A performance comparison of heuristics for scheduling jobs in hybrid mobile topologies Hardware improvements of mobile devices have allowed them to be considered as firstclass resources at the edge, which has led to the need to propose new techniques and scheduling heuristics to make efficient usage of these devices. Current scheduling heuristics available in the literature do not take into account mobile grid nodes that do not rely on batteries to work, a factor that is of great importance in the advent of mobileedge computing. In this paper we propose a simple algorithm that incorporates a more advanced awareness of a mobile deviceu0027s power supply and we show that incorporating this characteristic can enable these heuristics to manage edge resources in a more efficient way.

CrossServer Computation Offloading for MultiTask Mobile Edge Computing As an emerging network architecture and technology, mobile edge computing (MEC) can alleviate the tension between the computationintensive applications and the resourceconstrained mobile devices. However, most available studies on computation offloading in MEC assume that the edge severs host various applications and can cope with all kinds of computation tasks, ignoring limited computing resources and storage capacities of the MEC architecture. To make full use of the available resources deployed on the edge servers, in this paper, we study the crossserver computation offloading problem to realize the collaboration among multiple edge servers for multitask mobile edge computing, and propose a greedy approximation algorithm as our solution to minimize the overall consumed energy. Numerical results validate that our proposed method can not only give nearoptimal solutions with much higher computational efficiency, but also scale well with the growing number of mobile devices and tasks.