Energy-efficient migration techniques for cloud environment: a step toward green computing

Bhattacherjee, Srimoyee; Das, Rituparna; Khatua, Sunirmal; Roy, Sarbani

doi:10.1007/s11227-019-02801-0

Cited by 28 publications

(15 citation statements)

References 42 publications

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“…It evaluates the energy usage in resource allocation, and reduces power consumption on data centers [15]. Bhattacherjee et al [16] proposed VM placement and migration policy using a dynamic thresholding approach. Markov chain has been used for predicting future requirements based on history.…”

Section: Related Workmentioning

confidence: 99%

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QRAS: efficient resource allocation for task scheduling in cloud computing

2021

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Cloud resource allocation, a real-time problem can be dealt with efficaciously to reduce execution cost and improve resource utilization. Resource usability can fulfill customers’ expectations if the allocation has performed according to demand constraint. Task Scheduling is NP-hard problem where unsuitable matching leads to performance degradation and violation of service level agreement (SLA). In this research paper, the workflow scheduling problem has been conducted with objective of higher exploitation of resources. To overcome scheduling optimization problem, the proposed QoS based resource allocation and scheduling has used swarm-based ant colony optimization provide more predictable results. The experimentation of proposed algorithms has been done in a simulated cloud environment. Further, the results of the proposed algorithm have been compared with other policies, it performed better in terms of Quality of Service parameters.

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Section: Related Workmentioning

confidence: 99%

“…Markov chain has been used for predicting future requirements based on history. Its reduces energy consumption in data center resources [16]. Mansouri et al [17] proposes cost-based job scheduling (CJS) strategy for optimizing resource usage.…”

Section: Related Workmentioning

confidence: 99%

QRAS: efficient resource allocation for task scheduling in cloud computing

2021

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“…One of the big challenges cloud computing is load balancing; load balancing is the way of dividing workloads over various computing resources. The major aim of cloud load balancing is minimization in costs related with document management systems, response time, energy consumption and increase in availability of resources and performance [25]. Load balancing in cloud computing be between hosts or VMs; the balancing mechanism spreads the loads equally between the nodes (VMs or hosts).…”

Section: Cloud Computingmentioning

confidence: 99%

Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm

Abdulhammed

2021

J Supercomput

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IoT is one of the most important technologies that are used over the past few years, where everything is connected to the Internet; it is used in many fields; one of these fields is healthcare system that includes mobile health and remote patient monitoring (patients with kidney, heart disease, cancer, blood pressure, diabetes, respiratory disease and stroke). Integration of IoT and cloud computing can improve the performance of healthcare system and the development of the innovative applications in future. One of the major problems that cannot be ignored in cloud computing system is load balancing. Solving that problem leads to reduce the response time, power consumption, cost and increase server availability. This paper is comprised of two parts which are creating and implementing healthcare system by using IoT, and solving the problem of load balancing of the cloud computing by using intelligent algorithm called sparrow search algorithm (SSA). The SSA is used to select the best virtual machine (VM) among a group of VMs depending on the its fitness value; also many and varied tasks are scheduled with priority and assign to the best VMs depending on the its instruction millions (IM), where the task that has high IM is assigned to the best VM that has high fitness value. The outcomes demonstrated that the proposed method focuses to reduce the latency and packet loss while maximizing the throughput in healthcare systems; also the SSA has proved its robustness, efficiency and success when compared to other methods in terms of reducing makespan time, total processing time and provides load balancing among VMs, where the value of makespan time, processing time and degree of imbalance has decreased into (23.05), (899.8979) and (177.7675), respectively, in case of applying 500 tasks.

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“…Zolfaghari and Rahmani [6] conduct a systematic review of researches on it in cloud environments. Bhattacherjee et al [7] propose a prediction-based migration approach. Tarahomi et al [8] propose an effective energysaving micro-genetic algorithm for destination host selection in dynamic VM consolidation.…”

Section: Related Workmentioning

confidence: 99%

“…For the host resource management, reducing energy consumption is the primary goal of this paper, which can be expressed as (7).…”

Section: Problem Formulationmentioning

confidence: 99%

An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

Zhang

Wang

et al. 2021

IEEE Access

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Energy-efficient migration techniques for cloud environment: a step toward green computing

Cited by 28 publications

References 42 publications

QRAS: efficient resource allocation for task scheduling in cloud computing

QRAS: efficient resource allocation for task scheduling in cloud computing

Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm

An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

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