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Volunteer Down: How COVID-19 Created the Largest Idling Supercomputer on Earth

  • Lübeck University of Applied Sciences

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From close to scratch, the COVID-19 pandemic created the largest volunteer supercomputer on earth. Sadly, processing resources assigned to the corresponding Folding@home project cannot be shared with other volunteer computing projects efficiently. Consequently, the largest supercomputer had significant idle times. This perspective paper investigates how the resource sharing of future volunteer computing projects could be improved. Notably, efficient resource sharing has been optimized throughout the last ten years in cloud computing. Therefore, this perspective paper reviews the current state of volunteer and cloud computing to analyze what both domains could learn from each other. It turns out that the disclosed resource sharing shortcomings of volunteer computing could be addressed by technologies that have been invented, optimized, and adapted for entirely different purposes by cloud-native companies like Uber, Airbnb, Google, or Facebook. Promising technologies might be containers, serverless architectures, image registries, distributed service registries, and all have one thing in common: They already exist and are all tried and tested in large web-scale deployments.
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future internet
Volunteer Down: How COVID-19 Created the Largest
Idling Supercomputer on Earth
Nane Kratzke
Department for Electrical Engineering and Computer Science, Lübeck University of Applied Sciences,
23562 Lübeck, Germany;
Received: 25 May 2020; Accepted: 3 June 2020; Published: 6 June 2020
From close to scratch, the COVID-19 pandemic created the largest volunteer supercomputer
on earth. Sadly, processing resources assigned to the corresponding Folding@home project cannot be
shared with other volunteer computing projects efficiently. Consequently, the largest supercomputer
had significant idle times. This perspective paper investigates how the resource sharing of future
volunteer computing projects could be improved. Notably, efficient resource sharing has been
optimized throughout the last ten years in cloud computing. Therefore, this perspective paper
reviews the current state of volunteer and cloud computing to analyze what both domains could
learn from each other. It turns out that the disclosed resource sharing shortcomings of volunteer
computing could be addressed by technologies that have been invented, optimized, and adapted
for entirely different purposes by cloud-native companies like Uber, Airbnb, Google, or Facebook.
Promising technologies might be containers, serverless architectures, image registries, distributed
service registries, and all have one thing in common: They already exist and are all tried and tested
in large web-scale deployments.
volunteer computing; cloud computing; grid computing; HPC; supercomputing;
microservice; nanoservice; container; cloud-native; serverless; platform; lessons-learned; COVID-19
1. Introduction
On 28 April 2020, Greg Bowman—director of the volunteer computing (VC) project Folding@home—
posted this on Twitter
@Folding@home is continuing its growth spurt! There are now 3.5 M devices participating, including
2.8 M CPUs (19 M cores!) and 700 K GPUs.
According to Figure 1, the volunteer computing project Folding@home had even access to more
computing power than all TOP-500 supercomputers together could provide! Just two months earlier,
only 30,000 devices contributed to this project. Thus, what caused this tremendous increase in
processing power? It was COVID-19 and the willingness of hundreds of thousands of “nerds” to fight
COVID-19 by supporting Sars-CoV-2 computational bioscience research that was processed by the
Folding@home project.
Our research is mainly dealing with cloud computing and corresponding software engineering
and architecture questions. However, like many others, we surprisingly faced the COVID-19
shutdown impacts and decided on 23 March 2020 spontaneously to support Sars-CoV-2 computational
bioscience-related research. Our lab set up two 32 CPU virtual machines from our virtualization cluster
that is usually used for our cloud computing research and student projects.
Moreover, we called our students for support, and our students boarded almost immediately
providing their most valuable assets: Gaming graphic cards. On 1 May 2020, we ranked on place
2145 of 252,771 teams worldwide ( Thus, we were
Future Internet 2020,12, 98; doi:10.3390/fi12060098
Future Internet 2020,12, 98 2 of 20
suddenly among the top 1% of contributors as a very modest-sized University of Applied Sciences!
Even the local newspaper reported about this. Furthermore, it is essential to mention that such kind
of teams not only grew up in Lübeck, but this also happened all over the world. In total, the biggest
supercomputer on earth emerged somehow by accident.
Figure 1.
Processing speed posted by Folding@home project, 13 April 2020,
We were impressed by our students and by these numbers. Nevertheless, we also looked at the
usage statistics of our provided machines gathered by our virtualization cluster. However, these data
were so-so (see Figure 2). Our machines, although being configured to run at full power, had only usage
rates between 40% or 50%. In other words, we had assigned two machines, but, on average, only one
was used. At the end of March, this was even more severe. In that phase, many teams and individual
contributors boarded and the Folding@home network grew massively. However, the effect was that
our machines ran at ridiculous low usage rates. We first checked whether we had misconfigured the
machines, but our machines were operating correctly. The processing pipelines were empty, and the
master nodes needed hardware upgrades to handle all these new processing nodes. The control plane
of the Folding@home project could not scale-out fast enough. In cloud computing, we would say that
Folding@home was not elastic enough. It took all of April to make full use of the provided volunteer
resources (see Figure 2).
Figure 2.
Example usage data of two virtual machines provided to the Folding@home project (compiled
from our own data); red area: overload of master nodes, white area: recovering phase, green area:
normal operation.
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One obvious question arises here. Would it not be better in such a situation to use the amount
of contributed nodes for other VC projects? If we cannot fight COVID-19, we might contribute to
mathematical research, climate research, cancer research, or any other computationally intensive kind
of research. The most of contributors will contribute their resources regardless of the specific aim
of a VC project. Sadly, the reader will see throughout this paper that VC is not well prepared for
shifting processing resources across projects that can lead to such undesirable usage scenarios shown
in Figure 2.
Therefore, this paper deals with the question of how to improve the resource sharing of future
VC projects. We have done some similar transfer research for another domain and asked what
could be shifted auspiciously from the cloud computing domain to the simulation domain [
Because we derived some stupendous insights for the simulation domain, we will follow a quite
similar methodology here (see Figure 3).
Figure 3. Research methodology.
The main contribution of this paper is to provide and explain engineering ideas and
principles taken from the cloud computing domain that have been successfully invented, optimized,
and successfully implemented by so-called cloud-native companies like Netflix, Uber, Airbnb,
and many more. COVID-19 demonstrated that VC has some shortcomings regarding elasticity and
efficient use of shared resources. Precisely, these two points have been optimized throughout the
last ten years in cloud computing [
]. Cloud-native companies invented a lot of technology to make
efficient use of shared resources elastically [3]. These companies were forced to invent these resource
optimization technologies because it turned out that cloud computing can be costly if used inefficiently.
Thus, this paper strives to transfer some of these lessons learned [
] from the cloud computing
domain to the VC domain that might be beneficial. Consequently, VC could be better prepared for
flexible sharing of processing resources across different VC projects. VC could share more and not
claim donated resources.
We start by reviewing the current state of VC in Section 2doing the same for the cloud computing
domain in Section 3. Section 4will analyze what both domains could learn from each other and will
derive some requirements and promising architectural opportunities for future VC projects. Section 5
will present the corresponding related work from the cloud and VC domain to provide interesting
follow-up readings for the reader. We will conclude about our insights in Section 6and forecast more
standardized deployment units and more integrated but decentralized control-planes for VC.
