| Summary: | Mobile cloud computing has been proposed as an effective solution to augment the capabilities of resource-poor mobile devices. In this paper, we investigate energy-efficient collaborative task execution to reduce the energy consumption on mobile devices. We model a mobile application as a general topology, consisting of a set of fine-grained tasks. Each task within the application can be either executed on the mobile device or on the cloud. We aim to find out the execution decision for each task to minimize the energy consumption on the mobile device while meeting a delay deadline. We formulate the collaborative task execution as a delay-constrained workflow scheduling problem. We leverage the partial critical path analysis for the workflow scheduling; for each path, we schedule the tasks using two algorithms based on different cases. For the special case without execution restriction, we adopt one-climb policy to obtain the solution. For the general case where there are some tasks that must be executed either on the mobile device or on the cloud, we adopt Lagrange Relaxation based Aggregated Cost (LARAC) algorithm to obtain the solution. We show by simulation that the collaborative task execution is more energy-efficient than local execution and remote execution.
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