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 Self-Calibrated Edge Computation for Unmodeled Time-Sensitive IoT Offloading Traffic
Tác giả hoặc Nhóm tác giả: Nhu-Ngoc Dao, Thi-Thao Nguyen, Minh-Quan Luong, Thuy Nguyen-Thanh, Woongsoo Na, Sungrae Cho
Nơi đăng: IEEE Access: Computer Science, Information Systems (Q1 SCIE); Số: ISSN 2169-3536, DOI 10.1109/ACCESS.2020.3001572;Từ->đến trang: 110316 – 110323;Năm: 2020
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
With the characterizing benefits of ultra-low latency, contextual computing, and mobile scalability, mobile edge computing (MEC) is considered as a key enabler for realizing a tremendous boom in heterogeneously time-sensitive Internet-of-Things (IoT) services in the fifth-generation (5G) ecosystems.
However, achieving low-latency comes at a cost of energy-efficiency reduction. To address and balance this tradeoff, this paper proposes a joint optimization of energy consumption and latency satisfaction in MEC servers, called latency-aware green (LAG) computing algorithm. To fully consider the heterogeneity of IoT services offloaded to the MEC servers, offloading traffic at the MEC servers is assumed to be unmodeled and unpredictable. Using the proposed LAG algorithm, each MEC server autonomously and dynamically calibrates its own computing frequency based on the current status of the workload buffer size and computational workload arrival rate. This dynamic calibration provides minimum energy consumption for the workload computation while maintaining the computational latency stabilized under a desired threshold. Evaluation results show that the proposed algorithm maintains stable MEC servers in an energyefficient manner.
ABSTRACT
With the characterizing benefits of ultra-low latency, contextual computing, and mobile scalability, mobile edge computing (MEC) is considered as a key enabler for realizing a tremendous boom in heterogeneously time-sensitive Internet-of-Things (IoT) services in the fifth-generation (5G) ecosystems.
However, achieving low-latency comes at a cost of energy-efficiency reduction. To address and balance this tradeoff, this paper proposes a joint optimization of energy consumption and latency satisfaction in MEC servers, called latency-aware green (LAG) computing algorithm. To fully consider the heterogeneity of IoT services offloaded to the MEC servers, offloading traffic at the MEC servers is assumed to be unmodeled and unpredictable. Using the proposed LAG algorithm, each MEC server autonomously and dynamically calibrates its own computing frequency based on the current status of the workload buffer size and computational workload arrival rate. This dynamic calibration provides minimum energy consumption for the workload computation while maintaining the computational latency stabilized under a desired threshold. Evaluation results show that the proposed algorithm maintains stable MEC servers in an energyefficient manner.
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