Power Aware Scheduling of Bag-of-Tasks Applications with Deadline Constraints on DVS-enabled Clusters

Kim, Kyong Hoon; Buyya, Rajkumar; Kim, Jong

doi:10.1109/ccgrid.2007.85

Cited by 253 publications

(165 citation statements)

References 23 publications

Supporting

Mentioning

156

Contrasting

Unclassified

Order By: Relevance

“…In [3] and [4], the main focus is rather on temperature and/or heat emissions of resources in data centers. In the meantime, there also have been a considerable amount of research conducted using software approaches, such as scheduling and resource allocation [5][6][7][8][9] and task consolidation [10][11][12][13]. The scheduling and resource allocation approach is primarily enabled using slack reclamation with the support of dynamic voltage/frequency scaling (DVFS; more spherically 'processor undervaluing') [14] incorporated into many recent commodity processors.…”

Section: Related Workmentioning

confidence: 99%

Qualitative Analyzing and Evaluating Energy Efficient Datacenters Based on Cloud Model in Cloud Environment

Han¹,

Cai²

2015

Proceedings of the 2nd International Conference on Civil, Materials and Environmental Sciences

View full text Add to dashboard Cite

Abstract-The green cloud becomes a center of attention, how to evaluate the energy efficiency of the cloud environment has been one of difficult and attractive field . Cloud models are the effective tools in transforming between qualitative concepts and their quantitative expressions. This paper designs an uncertainty enhanced energy-efficiency evaluation model based on cloud model theory, through the cloud model we convert the energy, performance and QoS parameters into qualitative concept, and then we propose a new tasks scheduling algorithm to optimize the energy-efficiency of cloud datacenters. Our research results can be integrated into Cloud systems to monitor energy efficiency and support static or dynamic system-level energy-efficiency optimization.

show abstract

Section: Related Workmentioning

confidence: 99%

Qualitative Analyzing and Evaluating Energy Efficient Datacenters Based on Cloud Model in Cloud Environment

Han¹,

Cai²

2015

Proceedings of the 2nd International Conference on Civil, Materials and Environmental Sciences

View full text Add to dashboard Cite

show abstract

“…Significant research exists on efficiency enhancement of clusters at processor level [29,112], virtualization based resource managers level [134,135], and cluster resource managers level [61,137]; however only a small portion of it deals with improving energy efficiency of HPC platforms. In the area of global Grids, many meta-schedulers are in use.…”

Section: Energy -Aware Resource Allocationmentioning

confidence: 99%

Survey on Grid Resource Allocation Mechanisms

et al. 2014

View full text Add to dashboard Cite

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.

show abstract

“…The energy consumption of modern processor for job execution, ξ, can be divided into two parts, dynamic energy consumption ξ dynamic , and static energy consumption ξ static [37]:…”

Section: B Energy Modelmentioning

confidence: 99%

Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS

Wang

Laszewski

Dayal

et al. 2010

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing

189

108

View full text Add to dashboard Cite

Abstract-Reducing energy consumption for high end computing can bring various benefits such as, reduce operating costs, increase system reliability, and environment respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cluster with the Dynamic Voltage Frequency Scaling (DVFS) technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task's execution time as a whole. Additionally, Green Service Level Agreement is also considered in this paper. By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time. Models and scheduling heuristics are examined with a simulation study. Test results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.

show abstract

Power Aware Scheduling of Bag-of-Tasks Applications with Deadline Constraints on DVS-enabled Clusters

Abstract: Abstract

Cited by 253 publications

References 23 publications

Qualitative Analyzing and Evaluating Energy Efficient Datacenters Based on Cloud Model in Cloud Environment

Qualitative Analyzing and Evaluating Energy Efficient Datacenters Based on Cloud Model in Cloud Environment

Survey on Grid Resource Allocation Mechanisms

Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS

Contact Info

Product

Resources

About