC.
4. Enable external access to the cluster. In order to access the cluster using
SSH, the IP address of the front-end node of the CVEC is required. How-370
ever, the IP addresses in the Docker swarm cluster will be private to the
overlay network for the cluster and, therefore, they are not accesible from
outside networks. To solve this problem, we use IPFloater12, a tool able
to redirect the traffic from a public IP to a private IP inside a local area
network (LAN), thus, simulating the floating IPs offered by OpenStack375
12IPFloater is available at https://github.com/grycap/ipfloater
Docker Hub
SSH/Web Client
User’s side
Main node (Public IP)
Physical Infrastructu re
Connect and launch jobs
Docker Swarm
Docker Host
Working node
Docker Host
Working node
Docker Host
Docker Swarm Node
Docker Swarm Node
EC4Docker WN
EC4Docker WN
EC4Docker WN
EC4Docker front-end
Admin’s side
Overlay network
Create Docker
Figure 3: Architecture of a container-based virtual elastic cluster deployed on top of a
physical infrastructure and managed with Docker Swarm and EC4Docker.
Once the workflow has finished, the user is provided with the IP address of
the front-end node of the CVEC. Then, the end users can connect to the cluster
via SSH or by means of a web browser (in case of accessing a web application
like the Galaxy Portal [44]) and submit their jobs to the selected LRMS as they380
would do with a physical cluster.
The CVEC deployed using EC4Docker dynamically manages the size of the
cluster, with the novelty of running the jobs that are going to be executed in the
container-based working nodes, instead of using the traditional VM-based work-
ing nodes. This way, jobs will enjoy the advantages of light-weight virtualization385
with a reduced overhead in CPU and memory.
The self-managed elasticity is carried out by CLUES (step 5 in Figure 3),
that forms part of the container image used by EC4Docker to deploy the front-
end of the cluster. CLUES running inside the EC4Docker container detects
job submissions to the LRMS in the container-based cluster. Then, if there390
are no available nodes to satisfy the requirements of the job, it requests an
EC4Docker node container to the Docker Swarm Manager. This container will
be deployed by Docker Swarm in one of the bare-metal nodes that compose the
infrastructure, and will act as a container-based working node of the computer
cluster, automatically integrated in the LRMS. The EC4Docker node container395
will be also connected to the overlay network specifically created for the CVEC,
interconnecting the new container with the rest of the CVEC.
As we have mentioned, the scheduling of the location of the containers that
represent the cluster is carried out by Docker Swarm. Docker Swarm works with
rankings to decide where to execute the container. The node with the highest400
ranking is the one that is chosen to run the new container. The policies offered
by Docker Swarm are: spread (default), binpack and random. The first two
policies care about the number of containers deployed in the node and the CPU
and RAM free for each node, while the latter policy (random) simply returns
a random value for each node. Through the spread policy, the node chosen to405
host the new container depends on the number of containers running on the
node, regardless of their status. With the same resources (CPU and RAM), the
node that has fewer containers will run the new container. The binpack policy,
on the other hand, tries to pack the containers in a node, trying to leave free
enough space in other nodes to hold containers with higher requirements. Thus,410
it avoids fragmentation. It is noteworthy that, for all the policies, if all nodes
get the same ranking, the election is performed randomly.
3.3. Elasticity Rules
As stated earlier, elasticity in EC4Docker is managed by CLUES. This soft-
ware implements different policies that aim at balancing the trade-off that arises415
when trying to minimize the waiting time for the jobs (which involves a larger
number of available nodes) and the minimization of the infrastructure cost,
which involves a reduced number of nodes, which generate a cost in electricity
(for physical infrastructures) or in resources (for public cloud providers). In
the context of containers, the creation of a container results in less available420
resources for the subsequent containers deployed on the same host. Therefore,
it is important to submit the containers only when they are really necessary.
The policies implemented by CLUES can be divided in two groups: the
policies used to decide when to increase the capacity of the cluster (scale-out)
and those used to decide when to decrease the size of the cluster (scale-in).425
Regarding the scale-out policies, CLUES can interact with the LRMS at two
levels. On the one hand, it intercepts the submitted jobs before they reach the
LRMS. On the other hand, CLUES also monitors the queued jobs at the LRMS
to check if these jobs require additional nodes to be added to the cluster. The
policies available are:430
1:1 start. For each job launched, if no working nodes are available for its
execution, then a new node is deployed. Therefore, the jobs will wait for
the deployment of the node before they start their execution.
