(untitled) (International Journal of Engineering and Computer Science, 2017)
Load balancing is one of the challenging issues in dynamic load distribution systems and it has a direct impact on system performance, resource consumption and system dependability. Static methods that have been used in the original paradigm do not scale well to accommodate unpredictable levels of workload since resources may be either wastefully consumed or become a bottleneck. The approach presented in this paper is to adapt graph theory for dynamic load balancing of distributed systems in real time. Implementing computational nodes as vertices and communication links as edges the given methodology utilizes path and cut searching algorithms for identification and dynamic resettling of workloads. In this paper, by using computer simulations and case studies we show that the proposed approach is indeed superior to the conventional ones in throughput, latency and scalability. Despite the growth and complexity of contemporary distributed systems, the scalable graph-based model is flexible enough to foster further integration with artificial-intelligence-driven optimizations for systems. This work also examines the revolutionary applicability of graph theory in solving some of the most essential problems in distributed systems.