A DVFS Based Energy-Efficient Tasks Scheduling in a Data Center

Wang, Songyun; Qian, Zhuzhong; Yuan, Jian; You, Ilsun

doi:10.1109/access.2017.2724598

Cited by 64 publications

(27 citation statements)

References 17 publications

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“…However, the training time of the SVM algorithm is slow. Reference [23] presented a load balancing method based on constraint measure. The capacity and load of each VM are initially computed.…”

Section: Related Workmentioning

confidence: 99%

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

et al. 2019

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Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.

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Section: Related Workmentioning

confidence: 99%

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

et al. 2019

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“…Wang et al [29] introduced a new task model that illustrated the QoS requirements of tasks with the minimum frequency. Energy consumption ratio (ECR) was suggested to assess the efficiency of different frequencies under which to perform a take.…”

Section: Related Workmentioning

confidence: 99%

Hypercube Based Genetic Algorithm for Efficient VM Migration for Energy Reduction in Cloud Computing

Singh¹,

Dhir²

2019

Stat., optim. inf. comput.

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If we choose to compare computing technology to coral reef then cloud computing technology is its very live and growing end. Its challenges are new and demand innovative measure to bring the size of its expending data centers under calipers and bridle its energy consumptions. Reduction in the consumption of energy is to be brought about without compromising quality-of-service and efficacy. For this, we purpose a Hypercube based Genetic Algorithm (HBGA) for efficient VM migration for energy reduction in cloud computing under QoS (Quality-of-service) constraint. The proposed HBGA technique can be implemented in two phases. First, in a data center the physical machines organize themselves in such a way as to acquire a highly scalable structure called Hypercube. The hypercube imperceptibly grates itself up or dips low in sympathy with VM instances as they mount up or get depleted. Secondly on the basis of this representation model of the compute nodes, and given the hypercube topology in which they are organized we propose three algorithms: (a) Hypercube based Node Selection Algorithm to minimize energy consumption (b) Hypercube based VM Selection Algorithm which minimizes the number of VM to be migrated. (c) To solve the problem of VM Placement we propose Hypercube based Genetic algorithm. Experimental results of comparisons between the proposed HBGA method viz-a-viz the existing solutions show a marked reduction in energy consumption of cloud computing environment.

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“…Thus, this is why the frequency can represent the relative task execution time. In large environment, it is very difficult to measure the execution time with minimum frequency [91]. The bottleneck of some tasks is not the computing, but users may need a lower frequency to match the bottleneck of other resources, and avoid wasting the economic cost of computing resources.…”

Section: Energymentioning

confidence: 99%

“…The bottleneck of some tasks is not the computing, but users may need a lower frequency to match the bottleneck of other resources, and avoid wasting the economic cost of computing resources. Service providers can dynamically or statically recommend prices for different frequencies so that the system can use Dynamic Voltage and Frequency Scaling (DVFS) based energy saving strategies [91] [92] to reduce electricity cost. Users could require tasks' running speed based on the price of services and the property of tasks.…”

Section: Energymentioning

confidence: 99%

Review of the quality of service scheduling mechanisms in cloud

RemeshBabu¹,

Samuel²

2018

IJET

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Cloud computing provides on demand access to a large pool of heterogeneous computational and storage resources to users over the internet. Optimal scheduling mechanisms are needed for the efficient management of these heterogeneous resources. The optimal scheduler can improve the Quality of Services (QoS) as well as maintaining efficiency and fairness among these tasks. In large scale distributed systems, the performance of these scheduling algorithms is crucial for better efficiency. Now the cloud customers are charged based upon the amount of resources they are consumed or held in reserve. Comparing these scheduling algorithms from different perspectives is needed for further improvement. This paper provides a comparative study about different resource allocation, load balancing and virtual machine consolidation algorithms in cloud computing. These algorithms have been evaluated in terms of their ability to provide QoS for the tasks and Service Level Agreement (SLA) guarantee amongst the jobs served. This study identifies current and future research directions in this area for QoS enabled cloud scheduling.

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A DVFS Based Energy-Efficient Tasks Scheduling in a Data Center

Cited by 64 publications

References 17 publications

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

Hypercube Based Genetic Algorithm for Efficient VM Migration for Energy Reduction in Cloud Computing

Review of the quality of service scheduling mechanisms in cloud

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