Valeria Cardellini

University of Rome Tor Vergata | UNIROMA2 · Dipartimento di Ingegneria Civile e Ingegneria Informatica (DICII)

Microservices are widely used to enable agility and scalability in modern software systems, while cloud computing offers cost-effective ways to provision computing resources on demand. However, ensuring the correctness of scaling decisions and their impact on energy consumption is a challenging problem that has not been sufficiently addressed in pr...

The flexibility and the variety of computing resources offered by the cloud make it particularly attractive for executing user workloads. However, IaaS cloud environments pose non-trivial challenges in the case of workflow scheduling under deadlines and monetary cost constraints. Indeed, given the typical uncertain performance behavior of cloud res...

Modern computing environments are evolving towards the compute continuum paradigm, which promises to manage the heterogeneity and dynamism of geographically spread computing resources, supporting the execution of distributed and pervasive applications. As a complete understanding of all the implications and challenges posed by this new paradigm is...

Recent trends in deep learning (DL) imposed hardware accelerators as the most viable solution for several classes of high-performance computing (HPC) applications such as image classification, computer vision, and speech recognition. This survey summarizes and classifies the most recent advances in designing DL accelerators suitable to reach the pe...

Data Stream Processing (DSP) applications analyze data flows in near real-time by means of operators, which process and transform incoming data. Operators handle high data rates running parallel replicas across multiple processors and hosts. To guarantee consistent performance without wasting resources in face of variable workloads, auto-scaling te...

Intrusion Response is a relatively new field of research. Recent approaches for the creation of Intrusion Response Systems (IRSs) use Reinforcement Learning (RL) as a primary technique for the optimal or near-optimal selection of the proper countermeasure to take in order to stop or mitigate an ongoing attack. However, most of them do not consider...

Microservices can independently adjust their capacity to match demand while the autoscaling feature in cloud computing facilitates the users (i.e., developers) to provision resources required by their applications with less human intervention. Kubernetes is one of the well-known technologies used to deploy microservice-based applications and many a...

Intrusion Response is a relatively new field of research. Recent approaches for the creation of Intrusion Response Systems (IRSs) use Reinforcement Learning (RL) as a primary technique for the optimal or near-optimal selection of the proper countermeasure to take in order to stop or mitigate an ongoing attack. However, most of them do not consider...

Data Stream Processing (DSP) has emerged over the years as the reference paradigm for the analysis of continuous and fast information flows, which have often to be processed with low-latency requirements to extract insights and knowledge from raw data. Dealing with unbounded data flows, DSP applications are typically long-running and, thus, likely...

Cloud-native applications increasingly adopt the microservices architecture, which favors elasticity to satisfy the application performance requirements in face of variable workloads. To simplify the elasticity management, the trend is to create an auto-scaler instance per microservice, which controls its horizontal scalability by using the classic...

Data-intensive applications have attracted considerable attention in recent years. Business organizations are increasingly becoming data-driven and therefore look for novel ways to collect, analyze, and leverage the data at their disposal. The goal of this chapter is to overview some recurring performance management activities for data-intensive ap...

Emerging fog and edge computing environments enable the analysis of Big Data collected from devices (e.g., IoT sensors) with reduced latency compared to cloud-based solutions. In particular, many applications deal with continuous data flows in latency-sensitive domains (e.g., healthcare monitoring), where Data Stream Processing (DSP) systems repres...

The microservice architecture structures an application as a collection of loosely coupled and distributed services. Since application workloads usually change over time, the number of replicas per microservice should be accordingly scaled at run-time. The most widely adopted scaling policy relies on statically defined thresholds, expressed in term...

The fast increasing presence of Internet-of-Things and fog computing resources exposes new challenges due to heterogeneity and non-negligible network delays among resources as well as the dynamism of operating conditions. Such a variable computing environment leads the applications to adopt an elastic and decentralized execution. To simplify the ap...

Software containers are changing the way applications are designed and executed. Moreover, in the last few years, we see the increasing adoption of container orchestration tools, such as Kubernetes, to simplify the management of multi-container applications. Kubernetes includes simple deployment policies that spread containers on computing resource...

Given the always increasing size of computer systems, manually protecting them in case of attacks is unfeasible and error-prone. For this reason, until now, several model-based Intrusion Response Systems (IRSs) have been proposed with the purpose of limiting the amount of work of the system administrators. However, since the most advanced IRSs adop...

