An Optimization Model for Task Scheduling in Mobile Cloud Computing

An Optimization Model for Task Scheduling in Mobile Cloud Computing

Rashid Alakbarov
Copyright: © 2022 |Pages: 17
DOI: 10.4018/IJCAC.297102
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The rapid increase in the number of mobile users in mobile cloud computing (MCC), the cloud servers' remoteness, and the Internet loading have caused significant delays in the delivery of processed data to the user. The selection of the most suitable cloudlet that allows running users' applications rapidly in the cloudlet is still an urgent problem. In the paper we propose a strategy for selecting a cloudlet with high computing productivity, which provides a fast solution, considering the complexity degree of the application (file type). Here also noted that the balanced distribution of users' application software in the cloudlet network ensures the reduction of delays. In the paper, for the proposed strategy, a mathematical model of the optimal distribution of applications in the cloudlet has been proposed considering the loading degree of cloudlets which provides energy consumption on mobile devices, uploading the issue to the cloud, obtaining results, reducing network delays.
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During the day, users can prepare documents, watch movies, use social networks and online stores, etc., by using mobile devices. Also, users widely use email services and audio-video information on mobile devices. Given benefits can cause quick depletion of the mobile devices' power supply. Recently used applications used by mobile users require considerable computing and memory resources. Mobile devices have restricted computing, memory, and power resources, which create difficulties in solving complex problems requiring enormous computing and memory resources. These problems can be solved by using new paradigms such as cloud technology. Thus, with the help of a new paradigm, cloud technology, users use mobile devices to perform applications requiring large computing and memory resources. Cloud technologies provide mobile users with sufficient computing and memory resources by running tasks on a cloud platform (cloud servers).

Although widely used traditional (centralized) cloud computing has high computing and memory resources, it cannot deliver processed data to users at high speeds due to network delays. Furthermore, recently, due to the rapid increase in the number of mobile users in the network, the remote delivery of cloud servers, and the Internet loading, there are delays in delivering processed data to the user. Therefore, cloud computing resources should be placed close to the user to solve these problems (resource scarcity, power consumption, and delays in communication channels, etc.). Thus, the creation of cloudlet-based mobile cloud computing is an actual issue for solving these problems.

Problems and Aim of the Proposed Work

The computer devices used to create cloudlet networks have various technical capacities. In traditional cloudlet-based mobile computing networks, the Resource Management Center (RMC) routed and executed the user application to any cloud with free resources. Therefore, the type of application software (degree of complexity) and the selection of the appropriate cloudlet are not analyzed. Also, the issue of proximity between users and cloudlets is not considered here. Additionally, if the applications in a cloudlet are not optimally distributed in a balanced way (some cloudlets are fully loaded, some are empty), their execution time is extended, and the power consumption of the mobile device increases. Thus, the noted problems increase the power consumption and network delays of mobile devices. For solving these problems, applications with a high degree of complexity should be solved in a cloud with increased technical capabilities. If applications with high complexity are solved in cloudlets with advanced technological capabilities, then the problem-solving time will be less. Firstly, the type (level of complexity) of the user application is determined. Then, according to the kind of application, we select the appropriate cloudlet with the highest technical capabilities, close to the user from the appropriate cloudlets. The proposed strategy (planning) reduces the power consumption of mobile devices and the implementation delays of the application by loading them to the appropriate cloudlets. The proposed model also prevents the overloading of any cloudlet close to the problems of many users. When the cloudlets (virtual machines) are fully loaded (when the download rate is 100%), the time to solve the problems is extended. Therefore, the proposed model also considers the balanced distribution of user applications (issues) in the cloud under certain conditions. Thus, in the paper mathematical model for solving the following problems has been proposed:

  • Reduction of energy consumption in mobile devices.

  • Selection of cloudlets according to the type of application.

  • Decreasing of network delays.

  • Balanced distribution of loads in cloudlets.

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