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An efficient framework for scheduling security-critical tasks in resource-limited mobile edge computing using
Kapil Vhatkar1, Shweta Koparde1, Neeta Deshpande2
1Department of Computer Engineering at School of Computer Science and Information Technology, Symbiosis Skills and Professional University, Pune, India.
Abstract:
Mobile Edge Computing (MEC) is an advanced technology that has the ability to decentralize and transform the working functionality of phone networks. The MEC is implanted in the cell phone base stations. The available resources in the mobile applications are processed by the MEC. Yet, the user experience and Quality of Service (QoS) are affected by the problem of inevitable optimization. A practical and efficient option to transfer workloads is MEC servers that are equipped with tiny or large base stations. By shifting the tasks from mobile devices to edge servers, MEC can provide low-latency computing services and high throughput. This research work aims at scheduling security-critical workflow tasks in the MEC environment that significantly improves the computing power of the devices by scheduling the service workflows from computing mobile devices to the edges of the mobile network. The major objective to be considered during the scheduling of critical tasks is the minimization of workflow execution time and total energy consumption. The major contributions of the recommended security-critical tasks scheduling approach in resource-limited MEC are listed here.•To present a security-critical tasks scheduling method in resource-limited MEC to improve the offloading performance and energy efficiency to reduce the latency issues.•To secure the security-critical features of the tasks to schedule the tasks in resource-limited MEC for enhancing the user experience as well as quality of service.•To schedule the security-critical tasks in resource-limited MEC using the developed HGR-GJOS. It helps to minimize the workflow execution time and total energy consumption of the devices by optimizing which of the tasks are assigned to which machine.
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