摘要 : With mobile devices increasingly able to connect to cloud servers from anywhere, resource-constrained devices can potentially perform offloading of computational tasks to either save local resource usage or improve performance. It... 展开 With mobile devices increasingly able to connect to cloud servers from anywhere, resource-constrained devices can potentially perform offloading of computational tasks to either save local resource usage or improve performance. It is of interest to find optimal assignments of tasks to local and remote devices that can take into account the application-specific profile, availability of computational resources, and link connectivity, and find a balance between energy consumption costs of mobile devices and latency for delay-sensitive applications. We formulate an NP-hard problem to minimize the application latency while meeting prescribed resource utilization constraints. Different from most of existing works that either rely on the integer programming solver, or on heuristics that offer no theoretical performance guarantees, we propose Hermes, a novel fully polynomial time approximation scheme (FPTAS). We identify for a subset of problem instances, where the application task graphs can be described as serial trees, Hermes provides a solution with latency no more than times of the minimum while incurring complexity that is polynomial in problem size and . We further propose an online algorithm to learn the unknown dynamic environment and guarantee that the performance gap compared to the optimal strategy is bounded by a logarithmic function with time. Evaluation is done by using real data set collected from several benchmarks, and is shown that Hermes improves the latency by percent compared to a previously published heuristic and increases CPU computing time by only percent of overall latency. 收起