Dynamic Load Balancing and Migration in Multi-Cloud Environments

Abstract

As organizations increasingly adopt cloud computing, the ability to dynamically balance workloads across cloud providers and migrate VMs between them becomes critical for optimizing performance and costs. This paper explores algorithms and techniques for dynamic load balancing and live migration of VMs in multi-cloud environments. We first provide background on cloud computing and define key concepts related to load balancing and migration. We then survey different load balancing algorithms such as round-robin, least connections, and shortest job first, analyzing their trade-offs between complexity, overhead, and adaptability. For VM migration, we examine pre-copy, post-copy, and hybrid migration approaches and how to minimize downtime during transfers. The factors impacting live migration feasibility and performance are also discussed. We then propose a multi-cloud load balancing and migration framework that combines these techniques. The goal is to dynamically reallocate VMs based on utilization and costs to improve resource utilization and lower expenses. The framework consists of monitors to collect VM metrics, a load evaluation module that aggregates and analyzes data, a VM selection algorithm that chooses migration candidates, and executors to perform the transfers. Both push and pull migration models are supported to allow proactive and reactive movement of VMs. Several optimizations are suggested, including using machine learning techniques to build VM usage models and predict future demand, thereby guiding preemptive migrations. Approaches for minimizing data transfers such as compression, deduplication, and sending differences are examined to reduce migration overhead. We also describe optimization algorithms that take into account migration costs when making load balancing decisions. The paper concludes with an evaluation of the proposed techniques through simulations and testbed experiments. Different cloud deployment scenarios are evaluated, including private clouds, public clouds, and hybrid models. The impact of factors such as VM sizes, utilization levels, and network conditions on migration decisions and overhead is analyzed. Results demonstrate the ability of the system to reduce costs and improve quality-of-service through intelligent VM placement and migration compared to static approaches. The limitations of current techniques and open challenges are also discussed as areas for further research.