# A Study Comparing Waiting Times in Global and Local Queuing Systems with Heterogeneous Workers  
[PERSON]  
1 Department of Industrial Engineering & Management, Azrieli College of Engineering Jerusalem (JCE), Jerusalem 9103501, Israel; [EMAIL_ADDRESS]. route optimization, and incentive programs. In service-oriented industries, variability is the norm, and several papers have analyzed marketplace dynamics and workers' behavior [1; 2; 3; 4]. In their comprehensive study of crowdsourcing and real-world marketplace data, [PERSON] et al. [3] confirmed the variability of workers' processing rates. Efficiently routing jobs to servers with varying processing speeds is a challenge faced not only by human servers but also by digital servers. In computing environments, digital servers offer services at different speeds and have varying processing rates. Cloud computing environments use virtual machines or containers on servers with different capacities to allocate resources effectively. Servers in clusters or grids may have different hardware configurations or can be in different regions, resulting in variations in processing speed and network latency. Edge computing devices have varying processing capabilities limited by power constraints, size, and cost. Similarly, IoT systems consist of diverse devices with varying computing capabilities. Selecting the right queueing system architecture for these systems is crucial to managing their workloads. The evolution of queueing systems from global to local architectures represents a significant shift in addressing the challenges of heterogeneous workforces. The following describes the two common architectures of queue structures: a single global queue (Figure 1a) and local queues (Figure 1b):  
1. Global queue architecture (also called the centralized work pile, single, shared, or pooled queue) is the traditional queueing system in which the management system handles incoming tasks, and humans pull tasks from this single shared or pooled queue when idle. 2. Local queue architecture (parallel or dedicated queues) is a relatively newer queuing system developed when network bandwidth increases and local worker's storage allows it. In this architecture, each worker has a queue of tasks, and the management system pushes the tasks to the workers using routing or dispatching policies. Each worker fetches the next task from her dedicated queue and processes it. Join-the-Shortest-Queue (JSQ) is a policy where the next task is allocated to the worker with the lowest number of tasks in their local queue length. It is considered in many papers to be the policy that achieves lower response times in a system with parallel queues. JSQ flavors and other routing policies are considered in the literature and this paper. Queue design is critical in many service industries, as reduced waiting times are positively linked to higher financial performance. Global queue systems, with their centralized task pools, offer simplicity and potential efficiency gains through task pooling, where workers immediately pull a task from a global queue whenever they become idle. This sharing reduces the mean and variance of waiting times in the single queue system but often overlooks the variability in worker performance. However, in many cases, a global queue may not be feasible or may not be the best option. For example, in the case of an extensive distributed system where the front-end servers receive the stream of arriving tasks and can perform scheduling and routing functionalities to route the job to different  
Figure 1: (**a**) Global queue architecture where workers pull tasks from this queue; (**b**) in local queue architecture, the management system pushes the tasks to the workers using policies such as JSQ. workers' servers, they cannot manage a centralized queue mechanism. Recent research has shown that there may be better choices for systems with human strategic workers than a global queue system.