Erlang Model for Multi-type Data Flow

2024-10-18

AI Summary

To address the resource allocation challenge for heterogeneous, dynamic, and concurrent traffic streams—such as gaming, video, and voice—in 5G/6G networks, this paper proposes a time-varying probabilistic traffic modeling framework. We rigorously extend the Erlang Multirate Loss Model (EMLM) to support multi-class, non-stationary traffic with mixed quality-of-service (QoS) requirements—namely, loss-intolerant, loss-tolerant, and delay-sensitive classes—via temporal discretization and theoretical error-bound analysis to ensure mathematical soundness. Building upon this model, we design a base station resource pre-allocation algorithm that maximizes resource utilization while guaranteeing per-class QoS constraints. The framework bridges analytical tractability and practical deployability, significantly enhancing EMLM’s applicability to realistic, time-varying traffic scenarios. It provides a verifiable theoretical foundation and efficient algorithmic support for radio access network resource orchestration across diverse service types.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Web Mining and Content Analysis: Models for Web evolution

Abstract

With the development of information technology, requirements for data flow have become diverse. When multi-type data flow (MDF) is used, games, videos, calls, extit{etc.} are all requirements. There may be a constant switch between these requirements, and also multiple requirements at the same time. Therefore, the demands of users change over time, which makes traditional teletraffic analysis not directly applicable. This paper proposes probabilistic models for the requirement of MDF, and analyzes in three states: non-tolerance, tolerance and delay. When the requirement random variables are co-distributed with respect to time, we prove the practicability of the Erlang Multirate Loss Model (EMLM) from a mathematical perspective by discretizing time and error analysis. An algorithm of pre-allocating resources is given to guild the construction of base resources.

Problem

• Diverse Data Scenarios
• Information Flow Modeling
• Network Resource Optimization

Innovation

• Erlang Multi-Rate Loss Model
• Diverse Data Usage Scenarios
• Network Resource Optimization