Load Balancing Strategies For Scalable And Resilient Cloud Systems
Keywords:
Cloud Computing, Scalability, Virtualisation, Energy Consumption, Load balancing, Elasticity, TaxonomyAbstract
Task scheduling in cloud environments pose NP-hard optimization challenges due to diverse user requests and infrastructure configurations. Load imbalance, whether underloaded or overloaded, leads to system failures impacting electricity consumption, execution time, and machine reliability. Effective load balancing is crucial to mitigate these issues. This involves distributing tasks, dependent or independent, across Virtual Machines (VMs) to achieve load equilibrium. Various types of loads such as memory, CPU, and network contribute to the complexity. Researchers have proposed diverse load-balancing approaches aimed at optimizing different performance metrics. This paper presents a taxonomy of load-balancing algorithms in cloud computing, along with a discussion on thirteen performance parameters of 10 scheduling algorithms.
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