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Abstract
Due to limited energy and computing power of IoT devices, they cannot handle complex tasks. Edge computing technology effectively solves the requirements of computing power and response delay for complex tasks in devices by migrating computing power to the vicinity of IoT devices. For a separable complex task on IoT terminal, we focus on the effects of data distribution, dependencies, and offloading sequence of subtasks on its total delay when it is offloaded to edge servers. Through comprehensively considering these factors, we study the slicing and choreographing method during the offloading process of a complex task. Firstly, a task slicing method based on hierarchical clustering is presented and an improved hierarchical clustering algorithm is used to obtain the optimal solution of task partitioning. Secondly, a task choreographing method based on overlapping the longest path is presented. Finally, through the simulation experiments of complex tasks with different structures and loads, the effectiveness of our method is verified.
Highlights
In recent years, as the mobile Internet industry matures, the rapid explosion of the Internet of ings (IoT) leads the vigorous development of mobile intelligent terminal devices, which are widely used in transportation, health, entertainment, and other fields
We focus on the slicing and choreographing method of a complex delay-sensitive task at edge servers. e main research work and contributions include the two following aspects: (1) Aiming at the problem that the response time of offloading task is too long to meet the delay requirements of IoT devices, the task slicing method based on hierarchical clustering is improved to reduce the communication cost of subtasks on different servers and minimize the time consumption of task workflow while supporting the parallel and distributed execution of subtasks
Starting from the first node of the task workflow, we look for the subtasks that can be grouped into one task slice from top to bottom. e rule of merging is that each subtask is merged with one of the subsequent subtask which has the lowest weight with it until the last node in the workflow
Summary
As the mobile Internet industry matures, the rapid explosion of the Internet of ings (IoT) leads the vigorous development of mobile intelligent terminal devices, which are widely used in transportation, health, entertainment, and other fields. E other way is to combine the edge computing with the cloud computing to work together to complete the tasks, which can improve the processing capacity and shorten the transmission time In both cases, we need to divide complex tasks into smaller ones and choreograph them to multiple edge servers or the cloud. E main research work and contributions include the two following aspects: (1) Aiming at the problem that the response time of offloading task is too long to meet the delay requirements of IoT devices, the task slicing method based on hierarchical clustering is improved to reduce the communication cost of subtasks on different servers and minimize the time consumption of task workflow while supporting the parallel and distributed execution of subtasks. We focus on the slicing and choreographing method of a complex delay-sensitive task at edge servers. e main research work and contributions include the two following aspects: (1) Aiming at the problem that the response time of offloading task is too long to meet the delay requirements of IoT devices, the task slicing method based on hierarchical clustering is improved to reduce the communication cost of subtasks on different servers and minimize the time consumption of task workflow while supporting the parallel and distributed execution of subtasks. (2) On the basis of task slicing, a subtasks choreography method combining static, dynamic, and the earliest start time of the task workflow is proposed, and a scheduling algorithm of task workflow on edge servers is designed
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