Volume 18, Number 2

An Adaptive Hybrid Scheduling Approach for Sustainable and Reliable Cloud Services

  Authors

Rahul Bhatt¹, Ritika Mehra¹ and Kamal Upreti², ¹Dev Bhoomi Uttarakhand University, India, ²Christ University, India

  Abstract

The modern cloud computing systems have to plan the heterogeneous workloads and balance performance effectiveness, service availability, and sustainability. In this study, an adaptive hybrid scheduling framework is developed Adaptive Ant-guided Min-Max (AAMM) combining ant-guided optimization with dynamic Min-Min and Max-Min in deciding how to allocate cloud tasks as a multi-objective. The scheduler jointly evaluates task completion time, the likelihood of Service Level Agreement violations, energy consumption, and monetary cost within a unified scoring framework, enabling informed trade-offs among competing objectives. AAMM is assessed based on a real disaggregated Deep Learning Recommendation Model workload of 1,544 heterogeneous tasks, running on heterogeneous virtual machines. Comparative experiments are done with Min-Min, Max-Min and ACO-guided Min-Min scheduling strategies. According to experimental findings, the suggested approach has been very effective in reducing energy per task, cost per task, SLA violations are significantly lowered, and flow time stability is enhanced. Though moderate growth in the makespan is witnessed, the accompanying trade-off has created equal distribution of resources and service reliability.

  Keywords

Cloud computing; Task scheduling; Adaptive scheduling; Multi-objective optimization; SLA-aware systems; Energy-efficient cloud computing, Adaptive Ant-guided Min–Max (AAMM)