Group-based start. Every time a new node is required, a group of them
are started. This policy assumes a workload model in which as soon as a435
job reaches the LRMS, there is a high probability that other subsequent
jobs will be submitted in a short period of time. By over-provisioning a
larger number of nodes, the waiting time of the subsequent jobs will be
In order to decide when to shutdown a node (scale-in policies), the strategy440
is to remove a node from the computer cluster when it has been idle for a
specified amount of time. The selection of this time depends on the workload
of the computer cluster and it is important to achieve a good trade-off between
the used resources and the waiting time of the jobs. These are the available
Queued jobs. Idle working nodes are terminated when there are no
pending jobs in the LRMS.
Delayed shutdown. Idle working nodes are terminated after a certain
amount of configurable time. This is of interest when using public Clouds
that bill by the hour, where idle nodes are kept available for job executions450
before the hour expires, even if no jobs are available to be executed at the
Keeping some nodes always active. The computer cluster will have
a set of nodes deployed waiting for jobs. This way, the computer cluster
tries to prevent incoming jobs from waiting while nodes are started.455
4. Case study
In order to assess the effectiveness of the self-managed CVECs deployed with
EC4Docker, we present a case study based on a bioinformatics community of
users that need to execute several scientific tasks for their research. In partic-
ular, the application used is MrBayes [45] (Bayesian Inference of Phylogeny).460
MrBayes is a program for Bayesian inference and model choice across a wide
range of phylogenetic and evolutionary models. MrBayes uses Markov Chain
Monte Carlo (MCMC) methods to estimate the posterior distribution of model
parameters. MrBayes has several dependencies in order to work properly, like
an MPI implementation or the Beagle library. For this, we installed, among oth-465
ers, OpenMPI together with the gcc compiler. The case study also analyzes the
performance of containers comparing to VMs, trying to prove the advantages of
light-weight virtualization in contrast with traditional virtualization based on
The overall scenario consists of three different executions of the same job470
pattern submission (represented in Figure 4 (a)) on two different computing
scenarios, but on top of the very same physical resources. On the one hand,
scenario a) involves a container-based virtual computer cluster managed by
EC4Docker. All the containers submitted during this execution were limited
to 1 CPU and 1 GiB of RAM. On the other hand, scenarios b) and c) involve475
a VM-based virtual computer cluster deployed on an OpenNebula on-premises
Cloud by means of EC3. Each one is configured with different idle times to
trigger the scale-in policy: scenario b) is configured with a maximum value for
idle nodes of 1800 seconds (30 min.), and scenario c) will power off nodes that
were idle for more than 600 seconds (10 min.). Each VM deployed has 1 CPU480
and 1 GiB of RAM. The VMI employed is based on Ubuntu 14.04 LTS. In this
case, two different executions for each configuration were carried out, one in
which the software is dynamically deployed on vanilla VMs and the other in
which the software (SLURM, OpenMPI, NFS, MrBayes and its dependencies)
is pre-installed in the VMI, thus reducing the time for contextualization, i.e.,485
installation and configuration of the software applications. This last option was
the one chosen to compare with the execution of the container-based cluster,
since Docker containers are created out of pre-configured Docker images.
The physical infrastructure used to deploy the case study is the same for
both scenarios for the sake of a fair comparison. It comprised eight physical490
nodes with a total of 224 cores (28 cores per node), 512 GB of RAM (64 GB of
RAM per node) and a shared storage system of 16 TB. For the scenario a) we
deployed Docker Swarm and the main node includes the IPFloater tool in order
to associate a public IP to each container-based front-end. In scenarios b) and
c), an OpenNebula 4.8.0 on-premises Cloud deployment is used.495
The limit size of the cluster was fixed to 6 nodes in all scenarios. A total of
15 Bayesian tasks with an average duration of 17.5 minutes is executed for each
test. The dataset employed is cynmix.nex [46], a partitioned dataset consisting
of data from four genes and morphology for 30 taxa of gall wasps and outgroups.
The number of generations has been fixed to 170.000. The following subsections500
describe and analyze the obtained results for this case study.
