Effective task migration to reduce execution time in mobile cloud computing

Saha, Sajeeb; Hasan, Mohammad S.

doi:10.23919/iconac.2017.8081998

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…P. Balakrishnan in [13] believed that the task scheduling of mobile cloud computing needs to consider not only the energy consumption of mobile devices but also the energy consumption of the CPU in mobile devices at any time. S. Saha in [14] showed a migration decision algorithm for migrating tasks from mobile to cloud execution, which finally achieves the optimization of completion time. M. R. Ra in [15] showed an environment for incremental greedy policies for mobile cloud computing.…”

Section: Related Workmentioning

confidence: 99%

Research on Task Scheduling Strategy under Mobile Cloud Computing Based on ICSO

Chen¹,

Chen²

2022

Journal of Internet Technology

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<p>With the gradual application of mobile terminals such as cell phones in production and life, mobile cloud computing has become an important part of the internet. Different from traditional cloud computing task scheduling methods, mobile cloud computing task scheduling needs to consider not only task time minimization but also the lowest possible mobile device energy consumption. We propose an improved chicken swarm optimization (ICSO) algorithm applied to the task scheduling strategy under mobile cloud computing. First, we establish a multiobjective optimization strategy with minimum completion time and minimum energy consumption. Second, for the shortcomings of the chicken swarm optimization algorithm that easily fall into local optimums leading to algorithm stagnation, we use reverse learning initialization for the chicken flock population to expand the space of understanding and an adaptive strategy for learning factors and following coefficients. To illustrate the effectiveness of our algorithm in scheduling, we chose the number of mobile devices as 50, 100, and 150 and compared the improved chicken swarm optimization algorithm, ant colony algorithm, particle swarm algorithm, and chicken swarm optimization algorithm. The results illustrate that our proposed algorithm can reduce the task completion time, control the energy consumption of mobile devices well, and save energy.</p> <p> </p>

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Section: Related Workmentioning

confidence: 99%

Research on Task Scheduling Strategy under Mobile Cloud Computing Based on ICSO

Chen¹,

Chen²

2022

Journal of Internet Technology

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“…A worker device may bid for multiple advertised tasks, where each bid has been generated randomly considering given task size and deadline. When a worker device submits quality information in a bid, it includes communication latency with actual execution time [43].…”

Section: B Assumptionsmentioning

confidence: 99%

Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

et al. 2021

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Mobile device cloud (MDC) is a collaborative cloud computing platform over which neighboring smart devices form an alliance of shared resources to mitigate resource-scarcity of an individual user device for running compute-intensive applications. A major challenge of such a platform is maximizing user qualityof-experience (QoE) at minimum cost while providing attractive incentives to workers' mobile devices. In state-of-the-art works, either a voluntary task execution or merely resource-cost driven mechanism has been applied to minimize the task execution time while overlooking payment of any additional incentive to the worker devices for their quality services. In this paper, we develop a computational framework for MDC where the afore-mentioned challenging problem is formulated as a multi-objective linear programming (MOLP) optimization function that exploits reverse-auction bidding policy. Due to the NP-hardness of MOLP, we offer two greedy worker selection algorithms for maximizing user QoE or minimizing execution cost. In both algorithms, the amount of incentive awarded to a worker is determined following the QoE offered to a user. Theoretical proofs of desirable properties of the proposed incentive mechanisms are presented. Simulation results illustrate the effectiveness of our incentive algorithms compared to the stateof-the-art approaches.

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“…[5] Though the technology is getting increased day by day and the number of users are increased still the problems are also increasing, the main two problems faced by most of the mobile Cloud Users is Ease of Access and Security. [3] In this paper these two issues will be addressed with an intuition solve them at most.…”

Section: Fig (3) Android Smartphonementioning

confidence: 99%

“…Here the Dynamism is not applied at the time of collecting data it is applied at the time of processing the data. [3] And the last 6 Digits are going to be confidential always which needs to be remembered by the user i.e. registered time and location.…”

Section: Location Of Registrationmentioning

confidence: 99%

“…The entire algorithm will work as discussed above. So this will reduce the problem of security with respect to mobile cloud computing [3]. When it comes to Ease of Access of applications the network related issues need to be resolved first and the infrastructure has to be developed a way that the if internet connectivity is not there or poor the process should store data temporarily in cache once it is get into high speed network it should interact with the cloud for data process.…”

Section: How It Workmentioning

confidence: 99%

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Time Frame Based Dynamic Data Shift Algorithm for Data Security in Mobile Cloud Computing

2019

ijrte

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Mobile Computing is an Innovation in the Field of IT and it brought so many changes in the lives of Human Beings. Initially when mobile phone introduced it is a device useful only for Making and Receiving calls and Sending and Receiving Text Messages. As the technology changed drastically today’s mobile world has become Smart World. Where Mobile phone is not only for calls and messages, it will be used for different purposes starting from Storing The Data, Accessing Different Applications like MS Word, MS Excel etc.., to take photos, and listening songs etc..., So the mobile era brought so many changes in the life of human being, initially people are happy with 4GB memory devices now Smart Phones coming with 128GB and 256 GB still people need more memory. As the usage of Mobile Phone is getting increased storing data in the given space is becoming limited. To overcome this cloud computing has taken one more step to connect the applications running on mobile to connect with cloud to store and process the data. Initially the Application will be using only the memory of mobile phones but now the applications are moving to cloud. To give an example the applications like DROP BOX, Google Drive, One Drive are running on their servers now they moved to cloud where people can access the application with ease of access. The major problems faced by users are Security and Accessing the Applications without any interruption. In this Research Paper the major focus is on how to improve the security of the Data and the minor focus is on ease of access to applications. In order to provide security for the data two level security system will be implemented. For ease access there are few techniques and frameworks need to be changed according to the problem

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Effective task migration to reduce execution time in mobile cloud computing

Cited by 11 publications

References 11 publications

Research on Task Scheduling Strategy under Mobile Cloud Computing Based on ICSO

Research on Task Scheduling Strategy under Mobile Cloud Computing Based on ICSO

Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

Time Frame Based Dynamic Data Shift Algorithm for Data Security in Mobile Cloud Computing

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