A Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing in Cloud Systems

Abstract

The backbone of modern digital machines is cloud computing′s on-demand resources, which scale to increase throughput. However, the field is increasingly complex from the point of view of heterogeneous cloud environments: requiring paradigms for virtual machines, containers and serverless computing nodes that put further challenges into ensuring an optimal performance/cost/energy balance. In traditional resource allocation and load balancing strategies, these goals are usually treated separately. As a result, there may be inadequate system utilization by either party involved in the transaction; ends responded with slow feedback time while intermediary services performed worse than predicted; or Service Level Agreements weren't satisfied. To help meet these challenges, this paper proposes a Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing that spans two layers in heterogeneous cloud computing systems. The framework integrates Dynamic Voltage and Frequency Scaling (DVFS) with migration-aware task assignment, learning-aided metaheuristic optimization to dynamically adjust to fluctuations in load while minimizing power consumption and operational costs. The optimization engine blends predictive workload modelling implemented via machine learning for proactive decision-making and hybrid solution strategies. Among its multiple objectives, this mechanism offers the ability to jointly minimize SLA violations, response time as well power usage. Experiments using a larger CloudSim and actual workload traces (extended CloudSim) show that the proposed framework obtains 25-30% energy savings compared with ordinary methods, significantly reduces p95 latency and also achieves a relatively better Quality Of Service. These results suggest the framework may provide a sustainable schedule which responds to outside influences in real time, making it suitable for future intelligent cloud system with large-scale organization.