4.1. Results
First, we analyzed the time differences in the deployment and contextual-
ization processes for both containers and VMs used in our case study. Table 1
shows the average times for the deployment, configuration and execution times505
for the three scenarios. As we expected, the total average times for both the
front-end (FE) and working nodes (WN) were considerably higher with VMs,
even if we use a preconfigured VMI with SLURM, NFS, OpenMPI and Mr-
Bayes dependencies previously installed. In the last case, it was still necessary
to configure the SLURM configuration files, NFS system, and the application510
MrBayes, that takes an average time of [335-340] seconds in the case of the front-
end and [284-285] seconds for the working nodes. Even so, the time consumption
during the contextualization process was reduced significantly by starting from
a preconfigured VMI (about 65%). However, preparing a customized VMI is
not a trivial task so non-experienced users would refrain from using EC3 if they515
01000 2000 3000 4000 5000 6000 7000 8000
time (s)
(a) Job pattern submission of the case study.
01000 2000 3000 4000 5000 6000 7000 8000
Size of the cluster
Used Nodes
time (s)
Tiempo medio de espera
100.492308 s
(Este tiempo es calculando que si el nodo esta encendido y libre solo tarda 5 sg en asignarse y
Tiempo de trabajo
1083.1 s
Tiempo medio de espera
15.25 s
Tiempo de trabajo
994.3 s
(b) Execution on VMs (EC3 with idle time
for scale-in set to 30 min).
(c) Execution on containers (EC4Docker).
01000 2000 3000 4000 5000 6000 7000 8000
Size of the cluster
Used Nodes
time (s)
Nuevo tiempo de espera
(d) Execution on VMs (EC3 with idle time
for scale-in set to 10 min).
Figure 4: Execution results for both container-based cluster (a) and VM-based cluster (b)
where light blue represents the number of virtual nodes deployed, dark blue depicts used
nodes executing jobs and the red dashed line indicates the job pattern submission. The upper
grey dotted line represents the limit size of the cluster, fixed to six nodes.
are required to prepare their own VMIs.
In contrast, creating a Docker container image from a Dockerfile is a much
easier process than building a VMI. It is necessary to take into account that
the container times shown in the table do not consider the time required to
generate the container image from the Dockerfile, since this task only needs520
to be performed once by the administrator or the user. It is worth to point
out that the time needed to create the container image is equivalent to the
contextualization time employed by a non-preconfigured VM. Moreover, the
time to pull the container images if they are stored in Docker Hub has not been
included in the table, as this is performed only once, but it took an average of525
150 s. in our tests.
Scen. a) Scen. b) Scen. c)
Prec. Non prec. Prec. Non prec.
Deployment avg. time 2 35 35 35 35
Active SSH avg. time 1 30 30 30 30
Total avg. time machine ready 3 65 65 65 65
FE contextualization avg. time 0 340 830 335 853
Total avg. time FE ready 3 405 895 400 918
WN contextualization avg. time 0 219 702 220 684
Total avg. time WN ready in LRMS 16 284 767 285 749
Job avg. waiting time 15.25 101 305 209 449
Job avg. execution time 994 1076 1083 1064 1128
Table 1: Time analysis, in seconds, for the different phases of the scenarios. Scenario a) refers
to the container-based execution, scenario b) refers to the VM-based execution with an idle
time configuration of 30 minutes and scenario c) refers to the VM-based execution with an
idle time configuration of 10 minutes. In b) and c) the tests are carried out with preconfigured
Virtual Machines Images (VMIs) (Prec.) and without preconfigured VMIs (Non prec.).
Second, we present in Figure 4 the results obtained from the execution of
the job pattern submission show in Figure 4(a). Scenario a) is represented in
Figure 4(c), scenario b) is shown in Figure 4(b)) and scenario c) is addressed
in Figure 4(d). For the three executions, we have used conservative elasticity530
policies to ensure the minimum costs for the infrastructure in terms of energy
and resources consumption. Thus, CLUES has been configured to power on
nodes according to the 1:1 start strategy, i.e. when a job arrives to the LRMS
and there is no available node to execute it, a virtual node is deployed. On
the other hand, the power off policy selected was delayed shutdown, destroying535
nodes when they are idle for 2 minutes, for the scenario a) execution, 30 minutes
for the scenario b) execution and 10 minutes for the scenario c). The differences
in time for powering off a node are based on the time that a new virtual node
needs to be ready for task execution (16 seconds in case of a container node
and 285 seconds in average for a VM node). Scenarios b) and c) involve the540
same execution but the variations in the idle time to trigger the scale-in policy
introduced differences in the behaviour of the cluster, as it can be appreciated
in the figures.
