GRASP-based resource re-optimization for effective big data access in federated clouds

• Efficient re-optimization strategy for big data access in multi-tenant cloud infrastructures.
• Based on a Greedy Randomized Adaptive Search meta-heuristic working on a flexible federated cloud model.
• Performs end-to-end path rerouting and Virtual Machine migration to improve access to big data.
• Rebalances cloud resource usage so that more virtual machines can effectively access data sources.

Federated cloud organizations, spanning across multiple networked sites that provide both computing and storage resources, can be considered the state-of-the-art solutions for providing multi-tenant runtime services in modern distributed processing environments. In these scenarios, by re-optimizing the communication paths between virtual machines and big data sources, at evenly spaced interval or when required by circumstances, the overall communication and runtime resource utilization on the cloud infrastructure is re-balanced, so that more virtual machines can be allowed to access the needed big data sources with adequate bandwidth, thereby significantly improving the perceived performance and quality of service. The problem of re-optimization is tackled with a powerful meta-heuristic, the greedy randomized adaptive search procedure (GRASP), augmented by path re-linking. In order to evaluate the proposed approach, extensive simulations have been performed, leading to very interesting results, demonstrating the effectiveness and validity of the underlying ideas and their applicability to real large-scale federated cloud scenarios.