International Journal on Science and Technology

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Elastic Partition Scaling for Memory Efficient Distributed Storage Systems

Author(s) Arunkumar Sambandam
Country United States
Abstract Distributed storage systems rely on partitioning techniques to divide data across multiple nodes in order to support scalability and parallel processing. By spreading workload among several machines, these systems aim to improve resource utilization and maintain consistent performance under growing demand. However, conventional partitioning strategies typically employ static data placement, where partitions are assigned to nodes using fixed rules that remain unchanged during execution. Although this approach simplifies management, it often leads to uneven workload distribution as access patterns evolve over time. Busy nodes experience contention and increased processing delays, whereas lightly loaded nodes waste available CPU and memory capacity. As the number of nodes increases, this inefficiency becomes more pronounced, resulting in declining node utilization percentages and poor overall system efficiency. Adding more hardware does not necessarily improve performance, because additional nodes may remain underused while a few nodes become bottlenecks. Consequently, operational costs rise without proportional gains in throughput or responsiveness. Empirical observations indicate that static partitioning frequently produces fragmented load distribution, reduced hardware utilization, and inconsistent performance across nodes. Low node utilization directly reflects wasted computational resources and limits the effective scalability of distributed storage infrastructures. Systems that fail to balance workload dynamically are unable to fully exploit available capacity, especially under varying or unpredictable traffic conditions. These limitations highlight the need for partition management strategies that maintain balanced resource usage across all nodes. This paper addresses the problem of inefficient node utilization in distributed storage systems and focuses on improving uniform workload distribution to enhance resource efficiency and scalable performance.
Keywords Distributed, Storage, Partitioning, Scalability, Utilization, Balancing, Elasticity, Workload, Efficiency, Clustering, Allocation, Throughput, Optimization, Monitoring.
Field Engineering
Published In Volume 15, Issue 4, October-December 2024
Published On 2024-10-10
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