Abstract
Edge computing has gained momentum in recent years, and can provide more immediate analysis of streaming video data. However, the edge devices often lack the computing capabilities (processing power, memory) to guarantee reasonable performance (e.g., accuracy, latency, throughput) for complex video analytics tasks. To alleviate this critical problem, the prevalent trend is to offload some video analytics tasks from the edge devices to the cloud. However, existing offloading approaches fail to consider the dynamic nature of the video analytical tasks (e.g., varying encoding format for different video content) and are unable to adapt system dynamics (e.g., varying workload between the edge and the cloud). To overcome the limitation of existing approaches, we develop an edge-cloud offloading performance model based on the concept of hierarchical queues. The resource constraints (e.g., computing capacity and network bandwidth) of each edge nodes and dynamic edge-cloud network conditions are used to parameterize the performance model. Since finding optimal solutions for the performance model is NP-hard, we develop a two-stage gradient-based algorithm and compare it with some state-of-the-art (SOTA) solutions (e.g., FastVA, DeepDecision, Hill Climbing). Experiments have shown our performance model's advantages and the stability of the proposed offloading approach given different systems (edge-cloud) and video analytics application dynamics.
Original language | English |
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Pages (from-to) | 1178-1193 |
Number of pages | 16 |
Journal | IEEE Transactions on Computers |
Volume | 72 |
Issue number | 4 |
Early online date | 25 Jul 2022 |
DOIs | |
Publication status | Published - Apr 2023 |
Bibliographical note
Funding:This work was supported in part by the EPSRC under Projects EP/W003325/1 and EP/T021985/1, currently co-led by Prof. Ranjan and Prof. Zomaya. The work of Zhenyu Wen was supported by the NSFC under Grant 62072408. The work of Ye Yuan was supported in part by the NSFC under Grant 61932004 and in part by the Fundamental Research Funds for the Central Universities under Grant N181605012.
Keywords
- Video processing
- offloading
- optimization
- IoT