Design and implementation of energy-aware application-specific CPU frequency governors for the heterogeneous distributed computing systems

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

doi:10.1016/j.future.2016.05.011

Cited by 16 publications

(6 citation statements)

References 27 publications

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“…An optimized control strategy was developed to meet the workload processing deadlines. Papers [61], [79], and [56] address issues concerned with the structure of optimized energy-aware CPU frequency scaling rules. A class of CPU frequency switching rules, exploiting DVFS, is discussed.…”

Section: Energy-saving Technologiesmentioning

confidence: 99%

Infrastructure and Energy Conservation in Big Data Computing: A Survey

Niewiadomska-Szynkiewicz

Karpowicz

2019

JTIT

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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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Section: Energy-saving Technologiesmentioning

confidence: 99%

Infrastructure and Energy Conservation in Big Data Computing: A Survey

Niewiadomska-Szynkiewicz

Karpowicz

2019

JTIT

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“…Michał P. Karpowicz et al highlights in the 5th paper the design and implementation of energy-aware application-specific CPU frequency governors for the heterogeneous distributed computing systems [8]. The authors propose a benchmarking methodology to identify models of CPU workload dynamics.…”

Section: This Special Issuementioning

confidence: 99%

HPS-HDS: High Performance Scheduling for Heterogeneous Distributed Systems

Pop

Iosup

Prodan

2018

Future Generation Computer Systems

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Heterogeneous Distributed Systems (HDS) are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. Such type of systems are Cluster Computing, Grid Computing, Peer-to-Peer Computing, Cloud Computing and Ubiquitous Computing all involving elements of heterogeneity, having a large variety of tools and software to manage them. As computing and data storage needs grow exponentially in HDS, increasing the size of data centers brings important diseconomies of scale. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance. More, HDS are highly dynamic in its structure, because the user requests must be respected as an agreement rule (SLA) and ensure QoS, so new algorithm for events and tasks scheduling and new methods for resource management should be designed to increase the performance of such systems. In this special issues, the accepted papers address the advance on scheduling algorithms, energy-aware models, self-organizing resource management, dataaware service allocation, Big Data management and processing, performance analysis and optimization.

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“…In such a case it is possible to construct specialized power governors suitable for specialized usage scenarios, e.g. a web server, large scale computations or network traffic filtering [7], [8], [17]. The savings achieved by the algorithm presented in [7] are attained mostly by exploiting identified dynamics of applications running, and thus designing a control law that makes it possible to adequately react to load changes.…”

Section: Local Cpu Power Controlmentioning

confidence: 99%

“…r are power consumption values of the card c and the router r, respectively, M ek is throughput and ξ ek power consumption of link e in the state k, y ek = 1 if the energy state of link e is set to k (0 otherwise), z r = 1 if router r transmits any flow (0 otherwise), x c = 1 if card c transmits any flow (0 otherwise), l cp = 1 if port (one of link endpoints) p is on the card c (0 otherwise), u ed = 1 if path d leads through the link e (0 otherwise), binary constants a ep and b ep are used to define ingress and egress links (e) of port p, g rc is set to 1 if card c belongs to the router r.The complexity of the problem results from the fact that authors have combined the routing task -see flow continuity constraints (6)-(8) and link capacity constraint (9) -with hierarchic layout of the network node (router) -constraints (3)-(5) and the multiple energy state model of a linkconstraint(2). An efficient solution of such a complex task is possible by relying on heuristics, usually built as repetitive solving of simpler (usually relaxed) mathematic programming tasks.…”

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confidence: 99%

Energy Aware Data Centers and Networks: a Survey

Arabas

2018

JTIT

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The past years have brought about a great variety of clusters and clouds. This, combined with their increasing size and complexity, has resulted in an obvious need for power-saving control mechanisms. Upon presenting a basis on which such solutions - namely low-level power control interfaces, CPU governors and network topologies – are constructed, the paper summarizes network and cluster resources control algorithms. Finally, the need for integrated, hierarchical control is expressed, and speciﬁc examples are provided.

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Design and implementation of energy-aware application-specific CPU frequency governors for the heterogeneous distributed computing systems

Cited by 16 publications

References 27 publications

Infrastructure and Energy Conservation in Big Data Computing: A Survey

Infrastructure and Energy Conservation in Big Data Computing: A Survey

HPS-HDS: High Performance Scheduling for Heterogeneous Distributed Systems

Energy Aware Data Centers and Networks: a Survey

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