A key challenge in wide adoption of sophisticated context-aware applications is the requirement of continuous sensing and context computing. This paper presents Panorama, a middleware that identifies collaboration opportunities to offload context computing tasks to nearby mobile devices as well as cloudlets/cloud. At the heart of Panorama is a multi-objective optimizer that takes into account different constraints such as access cost, computation capability, access latency, energy consumption and data privacy, and efficiently computes a collaboration plan optimized simultaneously for different objectives such as minimizing cost, energy and/or execution time. Panorama provides support for discovering nearby devices and cloudlets/cloud, computing an optimal collaboration plan, distributing computation to participating devices, and getting the results back. The paper provides an extensive evaluation of Panorama via two representative context monitoring applications over a set of Android devices and a cloudlet/cloud under different constraints.
翻译:广泛采用精密背景意识应用的一个关键挑战是需要连续的遥感和背景计算。本文介绍了Panorama,这是一个中间软件,它确定了将背景计算任务卸到附近移动设备以及云层/云层/云层上的协作机会。在Panorama的核心是一个多目标优化器,它考虑到各种制约因素,如接入成本、计算能力、存取潜伏力、能源消耗和数据隐私等,并有效地计算了一项同时优化的合作计划,以达到各种目标,如最大限度地减少成本、能源和/或执行时间。Panorama为发现附近装置和云层/云层提供支持,计算最佳协作计划,向参与装置分配计算,并收回结果。本文通过对一套安卓装置和受不同制约的云盘/云层进行两个有代表性的背景监测应用,对全景进行了广泛的评估。