Stability, Memory, and Messaging Trade-Offs in Heterogeneous Service Systems

We consider a heterogeneous distributed service system consisting of n servers with unknown and possibly different processing rates. Jobs with unit mean arrive as a renewal process of rate proportional to n and are immediately dispatched to one of several queues associated with the servers. We assume that the dispatching decisions are made by a central dispatcher with the ability to exchange messages with the servers and endowed with a finite memory used to store information from one decision epoch to the next, about the current state of the queues and about the service rates of the servers. We study the fundamental resource requirements (memory bits and message exchange rate) in order for a dispatching policy to be always stable. First, we present a policy that is always stable while using a positive (but arbitrarily small) message rate and [Formula: see text] bits of memory. Second, we show that within a certain broad class of policies, a dispatching policy that exchanges [Formula: see text] messages per unit of time, and with [Formula: see text] bits of memory, cannot be always stable.

Entropy-Driven Stochastic Federated Learning in Non-IID 6G Edge-RAN

Scalable and sustainable AI-driven analytics are necessary to enable large-scale and heterogeneous service deployment in sixth-generation (6G) ultra-dense networks. This implies that the exchange of raw monitoring data should be minimized across the network by bringing the analysis functions closer to the data collection points. While federated learning (FL) is an efficient tool to implement such a decentralized strategy, real networks are generally characterized by time- and space-varying traffic patterns and channel conditions, making thereby the data collected in different points non independent and identically distributed (non-IID), which is challenging for FL. To sidestep this issue, we first introduce a new a priori metric that we call dataset entropy, whose role is to capture the distribution, the quantity of information, the unbalanced structure and the “non-IIDness” of a dataset independently of the models. This a priori entropy is calculated using a multi-dimensional spectral clustering scheme over both the features and the supervised output spaces, and is suitable for classification as well as regression tasks. The FL aggregation operations support system (OSS) server then uses the reported dataset entropies to devise 1) an entropy-based federated averaging scheme, and 2) a stochastic participant selection policy to significantly stabilize the training, minimize the convergence time, and reduce the corresponding computation cost. Numerical results are provided to show the superiority of these novel approaches.

Design a Semantic Scale for Passenger Perceived Quality Surveys of Urban Rail Transit: Within Attribute’s Service Condition and Rider’s Experience

A better understanding of passenger perceived quality helps urban rail transit managers adopt better strategies to improve the service quality of urban rail transit, which is beneficial to the sustainable development of an urban rail transit system itself and cities. This paper designs a semantic scale to survey passenger perceived quality of urban rail transit. The methodology is selecting specific features of an attribute and then describing the features to present the attribute’s service condition and the rider’s experience. The scale’s options can reduce cognitive steps and hesitation for riders to answer the survey questionnaire. Furthermore, it enables urban rail transit managers to understand passenger perceived quality more visually. After verifying the reliability and validity of the semantic scale, an empirical study was conducted to compare the evaluation results of the proposed semantic scale, Likert, and numeric scales. Compared to the Likert and numeric scales, the evaluation result of the semantic scale is fairer for attributes with homogeneous service conditions over operation periods from the transit agency perspective. Meanwhile, it is more homogeneous for attributes with homogeneous service conditions and is more heterogeneous for attributes with heterogeneous service conditions.