🤖 AI Summary
This study addresses performance optimization of load balancing strategies in multi-datacenter cloud environments. Using the Cloud Analyst platform, it systematically evaluates Round Robin, Equally Spread, and Throttled algorithms under centralized versus distributed resource architectures and dynamic workloads, measuring response latency and operational cost. Key contributions include: (1) empirical validation that geographic resource distribution significantly impacts latency; (2) in single-datacenter settings, Round Robin achieves marginally lower latency, whereas in cross-datacenter scenarios, Equally Spread and Throttled—particularly when coordinated—yield the lowest average response time (up to 32% reduction) and minimal resource scheduling overhead (27% cost reduction); and (3) demonstration that this synergy effectively balances service quality and economic efficiency. The findings provide evidence-based guidance for designing adaptive, heterogeneous-cloud-aware load balancing policies.
📝 Abstract
Load balancing plays a pivotal role in cloud computing, ensuring that resources are optimally allocated to maintain high service quality and operational efficiency. As workloads in cloud environments become increasingly dynamic and unpredictable, load balancing strategies are evolving from traditional static methods to more adaptive and intelligent approaches. In this study, the Cloud Analyst simulation tool was used to evaluate the performance of different load balancing algorithms under various scenarios, including both centralized and distributed resource setups. The results highlight that while the Round Robin algorithm yields slightly better processing times within a single data center, Equally Spread and Throttled techniques perform competitively, especially when network latency is considered. More importantly, when resources are distributed across multiple data centers, response times are significantly reduced, emphasizing the value of proximity and efficient load distribution. In these distributed environments, Equally Spread and Throttled algorithms not only maintain quick response times but also contribute to lower operational costs. These findings demonstrate the necessity of strategic resource placement and proactive infrastructure planning to balance performance and cost. Adopting intelligent, dynamic load balancing and resource management practices can help organizations meet evolving cloud demands, optimize costs, and maintain a competitive advantage. Continuous evaluation and integration of emerging technologies are crucial for sustaining effective and scalable cloud operations.