Based on the results represented in Figure 4 we can highlight that the
container-based cluster deployed in scenario a) fits almost perfectly to the work-545
load of the computing cluster. Indeed, containers only take a few seconds to be
ready to execute the jobs of the cluster since the contextualization process is
not required, and starting a container is faster than booting a VM. Therefore,
the average time that a job is queued up at the LRMS, i.e. in PENDING state,
does not exceed 15 seconds.550
In contrast, in scenarios b) and c) we can easily denote the differences de-
ploying a node, that takes an average of 285 seconds to be ready and detected
by the LRMS as an eligible node to execute jobs. This situation is represented
in Figure 4(b),(d) in light blue, and covers the time needed to deploy a new
VM, obtain SSH access to it and contextualize the job execution environment.555
For example, in Figure 4(b), for the first job this requires the initial 280 seconds
of the execution. This situation is repeated for the subsequent jobs that arrive
to the LRMS, when no available nodes are in the cluster. However, once the
cluster is fully deployed, new jobs do not need to wait for additional nodes to
be deployed. Instead, they just wait for other tasks to finish. This fact helps560
reducing the total average time of jobs waiting in the LRMS queue, which is
101 seconds. However, the resources are not properly exploited, because most
of the nodes were idle a long period of time.
This situation can be better addressed by reducing the idle time allowed for
nodes as it is done in scenario c). In this case, the available resources are better565
used, but the total time of execution increases (6989 seconds) like the job average
waiting time (209 seconds) in contrast with the other two scenarios a) and b).
However, despite the differences in the time required to provision new nodes
in all scenarios, the total execution time in scenarios a) and b) is very similar.
Container-based execution (scenario a)), requires 6579 seconds to complete all570
the submitted jobs while VM-based execution (scenario b)) takes 6661 seconds.
Note that, on the one hand, scenario b) is significantly impacted by requiring
to deploy additional nodes (VMs) at the beginning but the deployment and
configuration of the nodes is produced concurrently. However, once the new
nodes are up and running, jobs can be processed on a first-come-first-served575
basis. On the other hand, scenario c) is also impacted by the initial deployment
of new VMs. However, the infrastructure does not maintain the nodes active
and more time dedicated to deploy nodes os needed during the execution. These
facts reveal that in a VM-based execution, increasing the time that nodes are
idle, reduces the total execution time (no extra time is dedicated to deploy nodes580
to scale out) at the expense of wasting computational resources. Also, if the
idle time to trigger a scale in operation is reduced, the total time of execution
increases (due to the extra time required to provision additional nodes, which
increases the job waiting time) but the computational resources are better used.
It is of special relevance the differences in the average time for a single job585
execution, that is an 8.2% faster in the containers deployed in the scenario
(a) (994 seconds), than in VMs ([1064-1128] seconds). This fact confirms the
higher overheads in CPU and memory that VMs suffer, comparing with the
light-weight virtualization introduced by Docker containers.
Figure 4 does not represent the time required to deploy and configure the590
front-end of the cluster. This data is presented in Table 1, where for a container-
based cluster this task only requires deploying a container in Docker Swarm and
requesting a redirection to IPFloater (3 seconds). Meanwhile, for a VM-based
cluster, this task involves the creation of a new VM in OpenNebula, wait until
the SSH of the VM is active and complete the contextualization process ([895-595
918] seconds in average for a non-preconfigured VMI and [335-340] seconds for
a preconfigured VMI).
All the analyzed results suggest that containers are a proper solution to
execute groups of short HTC (High Throughput Computing) tasks, like Bag of
Tasks (BoT) applications. Indeed, for short tasks the required deployment time600
of a VM-based working node clearly outweighs the execution time of the tasks.
HPC tasks can also benefit from the reduced overheads that arise when using
containers. In contrast, for longer tasks, contextualization time may become
negligible with respect to the total execution time and, therefore, these tasks
can take advantage of the unlimited resources offered by Cloud Computing605
platforms in the shape of VMs.