We consider several Software as a Service (SaaS) providers that offer services using the Cloud resources provided by an Infrastructure as a Service (IaaS) provider which adopts a pay-per-use scheme similar to the Amazon EC2 service, comprising flat, on demand, and spot virtual machine instances. For this scenario, we study the virtual machine provi...

Chapter "In Situ Visualization of Performance-Related Data in Parallel CFD Applications" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

In the last few years, a large number of real-time analytics applications rely on the Data Stream Processing (DSP) so to extract, in a timely manner, valuable information from distributed sources. Moreover, to efficiently handle the increasing amount of data, recent trends exploit the emerging presence of edge/Fog computing resources so to decentra...

Software containers are changing the way dis-tributed applications are executed and managed on cloud com-puting resources. Interestingly, containers offer the possibilityof handling workload fluctuations by exploiting both horizontaland vertical elasticity “on the fly”. However, most of the existingcontrol policies consider horizontal and vertical...

Data Stream Processing (DSP) has emerged as a key enabler to develop pervasive services that require to process data in a near real-time fashion. DSP applications keep up with the high volume of produced data by scaling their execution on multiple computing nodes, so as to process the incoming data flow in parallel. Workloads variability requires t...

Given the always increasing size of computer systems, manually protecting them in case of attacks is infeasible and error-prone. For this reason, several Intrusion Response Systems (IRSs) have been proposed so far, with the purpose of limiting the amount of work of an administrator. However, since the most advanced IRSs adopt a stateful approach, t...

Software containers are ever more adopted to man-age and execute distributed applications. Indeed, they enable to quickly scale the amount of computing resources by means of horizontal and vertical elasticity. Most of the existing works consider the deployment of containers in centralized data centers.However, to exploit the diffused presence of ed...

Data Stream Processing (DSP) applications should be capable to efficiently process high-velocity continuous data streams by elastically scaling the parallelism degree of their operators so to deal with high variability in the workload. Moreover, to efficiently use computing resources, modern DSP frameworks should seamlessly support infrastructure e...

The “New Landscapes of the Data Stream Processing in the era of Fog Computing” special issue aims to present new research works on topics related to recent advances in Data Streaming Processing (DSP) computing paradigm in the emerging environments of Fog Computing and Internet of Things (IoT). The papers included in this special issue are relevant...

Many scientific applications require the solution of large and sparse linear systems of equations using Krylov subspace methods; in this case, the choice of an effective preconditioner may be crucial for the convergence of the Krylov solver. Algebraic MultiGrid (AMG) methods are widely used as preconditioners, because of their optimal computational...

By exploiting on-the-fly computation, Data Stream Processing (DSP) applications can process huge volumes of data in a near real-time fashion. Adapting the application parallelism at run-time is critical in order to guarantee a proper level of QoS in face of varying workloads. In this paper, we consider Reinforcement Learning based techniques in ord...

The capability of efficiently processing the data streams emitted by nowadays ubiquitous sensing devices enables the development of new intelligent services. Data Stream Processing (DSP) applications allow for processing huge volumes of data in near real-time. To keep up with the high volume and velocity of data, these applications can elastically...

Traditional networks are transformed to enable full integration of heterogeneous hardware and software functions, that are configured at runtime, with minimal time to market, and are provided to their end users on “as a service” principle. Therefore, a countless number of possibilities for further innovation and exploitation opens up. Network Funct...

Data Stream Processing (DSP) applications are widely used to develop new pervasive services, which require to seamlessly process huge amounts of data in a near real-time fashion. To keep up with the high volume of daily produced data, these applications need to dynamically scale their execution on multiple computing nodes, so to process the incomin...

In the Big Data era, Data Stream Processing (DSP) applications should be capable to seamlessly process huge amount of data. Hence, they need to dynamically scale their execution on multiple computing nodes so to adjust to unpredictable data source rate. In this paper, we present a hierarchical and distributed architecture for the autonomous control...

With the emerging IoT and Cloud-based networked systems that rely heavily on virtualization technologies, elasticity becomes a dominant system engineering attribute for providing QoS-aware services to their users. Although the concept of elasticity can introduce significant QoS and cost benefits, its implementation in real systems is full of challe...

