Edge computing is a popular paradigm in solving the problems of long time delay and high energy consumption in Internet of Things (IoT) network, which can effectively realize the IoT task offloading by collaboration of multiple edge servers. Nevertheless, how to choose the appropriate edge servers for offloading the dependent subtasks is still a big challenge, considering the limited resources and computing power of the edge servers as well as the start and end execution time of each subtask. These factors have a great impact on the execution efficiency of the whole task. At present, most of the research works focus on single-hop or multi-hop task offloading, where the edge servers farther away are not considered in the offloading decision. Such task offloading strategy is not optimal, and difficult to achieve high parallel execution of tasks, resulting in some delay-sensitive tasks not being completed within the specified time. In this paper, a two-stage optimization method is proposed to solve the resource allocation problem between edge servers and tasks. In the first stage, we group tasks according to their priorities, and the group with a higher priority is given the preference to resource allocation, thereby ensuring the timeliness of delay-sensitive tasks. Within the same group, resources are competed according to the game theory, and the total delay of all tasks is optimized. In the second stage, we aim to optimize the energy consumption of each task without increasing its completion time by allocating the computing resources to its subtasks based on their maximum completion time. For group resource allocation, we propose a spatial index tree to store the information of all edge servers for optimal server selection. During the selection process, an online learning based double prediction model is utilized to reduce the energy consumption caused by information transmission. We have evaluated the performance of the experiment on iFogSim simulator, and the experimental results show that our proposed method can achieve better performance in terms of time delay and energy consumption. 

Optimization Search Strategy for Task Offloading from Collaborative Edge Computing

With the rapid development of the Internet of Things and the popularization of 4G/5G wireless networks, the era of the Internet of Everything has arrived. However, the computing resources and power of mobile devices are limited, which cannot meet the needs of various computing-intensive applications. Edge computing emerged as an effective solution in this context. In order to improve the service quality of mobile device users, formulating efficient and reasonable offloading strategies when performing task offloading is the main research direction at present. Aiming at the multi-user and multi-task scenarios in the edge network environment, this paper describes the transaction process of users requesting resources and edge service providers selling resources based on the resource transaction mechanism, and proposes a real-time task offloading strategy for edge computing based on the resource transaction mechanism, aiming to improve While improving resource utilization, improve user service quality. And it is verified by simulation experiments that the algorithm can significantly optimize the system cost, and has obvious advantages in satisfying user experience. 

Joint Task Offloading and Resource Allocation Based on Transaction Mechanism

Owing to the ability to provide better services for latency-intensive tasks than the cloud paradigm, Mobile Edge Computing (MEC) has attracted increasing attention recently. However, due to limited resources, MEC cannot handle large amount of computation tasks as the cloud paradigm. Most of the existing works design offload strategies for MEC by sharing the responsibility of total computation tasks with the cloud to provide more services, but neglecting the fact that the profit can be shared when sharing responsibility, which decreases the profit. To address this issue, we propose a trade collaboration framework for the MEC and the cloud paradigm, where the MEC can purchase resources from the cloud paradigm to process computation tasks under latency constraints. Without accurate information about required resources, this paper has designed an efficient resource trade scheme for the MEC to achieve their optimal purchased resources, such that the expected profit of the MEC can be maximized. Simulation results show that the proposed scheme can maximize the profit of the MEC and guarantee latency requirements.

Edge-Cloud Resource Trade Collaboration scheme in Mobile Edge Computing

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Edge-Cloud Resource Trade Collaboration scheme in Mobile Edge Computing