4.7 Article

QoS-Aware Robotic Streaming Workflow Allocation in Cloud Robotics Systems

Journal

IEEE TRANSACTIONS ON SERVICES COMPUTING
Volume 14, Issue 2, Pages 544-558

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TSC.2018.2803826

Keywords

Robots; Cloud computing; Quality of service; Task analysis; Resource management; Optimization; Energy consumption; Networked cloud robotics; robotic streaming workflow; quality of service; computation offloading

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This study proposes a QoS-aware RSW allocation algorithm for NCR that jointly optimizes latency, energy efficiency, and cost while considering the characteristics of RSW and NCR. By constructing a data flow graph and transforming the problem into a linear programming problem, a heuristic algorithm is used to obtain a near-optimal solution in a reasonable time. Experimental comparisons demonstrate significant performance improvements with enhanced QoS and reduced execution times.
Computation offloading for cloud robotics is receiving considerable attention in academic and industrial communities. However, current solutions face challenges: 1) traditional approaches do not consider the characteristics of networked cloud robotics (NCR) (e.g., heterogeneity and robotic cooperation); 2) they fail to capture the characteristics of tasks in a robotic streaming workflow (RSW) (e.g., strict latency requirements and varying task semantics); and 3) they do not consider quality-of-service (QoS) issues for cloud robotics. In this paper, we address these issues by proposing a QoS-aware RSW allocation algorithm for NCR with joint optimization of latency, energy efficiency, and cost, while considering the characteristics of both RSW and NCR. We first propose a novel framework that combines individual robots, robot clusters, and a remote cloud for computation offloading. We then formulate the joint QoS optimization problem for RSW allocation in NCR while considering latency, energy consumption, and operating cost, and show that the problem is NP-hard. Next, we construct a data flow graph based on the characteristics of RSW and NCR, and transform the RSW allocation problem into a mixed-integer linear programming problem. To obtain a near-optimal solution in reasonable time, we also develop a heuristic algorithm. Experiments comparing our approach with others demonstrate significant performance gains, with improved QoS and reduced execution times.

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