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Casalicchio, EmilianoORCID iD iconorcid.org/0000-0002-3118-5058
Publications (10 of 14) Show all publications
Casalicchio, E. (2019). A study on performance measures for auto-scaling CPU-intensive containerized applications. Cluster Computing
Open this publication in new window or tab >>A study on performance measures for auto-scaling CPU-intensive containerized applications
2019 (English)In: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543Article in journal (Refereed) Epub ahead of print
Abstract [en]

Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes’ auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented.

Place, publisher, year, edition, pages
Springer New York LLC, 2019
Keywords
Auto-scaling, Autonomic computing, Container, Correlation, Docker, Kubernetes, Performance evaluation, Computer networks, Correlation methods, Software engineering, Performance evaluations, Containers
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17534 (URN)10.1007/s10586-018-02890-1 (DOI)2-s2.0-85059669161 (Scopus ID)
Available from: 2019-01-28 Created: 2019-01-28 Last updated: 2019-01-28Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2019). How to Measure Energy Consumption in Machine Learning Algorithms. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham. Paper presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018; Dublin; Ireland; 10 September 2018 through 14 September 2018 (pp. 243-255). , 11329
Open this publication in new window or tab >>How to Measure Energy Consumption in Machine Learning Algorithms
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2019 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham, 2019, Vol. 11329, p. 243-255Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.

Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11329
Keywords
Computer architecture, Energy efficiency, Green computing, Machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17209 (URN)10.1007/978-3-030-13453-2_20 (DOI)9783030134525 (ISBN)
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018; Dublin; Ireland; 10 September 2018 through 14 September 2018
Funder
Knowledge Foundation, 20140032
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2019-04-18Bibliographically approved
Anwar, M., Henesey, L. & Casalicchio, E. (2019). The feasibility of Blockchain solutions in the maritime industry. In: : . Paper presented at 31st NOFOMA CONFERENCE "Supply Chains and Sustainable Development of Societies", Oslo. Oslo, Norway: The Nordic Logistics Research Network (NOFOMA)
Open this publication in new window or tab >>The feasibility of Blockchain solutions in the maritime industry
2019 (English)Conference paper, Published paper (Other academic)
Abstract [en]

Purpose / Value

The concept of Blockchain technology in supply chain management is well discussed, yet

inadequately theorized in terms of its applicability, especially within the maritime industry,

which forms a fundamental node of the entire supply chain network. More so, the assumptive

grounds associated with the technology have not been openly articulated, leading to unclear

ideas about its applicability.

Design/methodology/approach

The research is designed divided into two Stages. This paper (Stage one) enhanced

literature review for data collection in order to gauge the properties of the Blockchain

technology, and to understand and map those characteristics with the Bill of Lading

process within maritime industry. In Stage two an online questionnaire is conducted to

assess the feasibility of Blockchain technology for different maritime use-cases.

Findings

The research that was collected and analysed partly from deliverable in the

Connect2SmallPort Project and from other literature suggests that Blockchain can be an

enabler for improving maritime supply chain. The use-case presented in this paper highlights

the practicality of the technology. It was identified that Blockchain possess characteristics

suitable to mitigate the risks and issues pertaining to the paper-based Bill of Lading process.

Research limitations

The study would mature further after the execution of the Stage Two. By the end of both

Stages, a framework for Blockchain adoption with a focus on the maritime industry would

be proposed.

Practical implications

The proposed outcome indicated the practicality of technology, which could be beneficial

for the port stakeholders that wish to use Blockchain in processing Bill of Lading or

contracts.

Social implications

The study may influence the decision makers to consider the benefits of using the Blockchain

technology, thereby, creating opportunities for the maritime industry to leverage the

technology with government’s support.

Place, publisher, year, edition, pages
Oslo, Norway: The Nordic Logistics Research Network (NOFOMA), 2019. p. 5
Series
Supply Chain Designs and Sustainable Development of Societies ; 1
Keywords
Digitalization, Blockchain, Maritime, Bill of Lading, Feasibility study
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-18483 (URN)
Conference
31st NOFOMA CONFERENCE "Supply Chains and Sustainable Development of Societies", Oslo
Projects
CONNECT2SMALLPORTS
Available from: 2019-07-16 Created: 2019-07-16 Last updated: 2019-09-06Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2018). Hoeffding Trees with nmin adaptation. In: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018): . Paper presented at 5th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), 1–4 October 2018, Turin (pp. 70-79). IEEE
Open this publication in new window or tab >>Hoeffding Trees with nmin adaptation
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2018 (English)In: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018), IEEE, 2018, p. 70-79Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient.In this paper we present the nmin adaptation method for Hoeffding trees. This method adapts the value of the nmin pa- rameter, which significantly affects the energy consumption of the algorithm. The method reduces unnecessary computations and memory accesses, thus reducing the energy, while the accuracy is only marginally affected. We experimentally compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation). The results show that VFDT-nmin consumes up to 27% less energy than the standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent of accuracy in a few datasets.

