ConcurrentOpt-AI: Intelligent Multi-Thread Optimization Framework for Distributed Systems
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I4P118Keywords:
Distributed Systems, Multi-Thread Optimization, Concurrent Computing, Reinforcement Learning, Scheduling Algorithms, Parallel Processing, AI-Driven Optimization, Performance Engineering, Workload Balancing, Intelligent FrameworksAbstract
Despite the fact that conventional methods based on single-threading or heuristics are not always efficient in adapting to fluctuating workloads and changing resource constraints, optimization remains a vital element in the achievement of energy saving and scaling in modern distributed systems. ConcurrentOpt-AI has invented a multi-thread AI-based optimization framework that is smart and can continuously improve the decisions made for resource allocation and scheduling. They aimed to achieve this by the implementation of distributed orchestration, adaptive learning models, and reinforcement-based feedback loops. Therefore, ConcurrentOpt-AI is in a position to go beyond the limitations that have been referred to as a result of this strategy. The system uses coordinated optimization agents running in parallel evaluation threads which exchange their findings through a lightweight orchestration layer to thereby reduce computational overhead, enhance task scheduling efficiency, and generally increase the system throughput. It has been shown that, in a large-scale microservices architecture, ConcurrentOpt-AI has significantly better throughput, latency, and resource consumption as compared to baseline heuristics and standard reinforcement learners. The use of case study evaluations was instrumental in accomplishing this demonstration. The results thus obtained demonstrate the potential of the framework to operate as a dependable, flexible, and scalable resource in the forthcoming cloud, edge, and high-performance computing systems.
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