Energy-Aware Virtual Machine Dynamic Provision and Scheduling for Cloud Computing

Lin, Ching-Chi; Liu, Pangfeng; Wu, Jan‐Jan

doi:10.1109/cloud.2011.94

Cited by 96 publications

(38 citation statements)

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“…The results obtained show 24.9% power saving and nearly 1.2% performance degradation. Also, an algorithm called Dynamic Round-Robin for energy-aware virtual machine scheduling and consolidation is proposed in [31]. Compared with other strategies such as Greedy, Round-Robin and PowerSave implemented in Eucalyptus, this reduces a signicant amount of power.…”

Section: Analysis Of Power Consumption In Heterogeneous Virtual Machimentioning

confidence: 99%

Analysis of power consumption in heterogeneous virtual machine environments

et al. 2016

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Reduction of energy consumption in Cloud computing datacenters today is a hot a research topic, as these consume large amounts of energy. Furthermore, most of the energy is used ineciently because of the improper usage of computational resources such as CPU, storage, and network. A good balance between the computing resources and performed workload is mandatory. In the context of data-intensive applications, a signicant portion of energy is consumed just to keep alive virtual machines or to move data around without performing useful computation. Moreover, heterogeneity of resources increases the degree of diculty, when try to achieve energy eciency. Power consumption optimization requires identication of those inefciencies in the underlying system and applications. Based on the relation between server load and energy consumption, we study the eciency of data-intensive applications, and the penalties, in terms of power consumption, that are introduced by dierent degrees of

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Section: Analysis Of Power Consumption In Heterogeneous Virtual Machimentioning

confidence: 99%

Analysis of power consumption in heterogeneous virtual machine environments

et al. 2016

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“…The approach was evaluated on CloudSim. Lin et al (2011) propose a dynamic round-robin scheduling scheme for deploying and migrating virtual machines to or across the servers on the cloud. The scheme uses two scheduling rules, whereby the first one avoids allocating additional virtual machines to a retiring physical machine that will be shut down while the second one speeds up the consolidation process.…”

Section: Vm Schedulingmentioning

confidence: 99%

“…More specifically, the proposed scheduling approach solves a global optimization problem in order to schedule the virtual machine on a host that results in the minimal system energy consumption. The work of Lin et al (2011) touches explicitly upon the energy issues on the cloud, however, the approach is limited to simply shutting down physical hosts without a global view of the entire system. The developed new vision cognitive algorithm speeds up the process of finding the best target host.…”

Section: 4mentioning

confidence: 99%

Using a vision cognitive algorithm to schedule virtual machines

Zhao

Mhedheb

Tao

et al. 2014

International Journal of Applied Mathematics and Computer Science

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Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption.

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“…In [14] Lin et al, proposed a Dynamic Round-Robin (DRR) algorithm for energy-aware virtual machine scheduling and consolidation during which VMs are moved from the retired physical machine to other physical machines in duty . According to the authors, the proposed algorithm was compared to GREEDY, ROUNDROBIN and POWERSAVE schedulers showing superiority in reducing the amount of consumed power although it did not consider the load and resources of the destination physical machines to which the VMs will be migrated to.…”

Section: Related Workmentioning

confidence: 99%

A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique

2017

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Cloud computing is a ubiquitous network access model to a shared pool of configurable computing resources where available resources must be checked and scheduled using an efficient task scheduler to be assigned to clients. Most of the existing task schedulers, did not achieve the required standards and requirements as some of them only concentrated on waiting time or response time reduction or even both neglecting the starved processes at all. In this paper, we propose a novel hybrid task scheduling algorithm named (SRDQ) combining Shortest-JobFirst (SJF) and Round Robin (RR) schedulers considering a dynamic variable task quantum. The proposed algorithms mainly relies on two basic keys the first having a dynamic task quantum to balance waiting time between short and long tasks while the second involves splitting the ready queue into two sub-queues, Q1 for the short tasks and the other for the long ones. Assigning tasks to resources from Q 1 or Q 2 are done mutually two tasks from Q 1 and one task from Q 2 . For evaluation purpose, three different datasets were utilized during the algorithm simulation conducted using CloudSim environment toolkit 3.0.3 against three different scheduling algorithms SJF, RR and Time Slice Priority Based RR (TSPBRR) Experimentations results and tests indicated the superiority of the proposed algorithm over the state of art in reducing waiting time, response time and partially the starvation of long tasks.

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Energy-Aware Virtual Machine Dynamic Provision and Scheduling for Cloud Computing

Cited by 96 publications

References 0 publications

Analysis of power consumption in heterogeneous virtual machine environments

Analysis of power consumption in heterogeneous virtual machine environments

Using a vision cognitive algorithm to schedule virtual machines

A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique

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