2. Review on Volunteer Computing
Over the past 20 years, VC projects have contributed to hundreds of scientific research publications
on a wide range of topics including climate change, clean energy, astrophysics, cancer research, malaria,
earthquakes, mathematics, and the search for extraterrestrial intelligence. This research has been made
possible by hundreds of thousands of people donating the unused processing power of their desktops,
laptops, and even smartphones to researchers. Figure 4shows the number and research disciplines
of papers that are related to one of the most popular VC platforms (BOINC). It has been compiled
Future Internet 2020,12, 98 4 of 20
from a systematic review of VC projects [
] and the official list of publications by BOINC projects [
This chart might be not representative, but it gives a good impression on the kind of questions VC is
used for.
Figure 4. Categories and research disciplines of VC projects, data taken from [5].
2.1. Categories of Volunteer Computing
Figure 5shows the publishing years of announcing VC solution proposal papers grouped by
their major categories. We see that grid approaches affected VC, especially in the very beginning.
However, since 2009, cloud as well as mobile approaches gained more and more influence on the
community. The majority of VC systems are geared towards embarrassingly parallel tasks that need
no or little communication (Bag of Tasks). According to [
], we classify such VC systems based on the
computational environments that they are deployed in.
Volunteer Grid Computing
makes use of the aggregated computing resources of volunteer
devices. “Volunteer grids are one way of fulfilling the original goal of Grid Computing, where anyone can
donate computing resources to the grid so that users can use it for their computational needs. Contrary to
the traditional grid infrastructure, which needs a dedicated infrastructure to run on, volunteer grid runs on
scavenging computing resources from desktop computers for computationally intensive applications.” [5]
These kinds of systems form the majority of all VC approaches (see Figure 4).
Volunteer Cloud Computing
provides volunteer clouds as opportunistic cloud systems that run
over donated quotas of resources of volunteer computers. Volunteer cloud systems come in
different shapes, such as desktop clouds, peer-to-peer clouds, social clouds, volunteer storage
cloud, and more [
]. The approach mimics the Cloud Computing service models (IaaS, PaaS, SaaS)
without relying on centralized data centers that are operated by hyperscaling service providers
like Amazon Web Services, Google, or Microsoft. These clouds are multipurpose and usually have
no specific mission like volunteer grid computing projects that are focused on a specific research
discipline or even a specific research question.
Mobile Volunteer Computing
makes use of advances in low-power consuming processors of
portable computers such as tablets and smartphones that can handle computationally intensive
applications. According to [
], nearly 50% of the worldwide population use smartphones
and tablets—more than conventional Laptop and PCs. The increasing computing power,
fast-growing number, and their power-efficient design make mobile devices interesting for
distributed computing [
]. Consequently, many traditional VC systems are extended to include
such devices. For example, BOINC provides an Android-based client (https://boinc.berkeley.
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Most VC systems support embarrassingly parallel tasks that need no or little communication
among the tasks [
]. However,
parallel VC
addresses use cases that need massive communication
among the tasks, based on MPI, MapReduce, or other platforms.
Figure 5. Publishing of VC solution proposals (project papers), data taken from [5].
Table 1lists all projects and corresponding follow-up references for the information of the reader.
We do not claim that this list is complete. This paper focuses mainly on Volunteer Grid Computing.
However, it considers other VC computational environments if this seems appropriate.
Table 1. Overview of some VC projects of recent years, data taken from [5].
Category Platforms and References (Ordered by Publication Date)
Volunteer grid computing
Condor [
], XtremWeb [
], SETI@home [
], Entropia [
], Farsite [
], CCOF [
], Kosha [
], OurGrid [
], Alchemi [
],FreeLoader [
LHC@home [
], Aneka [
], Cohesion [
], EDGeS [
], BitDew [
unaGrid [24], ATLAS@home [25]
Volunteer cloud computing
Storage@home [
], Cloud@home [
], Seattle [
], C3 [
], P3R3.O.KOM [
], UnaCloud [
], Personal Cloud [
], P2PCS [
], SoCVC [
Fatman [
], AdHoc Cloud [
], SASCloud [
], DIaaS [
], Nebula [
cuCloud [41]
Volunteer mobile computing
Mobiscope [
], AnonySense [
], Micro-blog [
], LiveCompare [
Bubblesensing [
], PRISM [
], CrowdLab [
], CWC [
], Serendipity [
Mobile Device Clouds [
], CellCloud [
], GEMCloud [
], FemtoCloud [
AirShower@home [55]
Volunteer parallel computing
VolpexPyMPI [
], MOON [
], MPIWS [
], ADAPT [
GiGi-MR [60], freeCycles [61], Adoop [62], CloudFinder [63]
2.2. Reference Model of Volunteer Computing
According to [
], VC follows mostly a master–worker parallel computing model as shown
in Figure 6a. In this model, the master decomposes massive tasks into small chunks and distributes
these small chunks among workers. The master can be itself composed of distributed nodes, but it
is a centralized concept for the overall architecture. Workers perform the required computation
and send results back. The master then verifies data results and aggregates them to compute final
results. The client nodes are inherently claimed by the master and used exclusively for a specific VC
project. By principle, it would be possible to install different VC client software on clients to contribute
to various VC projects. However, this is conceptually not considered and might end in problems.
For example, it is not clear whether and how different VC projects would be prioritized.
Middleware (see Figure 6b) handles all operations of a VC system. These operations compromise
splitting tasks into small chunks, scheduling these chunks and aggregating the chunk results,
and protecting privacy and security of VC computing devices [
]. However, it is also essential
to process truthfully. Thus, claimed applications and claimed work done by the middleware of a
project should be transparently stated. This truthfulness includes addressing questions like possible
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economic exploitation, expected results, the surrender of intellectual property, and similar aspects [
For example, a VC project should not claim to research about cancer but do crypto-mining instead.
(a) Exclusive use of volunteer workers (b) Master worker architecture
Figure 6. Common master worker model used by many VC projects.
Most VC projects rely on a controllable and exclusive environment to achieve this trust technically.
They provide the master nodes, and worker nodes attach to these master nodes exclusively. The trust
is expressed by installing the VC client software necessary to act as a worker for a specific VC project.
Consequently, the worker is bound to one particular project, and it is not handed over to another
VC project even if the overall workload would suggest this. Even if different VC projects share the
same middleware, this work is not frictionless. Consequently, VC projects that share no common
middleware are isolated (see Figure 6a).
2.3. Open Problems in Volunteer Computing
The COVID-19 case (Folding@home), recent reviews [
], and the critical discussion of the
current state of VC turned up the following (not necessarily complete) shortcomings of current
VC platforms.
Heterogeneity [64]:
Different VC devices have different power, memory and processing
capabilities, as well as different communication interfaces, making it hard to classify, design,
and assign device optimized work units.
Result verification [64]:
Volunteers perform their required computation and send data results
back to the master. The master then verifies data results and discards inadequate or erroneous
results. In this way, massive computation (several hours or even days are not unlikely) is wasted
as result verification is done at the end of processing. Intermediate result verification mechanisms
or smaller chunks could minimize this waste.