Processing data in a timely manner, data stream processing (DSP) applications are receiving an increasing interest for building new pervasive services. Due to the unpredictability of data sources, these applications often operate in dynamic environments; therefore, they require the ability to elastically scale in response to workload variations. In...

In order to reach challenging performance goals, computer architecture is expected to change significantly in the near future. Heterogeneous chips, equipped with different types of cores and memory, will force application developers to deal with irregular communication patterns, high levels of parallelism, and unexpected behavior.Load balancing amo...

Exploiting on-the-fly computation, Data Stream Processing (DSP) applications are widely used to process unbounded streams of data and extract valuable information in a near real-time fashion. As such, they enable the development of new intelligent and pervasive services that can improve our everyday life. To keep up with the high volume of daily pr...

Parallelism is a ubiquitous feature of modern computing architectures; indeed, we might even say that serial code is now automatically legacy code. Writing parallel code poses significant challenges to programs, and is often error-prone. Partitioned Global Address Space (PGAS) languages, such as Coarray Fortran (CAF), represent a promising developm...

The multiplication of a sparse matrix by a dense vector (SpMV) is a centerpiece of scientific computing applications: it is the essential kernel for the solution of sparse linear systems and sparse eigenvalue problems by iterative methods. The efficient implementation of the sparse matrix-vector multiplication is therefore crucial and has been the...

The ICT industry and specifically critical sectors, such as healthcare, transportation, energy and government, require as mandatory the compliance of ICT systems and services with legislation and regulation, as well as with standards. In the era of cloud computing, this compliance management issue is exacerbated by the distributed nature of the sys...

In the last few years, several processing approaches have emerged to deal with Big Data. Exploiting on-the-fly computation, Data Stream Processing (DSP) applications can process unbounded streams of data to extract valuable information in a near real-time fashion. To keep up with the high volume of daily produced data, the operators that compose a...

In this paper, we present a solution to the DEBS 2016 Grand Challenge that leverages Apache Flink, an open source platform for distributed stream and batch processing. We design the system architecture focusing on the exploitation of parallelism and memory efficiency so to enable an effective processing of high volume data streams on a distributed...

Data Stream Processing (DSP) applications are widely used to timely extract information from distributed data sources, such as sensing devices, monitoring stations, and social networks. To successfully handle this ever increasing amount of data, recent trends investigate the possibility of exploiting decentralized computational resources (e.g., Fog...

We consider a three-tier architecture for mobile and pervasive computing scenarios, consisting of a local tier of mobile nodes, a middle tier (cloudlets) of nearby computing nodes, typically located at the mobile nodes access points but characterized by a limited amount of resources, and a remote tier of distant cloud servers, which have practicall...

In High Performance Computing (HPC), minimizing communication overhead is one of the most important goals in order to get high performance. This is more than ever important on exascale platforms, where there will be a much higher degree of parallelism compared to petascale platforms, resulting in increased communication overhead with considerable i...

Dynamic service composition represents a key feature for service-based applications operating in dynamic and large scale network environments, as it allows leveraging the variety of offered services, and to cope with their volatility. However, the high number of services and the lack of central control pose a significant challenge for the scalabili...

Accelerators such as NVIDIA GPUs and Intel MICs are currently provided as co-processor devices, usable only through a CPU host. For Intel MICs it is planned that this constraint will be lifted in the near future: CPU and accelerator(s) will then form a single, many-core, processor capable of peak performance of several Teraflops with high energy e...

Storm is a distributed stream processing system that has recently gained increasing interest. We extend Storm to make it suitable to operate in a geographically distributed and highly variable environment such as that envisioned by the convergence of Fog computing, Cloud computing, and Internet of Things.

Fog computing is rapidly changing the distributed computing landscape by extending the Cloud computing paradigm to include wide-spread resources located at the network edges. This diffused infrastructure is well suited for the implementation of data stream processing (DSP) applications, by possibly exploiting local computing resources. Storm is an...

In this paper, we consider an application provider that executes simultaneously periodic long running jobs and needs to ensure a minimum throughput to guarantee QoS to its users; the application provider uses virtual machine (VM) resources offered by an IaaS provider. Aim of the periodic jobs is to compute measures on data collected over a specific...

