Resource optimization of edge servers dealing with priority-based workloads by utilizing service level objective-aware virtual rebalancing

Author

Amna Shahid, Mississippi State UniversityFollow

Issuing Body

Mississippi State University

Advisor

Khan, Samee U.

Committee Member

Luo, Chaomin

Committee Member

Luo, Yu

Date of Degree

8-8-2023

Document Type

Graduate Thesis - Open Access

Major

Electrical and Computer Engineering

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

IoT enables profitable communication between sensor/actuator devices and the cloud. Slow network causing Edge data to lack Cloud analytics hinders real-time analytics adoption. VRebalance solves priority-based workload performance for stream processing at the Edge. BO is used in VRebalance to prioritize workloads and find optimal resource configurations for efficient resource management. Apache Storm platform was used with RIoTBench IoT benchmark tool for real-time stream processing. Tools were used to evaluate VRebalance. Study shows VRebalance is more effective than traditional methods, meeting SLO targets despite system changes. VRebalance decreased SLO violation rates by almost 30% for static priority-based workloads and 52.2% for dynamic priority-based workloads compared to hill climbing algorithm. Using VRebalance decreased SLO violations by 66.1% compared to Apache Storm's default allocation.

Recommended Citation

Shahid, Amna, "Resource optimization of edge servers dealing with priority-based workloads by utilizing service level objective-aware virtual rebalancing" (2023). Theses and Dissertations. 5941.
https://scholarsjunction.msstate.edu/td/5941