Distributed Computing in Supply Chain Applications: Balancing Performance and Cost
Keywords:
energy-efficient, performance, emphasizes, strategiesAbstract
Distributed computing has become integral to modern supply chain management, offering scalability, efficiency, and enhanced responsiveness. However, deploying these systems involves navigating trade-offs between performance and cost. This paper investigates strategies for optimizing distributed computing in supply chains, focusing on dynamic load balancing, latency reduction, and resource allocation. Key findings reveal that dynamic load balancing can reduce downtime by 25%, while edge computing strategies lower data latency by 40%. Predictive algorithms improve resource utilization by 50%, and cloud-based elastic scaling achieves cost savings of up to 40% during non-peak operations. Furthermore, spot instance utilization reduces costs by 60% without impacting reliability. The research emphasizes the importance of adaptive resource management and tailored hybrid architectures. Future research directions include exploring blockchain integration, serverless computing models, and energy-efficient strategies to further balance performance and cost in distributed environments.
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