Energy-aware resource allocation for multicores with per-core frequency scaling

Zhao, Xinghui; Jamali, Nadeem

doi:10.1186/s13174-014-0009-x

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…The experiment was performed on Green Cloud and proved to minimize the total cost and thus, energy consumption. Zhao, X. et al in (13) defined Model "DREAM-CEP" to calculate energy consumption for multicore opearting systems. The model was tested on 2 test cases (static and dynamic workload).…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Review of Live VM Migration Techniques for Energy Management

Gupta¹,

Gupta²

2022

ECS Trans.

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The present computing era has been inclined towards providing services to the customers all round the clock and is driven by utilization of existing resources in its entirety to assist service providers in extracting maximized profits. The key technique in achieving the automated services despite of geographical locations is creation of virtual machines using virtualization hypervisor. Though it entails several benefits of increased resource utilization, but it faces challenges of energy mitigation and related migration overheads originated due to balancing of load on the physical servers, fault handling in VM or server, transfer of irrelevant data, etc. If the total time and energy invested in every single VM migration can be minimized, it can give better results in term of energy consumption, total downtime, and migration time. Thus, the paper outlines a detailed review about various components of VM migration along with existing techniques and related aspects.

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Section: Literature Reviewmentioning

confidence: 99%

A Systematic Review of Live VM Migration Techniques for Energy Management

Gupta¹,

Gupta²

2022

ECS Trans.

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“…Other systems such as ExaNest [11] aim to develop system level interconnect and non volatile storage based largely around low cost and low power components, ARM processors and FPGAs. This trend of designing for energy efficiency can be found in several research initiatives [16,4]. For example the ATLAS library [10] introduces methods for tuning linear algebra operations for both energy efficiency and execution time.…”

Section: Software Support For Exascale Systemsmentioning

confidence: 99%

Perspectives on Anomaly and Event Detection in Exascale Systems

Iuhasz

Petcu

2019

2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Sma

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The design and implementation of exascale system is nowadays an important challenge. Such a system is expected to combine HPC with Big Data methods and technologies to allow the execution of scientific workloads which are not tractable at this present time.In this paper we focus on an event and anomaly detection framework which is crucial in giving a global overview of a exascale system (which in turn is necessary for the successful implementation and exploitation of the system). We propose an architecture for such a framework and show how it can be used to handle failures during job execution.

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“…The study of [14] presents a line of investigation on Power management strategies for enterprise servers on the basis of Dynamic Voltage and Frequency Scaling i.e. (DVFS).…”

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confidence: 99%

Energy-Aware Scheduling Framework for resource allocation in a virtualized cloud data centre

Gupta¹,

Katiyar²

2017

IJET

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Abstract-Cloud paradigm is an embryonic computing model that in its vicinity stresses on proficient utilization of computing resources. Data centers that host and service cloud applications ingest enormous amount of energy, leading to massive emission of carbon footprints to the atmosphere and high operational expenditures. Consequently, there is a need to establish synergy between data centre resources for optimum resource utilization and strategies needs to be devised that can considerably reduce energy consumption in cloud data center. This paper elucidates an architectural framework for computation of energy spent in scheduling resources on hosts. The framework has been implemented for bin-packing techniques and explicates minutiae about broker components involved in scheduling process. Keyword-Cloud Computing, Energy Consumption, VM Migration, Resource Scheduling.I. INTRODUCTION Contemporary [1,2] resource-intensive enterprises has engendered demands for high performance computing infrastructures. Proliferation of IT services to be used by diverse range of cloud users has led to construction of large-scale energy hungry data centers that can facilitate computing services. Despite of the improvisations introduced in energy consumption models, service providers are confronted with challenges of reduction in energy consumption and CO 2 emission.The rationale [3] in the wake of explosion of energy emission is increase in number of computer usage due to increase in number of IT practitioners. As an upshot, size of data centres has increased. Moreover, exploiting energy-aware resource provisioning to its fullest extent can subsequently provide a solution to the forefront issues.[4] Service virtualization and consolidation are acting as inherent practices that can escort energyefficient datacenter architectures. It has effectually led to efficient resource utilization. VM provisioning can be sighted as a multidimensional bin packing problem comprising of capricious bin configurations and cost parameters. Virtualization technique embraces server consolidation process and a VM live-migration technique that has validated to be efficient in drastic reduction in energy consumption in high-performance cloud datacenters. However, I/O virtualization has excavated grounds for performance degradation posed by overheads encountered in vm migrations and needs to be addressed urgently.The organization of paper is laid out as literature review in beginning followed by research focus. The IV section elucidates architectural framework trailed by working prototype. The last section illustrates conclusion and future work.II. LITERATURE REVIEW The research work presented in [5] explores method to manage data intensive distributed programming paradigms (like MapReduce and Dryad) that assists practitioners to effortlessly parallelize the processing of huge data sets. Deployment of such data intensive computing infrastructures is of significant concern due to rise in cost. The work carried out in the study dynamically adjusts the size ...

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Energy-aware resource allocation for multicores with per-core frequency scaling

Cited by 5 publications

References 22 publications

A Systematic Review of Live VM Migration Techniques for Energy Management

A Systematic Review of Live VM Migration Techniques for Energy Management

Perspectives on Anomaly and Event Detection in Exascale Systems

Energy-Aware Scheduling Framework for resource allocation in a virtualized cloud data centre

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