Place, publisher, year, edition, pages
IEEE, 2018
Series
Proceedings of the International Conference on Data Science and Advanced Analytics, ISSN 2472-1573
Keywords
data stream mining; green artificial intelligence; energy efficiency; hoeffding trees; energy aware machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17207 (URN)10.1109/DSAA.2018.00017 (DOI)000459238600008 ()978-1-5386-5090-5 (ISBN)
Conference
5th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), 1–4 October 2018, Turin
Funder
Knowledge Foundation, 20140032
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2019-04-05Bibliographically approved
Nardelli, M., Cardellini, V. & Casalicchio, E. (2018). Multi-Level Elastic Deployment of Containerized Applications in Geo-Distributed Environments. In: Proceedings - 2018 IEEE 6th International Conference on Future Internet of Things and Cloud, FiCloud 2018: . Paper presented at 6th IEEE International Conference on Future Internet of Things and Cloud, FiCloud 2018; Barcelona; Spain; 6 August 2018 through 8 August 2018 (pp. 1-8). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Multi-Level Elastic Deployment of Containerized Applications in Geo-Distributed Environments
2018 (English)In: Proceedings - 2018 IEEE 6th International Conference on Future Internet of Things and Cloud, FiCloud 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

Containers are increasingly adopted, because they simplify the deployment and management of applications. Moreover, the ever increasing presence of IoT devices and Fog computing resources calls for the development of new approaches for decentralizing the application execution, so to improve the application performance. Although several solutions for orchestrating containers exist, the most of them does not efficiently exploit the characteristics of the emerging computing environment. In this paper, we propose Adaptive Container Deployment (ACD), a general model of the deployment and adaptation of containerized applications, expressed as an Integer Linear Programming problem. Besides acquiring and releasing geo-distributed computing resources, ACD can optimize multiple run-time deployment goals, by exploiting horizontal and vertical elasticity of containers. We show the flexibility of the ACD model and, using it as benchmark, we evaluate the behavior of several greedy heuristics for determining the container deployment. © 2018 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
Adaptation, Container, Optimal Deployment, Quality of Service, Run time Management, Virtual Machine, Fog computing, Integer programming, Internet of things, Application performance, Computing environments, Distributed computing resources, Distributed environments, Integer Linear Programming, Runtime management, Containers
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17456 (URN)10.1109/FiCloud.2018.00009 (DOI)000482232200001 ()2-s2.0-85057726990 (Scopus ID)9781538675038 (ISBN)
Conference
6th IEEE International Conference on Future Internet of Things and Cloud, FiCloud 2018; Barcelona; Spain; 6 August 2018 through 8 August 2018
Available from: 2019-01-09 Created: 2019-01-09 Last updated: 2019-09-11Bibliographically approved
Shirinbab, S., Lundberg, L. & Casalicchio, E. (2018). Performance Comparison between Horizontal Scaling of Hypervisor and Container Based Virtualization using Cassandra NoSQL Database. In: Proceeding of the 3rd International Conference on Virtualization Application and Technology: . Paper presented at 3rd International Conference on Virtualization Application and Technology (ICVAT 2018, Nov.16-18, Sanya, China.
Open this publication in new window or tab >>Performance Comparison between Horizontal Scaling of Hypervisor and Container Based Virtualization using Cassandra NoSQL Database
2018 (English)In: Proceeding of the 3rd International Conference on Virtualization Application and Technology, 2018, , p. 6Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing promises customers the ondemand ability to scale in face of workload variations. There are different ways to accomplish scaling, one is vertical scaling and the other is horizontal scaling. The vertical scaling refers to buying more power (CPU, RAM), buying a more expensive and robust server, which is less challenging to implement but exponentially expensive. While, the horizontal scaling refers to adding more servers with less processor and RAM, which is usually cheaper overall and can scale very well. The majority of cloud providers prefer the horizontal scaling approach, and for them would be very important to know about the advantages and disadvantages of both technologies from the perspective of the application performance at scale. In this paper, we compare performance differences caused by scaling of the different virtualization technologies in terms of CPU utilization, latency, and the number of transactions per second. The workload is Apache Cassandra, which is a leading NoSQL distributed database for Big Data platforms. Our results show that running multiple instances of the Cassandra database concurrently, affected the performance of read and write operations differently; for both VMware and Docker, the maximum number of read operations was reduced when we ran several instances concurrently, whereas the maximum number of write operations increased when we ran instances concurrently.

