计算机科学
随机算法
概率逻辑
作业车间调度
调度(生产过程)
云计算
任务(项目管理)
移动边缘计算
边缘计算
分布式计算
GSM演进的增强数据速率
近似算法
计算机网络
数学优化
算法
人工智能
操作系统
布线(电子设计自动化)
数学
管理
经济
作者
Varsha Kumari,Chapram Sudhakar
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-1
标识
DOI:10.1109/jiot.2023.3339219
摘要
Most of the previous research on task offloading in mobile edge computing has focused on clairvoyant task scheduling, which assumes that the characteristics of the tasks are known apriori. This assumption is unrealistic for many practical scenarios with randomly generated tasks in mobile edge computing. In this article, a randomized cost analysis of task offloading in mobile edge computing environment is done, where the characteristics of tasks are not known. The aim is to co-optimize the overall completion time and cost for executing a set of tasks. Task being executed may request some of the predefined services from cloud/edge devices. It is quite a challenge to ensure the quality of experience of end user and service provider in terms of time and cost due to the uncertain and diverse task characteristics. In order to tackle this problem, an efficient randomized task offloading model is formulated to reduce the overall execution time and cost. To this end, we present two service unaware and two service aware algorithms, namely SUN-BS, SUN-TS, SAN-BS and SAN-TS using randomized stochastic and randomized probabilistic approaches that provides near optimal solution. We further perform complexity analysis to show the theoretical upper bounds for makespan and total cost with certain performance guarantee. Numerical simulation experiments are conducted to demonstrate the performance improvement of proposed algorithms SUN-BS (54%-68%) in term of makespan and SAN-TS (7%-20%) in terms of cost over the traditional scheduling algorithms.
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