Project exclusiveness:
Currently, for most VC platforms, projects are organizations (usually
academic research groups) that need computational power. Each project runs on project-dedicated
master servers. On the one hand, this enables trust, but hinders sharing of devices across different
projects. This exclusiveness can result in situations that the Folding@home project faced in March
and April 2020 due to the massive COVID-19 scale-out. In this situation, all of the unused CPU
Future Internet 2020,12, 98 7 of 20
cycles (see Figure 2) could have been provided with ease to other VC projects dedicated to further
respectable motives. Thus, in March and April 2020, plenty of possible computations for cancer,
climate change, and further research projects could have been processed without disadvantaging
COVID-19 research. The COVID-19 pipelines were almost empty in that phase because there were
more devices than tasks. There was nothing to process.
Good security is multi-layered. For instance, the BOINC system maintains reasonable
security practices at several levels ( Let us
investigate them from a best-practice point of view.
All VC systems require that donators have to run executables provided by a third party—the
company or institution running the project. Thus, these third party executables are highly
suspicious from a security point of view. The following counter-measures are combined to
mitigate corresponding risks.
project security
measures very often include security audits of project code, enforcing
SSL communication with project infrastructure, and virus scanning of project files.
Code signing
can be used to provide valid official builds and to detect code injection attacks
on the client-side.
Result verification
is used on the master side to verify that malicious clients have not
manipulated results.
– Sandboxing
can limit the risk for donators from malicious or insecure project code. However,
sandboxing must be very client operation system-specific.
However, most VC projects are operated by domain-matter experts and not by IT-security experts.
It would be a benefit for the VC projects (reduced efforts) and the donators (improved security) if
both sides could rely on proven security infrastructures.
Scalability and elasticity [5]:
VC systems can have millions of volunteer nodes connected to them.
Moreover, this amount of nodes can grow exponentially and quickly as COVID-19 taught us. Thus,
more scalable and elastic approaches are required to handle this significant number of volunteer
nodes coupled with their intermittent availability. Using more decentralized architectures has
already been proposed and implemented [
]. However, the Folding@home project still could not
scale-out fast enough to handle all of the COVID-19 volunteers (see Figure 2).
3. A Review of the Current State of Cloud Computing
The reader should be aware that this section is mainly a summary of [
] to provide a more
convenient reading experience. As it already has been stated, the COVID-19 case of Folding@home
disclosed some “lock-in” shortcomings of VC regarding elasticity and efficient use of shared resources.
This “lock-in” shows some astonishing parallels with cloud computing. Precisely, these two points
(elasticity and resource utilization) have been mainly optimized throughout the last ten years in cloud
computing [
]. Theses lessons learned originate in profane economic considerations of cloud-native
companies like Netflix, Uber, Airbnb, and many more. However, the resulting technological solutions
might be rewarding for VC because they address by accident some of the identified shortcomings.
Therefore, we want to focus on these core insights because these insights might be used to address and
solve some of the identified problems of VC in Section 2.3.
According to our experiences and action research activities over the last ten years, cloud
computing is dominated by two major long-term trends. In the first adoption phase of cloud
computing, existing IT-systems were merely transferred to cloud environments. The original design
and architecture of these applications were not changed. Applications have only been migrated from
dedicated to virtualized hardware. Over the years, cloud system engineers implemented remarkable
improvements in cloud platforms (PaaS) and infrastructures (IaaS). In particular, we investigate
resource utilization improvements (Section 3.1) and the architectural evolution of cloud applications
(Section 3.2).
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3.1. A Review of the Resource Utilization Evolution
Cloud-native applications are built to be elastic. If not, cloud computing would very often not
be reasonable from an economic point of view [
]. Elasticity is understood as the degree to which
a system adapts to workload changes. Over time, systems were designed intentionally for such
elastic cloud infrastructures. Accordingly, the utilization rates of underlying computing infrastructures
increased. New deployment and design approaches like containers, microservices, or serverless
architectures evolved.
Figure 7shows a noticeable trend over the last decade. Machine virtualization consolidated plenty
of bare-metal machines and formed the technological backbone of IaaS cloud computing. Virtual
machines might be more lightweight than bare metal servers. However, containers are much more
fine-grained and improved two things: The way of standardized deployments, but they also increased
the utilization rates of virtual machines. Nevertheless, containers are still always-on components. Thus,
Function-as-a-Service (FaaS) approaches evolved and introduced time-sharing concepts in container
platforms. Using FaaS, units are only executed if requests have to be processed. Therefore, FaaS enables
scale-to-zero deployments and improves resource efficiency [
]. Thus, the technology stack—although
getting more complicated—followed the trend to run more workload on the same amount of physical
machines by shrinking the size of standardized deployment units, be it virtual machines, containers,
or functions.
Bare Metal Server
Bare Metal
Bare Metal Server
Bare Metal Server
FaaS Runtime
Virt ualiza tio n Con ta inerizati on
Dedicat ed Server
In c ase of de dicat ed serve rs
applications (A, B) are deployed
on physic al ser vers. In
consequence, the servers are
often ove r dimensione d and
have ine fficient utiliza tion rates.
Machine virtualization is
mainly used to consolidate
and isolate applications on
virtual m achine instead of
dedica ted servers. This
increases t he application
density on bare me tal
serve rs but the v irtual
machine images
(deployment unit) are very
To pragmatically operate more
than one application per
virtual m achine,
containerization established as
a trend. A container starts
faster than a virtual ma chine
and shares the operating
syste m wit h other conta iners,
thus reducing deployment unit
size s and increa sing
application density per virtual
But a cont ainer still re quests a
shar e of CPU , mem ory, a nd
stor age even if the provided
servi ce is hardly requeste d. It
is more resource efficient , if
servi ces w ould c onsume
resources only if there are
incoming requests. FaaS
runtime environments enable
that services can timeshare a
host. H owever, t his involves to
follow a serverless
architecture style.
1 2 3 4
Serverless, FaaS
Bare Metal
Figure 7. Observable trend of minimizing deployment unit sizes, taken from [3].
virtual-machine !container !function
resource utilization evolution is accompanied by
corresponding architectural approaches:
Service-oriented architectures (SOA)
fitted very well with monolithic deployment approaches
that can be provided using standardized virtual machines (IaaS).
Microservice architectures
are built on top of loosely coupled and independently deployable
services. These services can be provided via much smaller and standardized containers. We could
rate Microservices as a kind of standardized PaaS cloud service provision model.
serverless architectures
are mainly event-driven service-of-service architectures where
their functionality is provided as “nano”-services via functions. Serverless and FaaS are the latest
trends in cloud computing, so functions are not standardized yet. However, more and more
Future Internet 2020,12, 98 9 of 20
Cloud-native computing foundation (CNCF) hosted serverless approaches like Kubeless (https://, Knative (, or OpenWhisk ( make
use of containers to package and deploy functions. Thus, it seems likely that containers might
evolve as the de-facto deployment unit format not only for microservices, but also for functions.
3.2. A Review of the Architectural Evolution
The reader observes that cloud-native applications aim for better resource utilization by applying
more fine-grained deployment units—for instance, containers instead of virtual machines or functions
instead of containers. Improvements in resource utilization rates always had an impact on cloud-native
architecture styles. Let us now investigate the two major architectural trends that might be of most
interest from a VC perspective.