4.2. Discussion
As it occurs in physical clusters, in order to use the virtual cluster it is rec-
ommended to introduce some other tools that enhance the features of the cluster
and also take benefit from virtualization techniques. One of the most noticeable610
examples is the mechanisms that ensure the availability and the reliability of
the cluster. One benefit of virtual clusters with respect to physical clusters is
that virtualization facilitates the relocation of nodes. Indeed, incidents such as
power outages or network failures can introduce a downtime for users of phys-
ical clusters. In the case of virtual clusters, any of the computing nodes (i.e.615
front-end or working nodes) can be hosted in another virtualization infrastruc-
ture, thus maintaining the service to users. Concerning high availability, this
can be achieved in EC4Docker by deploying multiple containers configured to
act as front-ends and to configure high availability middleware, such as a load
balancer that supports failover.620
It is important to point out that container-based elastic clusters improve the
overall performance compared to VM-based elastic clusters. As demonstrated
by the case study, the reduced footprint of the container images with respect to
the virtual machine images enhances the ability of the elastic cluster to cushion
the workload peaks. Booting the container-based virtual working nodes takes625
significant less time than the VM-based ones. Therefore, the average waiting
time for a job to be running is considerably reduced.
Regarding the performance of the scientific computing clusters, containers
executed in one host take profit from the fact that the computational resources
are not allocated to a specific container. Instead, the default behaviour for the630
containers is to share the available resources, managed by the host OS. That
means that if one container is executed in an 8-core host, the application running
in the container will be able to use the 8 cores and the whole memory if there are
no other competing containers. However, a VM deployed with a fixed number
of cores and memory, will only be able to use that number of cores and amount635
of memory even if the rest of the physical host is idle.
5. Conclusions and Future Work
This paper has analyzed the feasibility of using Docker containers to support
the creation of virtual elastic computer clusters for the execution of scientific
applications. These clusters maintain the very same interfaces for end users640
but benefit from the reduced overheads introduced by containers. For this, we
introduced the open-source EC4Docker tool to support the deployment of such
clusters on a Container Orchestration Platform managed by Docker Swarm.
We have demonstrated the feasibility of adopting containers to execute sci-
entific applications, introducing two main advantages when compared to tradi-645
tional VMs: i) the low deploying times for new working nodes, and ii) potential
reductions in the overhead caused by VMs in CPU, memory and storage, offer-
ing near-native performance. Moreover, from the discussed case study, we can
conclude that container-based virtual clusters are an appropriate solution for
the execution of short HTC tasks.650
Future work involves the automatization of the generation of the container
images that EC4Docker uses to deploy the cluster. Currently, the administrator
or the users need to generate their own images including the Dockerfile provided
with EC4Docker in order to deploy their own applications in the container
cluster environment. A service will be implemented to facilitate this process for655
non-experienced users. Finally, a thorough scalability testing will be carried out
to quantify the benefits of the container technology versus virtual machines for
the processing of jobs on scientific computing virtual clusters.
This work has been developed under the support of the program “Ayudas660
para la contrataci´on de personal investigador en formaci´on de car´acter predoc-
toral, programa VALi+d”, grant number ACIF/2013/003, from the Conselleria
d’Educaci´o of the Generalitat Valenciana. The authors wish to thank the finan-
cial support received form The Spanish Ministry of Economy and Competitive-
ness to develop the project “CLUVIEM”, with reference TIN2013-44390-R.665
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... Container salah satu solusi yang ringan daripada Virtualisasi Hypervisor. Container mengujinkan untuk menjalankan proses yang terisolasi di sistem host dalam satu Sistem Operasi tanpa overhead berlebih pada CPU dan memory yang disebabkan oleh hypervisor [5], [6]. Kontainer menyediakan lingkugan virtual pada layer aplikasi [7]. ...
... Teknologi virtualisasi dimanfaatkan untuk menyelesaikan masalah heterogenitas (perbedaan versi library atau tools dari aplikasi website sehingga dapat menguruangi biaya dan kompleksitas hardware serta terciptanya lingkungan yang scalable, elastic, dan biaya yang efisien [5], [6], [9], [10]. Untuk mendukung scalable dan elastic sumber daya dapat diskalakan dengan mengatur jumlah sumberdaya fisik menggunakan Virtual Machine (VM) apabila menerapkan horizontal scaling atau Physical Machine (PM) apabila menerapkan Vertical Scaling [4]. ...
