Energy Saving Strategy for Task Migration Based on Genetic Algorithm

With the rapid development and popularization of mobile Internet and Internet of Things, People have entered the era of Internet of Everything. Mobile edge computing is the key technology to improving the user experience of 5G network in the future, which is close to the data source, so that it can effectively reduce the network transmission delay. Edge computing will sink the business to the edge of the network, provide computing services and storage services, and provide task migration platform for users. At present, the current battery development speed of mobile devices is far behind the development speed of its processor and memory. Therefore, to solve the problem of “how to realize the low energy migration of complex dependency application”, a fine-grained directed acyclic graph task partition model is established based on the characteristics of mobile edge computing, and the relationships among the divided subtasks are analyzed to construct the minimization energy consumption problem under the execution time limit, then uses the genetic algorithm to find the optimal solution, and obtains the result of the migration decision for each sub task, that is, the optimal energy-saving migration plan for the entire mobile terminal application. The experimental results show that the fine-grained task migration strategy proposed in this paper makes full use of the advantages of mobile edge computing, and can effectively reduce the energy consumption of mobile terminal devices on the premise of meeting the task execution delay.

[1]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[2]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[3]  Xiaojiang Du,et al.  Toward Vehicle-Assisted Cloud Computing for Smartphones , 2015, IEEE Transactions on Vehicular Technology.

[4]  Junyi Wang,et al.  Adaptive application offloading decision and transmission scheduling for mobile cloud computing , 2017, China Communications.

[5]  Sheetal Kalra,et al.  Comparative study of different cloud computing load balancing techniques , 2014, 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom).

[6]  Dongman Lee,et al.  An Adaptable Application Offloading Scheme Based on Application Behavior , 2008, 22nd International Conference on Advanced Information Networking and Applications - Workshops (aina workshops 2008).

[7]  Alec Wolman,et al.  MAUI: making smartphones last longer with code offload , 2010, MobiSys '10.

[8]  Sasu Tarkoma,et al.  Mobile search and the cloud: The benefits of offloading , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[9]  Hui Tian,et al.  Fine-granularity based application offloading policy in cloud-enhanced small cell networks , 2016, 2016 IEEE International Conference on Communications Workshops (ICC).

[10]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[11]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[12]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[13]  Yan Zhang,et al.  Mobile Edge Computing: A Survey , 2018, IEEE Internet of Things Journal.

[14]  Rajkumar Buyya,et al.  Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments , 2011, 2011 International Conference on Parallel Processing.

[15]  Dario Sabella,et al.  Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things , 2016, IEEE Consumer Electronics Magazine.