The rising popularity of smartphones and vehicles equipped with onboard sensors sheds lights on building a city-scale sensing system for urban surveillance. This paper proposes a novel metric, urban resolution, to measure the quality of urban sensing. Urban resolution describes how sensitivity the urban sensing system could achieve for environment monitoring applications. Then, we study the relationship between resolution r and number of sensing nodes s, and reveal the linear growth relationship between √r and √s . Furthermore, by employing a commonly used human/vehicle mobility model, SLAW, we find that the distribution model of urban sensing nodes is able to be described by a truncated Pareto distribution, and derive the complementary cumulative distribution function (CCDF) of urban resolution. The CCDF reveals the radio of the sub-regions which satisfy the required sensing quality to the whole region. Our findings provide valuable insights to infer the urban sensing quality according to the scale of urban sensing system or determine how many smartphone/vehicles needed for participating in urban sensing applications. Finally, based on five real datasets-three human/vehicle trajectory datasets and two environment monitoring datasets, we examine the metric of urban resolution and evaluate the main results in this paper.

Recent years, more and more enterprises migrate their applications to the cloud for cost saving. During the application migration, an application usually needs to be re-designed from a workflow to an execution plan on a cloud platform. Based on the execution plan, many scheduling optimization approaches have been proposed to improve the performance of the system. In this paper, we argue that the concurrency optimization of each task in the workflow can significantly improve the system efficiency under delay or resource constraint. To generate an efficient execution plan, we optimize the concurrency of the task based on the critical path of the workflow. Furthermore, we deal with the scheduling problem in two different scenarios respectively. To save the costs under a certain deadline, we propose a delay constrained task scheduling approach. To shorten the completion time under certain resources, we propose a resource constrained task scheduling approach. Finally, by deploying a video analysis application on a cloud platform testbed, we validate the proposed two scheduling approaches.