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Teknologi virtualisasi memerankan peran penting Infrastuktur TI meliputi private data centers dan platform public cloud. Ada beberapa jenis virtualisasi yaitu Virtualisasi Container dan Hypervisor. Virtualisasi berbasis container menggunakan kernel yang sama dan bekerja dalam layer software. Container memungkinkan menjalankan beberapa instance sistem operasi dan perangkat keras yang sama . Berbeda dengan container, hypervisor beroperasi pada level hardware memerlukan Operasi Sistem yang terpisah dengan sistem host. Ada beberapa platform virtualisasi yang dapat digunakan seperti Proxmox, VMWare ESX, dan OpenStack. Proxmox mendukung hypervisor KVM (Kernel-based Virtual Machine), dan LXC Container. KVM mempunyai performa CPU yang lebih baik daripada jenis virtualisasi lainnya seperti native, LXC, dan Docker. Penelitian ini bertujuan untuk mengimplementasi virtualisasi di PT.MKNT menggunakan platform virtualisasi PROXMOX. Hasil menunjukkan dengan menggunakan Platform PROXMOX dapat membantu untuk membuat dan mengelola VM dalam private server.
... Basically, Dockers has a container HUB works like container repository. [5] Virtualization is creating virtual version of something i.e. a server, network or storage devices. Infect it is a method for using share resources by many companies and organization that are geographically apart. ...
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Cloud computing emphasis on using and underlying infrastructure in a much efficient way. That's why it is gaining immense importance in today's industry. Like every other field, cloud computing also has some key feature for estimating the standard of working of every cloud provider. Elasticity is one of these key features. The term elasticity in cloud computing is directly related to response time (a server takes towards user request during resource providing and de-providing. With increase in demand and a huge shift of industry towards cloud, the problem of handling user requests also arisen. For a long time, the concept of virtualization held industry with all its merits and demerits to handle multiple requests over cloud. Biggest disadvantage of virtualization shown heavy load on underlying kernel or server but from past some Page 2 of 16 IJIST 2020 Vol 2 I1 decades an alternative technology emerges and get popular in a short time due to great efficiency known as containerization. In this paper we will discuss about elasticity in cloud, working of containers to see how it can help to improve elasticity in cloud for this will using some tools for analyzing two technologies i.e. virtualization and containerization. We will observe whether containers show less response time than virtual machine. If yes that's mean elasticity can be improved in cloud on larger scale which may improve cloud efficiency to a large extent and will make cloud more eye catching.
... The evaluation function is used as the fitness function of dynamic optimization. The reasons for choosing the queuing model M/M/s/K are as follows [33]: ...
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Elastic scaling is one of the techniques to deal with the sudden change of the number of tasks and the long average waiting time of tasks in the container cluster. The unreasonable resource supply may lead to the low comprehensive resource utilization rate of the cluster. Therefore, balancing the relationship between the average waiting time of tasks and the comprehensive resource utilization rate of the cluster based on the number of tasks is the key to elastic scaling. In this paper, an adaptive scaling algorithm based on the queuing model called ACEA is proposed. This algorithm uses the hybrid multiserver queuing model (M/M/s/K) to quantitatively describe the relationship among number of tasks, average waiting time of tasks, and comprehensive resource utilization rate of cluster and builds the cluster performance model, evaluation function, and quality of service (QoS) constraints. Particle swarm optimization (PSO) is used to search feasible solution space determined by the constraint relation of ACEA quickly, so as to improve the dynamic optimization performance and convergence timeliness of ACEA. The experimental results show that the algorithm can ensure the comprehensive resource utilization rate of the cluster while the average waiting time of tasks meets the requirement.
... According to the container networking paradigm, containers can interact with each other, while offering the opportunity to deploy novel applications over distributed and virtualized environments. 51 To reach this goal, however, it is necessary to integrate technologies implementing container engine, orchestrator, load balancer, and service discovery tools within a specific deployment platform. Regarding the deployment platform, two main solutions are adopted today: bare-metal and OpenStack cloud. ...