3.2.1. Microservice Architectures
Microservices form “an approach to software and systems architecture that builds on the well-established
concept of modularization but emphasizes technical boundaries. Each module—each microservice—is
implemented and operated as a small yet independent system, offering access to its internal logic and data through
a well-defined network interface. This architectural style increases software agility because each microservice
becomes an independent unit of development, deployment, operations, versioning, and scaling [
].” Faster
delivery, improved scalability, and greater autonomy are often mentioned benefits of microservice
architectures [
]. Different services are independently scalable based on actual request stimuli.
Because services can be developed and operated by different teams, they not only have a technological
but also an organizational impact. Thus, localized decisions per service regarding programming
languages, libraries, frameworks, and more are possible and enable best-of-breed approaches.
Besides the pure architectural point of view, the following tools, frameworks, services,
and platforms form the current understanding of the term microservice:
Service discovery technologies decouple services from each other. Services must not explicitly
refer to network locations.
Container orchestration technologies automate container allocation and management tasks.
Monitoring technologies enable runtime monitoring and analysis of the runtime behavior of
Latency and fault-tolerant communication libraries enable efficient and reliable service
communication in permanently changing configurations.
Service proxy technologies provide service discovery and fault-tolerant communication features
that are exposed over HTTP.
A complex tool-chain evolved to handle the continuous operation of microservice-based cloud
applications [3]. We should consider this for VC and took only the barely necessary concepts.
3.2.2. Serverless Architectures
The serverless computing model allocates resources dynamically and intentionally out of control
of the service customer. To scale to zero resources might be the most critical differentiator of serverless
platforms compared with other IaaS or PaaS-based cloud platforms. This scale-to-zero capability
excludes the most expensive always-on usage pattern [
]. Consequently, the term “serverless” is
getting more and more attraction [
]. However, where have all the servers gone? Processing resources
must still exist somehow.
Serverless architectures make substantial use of Function-as-a-Service (FaaS) concepts and
platforms [
] and integrate more intensively third-party backend services. Figure 7shows this
evolution over the last ten years. FaaS platforms realize time-sharing of resources and increase the
utilization factor of computing infrastructures. Cost reductions of 70% are possible [66].
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A FaaS platform is nothing more than an event processing system (see Figure 8). Serverless
platforms take an event and then determine which functions are registered to process the event [
If no function is present, a new one is created. Event-based applications are especially very much
suited for this approach [70,71].
Figure 8. Serverless platform architecture, taken from [3].
In summary, the following observable engineering decisions in serverless architectures are worth
being mentioned:
Cross-sectional logic, like authentication or storage, is sourced to external third party services.
End-user clients or edge devices do the Service composition. Thus, service orchestration is not
done by the service provider but by the service consumer via provided applications.
Endpoints using HTTP- and REST-based/REST-like communication protocols that can be
provided easily via API gateways are generally preferred.
Only very domain or service-specific functions are provided on FaaS platforms.
Thus, the serverless design is generally more decentralized and distributed. It makes more
intentional use of independently provided services and is therefore much more intangible compared
with microservice architectures.
In particular, this distribution characteristic seems to make it more suitable for VC. Thus, serverless
principles might be more preferable than microservice principles to consider for VC.
4. Discussion of Technological and Architectural Opportunities for Future Volunteer Computing
In Section 3, the reader got to know how the design of so-called cloud-native systems have
changed. Technologies that have been massively improved, integrated, and simplified throughout
the last ten years are containers, image registries, service registries, and service proxies. The primary
motivation for these changes has been economical. If this had not been done, cloud-deployed systems
would waste valuable (and costly) cloud resources. Some similar efficiency problems could be observed
during COVID-19 crisis in the Folding@home project (see Figure 2). Therefore, this section will
investigate whether and how these cloud-native improvements could be used to evolve the VC model
that has been summarized in Section 2.2 and Figure 6.
In short, this perspective paper proposes to transform the current situation of isolated VC project
networks into a more meshed variant (see Figure 9). Therefore, all VC projects must share their master
endpoints in a standardized way. The vision is that a worker should only know one IP address of the
global VC network to gain information about all other existing and available VC projects and endpoints.
It is then up to the worker to decide to which projects it would like to contribute. In addition, a worker
should always be able to contribute to more than one project at a time. Thus, if a favored project does
not request resources, the worker could fetch tasks from other VC projects according to a priority list
or any different kind of prioritization. If this was possible, the COVID-19 case of the Folding@home
Future Internet 2020,12, 98 11 of 20
project (see Figure 1) would not have happened. Unused resources had been automatically spent on
other projects that address climate change, cryptography, number theory, or whatever.
Figure 9. Transformation of VC networks to avoid project boundness of worker nodes.
4.1. Standardization of Deployment Units
Therefore, this perspective paper proposes in Figure 10 an evolved and container-based
master–worker architecture extending the typical VC master–worker architecture (see Figure 6).
Figure 10. Proposal of an evolved and container-based VC master worker architecture model.
The marked grey components are extending or changing specific parts of the VC reference
architecture explained in Section 2.2. First of all, the proposed architecture considers more than one
VC project. All master nodes provide a standard Service Registry component that is shared by all VC
projects to enable a complete VC project awareness for worker clients. Thus, a worker can choose
which project it would like to provide its computing resources. Therefore, all VC projects must provide
their workload in a standardized but flexible deployment format to make this freedom of choice
possible. Therefore, the architecture proposes to make use of container images as deployment format.
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Signed images are provided via public
Image Registries
. A VC project can operate a project-specific
image registry. Alternatively, public image registries (like DockerHub would
work as well.
Thus, the worker client can be minimized to a standardized container runtime engine (for example
Docker or any other OCI conform container runtime engine) that fetches provided images and
executes them as
Worker Functions
in a FaaS-like style (see Figure 8). However, worker functions
are accompanied by a so-called side-car container [
VC proxy
) that handles common VC
communication patterns and can be called by the VC Runtime Environment (a lightweight wrapper
around an OCI conform container runtime environment). VC proxy and the VC Runtime Environment
are designed to be standardized components that must be not adapted by a VC project. The VC proxy
provides a standard interface furthermore for worker functions. Obviously, the worker function is a
VC project-specific part that realizes data processing. However, because the proposal makes use of
container technologies, it can be provided as a standardized deployment unit. Container technologies
enable furthermore polyglot programming, so there is a freedom of programming language choice for
implementing worker functions. Current VC platforms like BOINC often enforce to include specific
libraries which are written very often in C/C++.
Table 2summarizes the selected and discussed cloud-native technologies and explains how
these technologies can be used to realize the proposed architecture (see Figure 10) to mitigate
mentioned VC open issues reported in the literature. To do this, well-tried technologies that
form the backbone of modern cloud-native architectures could be adopted: Containers, even more
fine-grained (but container-based) functions, image registries, and distributed service registries seem
very promising here. Thus, like the BOINC-approach, a middleware-based approach is proposed to
share resources between different VC projects. However, most parts of this middleware are already
existing (the technologies mentioned above and listed in Table 2). Thus, the proposal does not require a
complete new middleware or framework. Still, the proposal requires a VC-specific integration of these
technologies that go beyond the BOINC-approach (dating back to the early 2000 s, so to a pre-cloud
era). This VC-specific integration is called VC Runtime Environment in Figure 10.