Coarray Fortran is a set of features of the Fortran 2008 standard that make Fortran a PGAS parallel programming language. Two commercial compilers currently support coarrays: Cray and Intel. Here we present two coarray transport layers provided by the new OpenCoarrays project: one library based on MPI and the other on GASNet. We link the GNU Fortra...

The runtime management of Internet of Things (IoT) oriented applications deployed in multi-clouds is a complex issue due to the highly heterogeneous and dynamic execution environment. To effectively cope with such an environment, the cross-layer and multi-cloud effects should be taken into account and a decentralized self-adaptation is a promising...

Coarray Fortran is a set of features of the Fortran 2008 standard which makes Fortran a PGAS language. Currently, the coarray support is provided mainly by commercial compilers like Cray and Intel. In this work we present two coarray implementations on the GNU Fortran compiler. We present a performance comparison between our coarray implementations...

Hybrid nodes containing GPUs are rapidly becoming the norm in parallel machines. We have conducted some experiments regarding how to plug GPU-enabled computational kernels into PSBLAS, a MPI-based library specifically geared towards sparse matrix computations. In this paper, we present our findings on which strategies are more promising in the ques...

Exhaustive search is generally a last resort for solving a problem: each possible state of a system is generated and evaluated against a condition to find if the problem solution is attained. In some cases, for example in the reversal of cryptographic hash functions that make use of the salting technique, there are very few valid alternatives. Howe...

In this paper we consider a set of Software as a Service (SaaS) providers, that offer a set of Web services using the Cloud facilities provided by an Infrastructure as a Service (IaaS) provider. We assume that the IaaS provider offers a pay only what you use scheme similar to the Amazon EC2 service, comprising flat, on demand, and spot virtual mach...

We apply object-oriented software design patterns to develop code for scientific software involving sparse matrices. Design patterns arise when multiple independent developments produce similar designs which converge onto a generic solution. We demonstrate how to use design patterns to implement an interface for sparse matrix computations on NVIDIA...

A complex service-based system (CSBS), which comprises a multi-layer structure possibly spanning multiple organizations, operates in a highly dynamic and heterogeneous environment. At run time the quality of service provided by a CSBS may suddenly change, so that violations of the Service Level Agreements (SLAs) established within and across the bo...

Domain decomposition based on spatial locality is a classical data-parallel problem whose solution may improve by orders of magnitude when implemented on a GPU. Among the data structures involved in domain decomposition, uniform grids are widely used to speed up simulations in a number of fields, including computational physics and graphics. In thi...

Hybrid GPU/CPU clusters are becoming very popular in the scientific computing community, as attested by the number of such systems present in the Top 500 list. In this paper, we address one of the key algorithms for scientific applications: the computation of sparse matrix-vector products that lies at the heart of iterative solvers for sparse linea...

In this paper we consider several Software as a Service (SaaS) providers, that offer a set of applications using the Cloud facilities provided by an Infrastructure as a Service (IaaS) provider. We assume that the IaaS provider offers a pay only what you use scheme similar to the Amazon EC2 service, comprising flat, on demand, and spot virtual machi...

Service selection has been widely investigated by the SOA research community as an effective adaptation mechanism that allows a service broker, offering a composite service, to bind at runtime each task of the composite service to a corresponding concrete implementation, selecting it from a set of candidates which differ from one another in terms o...

Architecting software systems according to the service-oriented paradigm and designing runtime self-adaptable systems are two relevant research areas in today's software engineering. In this paper, we address issues that lie at the intersection of these two important fields. First, we present a characterization of the problem space of self-adaptati...

Service Oriented Systems (SOSs) based on the SOA paradigm are becoming popular thanks to a widely deployed internetworking infrastructure. They are composed by a possibly large number of heterogeneous third-party subsystems and usually operate in a highly varying execution environment, that makes it challenging to provide applications with Quality...

Cloud computing is an emerging paradigm used by an increasingly number of enterprises to support their business and promises to make the utility computing model fully realized by exploiting virtualization technologies. Free software is now mature not only to offer well-known server-side applications, but also to land on desktop computers. However,...

In the service computing paradigm, a service broker can build new applications by composing network-accessible services offered by loosely coupled independent providers. In this paper, we address the problem of providing a service broker, which offers to prospective users a composite service with a range of different Quality of Service (QoS) classe...