Ts2lbdp

Abstract

Few scheduling, models focus on service-level agreement (SLA) enforcement, which limits their real-time applicability. Thus, this research work proposes the design of an improved task-side service level (TS2L) agreement model for efficient task-scheduling using elephant herd optimization (EHO) with deadline (BDP) awareness. TS2LBDP incorporates pattern analysis using ensemble hierarchical, k-means, and fuzzy C means (FCM) clustering methods. A combination of these modules assists in improving task diversity and scheduling efficiency. SLA-based distribution enhances scheduling fairness and reduces mean waiting time for different clients. The proposed model was tested on parallel workload archive wherein different cloud workload logs and their respective VM configurations are described. It was observed that the proposed model improves scheduling efficiency by 20%, task diversity by 45%, and deadline hit ratio by 1%. It is scalable and can be used for several types of cloud deployments.