Title: On the Scalability of Four Multi-Agent Architectures for Load Control Management in Intelligent Networks
Paralleling the rapid advancement in the network evolution is the need for advanced network traffic management surveillance. The increasing number and variety of services being offered by communication networks has fuelled the demand for optimized load management strategies. The problem of Load Control Management in Intelligent Networks has been studied previously and four Multi-Agent architectures have been proposed. The objective of this thesis is to investigate one of the quality attributes namely, scalability of the four Multi-Agent architectures. The focus of this research would be to resize the network and study the performance of the different architectures in terms of Load Control Management through different scalability attributes. The analysis has been based on experimentation through simulations. It has been revealed through the results that different architectures exhibit different performance behaviors for various scalability attributes at different network sizes. It has been observed that there exists a trade-off in different scalability attributes as the network grows. The factors affecting the network performance at different network settings have been observed. Based on the results from this study it would be easier to design similar networks for optimal performance by controlling the influencing factors and considering the trade-offs involved.
Contents
1 INTRODUCTION
1.1 RESOURCES – A LIMITATION IN COMMUNICATION NETWORKS
1.2 INTELLIGENT NETWORK LOAD CONTROL PROBLEM
1.3 SCALABILITY
1.4 THE RESEARCH QUESTION
1.5 OUTLINE OF THE THESIS
2 INTELLIGENT NETWORKS
2.1 EVOLUTION OF INTELLIGENT NETWORK
2.2 THE OBJECTIVES
2.2.1 Broadened Range of Services
2.2.2 Increased Service Velocity at Low Cost
2.2.3 Enable Vendor-Independent Deployment
2.2.4 Evolve from Existing Networks
2.3 WHAT IS AN INTELLIGENT NETWORK (IN)
2.4 IN ARCHITECTURE
2.4.1 Service Switching Points (SSP)
2.4.2 Service Control Point (SCP)
2.4.3 Signal Transfer Point (STP)
2.4.4 Service Node (SN)
2.4.5 Service Creation Environment (SCE)
2.4.6 Service Management System (SMS)
2.5 INTELLIGENT NETWORK SERVICES
2.6 THE FUTURE OF INTELLIGENT NETWORKS
3 AGENT-BASED APPROACHES TO IN LOAD CONTROL
3.1 THE AGENT TYPES
3.1.1 Allocators
3.1.2 Quantifiers
3.1.3 Distributors
3.2 FOUR MULTI-AGENT ARCHITECTURES FOR IN LOAD CONTROL
3.2.1 The Centralized-Auction (CA) Architecture
3.2.2 The Hierarchically Distributed Auction (HA) Architecture
3.2.3 The Centralized Leaky Bucket (CLB) Architecture
3.2.4 The Mobile Broker (MB) Architecture
4 SCALABILITY, THE ATTRIBUTES
4.1 UTILIZATION OF RESOURCES
4.2 COMMUNICATION DELAYS
4.2.1 Responsiveness
4.2.2 Request Processing Delays
4.2.3 Messaging Delays
4.3 CALL ACCEPT/REJECT RATES
4.4 COMMUNICATION OVERHEAD
4.5 COMPUTATIONAL OVERHEAD
4.6 LOAD BALANCING
4.7 REACTIVITY
5 SIMULATION PRECONDITIONS & SETTINGS
5.1 SIMULATION PRECONDITIONS
5.1.1 General Network Configuration
5.1.2 Prediction of the Offered Load
5.1.3 Architecture Specific Configurations
5.2 TABULATION OF RESULTS
5.3 SIMULATION RUNS
6 THE ANALYSIS
6.1 UTILIZATION OF RESOURCES
6.2 COMMUNICATION DELAYS
6.2.1 Responsiveness
6.2.2 Request Processing Delays
6.2.3 Messaging Delays
6.3 CALL ACCEPT/REJECT RATES
6.4 OVERHEAD COMMUNICATION
6.5 OVERHEAD COMPUTATIONS
6.6 LOAD BALANCING
6.7 REACTIVITY
6.7.1 Overload Control
7 CONCLUSIONS AND FUTURE WORK
REFERENCES
Author: Raheel Ahmad
Source: Blekinge Institute of Technology
Reference URL 1: Visit Now