A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing (1607.06269v3)

Abstract: The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.

• The paper surveys load balancing algorithms for virtual machine (VM) placement in cloud computing, classifying strategies based on factors like scheduling, resource types, and dynamicity for this NP-hard problem.
• It categorizes algorithms into heuristic, meta-heuristic (showing up to 70% efficiency gains), and hybrid strategies, discussing their trade-offs in performance and computational cost.
• The survey identifies key research challenges and future directions, including the need for self-adaptive algorithms, analysis of communication costs, and empirical studies under unified setups.

An Academic Review of "A Survey on Load Balancing Algorithms for Virtual Machines Placement in Cloud Computing"

The paper authored by Minxian Xu and colleagues offers a comprehensive survey on load balancing algorithms concerning virtual machine (VM) placement within cloud computing environments. Cloud computing, primarily supported by virtualization technologies, allows dynamic and flexible allocation of computational resources. This landscape poses significant challenges for effective resource management, necessitating advanced load balancing strategies to ensure optimal performance and compliance with service level agreements (SLAs).

Core Focus and Methodology

The central theme of the paper is the exploration of VM placement algorithms designed to maintain load equilibrium in cloud data centers, characterized by heterogeneous resources and fluctuating workloads. Recognizing VM placement as an NP-hard problem, the authors scrutinize existing algorithms from multiple dimensions such as scheduling scenarios, management approaches, resource types, VM uniformity, and allocation dynamicity. A detailed classification is offered, providing a structured understanding of the landscape of VM load balancing strategies.

Technical Insights

The paper categorizes VM load balancing algorithms into heuristic, meta-heuristic, and hybrid strategies, highlighting the advantages and constraints associated with each. While heuristic algorithms offer time-efficient solutions, meta-heuristic approaches like Ant Colony Optimization and Particle Swarm Optimization provide improved load balancing outcomes albeit at increased computational costs. Hybrid algorithms, integrating both heuristic and meta-heuristic methodologies, are presented as potential solutions that balance optimization results and execution time.

Numerical Results and Comparative Analysis

Through simulations and realistic platform tests, various algorithms demonstrate their capability in reducing load variance, minimizing migration costs, and improving system scalability and reliability. For instance, the adoption of meta-heuristic strategies is shown to enhance load balancing efficiency by up to 70% compared to conventional heuristics. Moreover, hybrid approaches demonstrate a commendable reduction in imbalance levels and cost-makespan compared to standard methods.

Implications and Future Directions

This survey not only catalogs existing methodologies but also serves as a springboard for future innovation in VM load balancing algorithms. It calls attention to several research challenges such as the integration of self-adaptive algorithms for dynamic environments, investigation of distributed algorithm communication costs, and the influence of physical and virtual networking on migration strategies.

The authors recommend further empirical studies to evaluate these algorithms under a unified experimental setup. Such comparative studies could yield valuable insights into algorithm selection criteria based on specific performance metrics.

Conclusion

Xu et al.'s paper is a definitive resource for researchers exploring VM load balancing strategies within cloud computing. It rigorously analyzes existing methodologies and provides lucid insights into their operational efficacy, paving the way for future advancements in the domain. Researchers are encouraged to address highlighted challenges and pursue novel avenues, including hybrid algorithm designs and predictive analytics, to cater to increasingly complex cloud environments.