Dispatching Criteria in a Non-Congested Network in Distributed Service Networks

Abstract

The strategic planning and effective management of distributed service networks are crucial in both public and private sectors. To address this need, decision-makers often develop integrated models that can concurrently handle zoning, facility location, resource allocation, and related challenges. These models are designed by carefully balancing the required level of precision with the effort and resources available for model development. Key considerations in this process include the reliability of available data, how sensitive the results are to underlying assumptions, the potential impact of sub-optimal decisions on the overall objective, and the significance of the issue at hand. Consequently, the choice of a particular model is influenced by the complexity and specific nature of the real-world problem being addressed.In distributed service networks (DSNs), the process of dispatchingi.e., the allocation of incoming service requests to suitable servers or nodes, is a critical determinant of system performance. Traditionally, much of the scholarly attention has been directed toward optimizing dispatching strategies in congested or high-load environments, where the primary challenge lies in avoiding bottlenecks and ensuring balanced load distribution. However, the dynamics of dispatching in non-congested networks, where system resources are not fully utilized and server loads are relatively light, remain underexplored. In such scenarios, dispatching strategies must be re-evaluated, not for survival under pressure, but for maximizing operational efficiency, reducing latency, and making effective use of idle resources.This paper addresses this gap by conducting a systematic study of dispatching criteria specifically tailored for non-congested DSNs. The aim is to identify which dispatching strategies offer optimal performance when congestion is not a limiting factor, thus shifting the focus from mere load balancing to intelligent resource utilization and minimal response times. Central to the study is the development of a conceptual framework that integrates system parameters such as server capacity, request arrival rates, task complexity, and geographical proximity. Additionally, a mathematical model is formulated to analyze and compare multiple dispatching strategiesincluding static, randomized, and adaptive rule-based approaches—under conditions of low network stress.To validate the proposed framework and model, simulation experiments were carried out across a variety of non-congested scenarios. The results demonstrate that context-aware and adaptive dispatching rules consistently outperform traditional static policies. These intelligent strategies are capable of dynamically adjusting dispatching decisions based on real-time system information, such as server idleness, energy efficiency, and historical request patterns. The advantage of such adaptiveness becomes particularly pronounced in environments where service nodes are distributed across heterogeneous infrastructures with varying response capabilities.The findings underscore the importance of rethinking dispatching strategy design for non-congested DSNs. While simplistic methods may suffice under light loads, incorporating real-time analytics and adaptive rules leads to notable improvements in overall system responsiveness and resource efficiency. These results have significant implications for the design and implementation of distributed service networks, especially in emerging application domains such as edge computing, cloud services, and IoT-based platforms, where network states can fluctuate rapidly between congested and non-congested conditions.In conclusion, this paper contributes to the growing field of distributed systems by highlighting the distinct optimization opportunities present in non-congested environments. The proposed model and simulation-based evaluation provide a foundational reference for future research aiming to develop dispatching algorithms that are not only robust under stress but also smart and efficient during periods of low utilization. Future directions include integrating machine learning techniques for predictive dispatching and extending the framework to hybrid networks with mixed traffic patterns.