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This study proposes a novel adaptive meta-heuristics based scheduling policies for provisioning the VCPU resources among competing VM service domains in a cloud. Such provisioning guarantees to Service Level Agreement for each domain, with respect to the diverse workloads on-the-fly. The framework is built on CSIM models and tools, making it easy to understand and configure various virtualization setups. The study demonstrates the usefulness of the framework by evaluating proactive, reactive and adaptive VCPU scheduling algorithms. The paper evaluates how periodic/aperiodic execution of control actions can affect policy performance and speed of convergence. The periodic reactive resource allocation is used as the baseline for analysis and the average response time is the performance metric. Simulation based experiments using variety of real-world arrival traces and synthetic workloads results that the proposed provisioning technique detects changes in arrival pattern and resource demands and allocates resources accordingly to achieve application SLA targets. The proposed model improves CPU utilization and makes the best tradeoff between resource utilization and performance from 2 to 6% comparing with the default VMM scheduler configurations for diverse workloads. In addition, the results of the experiments show that the proposed Weighed Moving Average algorithm combined with the aperiodic policy significantly outperforms other dynamic VM consolidation algorithms in all cases, in regard to the SLA metric due to a substantially reduced level of response time violations and the frequency of algorithm invocation.

Workload Known VMM Scheduler for Server Consolidation for Enterprise Cloud Data Center