4.2. Client-Side Service Discovery Initiated Workflow
A publish–subscribe communication model between the client and the master nodes is
nearby. However, the publish–subscriber model assumes to some degree that both client-
and master-components are (not perfectly but to some degree reliable) always-on components.
This assumption is not entirely the case in volunteer computing, especially not on the client-side. Thus,
the proposed approach follows the publish–subscriber philosophy but evolves it as a straightforward
and purely client-side triggered approach. In VC, the clients form the ephemeral parts of the complete
system. Taking the insights of [
], one can expect simply less error tracking efforts if the ephemeral
components (clients) query and request the stable parts (masters).
In VC, we are talking about hundreds of VC projects operating thousands of master nodes that
mainly operate in an always-on mode. On the worker side, we are talking about a much more volatile
setting of millions of client nodes that are only sporadically available. Therefore, we generally advocate
client-side service discovery and distributed server-side service registries because the unchanging
parts are more on the master and less on the client-side. Thus, client-side service discovery has to query
much less moving and changing roles in this setting (thousands of service endpoints on the master-side
instead of millions of service endpoints on the client-side). Whenever a client is available, it can make
(well cacheable) client-side service discovery and ask for VC tasks and process them according to the
client preferences and priorities. Thus, client-side service discovery can be used to create a naturally
occurring workload sharing across different VC projects (and not just within a VC project).
Future Internet 2020,12, 98 13 of 20
Table 2. Mapping of recent cloud-native technologies to identified VC open issues.
VC Issues Container Function Image Registry Service Registry Service Proxy Remarks
HW heterogenity x x
Containers (and functions packaged as containers) are a standardized deployment
format that is useable on all primary desktop and server operating platforms
(Windows, Linux, Mac OS).
Verification of (large) results
Functions are used in cloud-native architectures to process events that must be
computed in a limited amount of time. Functions (if packaged as containers) can
be accompanied by trusted service proxies that could validate function results
before sending them to the master. Because of the time limitations (minutes
instead of hours or even days), the result verification would be faster and might
be even processed decentrally.
Code signing and updating x
Image content trust technologies provide the ability to use digital signatures for
image registry operations (push, pull). Publishers can sign their pushed images,
and image consumers can ensure that pulled images are signed. If images are
updated they can be fetched automatically by the clients in their next event
processing cycle. Current image registries like Harbor, DockerHub,,
GitLab registry, and many more provide signed images for automatic
deployments out of the box.
Sandboxing x x
The original intent of operating system virtualization (containers) was
sandboxing. Thus, containers (and functions packaged as containers) provide
inherent and reliable sandboxing out of the box. This sandboxing is much more
fine-grained than virtual machines and available on all major desktop and server
platforms (see HW heterogeneity).
Project exclusiveness x
A service registry is a database containing the network locations of service
instances. It consists typically of components that use a replication protocol.
Examples for reliable cloud-native products are etcd (,
consul (, or Zookeeper (
Such solutions can share VC project information and network locations of master
nodes for clients in a project agnostic format. Thus, clients that are bound to one
master component can do client-side service discovery of further VC projects.
Future Internet 2020,12, 98 14 of 20
The client-side initiated workflow loops through the steps to œshown in Figure 10:
In Step
, the
VC Runtime Environment
of a
worker node
queries periodically (for example,
each day, every six hours or similar) the distributed VC project discovery service that is formed
by master nodes of various VC projects. This updates a worker node’s VC project awareness to
decide which master nodes to ask for processing tasks.
In Step
, the
VC Runtime Environment
of a worker node
selects a master node
according to
its updated project awareness and fetches a task (including the data to be processed). If this fails
(for instance, the master node might be not available, has no jobs, etc.),
another task from another
master node (even from a different project) is fetched according to worker node preferences.
In Step
, the
VC Runtime Environment
analysis the task and triggers a corresponding
Function pull
from a
public image registry
to fetch (if not already present) and start the VC
Worker function
container image. Therefore, the address of the image registry,
the unique image name of the
Worker function
, and image version must be part of the task
description. Furthermore, the task description must contain the URL of the data to be processed.
In Step
, the
VC Runtime Environment
handles the control over to a
VC proxy
. This proxy
does communication with the Worker function and decouples the runtime environment from the
Worker function.
In Step
, the
VC proxy
calls the
Worker function
with the to be processed
and receives
the result.
Finally, in Step
, the
VC proxy
can even do the result verification on the Worker-side (and not
on the Master). Like the Worker function, the
VC proxy
is simply a container that is instantiated
from a trusted image and may, therefore, contain signed result verification logic that cannot be
tampered unnoticed. As a last step, the VC proxy transmits the result to the assimilation endpoint
of the master node (this endpoint must also be part of the task description).
The process would go on with step
(or step
if a periodic update of the VC project
awareness is necessary). It would address the resource sharing problem efficiently and with a simple
client-side strategy.
5. Critical Discussion and Related Work
The proposed approach targets mainly volunteer grid computing projects and, in particular,
the mentioned shortcomings of cross-project resource sharing. The main intention is to improve
resource sharing across VC projects and to set up VC projects more easily making use of established
and well-accepted cloud-native technologies. Therefore, it shares similar limitations like every other
volunteer grid computing project. Thus, the architecture supports embarrassingly parallel tasks that
need no or little communication among the tasks [
]. If this is not the case, volunteer parallel computing
projects or even HPC supercomputing might be a better fit. However, this paper does not focus on this
kind of parallel VC or even HPC. The paper does not even claim to have or provide answers of value
for these parallel computing or HPC supercomputing domains.
Furthermore, the proposed approach follows conceptually a middleware-based path and shares,
therefore, comparable limitations to the BOINC-approach [
]. To reach project awareness
and enable necessary trust are complex tasks in themselves, even in a single-research VC project.
Single-research projects might even have advantages here because contributors do not need any
complex overview of various research or other projects. However, this aspect is getting more
complicated for a middleware-based approach [
]. The contributors need project awareness, and some
project prioritization means. Some contributors want to support disease-related projects but might be
not interested in supporting prime-number mathematical research. In the case of BOINC, a kind of
trusted community already exists, and all BOINC-based projects have passed a kind of quality gate,
and all BOINC-based projects are research-focused. This gatekeeper role of BOINC makes it easier for
BOINC contributors to establish trust. However, projects that are not aware of the gatekeeper are not
aware to contributors as well. Thus, multi-project gatekeeping in VC always has a kind of censorship.
Future Internet 2020,12, 98 15 of 20
Therefore, the proposed approach enables intentionally to set up VC infrastructures that could
process arbitrary computations—for instance, doing mundane crypto-mining for purely monetary
reasons. We do not think that crypto-mining (or other doubtable motivations) would be a useful
and ethical form of VC. However, this should not be the decision of a non-democratic legitimized
gatekeeping institution but the personal choice of every single VC contributor according to their own
criteria [75,76].
Therefore, this proposal intentionally does not assume a well-trusted gatekeeper that filters
qualified from non-qualified projects. However, this missing gatekeeping role makes it more
complicated for contributors to select VC projects that are worth being supported.
