Author(s): Daniel K Osei and Mateusz Kowalczyk

Abstract: Distributed computing environments are increasingly adopted at entry level to support scalable execution of computational tasks across multiple low-cost nodes. In such environments, effective task distribution strategies play a critical role in determining system performance, responsiveness, and resource utilization. This research examines fundamental task distribution approaches suitable for beginner oriented distributed systems with limited hardware, minimal administrative complexity, and modest workload diversity. Common strategies including static partitioning, centralized scheduling, and simple dynamic allocation are analyzed under controlled simulated conditions. Performance metrics such as task completion time, load balance, queue waiting time, and processor idle ratios are used for comparative evaluation. The experimental framework models small scale distributed setups commonly found in academic laboratories, training clusters, and introductory cloud-based platforms. Results indicate that while static task distribution offers minimal overhead and implementation simplicity, it suffers from poor adaptability under heterogeneous workloads. Centralized scheduling demonstrates improved load balance but introduces scheduling latency and a single point of control. Basic dynamic strategies provide better responsiveness at the cost of moderate coordination overhead. The findings highlight trade-offs between simplicity, efficiency, and scalability that are especially relevant for novice developers and educators. Based on the observed behavior, the research proposes selection guidelines to align task distribution mechanisms with system size, workload predictability, and learning objectives. These insights aim to support informed design decisions and improve efficiency in entry level distributed computing deployments. Overall, the work contributes a structured evaluation of simple task distribution strategies and establishes a foundation for progressive exploration of advanced distributed scheduling techniques. The outcomes are intended to assist students, instructors, and early-stage practitioners in understanding practical performance implications before adopting complex frameworks. This focus supports gradual skill development and effective utilization of limited computational resources in real world educational and small organizational contexts without unnecessary system complexity.

DOI: 10.33545/27076636.2026.v7.i1a.154

Pages: 46-50 | Views: 83 | Downloads: 37

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