Performance Benchmarking of Load Balancers and Service Brokers in Heterogeneous Clouds using CloudAnalyst

Cloud computing's foundational challenge lies in efficiently distributing user requests across geographically distributed data centers (DCs) and subsequently across heterogeneous virtualized resources. This review paper systematically analyzes two critical components of this process: Service Broker Policies (SBPs), which determine DC selection, and Load Balancing (LB) Algorithms, which manage task distribution within Virtual Machines (VMs) in a DC. The study provides a detailed taxonomy and analysis of prevalent policies and algorithms, evaluating their mechanisms, strengths, and weaknesses. Furthermore, it presents a rigorous empirical performance analysis using the CloudAnalyst simulation tool. Three representative algorithms Round Robin (RR), Equally Spread Current Execution (ESCE), and Throttled LB (TLB) are evaluated under two distinct SBP: Closest DC and Optimized Response Time. The simulation models a realistic, heterogeneous cloud infrastructure subjected to dynamically varied workloads. Empirical results demonstrate that the TLB algorithm provides the most robust performance, characterized by highly competitive average response times and exceptional stability, as evidenced by its consistently minimal maximum latency. This is achieved through a proactive, capacity-aware distribution strategy that strategically directs workloads toward high-performance VMs. In stark contrast, the static RR algorithm proves fundamentally unsuitable for heterogeneous environments, incurring severe performance degradation and unstable response times due to its inability to account for disparate VM capacities. Although the SBP exerts a measurable influence on performance, the analysis conclusively establishes that the selection of the load balancing algorithm is the paramount factor in optimizing overall system performance, quality of service (QoS), and resource utilization in heterogeneous cloud environments.