Power-aware resource allocation in computer clusters using dynamic threshold voltage scaling and dynamic voltage scaling: comparison and analysis

Bilal, Kashif; Fayyaz, Ahmad; Khan, Samee U.; Usman, Saeeda

doi:10.1007/s10586-015-0437-9

Cited by 17 publications

(12 citation statements)

References 26 publications

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“…In the solution method for the cluster optimization problem, [19], [26]- [28] use heuristic or greedy algorithms on the whole. Reference [8] first reduces the number of variables by half through a theoretical analysis and then uses a binary search; [5] uses Generalized Benders Decomposition; [18] and [16] use GLPK and CPLEX, respectively; and [14] proposes a bisection method and several heuristic methods.…”

Section: Related Studiesmentioning

confidence: 99%

Online Power-Aware Deployment and Load Distribution Optimization for Application Server Clusters

et al. 2019

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The energy conservation of application server clusters is a pressing problem. In this paper, we propose an online power-aware deployment and load distribution optimization strategy for application server clusters, whose objective is to minimize the cluster's power while ensuring that the server's CPU utilization is not higher than a preset value. The strategy includes two schemes: a mixed integer linear programming (MILP)-based scheme and a mixed integer non-linear programming (MINLP)-based scheme. The former formulates the cluster optimization problem as a MILP problem and adopts a toolkit to solve it. When the cluster scale is small, it can find the global optimal solution quickly. So, the MILP-based scheme is applicable to small-scale clusters. In the MINLP-based scheme, we first formulate the cluster optimization problem as a non-linear programming problem, and then design a method to reduce the number of variables and reformulate it as an MINLP problem. We finally propose an efficient solution method based on the flower pollination algorithm. Due to the small number of variables and the high solution efficiency, the solution method can quickly obtain a high-quality solution, so the MINLP-based scheme can be applied to largescale clusters. The experimental results demonstrate the effectiveness of our strategy.

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Section: Related Studiesmentioning

confidence: 99%

Online Power-Aware Deployment and Load Distribution Optimization for Application Server Clusters

et al. 2019

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“…Some works [1,[7][8][9][10][11][12] use simulation tests to evaluate their strategies. However, [7][8] and [9] do not give simulation methods; [1] and [10] just simulate in computational level rather than cluster's actual operation; [11] and [12] use their own simulator to simulate, but do not give the design details of their simulators.…”

Section: Related Workmentioning

confidence: 99%

“…Researches show that servers are now gobbling up a huge amount of energy [1], but they, most of the time, are loaded between 10% and 50% of peak, with CPU utilization that rarely surpasses 40% [2]. This leads to an excessive waste of energy.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we need to dynamically manage cluster's deployment so as to reduce power consumption and meanwhile satisfy load performance demand. The deployment should include the on/off state, CPU frequency (CPU is the largest energy consumption component of a server, and mainstream CPUs all support dynamic frequency scaling technology [1] today) and request scheduling parameter(s) of each server.…”

Section: Introductionmentioning

confidence: 99%

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A Power and Performance Management Simulation Platform for Web Application Server Cluster

Xiong

Xue

Cai

et al. 2016

IJFGCN

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“…for computational fluid dynamics, geometric modelling, solving partial differential equations or image and video processing [1][2][3][4][5]. As computing time and memory usage grow linearly with the number of array elements in stencil computations our research targets highly parallel implementations of stencil codes together with task scheduling and optimization techniques taking into consideration energy cost and data locality [6][7][8][9][10]. We have proved during our experimental studies that recent changes introduced in heterogeneous computing hardware resulted in different performance and energy characteristics that are critical for highly efficient and scalable stencil computations [11].…”

Section: Introductionmentioning

confidence: 99%

Energy aware scheduling model and online heuristics for stencil codes on heterogeneous computing architectures

2016

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Performance of high-end supercomputers will reach the exascale through the advent of core counts in billions. However, in the upcoming exascale computing era it is important not only to focus on the performance, but also on scalability of fine-grained parallel applications, data locality and energy aware scheduling within the parallel code. In fact, parallel applications need to change even now by redesigning algorithms and data structures respectively to take advantage of the recent improvements in energy efficiency of heterogeneous computing hardware, including multicore processors and GPU accelerators. Over the next few years one of the biggest challenges for exascale will be the ability of parallel applications to fully exploit locality which will, in turn, be required to achieve expected performance and energy efficiency. Future highly parallel applications will have to deal with deep memory hierarchies taking into account energy cost in moving data off-chip. Therefore, they will have to apply new coordinated scheduling approaches to balance energy aware resource utilization and minimize work starvation during runtime. As new constraints and limits on memory bandwidth and energy will play a key role in high performance computing (HPC) in the future, more sophisticated and dynamic scheduling techniques will be needed and applied within the parallel code. In this paper we focus on an energy-aware distribution of the stencil workload on heterogeneous processors. Our analysis of energy and performance models focused on relevant class of stencil computations to explore the relationship between task scheduling algorithms and energy constraints. More precisely, we search for a schedule which minimizes the energy usage within a specified computation's deadline of the stencil workload on heterogeneous architectures. Since the problem is computationally intractable, we present an integer linear programming formulation for finding optimal schedules. As finding optimal schedules is time consuming we have developed four heuristics and tested them experimentally with respect to optimal solutions. In our work we focus on a single node configurations with heterogeneous processors. These configurations represent the state of the art multi-and many-core architectures.

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Power-aware resource allocation in computer clusters using dynamic threshold voltage scaling and dynamic voltage scaling: comparison and analysis

Cited by 17 publications

References 26 publications

Online Power-Aware Deployment and Load Distribution Optimization for Application Server Clusters

Online Power-Aware Deployment and Load Distribution Optimization for Application Server Clusters

A Power and Performance Management Simulation Platform for Web Application Server Cluster

Energy aware scheduling model and online heuristics for stencil codes on heterogeneous computing architectures

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