Publisher
p. 6
Keywords
Cassandra; Cloud computing; Docker container; Horizontal scaling; NoSQL database; Performance comparison; Virtualization; VMware virtual machine
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-17212 (URN)
Conference
3rd International Conference on Virtualization Application and Technology (ICVAT 2018, Nov.16-18, Sanya, China
Available from: 2018-11-02 Created: 2018-11-02 Last updated: 2018-11-06Bibliographically approved
Casalicchio, E., Cardellini, V., Interino, G. & Palmirani, M. (2018). Research challenges in legal-rule and QoS-aware cloud service brokerage. Future generations computer systems, 78(Part 1), 211-223
Open this publication in new window or tab >>Research challenges in legal-rule and QoS-aware cloud service brokerage
2018 (English)In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 78, no Part 1, p. 211-223Article in journal (Refereed) Published
Abstract [en]

Abstract 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 system and by the limited control that customers have on the services. Today, the cloud industry is aware of this problem (as evidenced by the compliance program of many cloud service providers), and the research community is addressing the many facets of the legal-rule compliance checking and quality assurance problem. Cloud service brokerage plays an important role in legislation compliance and QoS management of cloud services. In this paper we discuss our experience in designing a legal-rule and QoS-aware cloud service broker, and we explore relate research issues. Specifically we provide three main contributions to the literature: first, we describe the detailed design architecture of the legal-rule and QoS-aware broker. Second, we discuss our design choices which rely on the state of the art solutions available in literature. We cover four main research areas: cloud broker service deployment, seamless cloud service migration, cloud service monitoring, and legal rule compliance checking. Finally, from the literature review in these research areas, we identify and discuss research challenges.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Cloud computing, Autonomic computing, Legislation compliance checking, Optimization, Quality of service, Monitoring, Service migration, Service portability
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-13668 (URN)10.1016/j.future.2016.11.025 (DOI)000413127800016 ()
Funder
Knowledge Foundation, 20140032
Note

open access

Available from: 2016-12-26 Created: 2016-12-26 Last updated: 2018-01-13Bibliographically approved
Casalicchio, E. & Perciballi, V. (2017). Measuring Docker Performance: What a Mess!!!. In: ICPE 2017 - Companion of the 2017 ACM/SPEC International Conference on Performance Engineering: . Paper presented at 8th ACM/SPEC International Conference on Performance Engineering, ICPE L'Aquila; Italy (pp. 11-16). ACM
Open this publication in new window or tab >>Measuring Docker Performance: What a Mess!!!
2017 (English)In: ICPE 2017 - Companion of the 2017 ACM/SPEC International Conference on Performance Engineering, ACM , 2017, p. 11-16Conference paper, Published paper (Refereed)
Abstract [en]

Today, a new technology is going to change the way platforms for the internet of services are designed and managed. This technology is called container (e.g. Docker and LXC). The internet of service industry is adopting the container technology both for internal usage and as commercial offering. The use of container as base technology for large-scale systems opens many challenges in the area of resource management at run-time, for example: autoscaling, optimal deployment and monitoring. Specifically, monitoring of container based systems is at the ground of any resource management solution, and it is the focus of this work. This paper explores the tools available to measure the performance of Docker from the perspective of the host operating system and of the virtualization environment, and it provides a characterization of the CPU and disk I/O overhead introduced by containers.

