Evaluating a Single-Server Queue with Asynchronous Speed Scaling

Rumyantsev, Alexander; Зуева, П.А.; Kalinina, Ksenia; Golovin, Alexander

doi:10.1007/978-3-319-74947-1_11

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…To perform a comprehensive study, we adopt the so-called threelevel methodology, with cross-validated models of analytical, simulation and technical levels, first proposed in [31]. The validation results confirm an accuracy of more than 95% for the considered small-scale system between the three models.…”

Section: Introductionmentioning

confidence: 84%

“…We apply the multilevel methodology, first proposed in [31] Using the three-level approach, we cross-validate the analytical (MAM), simulation (GSMP) and technical level models in the intersecting conditions. At the same time, for the simulation and technical level models, the restricting conditions of analytical model can be significantly relaxed.…”

Section: Proposed Methodologymentioning

confidence: 99%

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A Three-Level Modelling Approach for Asynchronous Speed Scaling in High-Performance Data Centres

Rumyantsev

Basmadjian

Golovin

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

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In data centres, there exist several techniques for energy efficiency purposes. When applied, most of those techniques have impact on the quality (e.g. performance) of the underlying services. A careful study is required in order to optimise such an energy-performance trade-off. In this paper, we study the speed scaling as an energy efficiency technique within the scope of high-performance computing (HPC) data centres. We propose a methodology based on threelevels: analytic, simulation and technical. At the analytical level, the matrix-analytic method (MAM) allows one to obtain energyperformance measures explicitly for a small-scale system. At the simulation level, discrete-event simulation (DES) based on the generalised semi-Markov processes (GSMP) is used to derive the corresponding estimates. Finally, at the technical level, a real small-scaled system in a controlled environment is used. The preliminary results demonstrate that simulation and technical models go well together with the theoretical one with an accuracy of more than 95%. CCS CONCEPTS• Hardware → Enterprise level and data centers power issues; • Mathematics of computing → Queueing theory; • Computing methodologies → Discrete-event simulation.

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Section: Introductionmentioning

confidence: 84%

Section: Proposed Methodologymentioning

confidence: 99%

A Three-Level Modelling Approach for Asynchronous Speed Scaling in High-Performance Data Centres

Rumyantsev

Basmadjian

Golovin

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…[37], wanted to be more flexible in changing the frequency by hand during their benchmarks. Since some of the generic scaling governors are overwritten (by using the same name) by intel_pstate and others are not usable, sometimes researchers [63,82,100,140,236] disabled it to use the generic ones. None of the aforementioned papers specified one of the first three options (1-3) explicitly in their papers.…”

Section: Related Workmentioning

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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“…Such a model is known in the literature as simultaneous service multiserver or multiserver job model, which had a long history [6,19] and a spike of interest recently [1,14,15,24]. Unlike these papers, hereinafter we continue the studies of the model with system speed scaling performed at customer arrival/departure epochs [22,23,25]. The speed scaling feature allows to address the well-known energy/performance tradeoff by altering the corresponding management parameters (policy), which is in the main focus of our research.…”

Section: Introductionmentioning

confidence: 99%

Evaluating asynchronous speed scaling policies in high-performance data centres with heavy tails

Rumyantsev

Basmadjian

Astafiev

et al. 2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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Complex scientific problems are solved by high-performance computing (HPC) data centres. To reduce their energy consumption, recently the concept of dynamically scaling the voltage and frequency (DVFS) of the processor has been adopted for HPC systems. In this paper, we address the energy-performance trade-off of those systems using the queuing theory methodology, to study the opportunity of speed scaling technique in HPC data centres. To this end, we consider a real-world environment of 8 servers as well as develop a simulator based on the generalised semi-Markov processes. After validating the correctness of the simulator against the configured real-world environment, the underlying simulator is used to derive optimal asynchronous speed scaling policies. We show that by setting the probabilities of switching the frequency of the processor at the arrival and departure epochs of the jobs (customers) to 0.65 and 0.7 respectively, an optimal point is achieved for the heterogeneous case with truncated Pareto distribution of the amount of work. CCS CONCEPTS• Hardware → Enterprise level and data centers power issues; • Mathematics of computing → Queueing theory; • Computing methodologies → Discrete-event simulation.

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Evaluating a Single-Server Queue with Asynchronous Speed Scaling

Cited by 7 publications

References 10 publications

A Three-Level Modelling Approach for Asynchronous Speed Scaling in High-Performance Data Centres

A Three-Level Modelling Approach for Asynchronous Speed Scaling in High-Performance Data Centres

A Simulation Framework for Function as a Service

Evaluating asynchronous speed scaling policies in high-performance data centres with heavy tails

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