TY  - EJOU
AU  - Wang, Dayong 
AU  - Bakar, Kamalrulnizam Bin Abu 
AU  - Isyaku, Babangida 
AU  - Lei, Liping 

TI  - An Asynchronous Data Transmission Policy for Task Offloading in Edge-Computing Enabled Ultra-Dense IoT
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 81
IS  - 3
SN  - 1546-2226

AB  - In recent years, task offloading and its scheduling optimization have emerged as widely discussed and significant topics. The multi-objective optimization problems inherent in this domain, particularly those related to resource allocation, have been extensively investigated. However, existing studies predominantly focus on matching suitable computational resources for task offloading requests, often overlooking the optimization of the task data transmission process. This inefficiency in data transmission leads to delays in the arrival of task data at computational nodes within the edge network, resulting in increased service times due to elevated network transmission latencies and idle computational resources. To address this gap, we propose an Asynchronous Data Transmission Policy (ADTP) for optimizing data transmission for task offloading in edge-computing enabled ultra-dense IoT. ADTP dynamically generates data transmission scheduling strategies by jointly considering task offloading decisions and the fluctuating operational states of edge computing-enabled IoT networks. In contrast to existing methods, the Deep Deterministic Policy Gradient (DDPG) based task data transmission scheduling module works asynchronously with the Deep Q-Network (DQN) based Virtual Machine (VM) selection module in ADTP. This significantly reduces the computational space required for the scheduling algorithm. The continuous dynamic adjustment of data transmission bandwidth ensures timely delivery of task data and optimal utilization of network bandwidth resources. This reduces the task completion time and minimizes the failure rate caused by timeouts. Moreover, the VM selection module only performs the next inference step when a new task arrives or when a task finishes its computation. As a result, the wastage of computational resources is further reduced. The simulation results indicate that the proposed ADTP reduced average data transmission delay and service time by 7.11% and 8.09%, respectively. Furthermore, the task failure rate due to network congestion decreased by 68.73%.
KW  - Bandwidth allocation; edge computing; internet of things; task offloading; reinforcement learning

DO  - 10.32604/cmc.2024.059616