Optimal Data Placement on Networks With Constant Number of Clients

We introduce optimal algorithms for the problems of data placement (DP) and page placement (PP) in networks with a constant number of clients each of which has limited storage availability and issues requests for data objects. The objective for both problems is to efficiently utilize each client's storage (deciding where to place replicas of objects) so that the total incurred access and installation cost over all clients is minimized. In the PP problem an extra constraint on the maximum number of clients served by a single client must be satisfied. Our algorithms solve both problems optimally when all objects have uniform lengths. When objects lengths are non-uniform we also find the optimal solution, albeit a small, asymptotically tight violation of each client's storage size by $\epsilon$lmax where lmax is the maximum length of the objects and $\epsilon$ some arbitrarily small positive constant. We make no assumption on the underlying topology of the network (metric, ultrametric etc.), thus obtaining the first non-trivial results for non-metric data placement problems

A GA-based simulation system for WMNs: comparison analysis for different number of flows, client distributions, DCF and EDCA functions

In this paper, we compare the performance of Distributed Coordination Function (DCF) and Enhanced Distributed Channel Access (EDCA) for normal and uniform distributions of mesh clients considering two Wireless Mesh Network (WMN) architectures. As evaluation metrics, we consider throughput, delay, jitter and fairness index metrics. For simulations, we used WMN-GA simulation system, ns-3 and Optimized Link State Routing. The simulation results show that for normal distribution, the throughput of I/B WMN is higher than Hybrid WMN architecture. For uniform distribution, in case of I/B WMN, the throughput of EDCA is a little bit higher than Hybrid WMN. However, for Hybrid WMN, the throughput of DCF is higher than EDCA. For normal distribution, the delay and jitter of Hybrid WMN are lower compared with I/B WMN. For uniform distribution, the delay and jitter of both architectures are almost the same. However, in the case of DCF for 20 flows, the delay and jitter of I/B WMN are lower compared with Hybrid WMN. For I/B architecture, in case of normal distribution the fairness index of DCF is higher than EDCA. However, for Hybrid WMN, the fairness index of EDCA is higher than DCF. For uniform distribution, the fairness index of few flows is higher than others for both WMN architectures.Peer ReviewedPostprint (author's final draft

Performance evaluation of WMN-GA for different mutation and crossover rates considering number of covered users parameter

Node placement problems have been long investigated in the optimization field due to numerous applications in location science and classification. Facility location problems are showing their usefulness to communication networks, and more especially from Wireless Mesh Networks (WMNs) field. Recently, such problems are showing their usefulness to communication networks, where facilities could be servers or routers offering connectivity services to clients. In this paper, we deal with the effect of mutation and crossover operators in GA for node placement problem. We evaluate the performance of the proposed system using different selection operators and different distributions of router nodes considering number of covered users parameter. The simulation results show that for Linear and Exponential ranking methods, the system has a good performance for all rates of crossover and mutation.Peer ReviewedPostprint (published version

Implementation and evaluation of a simulation system based on particle swarm optimisation for node placement problem in wireless mesh networks

With the fast development of wireless technologies, wireless mesh networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless internet connectivity. This paper implements a simulation system based on particle swarm optimisation (PSO) in order to solve the problem of mesh router placement in WMNs. Four replacement methods of mesh routers are considered: constriction method (CM), random inertia weight method (RIWM), linearly decreasing Vmax method (LDVM) and linearly decreasing inertia weight method (LDIWM). Simulation results are provided, showing that the CM converges very fast, but has the worst performance among the methods. The considered performance metrics are the size of giant component (SGC) and the number of covered mesh clients (NCMC). The RIWM converges fast and the performance is good. The LDIWM is a combination of RIWM and LDVM. The LDVM converges after 170 number of phases but has a good performance.Peer ReviewedPostprint (author's final draft