A Multi-Objective Approach to Real-Time In-Situ Processing of Mobile-Application Workflows

作者：Viswanathan Hariharasudhan^*; Lee Eun Kyung; Pompili Dario

来源：IEEE Transactions on Parallel and Distributed Systems, 2016, 27(11): 3116-3130.

DOI：10.1109/TPDS.2016.2532864

• Data uncertainty
• workflows
• mobile grid computing
• in-situ processing
• interval arithmetic
• multi-objective optimization

Innovative mobile applications that rely on real-time in-situ processing of data collected in the field need to tap into the heterogeneous sensing and computing capabilities of sensor nodes, mobile handhelds as well as computing and storage servers in remote datacenters. There is, however, uncertainty associated with the quality and quantity of data from mobile sensors as well as with the availability and capabilities of mobile computing resources on the field. Data and computing-resource uncertainty, if unchecked, may propagate up the "raw data -> information -> knowledge" chain and have an adverse effect on the relevance of the generated results. A generalized workflow representation scheme that can represent a wide variety of data-and task-parallel ubiquitous mobile applications is presented. A unified uncertainty-aware framework for data and computing-resource management to enable real-time, in-situ processing of applications is proposed and evaluated. The framework employs a two-phase solution that captures the propagation of data uncertainty up the data-processing chain using interval arithmetic in the first phase and that employs multi-objective optimization for task allocation in the second phase. The results of a case study to assess effectiveness the proposed framework are discussed in detail. Results reaffirm that i) data-uncertainty awareness helps control the uncertainty in the final result and ii) multi-objective combinatorial approach for task allocation significantly outperforms the single-objective approaches in terms of makespan (15 percent improvement), fairness in battery drain (56 percent improvement), and network load (54 percent improvement).

• 出版日期2016-11-1
• 单位rutgers