The Impact of Service Demand Variability on Data Center Performance

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Diletta Olliaro;Adityo Anggraito;Marco Ajmone Marsan;Simonetta Balsamo;Andrea Marin
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引用次数: 0

Abstract

Modern data centers feature an extensive array of cores that handle quite a diverse range of jobs. Recent traces, shared by leading cloud data center enterprises like Google and Alibaba, reveal that the constant increase in data center services and computational power is accompanied by a growing variability in service demand requirements. The number of cores needed for a job can vary widely, ranging from one to several thousands, and the number of seconds a core is held by a job can span more than five orders of magnitude. In this context of extreme variability, the policies governing the allocation of cores to jobs play a crucial role in the performance of data centers. It is widely acknowledged that the First-In First-Out (FIFO) policy tends to underutilize available computing capacity due to the varying magnitudes of core requests. However, the impact of the extreme variability in service demands on job waiting and response times, that has been deeply investigated in traditional queuing models, is not as well understood in the case of data centers, as we will show. To address this issue, we investigate the dynamics of a data center cluster through analytical models in simple cases, and discrete event simulations based on real data. Our findings emphasize the significant impact of service demand variability, both in terms of requested cores and service times, and allow us to provide insight for enhancing data center performance. In particular, we show how data center performance can be improved thanks to the control of the interplay between service and waiting times through the assignment of cores to jobs.
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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