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Optimal Joint Scheduling and Cloud Offloading for Multi-Component Applications - 2021

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Research Paper on Optimal Joint Scheduling and Cloud Offloading for Multi-Component Applications

Research Area:  Edge Computing

Abstract:

Cloud computing facilitates on-demand computing services like servers, storage, database, and networking over the Internet service. The concept of joint scheduling and computation offloading (JSCO) for multi-component applications is introduced, where an optimal decision is taken to offload some part of the application to be executed in the cloud platform. Executing part of the application on local machine/node and the remaining part on the cloud in parallel are faster and power saving for the local nodes, which are much suitable for today’s mobile-based applications. In JSCO, this approach is followed to provide solutions for many compute-intensive mobile applications like video gaming and graphics processing. In this work, we use a centralized broker that determines optimal solutions for scheduling tasks and offloading possible tasks on to the available cloud. Today, many companies provide cloud services for free, and once the tasks are ordered and scheduled to run on the cloud, any available cloud service is chosen dynamically by the broker node. The main job of the broker node is to schedule the tasks and maintain the availability of resources in each cloud for scheduling. In a resource augmentation environment (RAE), a mobile node can decide to offload tasks when the available resources are not adequate locally to execute their tasks or to achieve the desired performance by executing the tasks on high-speed computing nodes at the remote (e.g., mobile node executing resource-intensive applications like games).

Keywords:  

Author(s) Name:  S. L. Shiva Darshan, Abdul Mueez, Aakash S. Shetty, B. A. Mohan, S. Ashok Kumar & Roshan Fernandes

Journal name:  Emerging Research in Computing, Information, Communication and Applications

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/978-981-16-1342-5_33

Volume Information:  volume 83,(2021)