RETRACTED ARTICLE: Energy aware smartphone tasks offloading to the cloud using gray wolf optimization

Subramaniam, Erana Veerappa Dinesh; Krishnasamy, Valarmathi

doi:10.1007/s12652-020-01756-y

Cited by 12 publications

(8 citation statements)

References 25 publications

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“…In the current time, task offloading from smartphones to cloud servers has proven to be a promising technique for increasing smartphone functionality and battery life. The cost of communication and energy usage are used to determine the effectiveness of task offloading [17] Strength We offer a new framework that uses a task scheduler to reduce energy usage during HMCC task offloading [10]…”

Section: Gray Wolf Optimizationmentioning

confidence: 99%

“…The extreme achievement period is termed the makespan, which is given as makespan = max ðT t−1t , T t−2t ; ;T t−mt Þ for a cloudlet VM task offloading [10].…”

Section: Heuristic Algorithmmentioning

confidence: 99%

“…

then the quantity of the success period of the offloading task. The extreme achievement period is termed the makespan, which is given as makespan = max( T t −1 t , T t −2 t , T t − mt ) for a cloudlet VM task offloading [ 10 ].…”

Section: Related Workmentioning

confidence: 99%

“…We have to utilize the resources or offload the task to consume time, energy cost, power consumption, and computational power, to minimize the energy usage of the smart devices we offload tasks in the cloud network and check resource parameters by using some methods [3,[9][10][11]. Our future job, according to the computational findings, is to offload the task using some metaheuristic algorithm combo, and the imitations conclude that the current task offloading method achieved a well-traded-off presentation between power usage ability and task execution.…”

Section: Introductionmentioning

confidence: 99%

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Self-Adaptation Resource Allocation for Continuous Offloading Tasks in Pervasive Computing

Ehsan

Haider

Faisal

et al. 2022

Computational and Mathematical Methods in Medicine

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Advancement in technology has led to an increase in data. Consequently, techniques such as deep learning and artificial intelligence which are used in deciphering data are increasingly becoming popular. Further, advancement in technology does increase user expectations on devices, including consumer interfaces such as mobile apps, virtual environments, or popular software systems. As a result, power from the battery is consumed fast as it is used in providing high definition display as well as in charging the sensors of the devices. Low latency requires more power consumption in certain conditions. Cloud computing improves the computational difficulties of smart devices with offloading. By optimizing the device’s parameters to make it easier to find optimal decisions for offloading tasks, using a metaheuristic algorithm to transfer the data or offload the task, cloud computing makes it easier. In cloud servers, we offload the tasks and limit their resources by simulating them in a virtual environment. Then we check resource parameters and compare them using metaheuristic algorithms. When comparing the default algorithm FCFS to ACO or PSO, we find that PSO has less battery or makespan time compared to FCFS or ACO. The energy consumption of devices is reduced if their resources are offloaded, so we compare the results of metaheuristic algorithms to find less battery usage or makespan time, resulting in the PSO increasing battery life or making the system more efficient.

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Section: Gray Wolf Optimizationmentioning

confidence: 99%

“…The extreme achievement period is termed the makespan, which is given as makespan = max ðT t−1t , T t−2t ; ;T t−mt Þ for a cloudlet VM task offloading [10].…”

Section: Heuristic Algorithmmentioning

confidence: 99%

“…

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Self-Adaptation Resource Allocation for Continuous Offloading Tasks in Pervasive Computing

Ehsan

Haider

Faisal

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…To perform the selective offloading with multiple input tasks such that the overall network energy consumption of IoT and the overall network delay become minimum. 23 Evolutionary algorithms have the capability to explore and exploit the search space and get the best results.…”

Section: Related Workmentioning

confidence: 99%

Meta-heuristic-based offloading task optimization in mobile edge computing

Abbas

Raza

Aadil

et al. 2021

International Journal of Distributed Sensor Networks

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With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks.

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A Novel Fish Migration Optimization with QUasi-Affine TRansformation Evolutionary for Numerical Optimization and Application

Wang

He-tong

et al. 2022

Smart Innovation, Systems and Technologies

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RETRACTED ARTICLE: Energy aware smartphone tasks offloading to the cloud using gray wolf optimization

Cited by 12 publications

References 25 publications

Self-Adaptation Resource Allocation for Continuous Offloading Tasks in Pervasive Computing

Self-Adaptation Resource Allocation for Continuous Offloading Tasks in Pervasive Computing

Meta-heuristic-based offloading task optimization in mobile edge computing

A Novel Fish Migration Optimization with QUasi-Affine TRansformation Evolutionary for Numerical Optimization and Application

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