An Analytical Evaluation of Various Approaches for Load Optimization in Distributed System
Keywords:
Distributed systems, Load optimization, Static load balancing, Dynamic load balancing, Task scheduling, Resource allocation, Network topologyAbstract
This survey aims to investigate the various approaches for load optimization in distributed systems. Distributed systems are composed of multiple components that work together to achieve a common goal. Load optimization in such systems refers to the efficient distribution of resources and tasks among these components to ensure that the system operates at optimal performance levels. The survey focuses on the various techniques and algorithms that are used for load balancing, resource allocation, scheduling policies, application-specific load optimization, task migration, task replication, content distribution networks (CDNs), and machine learning-based load balancing load optimization. The study also considers the impact of various parameters, such as network topology, network traffic, and system resources, on the performance of load optimization techniques. In addition, the survey examines the trade-offs between the different approaches for load optimization, including their advantages and disadvantages. The study also highlights the limitations of current load optimization methods and the future directions for research in this field. Overall, this survey provides a comprehensive overview of the various approaches for load optimization in distributed systems and offers insights into the current state of the field and future research directions.
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