We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Prediction-Based Resource Deployment and Task Scheduling in Edge-Cloud Collaborative Computing.
- Authors
Su, Mingfeng; Wang, Guojun; Choo, Kim-Kwang Raymond
- Abstract
Edge computing is becoming increasingly commonplace, as consumer devices become more computationally capable and network connectivity improves (e.g., due to 5G). With the rapid development of edge computing and Internet of Things (IoT), the use of edge-cloud collaborative computing to provide service-oriented network application (i.e., task) in edge-cloud IoT has become an important research topic. In this paper, we present an edge-cloud collaborative computing framework and our resource deployment algorithm with task prediction (RDAP). Based on our paradigm, tasks in the cloud service center are predicted using the two-dimensional time series, and task classification aggregation and delay threshold determination are combined to optimize task resource deployment of edge servers. A task scheduling algorithm with Pareto improvement (TSAP) is also proposed. At the edge servers, the Pareto progressive comparison is conducted in two stages to obtain the tangent point or any intersection point of the two objective curves of user's quality of service and effect of system service to optimize task scheduling. The experimental results show that for varying user task scales and different Zipf distribution α parameters, combining RDAP and TSAP (RDAP-TSAP) can improve the average user task hit rate. In addition, the average task completion time of users, the overall system service effect, and the total task delay rate of RDAP-TSAP are better than TSAP and the benchmark algorithms for task scheduling.
- Subjects
EDGE computing; QUALITY of service; INTERNET of things; TASKS; TIME series analysis; CURVES
- Publication
Wireless Communications & Mobile Computing, 2022, p1
- ISSN
1530-8669
- Publication type
Article
- DOI
10.1155/2022/2568503