A new energy-aware task scheduling method for data-intensive applications in the cloud

Zhao, Qiuyan; Xiong, Congcong; Yu, Ce; Zhang, Chuanlei; Zhao, Xia

doi:10.1016/j.jnca.2015.05.001

Cited by 50 publications

(23 citation statements)

References 30 publications

(27 reference statements)

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“…A new energy-aware task scheduling method for data-intensive applications in the cloud [23] In this method, first, the datasets and tasks are modeled as a binary tree by a data correlation clustering algorithm, in which both the data correlations generated from the initial datasets and that from the intermediate datasets have been considered. Hence, the amount of global data transmission can be reduced greatly, which are beneficial to the reduction of SLA violation rate.…”

Section: Matlabmentioning

confidence: 99%

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Sultanpure¹,

Reddy²

2018

IJECE

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<p>Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads.</p>

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Section: Matlabmentioning

confidence: 99%

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Sultanpure¹,

Reddy²

2018

IJECE

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“…Cloud computing platform has three types of services and these are: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). These are also known as cloud services models [5]. The cloud can be classified From the last several years, the increased growth of the cloud also maximizes the number of cloud data center at unrivalled speeds.…”

Section: Cloud Computing Service Modelsmentioning

confidence: 99%

“…The other issue in cloud is called load offset that is a critical problem that becomes a big hurdle between the speedy developments of the cloud computing. The various number of users from the distinct regions demands services at high frequency in every individual second from cloud service provider [5]. Meanwhile, energy-awake scheduling approach with Ant colony optimization method is an assuring method to accomplish that objective [6].…”

Section: Cloud Computing Service Modelsmentioning

confidence: 99%

An Effective Technique to Decline Energy Expenditure in Cloud Platforms

Kaur¹,

Kaur²

2018

IJMECS

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Abstract-The cloud computing is the rapidly growing technology in the IT world. A vital aim of the cloud is to provide the services or resources where they are needed. From the user's prospective convenient computing resources are limitless thatswhy the client does not worry that how many numbers of servers positioned at one site so it is the liability of the cloud service holder to have large number of resources. In cloud data-centers, huge bulk of power exhausted by different computing devices.Energy conservancy is a major concern in the cloud computing systems. From the last several years, the different number of techniques was implemented to minimize that problem but the expected results are not achieved. Now, in the proposed research work, a technique called Enhanced -ACO that is developed to achieve better offloading decisions among virtual machines when the reliability and proper utilization of resources will also be considered and will use ACO algorithm to balance load and energy consumption in cloud environment. The proposed technique also minimizes energy consumption and cost of computing resources that are used by different processes for execution in cloud. The earliest finish time and fault tolerance is evaluated to achieve the objectives of proposed work. The experimental outcomes show the better achievement of prospective model with comparison of existing one. Meanwhile, energy-awake scheduling approach with Ant colony optimization method is an assuring method to accomplish that objective.

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“…Sousa et al [19] have given an algorithm based on Meta heuristic technique for energy resource scheduling in smart grids. Zhao et al [20] have discovered a task scheduling method for datasets and tasks. In this method there are two conditions (i) datasets of task scheduling (ii) tree to tree task scheduling.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Energy Consumption for Task Scheduling on Uni-Processor and Multiprocessor for Step Topology under Distributed Environment

Verma¹,

Saxena²

2017

IJCA

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Distributed computer networking plays a very crucial role in the Business, Industries, Education, Research and Development areas. Many users work on the heterogeneous devices which have different configurations. In distributed network communication takes place from one to one machine, one to many machines or many to one machine. Hence, tasks are migrated from one device to another device which is the important property of the distributed system. Due to rapid increase of the users on the devices connected across the distributed network, the management of the computer networks is a very big and challenging area of research. In the present work, different devices are connected across the step topological networks and an attempt is made to reduce the overall energy consumption when data is flowing from one device to another device. Optimization of energy consumption reduces the overall cost of transfer of data. Multiprocessor and Uni-processor cases are considered in special cases and computed results are represented in the form of tables. A well known Hungarian methodology is used for optimization of the overall energy.

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A new energy-aware task scheduling method for data-intensive applications in the cloud

Cited by 50 publications

References 30 publications

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

An Effective Technique to Decline Energy Expenditure in Cloud Platforms

Optimization of Energy Consumption for Task Scheduling on Uni-Processor and Multiprocessor for Step Topology under Distributed Environment

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