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Container networking is emerging as a game-changer paradigm for the deployment of virtualized service infrastructures in a faster and reliable way. Nevertheless, Small and Medium Enterprises are still skeptical to revise their business in this direction because of the absence of deep studies showing its effectiveness in real deployments leveraging local computing environments. To bridge this gap, this paper presents a quantitative cross-comparison of cutting-edge technologies for container networking (including Docker as a container engine, Docker Swarm and Kubernetes as orchestrators, bare-metal and OpenStack cloud as deployment platform), properly integrated to realize a virtualized service infrastructure within a commercial workstation. Initial experimental tests are conducted to identify the most suitable combination of technologies for high-load environments, where many clients contact the virtualized service infrastructure to download files of large size. Obtained results demonstrate that the combination of Docker and Kubernetes generally ensures better performance on the bare-metal deployment platform, thus emerging as mature and effective solutions to be used by Small and medium Enterprises. Finally, the behavior of the identified virtualized service infrastructure is also evaluated in a smart farm use case, where containers are in charge of processing images provided by mobile drones for monitoring purposes. Also, in this case, the conducted study highlights the promising capability offered by container networking in real deployments, exploiting local computing environments.
A novel multi-objective (cost, delay, and reliability) auto-scaling optimisation model is proposed for micro-service workflows in containerised hybrid clouds. We compare the container-based model with VM-based model and conclude that the former significantly supersedes. The benchmark of three mainstream algorithms is conducted by the Hypervolume metric, showed that the performance of MOEA/D is inferior to NSGA family, and NSGA-III is not always superior to NSGA-II. So we design an improved NSGA-II based on dynamically changing crossover and mutation operators, which outperforms NSGA-III both in stability and performance by over 60% and 80% in all multi-scale tests.
In the big data of complex application scenarios, there are many kinds of mixed computing mode jobs, in order to work, virtual cluster needs to maintain a variety of computing modes. In the calculation of the complexity of the load, time-varying, in terms of resource utilization, virtual cluster is not high. In order to improve the global resource utilization, this utilization is based on the big data application virtual cluster, the flexible resource management strategy is adopted to absorb the sudden change of resource demand when multiple computing modes are mixed. On the basis of docker container technology, change of operation according to requirements is proposed according to the change of job requirements. According to the dynamic requirements of computing load on resources, computing form based on virtual cluster is adjusted in real time, including the size and type of the calculation node. This model can not only realize the dynamic customization of user execution environment, but also achieves the purpose of peak shifting calculation. The simulation results show that the CPU utilization is increased by 5.3%, which is the result of the model using virtual nodes, it also optimizes the execution efficiency of computer jobs.
Containers such as Docker provide a lightweight virtualization technology. They have gained popularity in developing, deploying and managing applications in and across Cloud platforms. Container management and orchestration platforms such as Kubernetes run application containers in virtual clusters that abstract the overheads in managing the underlying infrastructures to simplify the deployment of container solutions. These platforms are well suited for modern web applications that can give rise to geographic fluctuations in use based on the location of users. Such fluctuations often require dynamic global deployment solutions. A key issue is to decide how to adapt the number and placement of clusters to maintain performance, whilst incurring minimum operating and adaptation costs. Manual decisions are naive and can give rise to: over‐provisioning and hence cost issues; improper placement and performance issues, and/or unnecessary relocations resulting in adaptation issues. Elastic deployment solutions are essential to support automated and intelligent adaptation of container clusters in geographically distributed Clouds. In this article, we propose an approach that continuously makes elastic deployment plans aimed at optimizing cost and performance, even during adaptation processes, to meet service level objectives (SLOs) at lower costs. Meta‐heuristics are used for cluster placement and adjustment. We conduct experiments on the Australia‐wide National eResearch Collaboration Tools and Resources Research Cloud using Docker and Kubernetes. Results show that with only a 0.5 ms sacrifice in SLO for the 95th percentile of response times we are able to achieve up to 44.44% improvement (reduction) in cost compared to a naive over‐provisioning deployment approach.
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Modern scientific research challenges require new technologies, integrated tools, reusable and complex experiments in distributed computing infrastructures. But above all, computing power for efficient data processing and analyzing. Containers technologies have emerged as a new paradigm to address such intensive scientific applications problems. Their easy deployment in a reasonable amount of time and the few required computational resource make them more suitable. Containers are considered light virtualization solutions. They enable performance isolation and flexible deployment of complex, parallel, and high-performance systems. Moreover, they gained popularity to modernize and migrate scientific applications in computing infrastructure management. Additionally, they reduce computational time processing. In this paper, we first give an overview of virtualization and containerization technologies. We discuss the taxonomies of containerization technologies of the literature, and then we provide a new one that covers and completes those proposed in the literature. We identify the most important application domains of containerization and their technological progress. Furthermore, we discuss the performance metrics used in most containerization techniques. Finally, we point out research gaps in the related aspects of containerization technology that require more research.