Place, publisher, year, edition, pages
ACM, 2017
Series
ICPE ’17 Companion
Keywords
cloud computing, container, docker, internet of service, microservices, monitoring, performance evaluation
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-14450 (URN)10.1145/3053600.3053605 (DOI)9781450348997 (ISBN)
Conference
8th ACM/SPEC International Conference on Performance Engineering, ICPE L'Aquila; Italy
Projects
BigData@BTH
Funder
Knowledge Foundation, 20140032
Available from: 2017-06-09 Created: 2017-06-09 Last updated: 2017-06-13Bibliographically approved
Cardellini, V., Casalicchio, E., Grassi, V., Iannucci, S., Lo Presti, F. & Mirandola, R. (2017). MOSES: A platform for experimenting with qos-driven self-adaptation policies for service oriented systems. In: Lecture Notes in Computer Science: . Paper presented at International Seminar on Software Engineering for Self-Adaptive Systems: Assurances, 2013; Dagstuhl Castle; Germany (pp. 409-433). Springer Verlag, 9640
Open this publication in new window or tab >>MOSES: A platform for experimenting with qos-driven self-adaptation policies for service oriented systems
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2017 (English)In: Lecture Notes in Computer Science, Springer Verlag , 2017, Vol. 9640, p. 409-433Conference paper, Published paper (Refereed)
Abstract [en]

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 chapter we present MOSES, a software platform supporting QoS-driven adaptation of service-oriented systems. It has been conceived for service-oriented systems architected as composite services that receive requests generated by different classes of users. MOSES integrates within a unified framework different adaptation mechanisms. In this way it achieves a greater flexibility in facing various operating environments and the possibly conflicting QoS requirements of several concurrent users. Besides providing its own self-adaptation functionalities, MOSES lends itself to the experimentation of alternative approaches to QoS-driven adaptation of service-oriented systems thanks to its modular architecture. © Springer International Publishing AG 2017.

Place, publisher, year, edition, pages
Springer Verlag, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9640
Keywords
Adaptive systems, Quality of service, Software engineering, Adaptation mechanism, Composite services, Modular architectures, Operating environment, Service Oriented, Service Oriented Systems, Software platforms, Unified framework, Service oriented architecture (SOA)
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-15930 (URN)10.1007/978-3-319-74183-3_14 (DOI)2-s2.0-85041824985 (Scopus ID)9783319741826 (ISBN)
Conference
International Seminar on Software Engineering for Self-Adaptive Systems: Assurances, 2013; Dagstuhl Castle; Germany
Available from: 2018-02-22 Created: 2018-02-22 Last updated: 2018-02-22Bibliographically approved
Shirinbab, S., Lundberg, L. & Casalicchio, E. (2017). Performance Evaluation of Container and Virtual Machine Running Cassandra Workload. In: Essaaidi, M Zbakh, M (Ed.), PROCEEDINGS OF 2017 3RD INTERNATIONAL CONFERENCE OF CLOUD COMPUTING TECHNOLOGIES AND APPLICATIONS (CLOUDTECH): . Paper presented at 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), Rabat (pp. 24-31).
Open this publication in new window or tab >>Performance Evaluation of Container and Virtual Machine Running Cassandra Workload
2017 (English)In: PROCEEDINGS OF 2017 3RD INTERNATIONAL CONFERENCE OF CLOUD COMPUTING TECHNOLOGIES AND APPLICATIONS (CLOUDTECH) / [ed] Essaaidi, M Zbakh, M, 2017, p. 24-31Conference paper, Published paper (Refereed)
Abstract [en]

Today, scalable and high-available NoSQL distributed databases are largely used as Big Data platforms. Such distributed databases typically run on a virtualized infrastructure that could be implemented using Hypervisorb ased virtualiz ation or Container-based virtualiz ation. Hypervisor-based virtualization is a mature technology but imposes overhead on CPU, memory, networking, and disk Recently, by sharing the operating system resources and simplifying the deployment of applications, container-based virtualization is getting more popular. Container-based virtualization is lightweight in resource consumption while also providing isolation. However, disadvantages are security issues and 110 performance. As a result, today these two technologies are competing to provide virtual instances for running big data platforms. Hence, a key issue becomes the assessment of the performance of those virtualization technologies while running distributed databases. This paper presents an extensive performance comparison between VMware and Docker container, while running Apache Cassandra as workload. Apache Cassandra is a leading NoSQL distributed database when it comes to Big Data platforms. As baseline for comparisons we used the Cassandra's performance when running on a physical infrastructure. Our study shows that Docker had lower overhead compared to the VMware when running Cassandra. In fact, the Cassandra's performance on the Dockerized infrastructure was as good as on the Non-Virtualized.

Keywords
Cassandra, Cloud computing, Containers, Docker, NoSQL databases, Virtual machine, VMware, Big Data, Performance evaluation
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16000 (URN)000426451400004 ()978-1-5386-1115-9 (ISBN)
Conference
3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), Rabat
Available from: 2018-03-23 Created: 2018-03-23 Last updated: 2018-11-06Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3118-5058

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