The requirement in addition to consequences in refining efficiency of energy in cloud applications have enlarged owing to speedy evolution and explosion of data center amenities everywhere in the globe. Virtual machines (VMs) embrace pillar of utmost and are frequently associated on the Physical Machine (PM) towards proficiently employ its assets. Various workload scheduling algorithms are proposed to utilize the resources, however they failed to consider the machine heterogeneity resulting in more energy consumption. To overcome these issues, we introduced Job Consolidation Algorithm (JCA) that efficiently utilize the resource in the cloud considering machine heterogeneity, and we implemented DVFS technique which remains efficiently to produce liable replacement among jobs guaranteeing reduction in energy consumption.

Viswanathan P

School of Computer Science and Engineering

Chennai Campus

Sanjeevi P

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Towards energy-aware job consolidation scheduling in cloud

2016 International Conference on Inventive Computation Technologies (ICICT)

The major issue threatening the cloud computing is that it consumes an enormous amount of energy for providing valuable computational services. Many workload consolidation attempts were taken to reduce the energy consumption of data centre. Broad research on all workload consolidation issues in the cloud environment is extremely challenging since it entails developers to deliberate network setup and the cloud environment, which could be beyond the control. Moreover, the consolidation of workload cannot be predicted or restrained. Though, existing literature lacks the crucial agility for workload consolidation to reduce energy in cloud data centre, producing poor responsiveness and inadequate scalability. In this paper, we present the background and motivation of workload consolidation techniques in the cloud. Moreover, we acquaintance four recent workload consolidation algorithms considering metrics for reducing energy and the features of the best workload consolidation algorithm is highlighted. Finally, research areas and challenges are boasted based on the strengths and weakness of prevailing workload consolidation methods. Copyright © 2017 Inderscience Enterprises Ltd.

International Journal of Internet Protocol Technology

Workload consolidation techniques to optimise energy in cloud: review

The cloud data center is accommodated with many servers for cloud-based services which cause more consumption of energy and menace cost factors in computing tasks. Many existing scheduling techniques hinge on allocating task where scheduling algorithm is not based on assigning tasks through urgent and non-urgent task scheduling using dynamic voltage frequency scaling (DVFS) controller. In demand to reduce energy consumption and to maintain the quality of services, this paper proposes non-urgent and urgent task scheduling (NUTS) algorithm using DVFS, to restraint and scheduling of task in the more efficient way for minimizing the power consumption of the IT equipment. To increase the energy efficiency, we proposed scheduling queue and non-completed task queue for scheduling urgent, non-urgent and non-completed tasks to ally utilization of resources efficiently and to decrease the consumption of energy in the data center. In this paper, we compared proposed algorithm with two existing standard scheduling algorithms. The experimental results boast that NUTS algorithm performs better than the existing algorithms and can centrist energy efficiency in cloud data center. © 2017, Springer-Verlag Wien.

Computing

NUTS scheduling approach for cloud data centers to optimize energy consumption

Cloud computing grow into more emerging technology, energy consumption in cloud needs to be paid more attention. In the data center virtualization technology is used to reduce the energy consumption of cloud. Virtual machine (VM) relocation and merging has been used as a vital backbone for cloud data center. Consequently, we requisite Green Cloud resolutions that able to not only decrease operating costs and moreover to decrease the environmental impact caused through data center. Various queuing algorithms are proposed to utilize the resources and assigning task in an efficient manner. We introduce a new energy consumption model and new scheduling strategy for Cloud environments. Our proposed model incorporates the module called as certainty and uncertainty scheduling which pledges involuntary relocation of VM to realm the green computing environment. © 2015 IEEE.

2015 International Conference on Computing and Network Communications (CoCoNet)

A green energy optimized scheduling algorithm for cloud data centers

Journal of Computer and System Sciences

Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds

Computers & Electrical Engineering

Use of proactive and reactive hotspot detection technique to reduce the number of virtual machine migration and energy consumption in cloud data center

2015 International Conference on Parallel Architecture and Compilation (PACT)

DVFS-Aware Consolidation for Energy-Efficient Clouds

Future Generation Computer Systems

Energy efficient scheduling of virtual machines in cloud with deadline constraint

Journal	Data powered by Typeset2016 International Conference on Inventive Computation Technologies (ICICT)
Publisher	Data powered by TypesetIEEE
Open Access	No