A Context Sensitive with Effective Task Migration in Mobile Cloud Computing Services
Gurujukota Ramesh Babu, Phaneendra Varma Chintalapati, Kiran Sree Pokkuluri, Archana Pudi, Chintha Venkata Ramana · 2023
Cloud computing has gained popularity in recent years and is modernizing the internet computer architecture. Mobile applications and gadgets are also rapidly transforming. As the acceptance and functionality of mobile devices have increased recently, Mobile Cloud Computing (MCC) has attracted a lot of research interest. As mobile technologies progress, new compute-intensive tasks are quickly appearing. On mobile devices, though, these tasks are challenging to complete due to resource restrictions. By conducting an operation in the cloud and then transmitting the result to the user's mobile device, cloud transportation is utilised to solve this issue. A prototype MCC offloading system used in this analysis offers an adaptable MCC service that takes into consideration a variety of cloud services, including mobile ad-hoc networks, cloudlets, and public clouds. They describe a context-aware offloading with effective cloud migration decision making algorithm in this analysis, with the goal of providing code offloading decisions and compute-intensive tasks at runtime on selecting wireless medium and which potential cloud resources as the offloading location based on the device context. Whether a function is on a cloud server or a mobile device depends on the decision-making process around its transfer. Also examined how well mobile and cloud execution performed, demonstrating how using a cloud server can speed up task execution. To evaluate the effectiveness of the presented algorithm, they also run actual experiments on the system that has been put into place. Based on the various mobile device contexts, the system and embedded decision algorithm are able to select suitable wireless mediums and cloud resources, resulting in a significant performance boost, according to the findings.In this case, the presented algorithm will outperform other approaches in terms of execution time and energy consumption in this analysis.