The reader should take additional surveys on VC, like [
] or cloud
computing [2,3,67,69,70]
into account to derive their own conclusions and discuss this perspective paper critically. For instance,
Ref. [
] provides a broad and excellent overview of several grid-based, cloud-based, mobile,
and parallel VC projects, frameworks, and technological approaches. As the reader may have noticed,
this perspective paper is highly influenced by [
]. However, to the best of the author’s knowledge,
there does not exist any survey that covers cloud computing and VC in parallel focusing particularly
on the aspect of how to combine concepts of both domains to overcome the project exclusiveness
problem in VC.
It is interesting to see that grid- and cloud-based projects form the majority of VC projects
(see Figure 4). Thus, there is some kind of attraction in adopting Cloud technologies for VC
(see Figure 5). In particular, recent cloud-native trends like standardization of fine-grained deployment
units via containers provide exciting opportunities. “The efficient use of available resources and tight
integration with the host operating system makes container technologies a plausible choice for VC. [...] More
research is important to investigate the suitability and efficiency of container technologies for VC, specifically for
volunteer cloud systems [64]”.
However, this paper shifted the focus less on volunteer clouds but postulated to adopt container
technologies in VC to improve the overall share- and portability between VC projects to enable
overflow processing between different projects. Projects like [
] strive to do something similar by
integrating supercomputing with VC and Cloud Computing. However, their focus is more on how to
make the high-performance end of supercomputing data centers available for VC.
6. Conclusions
The COVID-19 pandemic created the largest volunteer supercomputer on earth. However,
this largest supercomputer on the planet ran idle for a significant amount of time—what a waste of
resources. Therefore, this perspective paper investigated how the sharing of donated resources across
VC projects could be improved. If one cannot fight COVID-19, one might contribute to mathematical
research, climate research, other disease research, or any different computationally intensive kind
of research. Most of the VC donators will provide their resources regardless of the specific aim of a
VC project.
This perspective paper proposes to tackle the disclosed resource sharing shortcomings of
volunteer computing using technologies that have been invented, optimized, and adapted for
entirely different purposes by cloud-native companies like Uber, Airbnb, Google, or Facebook. Such
promising technologies might be containers, serverless architectures, image registries, distributed
service registries that can address problems like hardware heterogeneity, sandboxing, code signing
and updating, result verification, and most importantly to overcome project exclusiveness. All these
cloud-native technologies mentioned have one thing in common: They already exist and are all tried
and tested in large web-scale deployments.
However, the reader should keep in mind that this paper is a perspective paper. It does not present
a validated solution proposal intentionally. Nevertheless, it offered a detailed list of technologies to
overcome current shortcomings of VC that the COVID-19 case disclosed. This concrete strategy does
not claim the “philosopher’s stone” but strives to foster discussions in the VC community on how
Future Internet 2020,12, 98 16 of 20
VC could transform into a more global VC grid of cooperating VC projects that share and do not
claim resources. In addition, a lot of interesting research questions will appear if this path is followed.
This path compromises the fact that each VC project is still operating their own master nodes and
control plane infrastructure but yielding access to donated resources.
Consequently, a win–win-situation can be expected: Future VC projects should gain access to
a much broader set of donated resources. COVID-19 showed us that VC projects can easily build
the biggest supercomputer on earth. In addition, VC donors would gain access to a much more
multifarious spectrum of research projects. The middleware must simply be more standardized,
and more focused on resource sharing. Cloud computing has followed this exact path successfully for
a bit more than a decade. Perhaps VC should also have a look?
Funding: This research received no external funding.
Let me thank our students at the Lübeck University of Applied Sciences to support the
Folding@home project and Sars-CoV-2 research providing precious assets: Gaming PCs with powerful GPUs.
However, the primordial and likely unconscious initiator of this paper was Josef Adersberger (we normally
cooperate in several cloud-native publication projects). He is the CEO of the cloud-native consulting company
QAware GmbH and a cloud-native computing expert. During the COVID-19 pandemic, employees of QAware
formed a Folding@home group to support Sars-CoV-2 research, and Josef Adersberger posted their company rank
(top 3% of worldwide contributors) on Twitter. We accepted his “challenge” and supported the Folding@home
project as well (like many others worldwide). Without this “challenge”, this perspective paper would never have
been written.
Conflicts of Interest: The author declares no conflict of interest.
The following abbreviations are used in this manuscript:
API Application Programming Interface
BOINC Berkeley Open Infrastructure for Network Computing (
CNCF Cloud-Native Computing Foundation (
HTTP Hypertext Transfer Protocol
HW Hardware
IaaS Infrastructure as a Service
IP Internet Protocol
OCI Open Container Initiative (
PaaS Platform as a Service
REST Representational State Transfer
SaaS Software as a Service
SOA Service Oriented Architecture
URL Uniform Resource Locator
VC Volunteer Computing
QoS Quality of Service
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... Cloud services enable users (i.e., COVID-19 patients, etc.) to access the electronic healthcare records shared on their smartphones using the internet network. This will ultimately make the improved patient's service possible in various ways in the ongoing COVID-19 Pandemic Al-Qaness et al., 2020;Kratzke, 2020). Figure 5 briefs the workflow process through the cloud computing concept in the framework of remote location services. ...
Cloud computing facilitates collaboration, communication, and essential online services during the COVID-19 crisis. The current situation of the COVID-19 pandemic has compelled people to work from their homes, but they have to communicate, collaborate online. Thus, we see an essential role of cloud computing in taking up this challenge of working from home and delivering efficiently. A brief review of Cloud Computing service in the context of COVID-19 pandemic is done using recent papers' by searching keywords such as "Cloud Computing" and "COVID-19" from PubMed's database SCOPUS and Google Scholar. During the lockdown situation, cloud computing technology helps provide commendable service in the healthcare domain. It provides an advanced infrastructure for facilitating digital transformation. A brief discussion has been made on how cloud computing components are vital for overcoming the ongoing situation. This paper also studies the remote working of cloud computing for the COVID-19 pandemic and finally identified significant cloud computing applications for the COVID-19 pandemic. All countries focus on reducing this virus's spread, so this technology helps minimize the spread of this virus by providing online services. It provides an innovative environment that enhances the creativity and productivity of healthcare workers. This technology is efficient in detecting, tracking, and monitoring newly infected patients. In the future, this technology will insight and provide control over this infection to save millions of lives worldwide. This technology is also quite helpful to forecast the future impact of the SARS-Co-2 virus.
... Folding@home became 15x faster than any current supercomputers such as IBM Summit. 15 In the OpenPandemics-COVID-19 P.V.C project, more than 798,000 volunteers have so far achieved the equivalent of 70,000 years' worth of computing (by way of explanation, a single-processor PC would need to work that long to complete the computations) and Identified 70 chemical compounds, from a large collection of 80 million molecule candidates to fight against COVID-19 in a short time. 16 ...
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SETI@home was a well-known groundbreaking public volunteer computing (P.V.C) project in which 5.2 million participants donated their computers' unused resources to analyze radio signals from space to find extraterrestrial intelligence. Folding@home is another P.V.C project, aimed at fighting COVID-19, accessing more computing power than all top-500 of the world's supercomputers combined. Despite several advantages of P.V.C projects such as low cost, this approach has yet to be used in dentistry. The authors strongly encourage the use of this innovative technology by dental researchers and clinicians.
... The main challenge of the implementation lies in the fact that the Ethereum blockchain stores a massive amount of heterogeneous data, smart-contracts included, which enormously grow in time. For this reason, Smart Corpus was designed to be scalable, by adopting the latest cutting-edge technology, such as document-oriented database, graph query language and serverless computing platform (12). ...
