Computation off-loading, i.e., remote execution, has been shown to be effective for extending the computational power and battery life of resource-restricted devices, e.g., hand-held, wearable, and pervasive computers. Remote execution systems must predict the cost of executing both locally and remotely to determine when off-loading will be most beneficial. These costs however, are dependent upon the execution behavior of the task being considered and the highly-variable performance of the underlying resources, e.g., CPU (local and remote), bandwidth, and network latency. As such, remote execution systems must employ sophisticated, prediction techniques that accurately guide computation off-loading. Moreover, these techniques must be efficient, i.e., they cannot consume significant resources, e.g., energy, execution time, etc., since they are performed on the mobile device.
In this paper, we present NWSLite, a computationally efficient, highly accurate prediction utility for mobile devices. NWSLite is an extension to the Network Weather Service (NWS), a dynamic forecasting toolkit for adaptive scheduling of high-performance Computational Grid applications. We significantly scaled down the NWS to reduce its resource consumption yet still achieve accuracy that exceeds that of extant remote execution prediction methods. We empirically analyze and compare both the prediction accuracy and the cost of NWSLite and a number of different forecasting methods from existing remote execution systems. We evaluate the efficacy of the different methods using a wide range of mobile applications and resources.
This material is based upon work supported by the National Science Foundation under Grant No. EIA-0080134. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.