An adaptive task allocation technique for green cloud computing

Mishra, Sambit Kumar; Puthal, Deepak; Sahoo, Bibhudatta; Jena, Sanjay Kumar; Obaidat, Mohammad S.

doi:10.1007/s11227-017-2133-4

Cited by 85 publications

(33 citation statements)

References 19 publications

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“…Mishra et al [6] addressed the issue of computational power consumption in Cloud data center. They proposed an adaptive task allocation algorithm (by presenting a system model/task model) to minimize makespan along with energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…So, to calculate the energy consumption of virtual machine, both active and idle state energy consumptions are considered. It is analyzed in the literature available that the energy consumption of VM in idle state is 60% of energy consumption of VM during active state [6]. Hence, the idle state time of a VM is calculated by subtracting the active state time from the makespan of the system.…”

Section: Makespan (M) = (I)mentioning

confidence: 99%

“…The proposed PADTS Algorithm is derived from Adaptive Task Allocation algorithm discussed in [6] and further it carried out more functions. The proposed algorithm consider the interdependent and storage bound task which was not considered in [6].…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…The proposed algorithm consider the interdependent and storage bound task which was not considered in [6]. As it consider interdependent task, it checks the dependency of task before the resource allocation.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…We have considered urgency and dependency of tasks as shown in table 1. Our algorithm performs row update and column update functions as describe in [6]. This technique is useful to indentify efficient VM for task.…”

Section: Evaluation Of Algorithm: Example Scenariomentioning

confidence: 99%

See 4 more Smart Citations

Power Aware Task Scheduling in Compute Cloud

Modi¹,

Shrimali²,

Hiren³

2018

IJCA

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Cloud computing has become an attractive computing paradigm in recent years to offer on demand computing resources for users worldwide. Computing resources are delivered in the form of virtual machines. In such a scenario, task scheduling algorithms play an important role to schedule the tasks effectively to achieve reduction in power consumption and makespan with improvement in resource utilization. Many task scheduling algorithms are introduced to improve energy efficiency of data center. In our work, we have proposed and discussed a power aware dependent task scheduling (PADTS) algorithm and compare it with existing ones.

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Section: Related Workmentioning

confidence: 99%

Section: Makespan (M) = (I)mentioning

confidence: 99%

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Evaluation Of Algorithm: Example Scenariomentioning

confidence: 99%

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Power Aware Task Scheduling in Compute Cloud

Modi¹,

Shrimali²,

Hiren³

2018

IJCA

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Applications of Federated Learning in Computing Technologies

Mishra¹,

Sindhu²,

Teja³

et al. 2023

Convergence of Cloud With AI for Big Data Analytics

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A cost‐efficient and QoS‐aware adaptive placement of applications in fog computing

Wang

et al. 2023

Concurrency and Computation

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SummaryWith the rapid development of the Internet of Things (IoT), fog computing has emerged as a complementary solution to address the issues faced in cloud computing. However, it is a challenging issue to ensure both of the high Quality of Service (QoS) and low cost for different requests when dealing with computing resources. In this article, we propose a new approach on adaptive cost‐efficient and QoS‐aware application placement for fog computing called DATSS. Specifically, this approach consists of a QoS state driven strategy and a credibility rating mechanism. The algorithm divides the QoS into three different states by calculating the QoS violation rate and adaptively selects the best scheduling strategy. Then evaluates the stability of the node based on the task history, and prioritize the task on the node with a higher rating. Performance results show that the cost, delay and energy consumption of the proposed algorithm can be reduced by 20.9%, 15.7%, and 12.6% respectively compared with other algorithms at most. The proposed algorithm can effectively improve cost efficiency and stability under QoS constraints in fog computing environments.

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An adaptive task allocation technique for green cloud computing

Cited by 85 publications

References 19 publications

Power Aware Task Scheduling in Compute Cloud

Power Aware Task Scheduling in Compute Cloud

Applications of Federated Learning in Computing Technologies

A cost‐efficient and QoS‐aware adaptive placement of applications in fog computing

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