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Docker provides a good basis to run composite applications in the cloud, especially if those are not cloud-aware, or cloud-native. However, Docker concentrates on managing containers on one host, but SaaS provi¬ders need a container management solution for multiple hosts. Therefore, a number of tools emerged that claim to solve the problem. This paper classifies the solutions, maps them to requirements from a case study and identifies gaps and integration requirements. We close some of these gaps with our own integration components and tool enhancements, resulting in the currently most complete management suite.
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Linux container technology has more than proved itself useful in cloud computing as a lightweight alternative to virtualisation, whilst still offering good enough resource isolation. Docker is emerging as a popular runtime for managing Linux containers, providing both management tools and a simple file format. Research into the performance of containers compared to traditional Virtual Machines and bare metal shows that containers can achieve near native speeds in processing, memory and network throughput. A technology born in the cloud, it is making inroads into scientific computing both as a format for sharing experimental applications and as a paradigm for cloud based execution. However, it has unexplored uses in traditional cluster and grid computing. It provides a run time environment in which there is an opportunity for typical cluster and parallel applications to execute at native speeds, whilst being bundled with their own specific (or legacy) library versions and support software. This offers a solution to the Achilles heel of cluster and grid computing that requires the user to hold intimate knowledge of the local software infrastructure. Using Docker brings us a step closer to more effective job and resource management within the cluster by providing both a common definition format and a repeatable execution environment. In this paper we present the results of our work in deploying Docker containers in the cluster environment and an evaluation of its suitability as a runtime for high performance parallel execution. Our findings suggest that containers can be used to tailor the run time environment for an MPI application without compromising performance, and would provide better Quality of Service for users of scientific computing.
Conference Paper
Profiling job activities on different batch systems in the data center helps understand the patterns of usage by different users. With these patterns the system administrators in the data center are able to reorganize or rearrange their resources in a way that the overall resource utilization is improved. In this paper, we extract wall and CPU time from job accounting information on different batch systems of which Global Science experiment Data hub Center (GSDC) at Korea Institute of Science and Technology Information (KISTI) provides to its various user communities and we profile job activities of each batch system for months. We evaluate batch system usage and prioritize jobs upon the profiles.
Conference Paper
Workflows are a widely used abstraction for representing large scientific applications and executing them on distributed systems such as clusters, clouds, and grids. However, workflow systems have been largely silent on the question of precisely what environment each task in the workflow is expected to run in. As a result, a workflow may run correctly in the environment in which it was designed, but when moved to another machine, is highly likely to fail due to differences in the operating system, installed applications, available data, and so forth. Lightweight container technology has recently arisen as a potential solution to this problem, by providing a well-defined execution environments at the operating system level. In this paper, we consider how to best integrate container technology into an existing workflow system, using Makeflow, Work Queue, and Docker as examples of current technology. A brief performance study of Docker shows very little overhead in CPU and I/O performance, but significant costs in creating and deleting containers. Taking this into account, we describe four different methods of connecting containers to different points of the infrastructure, and explain several methods of managing the container images that must be distributed to executing tasks. We explore the performance of a large bioinformatics workload on a Docker-enabled cluster, and observe the best configuration to be locally-managed containers that are shared between multiple tasks.
This paper describes the developments to produce EC3 (Elastic Cloud Computing Cluster), a tool that creates self-managed cost-efficient virtual hybrid elastic clusters on top of Infrastructure as a Service (IaaS) Clouds. Using spot instances, together with checkpointing techniques, EC3 can significantly reduce the total cost of executions while introducing automatic fault tolerance. Moreover, EC3 can deploy and manage hybrid clusters across on-premises and public Cloud resources, thus introducing Cloud bursting capabilities. A case study is presented to assess the effectiveness of the tool featuring the structural dynamic analysis of buildings. In addition, checkpointing algorithms are evaluated in a real Cloud environment with existing workloads to study their effectiveness. The results show the feasibility and benefits of this type of clusters for computationally intensive applications.