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Many empirical software engineering studies show that there is a need for repositories where source codes are acquired, filtered and classified. During the last few years, Ethereum block explorer services have emerged as a popular project to explore and search for Ethereum blockchain data such as transactions, addresses, tokens, smart contracts’ source codes, prices and other activities taking place on the Ethereum blockchain. Despite the availability of this kind of service, retrieving specific information useful to empirical software engineering studies, such as the study of smart contracts’ software metrics, might require many subtasks, such as searching for specific transactions in a block, parsing files in HTML format, and filtering the smart contracts to remove duplicated code or unused smart contracts. In this paper, we afford this problem by creating Smart Corpus, a corpus of smart contracts in an organized, reasoned and up-to-date repository where Solidity source code and other metadata about Ethereum smart contracts can easily and systematically be retrieved. We present Smart Corpus’s design and its initial implementation, and we show how the data set of smart contracts’ source codes in a variety of programming languages can be queried and processed to get useful information on smart contracts and their software metrics. Smart Corpus aims to create a smart-contract repository where smart-contract data (source code, application binary interface (ABI) and byte code) are freely and immediately available and are classified based on the main software metrics identified in the scientific literature. Smart contracts’ source codes have been validated by EtherScan, and each contract comes with its own associated software metrics as computed by the freely available software PASO. Moreover, Smart Corpus can be easily extended as the number of new smart contracts increases day by day.
Märkte verändern sich immer schneller, Kundenwünsche stehen im Mittelpunkt – viele Unternehmen sehen sich 𝗛𝗲𝗿𝗮𝘂𝘀𝗳𝗼𝗿𝗱𝗲𝗿𝘂𝗻𝗴𝗲𝗻 gegenüber, die nur 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗯𝗲𝗵𝗲𝗿𝗿𝘀𝗰𝗵𝗯𝗮𝗿 sind. Um diese Anforderungen zu bewältigen, bietet sich der Einsatz von 𝗖𝗹𝗼𝘂𝗱-𝗻𝗮𝘁𝗶𝘃𝗲-𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝗻 an. Dabei reicht es jedoch nicht aus, einen Account bei einem Cloud-Anbieter anzulegen. Es geht auch darum, die unterschiedlichen Faktoren zu verstehen, die den Erfolg von Cloud-native-Projekten beeinflussen. Anhand von realen 𝗣𝗿𝗮𝘅𝗶𝘀𝗯𝗲𝗶𝘀𝗽𝗶𝗲𝗹𝗲𝗻 wird gezeigt, was bei der Umsetzung in 𝘂𝗻𝘁𝗲𝗿𝘀𝗰𝗵𝗶𝗲𝗱𝗹𝗶𝗰𝗵𝗲𝗻 𝗕𝗿𝗮𝗻𝗰𝗵𝗲𝗻 gut und was schlecht gelaufen ist und welche Best Practices sich daraus ableiten lassen. Dabei wird auch die Migration von Legacy-Code berücksichtigt. 𝗜𝗧-𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗸𝘁𝗲𝗻 𝘃𝗲𝗿𝗺𝗶𝘁𝘁𝗲𝗹𝘁 𝗱𝗶𝗲𝘀𝗲𝘀 𝗕𝘂𝗰𝗵 𝗱𝗮𝘀 𝗴𝗿𝘂𝗻𝗱𝗹𝗲𝗴𝗲𝗻𝗱𝗲 𝗪𝗶𝘀𝘀𝗲𝗻, 𝘂𝗺 𝗖𝗹𝗼𝘂𝗱-𝗻𝗮𝘁𝗶𝘃𝗲-𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝗻 𝘂𝗻𝗱 𝗱𝗶𝗲 𝗗𝗲𝘃𝗢𝗽𝘀-𝗞𝘂𝗹𝘁𝘂𝗿 𝗶𝗻 𝗶𝗵𝗿𝗲𝗺 𝗣𝗿𝗼𝗷𝗲𝗸𝘁 𝗼𝗱𝗲𝗿 𝗶𝗺 𝗴𝗲𝘀𝗮𝗺𝘁𝗲𝗻 𝗨𝗻𝘁𝗲𝗿𝗻𝗲𝗵𝗺𝗲𝗻 einzuführen. Das Buch beleuchtet den 𝗖𝗹𝗼𝘂𝗱-𝗻𝗮𝘁𝗶𝘃𝗲-𝗪𝗮𝗻𝗱𝗲𝗹 aus 𝘂𝗻𝘁𝗲𝗿𝘀𝗰𝗵𝗶𝗲𝗱𝗹𝗶𝗰𝗵𝗲𝗻 𝗣𝗲𝗿𝘀𝗽𝗲𝗸𝘁𝗶𝘃𝗲𝗻: Von der Unternehmenskultur, der Cloud-Ökonomie und der Einbeziehung der Kunden (Co-Creation) über das Projektmanagement (Agilität) und die Softwarearchitektur bis hin zu Qualitätssicherung (Continuous Delivery) und Betrieb (DevOps). ▪️ Grundlagen des Cloud Computings (Service-Modelle und Cloud-Ökonomie) ▪️ Das Everything-as-Code-Paradigma (DevOps, Deployment Pipelines, IaC) ▪️ Den Systembetrieb mit Container-Orchestrierung automatisieren ▪️ Microservice- und Serverless-Architekturen verstehen und Cloud-native-Architekturen mit Domain Driven Design entwerfen EXTRA: 𝗖𝗖𝟬-𝗹𝗶𝘇𝗲𝗻𝘀𝗶𝗲𝗿𝘁𝗲 𝘂𝗻𝗱 𝗲𝗱𝗶𝘁𝗶𝗲𝗿𝗯𝗮𝗿𝗲 𝗛𝗮𝗻𝗱𝗼𝘂𝘁𝘀 und Labs für Trainer:innen und Dozent:innen finden sich übrigens hier:
Many empirical software engineering studies show that there is a great need for repositories where source code is acquired, filtered and classified. During the last few years, Ethereum block explorer services have emerged as a popular project to explore and search Ethereum blockchain data such as transactions, addresses, tokens, smart-contracts' source code, prices and other activities taking place on the Ethereum blockchain. Despite the availability of this kind of services, retrieving specific information useful to empirical software engineering studies, such as the study of smart-contracts' software metrics might require many sub-tasks, such as searching specific transactions in a block, parsing files in HTML format and filtering the smart-contracts to remove duplicated code or unused smart-contracts. In this paper we afford this problem creating Smart Corpus', a Corpus of Smart Contracts in an organized reasoned and up to date repository where Solidity source code and other metadata about Ethereum smart contracts can easily and systematically be retrieved. We present the Smart Corpus' design and its initial implementation and we show how the data-set of smart contracts' source code in a variety of programming languages can be queried and processed, get useful information on smart contracts and their software metrics. The Smart Corpus aims to create a smart-contracts' repository where smart contracts data (source code, ABI and byte-code) are freely and immediately available and also classified based on the main software metrics identified in the scientific literature. Smart-contracts source code has been validated by EtherScan and each contract comes with its own associated software metrics as computed by the freely available software PASO. Moreover, Smart Corpus can be easily extended, as the number of new smart-contracts increases day by day.
