DependencyAware Task Allocation Algorithm for Distributed Edge Computing To overcome the limitation of standalone edge computing in terms of computing power and resource, a concept of distributed edge computing has been introduced, where application tasks are distributed to multiple edge clouds for collaborative processing. To maximize the effectiveness of the distributed edge computing, we formulate an optimization problem of task allocation minimizing the application completion time. To mitigate high complexity overhead in the formulated problem, we devise a lowcomplexity heuristic algorithm called dependencyaware task allocation algorithm (DATA). Evaluation results demonstrate that DATA can reduce the completion time up to by 18% compared to conventional dependencyunaware task allocation schemes.

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.