A novel energy estimation model for constraint based task offloading in mobile cloud computing

• Published in: Journal of ambient intelligence and humanized computing 2020-11, Vol.11 (11), p.5477-5486
• Main Authors: Erana Veerappa Dinesh, S., Valarmathi, K.
• Format: Article
• Language: English
• Subjects: Algorithms; Applications programs; Artificial Intelligence; Cloud computing; Clustering; Collaboration; Communication; Computation offloading; Computational Intelligence; Constraint modelling; Decision making; Edge computing; Efficiency; Electronic devices; Energy consumption; Engineering; Integer programming; Mobile computing; Original Research; Robotics and Automation; Scheduling; Smartphones; Task scheduling; User Interfaces and Human Computer Interaction; Virtual environments
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-020-01903-5

The utility of mobile applications has been increased enormously due to the advancements in science and technology to assist the users for various purposes. The main intention of integrating cloud services and resources with the mobile application is to reduce battery usage and to improve the efficiency of mobile devices. Thus the process of shifting a task that can be run on the cloud resources for assistance is referred to as task offloading. The process of task offloading is critical in the field of Mobile Cloud Computing. The major issue that pairs with task offloading are the communication cost estimation of the devices. To overcome the above-mentioned issues and to create an effective task offloading model, A Novel Mobile Cloud Computing framework called Rule Generation based Energy Estimation Model (RG-EEM) is designed. The energy required for executing the task in the local mobile device and cloud is estimated by implementing an energy estimation algorithm. Then a novel constraints specific rule generation algorithm is used to estimate the task execution time and memory utilization of the task, from which the decision has to be taken for offloading or local execution by considering all the possible affecting characteristics. Further, a novel task clustering and scheduling algorithm are implemented to execute the task in the cloud server effectively which will help in allocating similar tasks to a particular virtual machine in the cloud. The RG-EEM algorithm will also help in partitioning the task and parallel execution. The effectiveness of the RG-EEM technique is evaluated using the parametric measures and compared with the existing techniques.