Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

Mobile edge computing(MEC),as a new distributed computing model,satisfies the low energy consumption and low latency requirements of computation-intensive services.The task offloading of MEC has become an important research hotspot,as it solves the problems of insufficient computing capability and battery capacity of Internet of things(IoT)devices.This study investigates task offloading scheduling in a dynamic MEC system.By integrating energy harvesting technology into IoT devices,we propose a hybrid energy supply model.We jointly optimize local computing,offloading duration,and edge computing decisions to minimize system cost.On the basis of stochastic optimization theory,we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME.DTOME can make task offloading decisions by weighing system cost and queue stability.We quote dynamic programming theory to obtain the optimal task offloading strategy.Simulation results verify the effectiveness of DTOME,and show that DTOME entails lower system cost than two baseline task offloading strategies.

＂Smart Cafe＂： A Mobile Local Computing System Based On Indoor Virtual Cloud

With network developing and virtualization rising, more and more indoor environment （POIs） such as care, library, office, even bus and subway can provide plenty of bandwidth and computing resources. Meanwhile many people daily spending much time in them are still suffering from the mobile device with limited resources. This situation implies a novel local cloud computing paradigm in which mobile device can leverage nearby resources to facilitate task execution. In this paper, we implement a mobile local computing system based on indoor virtual cloud. This system mainly contains three key components： 1）As to application, we create a parser to generate the ＂method call and cost tree＂ and analyze it to identify resource- intensive methods. 2） As to mobile device, we design a self-learning execution controller to make offtoading decision at runtime. 3） As to cloud, we construct a social scheduling based application-isolation virtual cloud model. The evaluation results demonstrate that our system is effective and efficient by evaluating CPU- intensive calculation application, Memory- intensive image translation application and I/ O-intensive image downloading application.

Dynamic computation offloading in time-varying environment for ultra-dense networks:a stochastic game approach

To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,combining ultra-dense networks(UDNs)and mobile edge computing(MEC)are considered as important solutions.In the MEC enabled UDNs,one of the most important issues is computation offloading.Although a number of work have been done toward this issue,the problem of dynamic computation offloading in time-varying environment,especially the dynamic computation offloading problem for multi-user,has not been fully considered.Therefore,in order to fill this gap,the dynamic computation offloading problem in time-varying environment for multi-user is considered in this paper.By considering the dynamic changes of channel state and users’queue state,the dynamic computation offloading problem for multi-user is formulated as a stochastic game,which aims to optimize the delay and packet loss rate of users.To find the optimal solution of the formulated optimization problem,Nash Q-learning(NQLN)algorithm is proposed which can be quickly converged to a Nash equilibrium solution.Finally,extensive simulation results are presented to demonstrate the superiority of NQLN algorithm.It is shown that NQLN algorithm has better optimization performance than the benchmark schemes.