Energy and Power Efficiency in Cloud

Karpowicz, Michał P.; Niewiadomska-Szynkiewicz, Ewa; Arabas, Piotr; Sikora, Andrzej

doi:10.1007/978-3-319-44881-7_6

Cited by 10 publications

(7 citation statements)

References 86 publications

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“…This is different to long running applications in a PaaS area where multi-threaded implementations of pieces of functionality are suspended for code readability and maintenance. Avoiding multi-threaded code is also addressed by static code quality tools 117 .…”

Section: Checklist For Performing Faas Benchmarksmentioning

confidence: 99%

A Simulation Framework for Function as a Service

Manner

2024

Schriften Aus Der Fakultät Wirtschaftsinformatik Und Angewandte Informatik Der Otto-Friedrich-Universität Bamberg

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Serverless Computing is seen as a game changer in operating large-scale applications. While practitioners and researches often use this term, the concept they actually want to refer to is Function as a Service (FaaS). In this new service model, a user deploys only single functions to cloud platforms where the cloud provider deals with all operational concerns – this creates the notion of server-less computing for the user. Nonetheless, a few configurations for the cloud function are necessary for most commercial FaaS platforms as they influence the resource assignments like CPU time and memory. Due to these options, there is still an abstracted perception of servers for the FaaS user. The resource assignment and the different strategies to scale resources for public cloud offerings and on-premise hosted open-source platforms determine the runtime characteristics of cloud functions and are in the focus of this work. Compared to cloud offerings like Platform as a Service, two out of the five cloud computing characteristics improved. These two are rapid elasticity and measured service. FaaS is the first computational cloud model to scale functions only on demand. Due to an independent scaling and a strong isolation via virtualized environments, functions can be considered independent of other cloud functions. Therefore, noisy neighbor problems do not occur. The second characteristic, measured service, targets billing. FaaS platforms measure execution time on a millisecond basis and bill users accordingly based on the function configuration. This leads to new performance and cost trade-offs. Therefore, this thesis proposes a simulation approach to investigate this tradeoff in an early development phase. The alternative would be to deploy functions with varying configurations, analyze the execution data from several FaaS platforms and adjust the configuration. However, this alternative is time-consuming, tedious and costly. To provide a proper simulation, the development and production environment should be as similar as possible. This similarity is also known as dev-prod parity. Based on a new methodology to compare different virtualized environments, users of our simulation framework are able to execute functions on their machines and investigate the runtime characteristics for different function configurations at several cloud platforms without running their functions on the cloud platform at all. A visualization of the local simulations guide the user to choose an appropriate function configuration to resolve the mentioned trade-off dependent on their requirements.

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Section: Checklist For Performing Faas Benchmarksmentioning

confidence: 99%

A Simulation Framework for Function as a Service

Manner

2024

Schriften Aus Der Fakultät Wirtschaftsinformatik Und Angewandte Informatik Der Otto-Friedrich-Universität Bamberg

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“…• cpufreq governor [61], [56], [72]. In [73], a design of a feedback controller for solving the problem of low utilization of servers in a data-center running I/O-intensive applications is proposed.…”

Section: Energy-saving Technologiesmentioning

confidence: 99%

“…The ability to control the activity of computing nodes is provided by various tools and platforms described in Sections 3 and 4, for instance by Slurm. Moreover, various efforts have been undertaken to develop energy-efficient task scheduling, load balancing and green routing protocols [83]- [85], [72]. However, optimized task scheduling and allocation becomes much more difficult with the classic makespan criterion with energy-efficiency and user-perceived QoS [86], [87] taken into consideration.…”

Section: Energy-saving Technologiesmentioning

confidence: 99%

Infrastructure and Energy Conservation in Big Data Computing: A Survey

Niewiadomska-Szynkiewicz

Karpowicz

2019

JTIT

Self Cite

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Progress in life, physical sciences and technology depends on eﬃcient data-mining and modern computing technologies. The rapid growth of data-intensive domains requires a continuous development of new solutions for network infrastructure, servers and storage in order to address Big Datarelated problems. Development of software frameworks, include smart calculation, communication management, data decomposition and allocation algorithms is clearly one of the major technological challenges we are faced with. Reduction in energy consumption is another challenge arising in connection with the development of eﬃcient HPC infrastructures. This paper addresses the vital problem of energy-eﬃcient high performance distributed and parallel computing. An overview of recent technologies for Big Data processing is presented. The attention is focused on the most popular middleware and software platforms. Various energy-saving approaches are presented and discussed as well.

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“…Another significant part of research focuses on hardware. For example Karpowichz et al [13] study on energy-aware design in hardware, middleware and software layers. They note that to get benefit on hardware development, a holistic view to the whole system must be taken.…”

Section: Related Workmentioning

confidence: 99%

Towards Green Big Data at CERN

Niemi

Nurminen

Liukkonen³

et al. 2018

Future Generation Computer Systems

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High-energy physics studies collisions of particles traveling near the speed of light. For statistically significant results, physicists need to analyze a huge number of such events. One analysis job can take days and process tens of millions of collisions. Today the experiments of the large hadron collider (LHC) create 10 GB of data per second and a future upgrade will cause a tenfold increase in data. The data analysis requires not only massive hardware but also a lot of electricity. In this article, we discuss energy efficiency in scientific computing and review a set of intermixed approaches we have developed in our Green Big Data project to improve energy efficiency of CERN computing. These approaches include making energy consumption visible to developers and users, architectural improvements, smarter management of computing jobs, and benefits of cloud technologies. The open and innovative environment at CERN is an excellent playground for different energy efficiency ideas which can later find use in mainstream computing.

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Energy and Power Efficiency in Cloud

Cited by 10 publications

References 86 publications

A Simulation Framework for Function as a Service

A Simulation Framework for Function as a Service

Infrastructure and Energy Conservation in Big Data Computing: A Survey

Towards Green Big Data at CERN

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