Autonomic power and performance management for computing systems

Khargharia, Bithika; Hariri, Salim; Yousif, Mazin

doi:10.1007/s10586-007-0043-6

Cited by 69 publications

(24 citation statements)

References 22 publications

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“…Therefore, VFS-enabled systems perform better in energy saving in such environments. Using VFS at CPU level can save a lot of energy by scaling down the CPU frequency either manually [36,86] or dynamically (DVFS) [20, 30, 41-43, 55-57, 62, 80] by means of processor technologies like Intel's SpeedStep [58]. Thus the Grid meta-scheduler, along with other factors, should decide to assign jobs to resource sites at the time in which the jobs can be executed at minimum CPU frequency level to save power consumption.…”

Section: Energy -Aware Resource Allocationmentioning

confidence: 99%

Survey on Grid Resource Allocation Mechanisms

et al. 2014

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Grid is a distributed high performance computing paradigm that offers various types of resources (like computing, storage, communication) to resource-intensive user tasks. These tasks are scheduled to allocate available Grid resources efficiently to achieve high system throughput and to satisfy A. Y. Zomaya University of Sydney, Sydney, Australia user requirements. The task scheduling problem has become more complex with the ever increasing size of Grid systems. Even though selecting an efficient resource allocation strategy for a particular task helps in obtaining a desired level of service, researchers still face difficulties in choosing a suitable technique from a plethora of existing methods in literature. In this paper, we explore and discuss existing resource allocation mechanisms for resource allocation problems employed in Grid systems. The work comprehensively surveys Gird resource allocation mechanisms for different architectures (centralized, distributed, static or dynamic). The paper also compares these resource allocation mechanisms based on their common features such as time complexity, searching mechanism, allocation strategy, optimality, operational environment and objective function they adopt for solving computing-and data-intensive applications. The comprehensive analysis of cutting-edge research in the Grid domain presented in this work provides readers with an understanding of essential concepts of resource allocation mechanisms in Grid systems and helps them identify important and outstanding issues for further investigation. It also helps readers to choose the most appropriate mechanism for a given system/application.

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Section: Energy -Aware Resource Allocationmentioning

confidence: 99%

Survey on Grid Resource Allocation Mechanisms

et al. 2014

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“…These papers, however, concentrate on specific subsystems of Large Scale Distributed Systems, as [13] on the performance of memory systems. Furthermore, Petrucci [20] and Bichler [4] investigate only one general resource constraint.…”

Section: Related Workmentioning

confidence: 99%

Self-Adaptive and Resource-Efficient SLA Enactment for Cloud Computing Infrastructures

Maurer

Brandić

Sakellariou

2012

2012 IEEE Fifth International Conference on Cloud Computing

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Abstract-Cloud providers aim at guaranteeing Service Level Agreements (SLAs) in a resource-efficient way. This, amongst others, means that resources of virtual (VMs) and physical machines (PMs) have to be autonomically allocated responding to external influences as workload or environmental changes. Thereby, workload volatility (WV) is one of the crucial factors that influence the quality of suggested allocations. In this paper we devise a novel approach for self-adaptive and resourceefficient decision-making considering the three conflicting goals of minimizing the number of SLA violations, maximizing resource utilization, and minimizing the number of necessary time-and energy-consuming reconfiguration actions. We propose self-adaptive rule-based knowledge management for autonomic VM reconfiguration considering the rapidness of changes in the workload, i.e., WV. We introduce a novel WV categorization and present cost and volatility based methods for self-tuning. We evaluate these methods by a large variety of synthetically generated workloads, and by real-world measurements gathered from an image rendering application and a scientific workflow for RNA sequencing. Evaluation shows that in most cases the self-adaptive approach outperforms the static approach.

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“…Early systems work such as [11] achieved substantial savings despite ignored switching costs in their design. Other designs have focused on decentralized frameworks, e.g., [12] and [13]. A recent survey is [14].…”

Section: Related Workmentioning

confidence: 99%

Online dynamic capacity provisioning in data centers

Lin

Wierman

Andrew

et al. 2011

2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Abstract-Power consumption imposes a significant cost for implementing cloud services, yet much of that power is used to maintain excess service capacity during periods of low load. In this work, we study how to avoid such waste via an online dynamic capacity provisioning. We overview recent results showing that the optimal offline algorithm for dynamic capacity provisioning has a simple structure when viewed in reverse time, and this structure can be exploited to develop a new 'lazy' online algorithm which is 3-competitive. Additionally, we analyze the performance of the more traditional approach of receding horizon control and introduce a new variant with a significantly improved worst-case performance guarantee. I. INTRODUCTIONEnergy costs represent a significant fraction of a data center's budget [1] and this fraction is expected to grow. Hence, there is a growing push to improve the energy efficiency of the data centers. A promising approach for making data centers more energy efficient is using software to dynamically 'rightsize' the data center, i.e., adapt the dispatching so that during periods of low load some servers are allowed to enter a powersaving mode (e.g., go to sleep or shut down).However, entering and leaving sleep mode incurs a penalty ("switching cost"), in terms of latency, energy consumption, or wear-and-tear. This means that decisions to sleep cannot be made independently at different time instants. The problem is challenging due to the lack of knowledge about future workloads, which means that a server that is put to sleep may soon need to be woken again. There is a significant and growing literature on this topic [2]- [5].This paper provides an overview of recent results providing online algorithms to decide the provisioning at each time instant without or with a little information of future workload. To this end, we discuss a simple but general model that captures the major issues of right-sizing. With this model, we first analytically characterize the optimal offline solution (Section III-A). We show that it exhibits a simple, 'lazy' structure when viewed in reverse time. Second, we discuss a novel, practical online algorithm motivated by this structure (Section III-B). The algorithm, named Lazy Capacity Provisioning (LCP), mimics the 'lazy' structure of the optimal algorithm, but proceeding forwards instead of backwards in time. Importantly, LCP is 3-competitive, i.e., its cost is at most 3 times that of the optimal offline solution. Third, we analyze the traditional approach of Receding Horizon Control (RHC). We show that RHC performs well when servers are homogeneous; specifically, it has performance that quickly tends toward optimality as the prediction window increases. However, we also show that RHC can perform badly when servers are heterogeneous, regardless of the length of the prediction window. To address this issue, we discuss a variant of RHC that is guaranteed to perform well in heterogeneous settings. Specifically, under both homogeneous and heterogeneous settings, their...

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Autonomic power and performance management for computing systems

Cited by 69 publications

References 22 publications

Survey on Grid Resource Allocation Mechanisms

Survey on Grid Resource Allocation Mechanisms

Self-Adaptive and Resource-Efficient SLA Enactment for Cloud Computing Infrastructures

Online dynamic capacity provisioning in data centers

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