Full-text available
Cloud computing can be a game-changer for computationally intensive tasks like simulations. The computational power of Amazon, Google, or Microsoft is even available to a single researcher. However, the pay-as-you-go cost model of cloud computing influences how cloud-native systems are being built. We transfer these insights to the simulation domain. The major contributions of this paper are twofold: (A) we propose a cloud-native simulation stack and (B) derive expectable software engineering trends for cloud-native simulation services. Our insights are based on systematic mapping studies on cloud-native applications, a review of cloud standards, action research activities with cloud engineering practitioners, and corresponding software prototyping activities. Two major trends have dominated cloud computing over the last 10 years. The size of deployment units has been minimized and corresponding architectural styles prefer more fine-grained service decompositions of independently deployable and horizontally scalable services. We forecast similar trends for cloud-native simulation architectures. These similar trends should make cloud-native simulation services more microservice-like, which are composable but just ''simulate one thing well.'' However, merely transferring existing simulation models to the cloud can result in significantly higher costs. One critical insight of our (and other) research is that cloud-native systems should follow cloud-native architecture principles to leverage the most out of the pay-as-you-go cost model.
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“Volunteer computing” is the use of consumer digital devices for high-throughput scientific computing. It can provide large computing capacity at low cost, but presents challenges due to device heterogeneity, unreliability, and churn. BOINC, a widely-used open-source middleware system for volunteer computing, addresses these challenges. We describe BOINC’s features, architecture, implementation, and algorithms.
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Volunteer Computing is a kind of distributed computing that harnesses the aggregated spare computing re- sources of volunteer devices. It provides a cheaper and greener alternative computing infrastructure that can complement the dedicated, centralized, and expensive data centres. The aggregated idle computing resources of devices ranging from desktop computers to routers and smart TVs are being utilized to provide the much needed computing infrastructure for compute intensive tasks such as scientific simulations and big data analysis. However, the use of Volunteer Computing is still dominated by scientific applications and only a very small fraction of the potential volunteer nodes are participating. This paper provides a comprehensive survey of Volunteer Computing, covering key technical and operational issues such as security, task distribution, resource management, and incentive models. The paper also presents a taxonomy of Volunteer Computing systems, together with discussions of the characteristics of specific systems in each category. In order to harness the full potentials of Volunteer Computing and make it a reliable alternative computing infrastructure for general applications, we need to improve the existing techniques and device new mechanisms. Thus, this paper also sheds light on important issues regarding the future research and development of Volunteer Computing systems with the aim of making them a viable alternative computing infrastructure.
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This paper presents a review of cloud application architectures and its evolution. It reports observations being made during a research project that tackled the problem to transfer cloud applications between different cloud infrastructures. As a side effect, we learned a lot about commonalities and differences from plenty of different cloud applications which might be of value for cloud software engineers and architects. Throughout the research project, we analyzed industrial cloud standards, performed systematic mapping studies of cloud-native application-related research papers, did action research activities in cloud engineering projects, modeled a cloud application reference model, and performed software and domain-specific language engineering activities. Two primary (and sometimes overlooked) trends can be identified. First, cloud computing and its related application architecture evolution can be seen as a steady process to optimize resource utilization in cloud computing. Second, these resource utilization improvements resulted over time in an architectural evolution of how cloud applications are being built and deployed. A shift from monolithic service-oriented architectures (SOA), via independently deployable microservices towards so-called serverless architectures, is observable. In particular, serverless architectures are more decentralized and distributed and make more intentional use of separately provided services. In other words, a decentralizing trend in cloud application architectures is observable that emphasizes decentralized architectures known from former peer-to-peer based approaches. This is astonishing because, with the rise of cloud computing (and its centralized service provisioning concept), the research interest in peer-to-peer based approaches (and its decentralizing philosophy) decreased. However, this seems to change. Cloud computing could head into the future of more decentralized and more meshed services.
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Emerging cloud systems, such as volunteer clouds and mobile clouds, are getting momentum among the current topics that dominate the research landscape of Cloud Computing. Volunteer cloud computing is an economical, secure, and greener alternative solution to the current Cloud Computing model that is based on data centers, where tens of thousands of dedicated servers are setup to back the cloud services. This paper presents cuCloud, a Volunteer Computing as a Service (VCaaS) system that is based on the spare resources of personal computers owned by individuals and/or organizations. The paper addresses the design and implementation issues of cuCloud, including the technical details of its integration with the well-known open source IaaS cloud management system, CloudStack. The paper also presents the empirical performance evidence of cuCloud in comparison with Amazon EC2 using a big-data application based on Hadoop.
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Microservices are an architectural approach emerging out of service-oriented architecture, emphasizing self-management and lightweightness as the means to improve software agility, scalability, and autonomy. This article examines microservice evolution from the technological and architectural perspectives and discusses key challenges facing future microservice developments.
Conference Paper
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Microservices is an architectural style increasing in popularity. However, there is still a lack of understanding how to adopt a microservice-based architectural style. We aim at characterizing different microservice architectural style patterns and the principles that guide their definition. We conducted a systematic mapping study in order to identify reported usage of microservices and based on these use cases extract common patterns and principles. We present two key contributions. Firstly, we identified several agreed microservice architecture patterns that seem widely adopted and reported in the case studies identified. Secondly, we presented these as a catalogue in a common template format including a summary of the advantages, disadvantages, and lessons learned for each pattern from the case studies. We can conclude that different architecture patterns emerge for different migration, orchestration, storage and deployment settings for a set of agreed principles.
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Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services. It represents an evolution of cloud programming models, abstractions, and platforms, and is a testament to thematurity and wide adoption of cloud technologies. In this chapter, we survey existing serverless platforms from industry, academia, and open-source projects, identify key characteristics and use cases, and describe technical challenges and open problems.
Volunteer Computing (VC) is a computing model that uses donated computing cycles on the devices such as laptops, desktops, and tablets to do scientific computing. BOINC is the most popular software framework for VC and it helps in connecting the projects needing computing cycles with the volunteers interested in donating the computing cycles on their resources. It has already enabled projects with high societal impact to harness several PetaFLOPs of donated computing cycles. Given its potential in elastically augmenting the capacity of existing supercomputing resources for running High-Throughput Computing (HTC) jobs, we have extended the BOINC software infrastructure and have made it amenable for integration with the supercomputing and cloud computing environments. We have named the extension of the BOINC software infrastructure as BOINC@TACC, and are using it to route *qualified* HTC jobs from the supercomputers at the Texas Advanced Computing Center (TACC) to not only the typically volunteered devices but also to the cloud computing resources such as Jetstream and Chameleon. BOINC@TACC can be extremely useful for those researchers/scholars who are running low on allocations of compute-cycles on the supercomputers, or are interested in reducing the turnaround time of their HTC jobs when the supercomputers are over-subscribed. We have also developed a web-application for TACC users so that, through the convenience of their web-browser, they can submit their HTC jobs for running on the resources volunteered by the community. An overview of the BOINC@TACC project is presented in this paper. The BOINC@TACC software infrastructure is open-source and can be easily adapted for use by other supercomputing centers that are interested in building their volunteer community and connecting them with the researchers needing multi-petascale (and even exascale) computing power for their HTC jobs.