Energy-Aware Scheduling for Precedence-Constrained Parallel Virtual Machines in Virtualized Data Centers

Ebrahimirad, Vahid; Goudarzi, Maziar; Rajabi, Aboozar

doi:10.1007/s10723-015-9327-x

Cited by 28 publications

(11 citation statements)

References 49 publications

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“…The authors in [41] presented two scheduling algorithms for precedence-constrained parallel VMs in a virtualized data center. The proposed algorithms used a new insertion policy to insert VMs among previously scheduled ones.…”

Section: Software Level Solutionsmentioning

confidence: 99%

Power management in virtualized data centers: state of the art

et al. 2016

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Cloud computing is an emerging technology in the field of computing that provides access to a wide range of shared resources. The rapid growth of cloud computing has led to establishing numerous data centers around the world. As data centers consume huge amounts of power, enhancing their power efficiency has become a major challenge in cloud computing. This paper surveys previous studies and researches that aimed to improve power efficiency of virtualized data centers. This survey is a valuable guide for researchers in the field of power efficiency in virtualized data centers following the cloud computing model.

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Section: Software Level Solutionsmentioning

confidence: 99%

Power management in virtualized data centers: state of the art

et al. 2016

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“…. , }; (14) Check the number of tasks; (15) Find a V having the least number of tasks; (16) e n df o r (1) // for over-utilized hosts ∈ (2) Find a ℎ with the highest CPU utilization ∈ ;…”

Section: Task Classification Based Energy-aware Consolidation Algoritmentioning

confidence: 99%

Task Classification Based Energy-Aware Consolidation in Clouds

Choi

Lim

et al. 2016

Scientific Programming

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We consider a cloud data center, in which the service provider supplies virtual machines (VMs) on hosts or physical machines (PMs) to its subscribers for computation in an on-demand fashion. For the cloud data center, we propose a task consolidation algorithm based on task classification (i.e., computation-intensive and data-intensive) and resource utilization (e.g., CPU and RAM). Furthermore, we design a VM consolidation algorithm to balance task execution time and energy consumption without violating a predefined service level agreement (SLA). Unlike the existing research on VM consolidation or scheduling that applies none or single threshold schemes, we focus on a double threshold (upper and lower) scheme, which is used for VM consolidation. More specifically, when a host operates with resource utilization below the lower threshold, all the VMs on the host will be scheduled to be migrated to other hosts and then the host will be powered down, while when a host operates with resource utilization above the upper threshold, a VM will be migrated to avoid using 100% of resource utilization. Based on experimental performance evaluations with real-world traces, we prove that our task classification based energy-aware consolidation algorithm (TCEA) achieves a significant energy reduction without incurring predefined SLA violations.

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“…Combining the resources of the data center including CPU and memory, the work in [37] proposes a task scheduling algorithm with bandwidth perception with a consideration of task scheduling of cloud computing. The work in [38] presents two scheduling algorithms for precedence-constrained parallel Virtual Machines (VM) in a virtualized data center where each VM represents a sub-service of the Internet-scale service. The work in [39] designs a rolling horizon scheduling structure for the virtual data center, based on which an energy-perceivable real-time task scheduling algorithm is developed.…”

Section: Related Workmentioning

confidence: 99%

“…Although it satisfies the processing fairness of the server requests, it does not make full use of the access ability of new servers, which affects the capacity of the access cluster’s load balancing when processing requests for a longer time. The research in [23,24] mainly considers the real-time processing; the work in [25,26] centres on the reliability of message transmission; the work in [27,28] mainly considers the network resource quality’s impact on access to the virtual data center; the proposed methods in [29,30,31] concentrate on network availability and data center stability; the research in [32,33] mainly centres on the optimization of the network resources cost between virtual machines; the methods in [34,35] focus on privacy protection and visiting safety; the work in [36,37,38,39] mainly consider data center access from the aspect of task optimization and load scheduling; and the work in [40,41,42] solves access problems from the perspective of virtual data center resource reallocation and virtual machine migration.…”

Section: Related Workmentioning

confidence: 99%

An Effective Massive Sensor Network Data Access Scheme Based on Topology Control for the Internet of Things

Meng

Chen

Xiong

2016

Sensors

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This paper considers the distributed access and control problem of massive wireless sensor networks’ data access center for the Internet of Things, which is an extension of wireless sensor networks and an element of its topology structure. In the context of the arrival of massive service access requests at a virtual data center, this paper designs a massive sensing data access and control mechanism to improve the access efficiency of service requests and makes full use of the available resources at the data access center for the Internet of things. Firstly, this paper proposes a synergistically distributed buffer access model, which separates the information of resource and location. Secondly, the paper divides the service access requests into multiple virtual groups based on their characteristics and locations using an optimized self-organizing feature map neural network. Furthermore, this paper designs an optimal scheduling algorithm of group migration based on the combination scheme between the artificial bee colony algorithm and chaos searching theory. Finally, the experimental results demonstrate that this mechanism outperforms the existing schemes in terms of enhancing the accessibility of service requests effectively, reducing network delay, and has higher load balancing capacity and higher resource utility rate.

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Energy-Aware Scheduling for Precedence-Constrained Parallel Virtual Machines in Virtualized Data Centers

Cited by 28 publications

References 49 publications

Power management in virtualized data centers: state of the art

Power management in virtualized data centers: state of the art

Task Classification Based Energy-Aware Consolidation in Clouds

An Effective Massive Sensor Network Data Access Scheme Based on Topology Control for the Internet of Things

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