A novel composite web service selection based on quality of service

Using the internet, as a dynamic environment thanks to its distributed characteristic, for web service deployment has become a crucial issue in QoS-driven service composition. An accurate adaption should be undertaken to provide a reliable service composition which enables the composited services are being executed appropriately. That is, the critical aspect of service composition is the proper execution of combination of web services while the appropriate service adaption performed with respect to predetermined functional and non-functional characteristics. In this paper, we attempts to deliberate the optimization approaches to devise the appropriate scheme for QoS-based composite web service selection

Energy Optimization of a Cellular Network with QoS Guarantee

The problem of energy optimization in a cellular network has been studied from different perspectives: sleep patterns, network interference, association of users and base stations, allocation of resources (bandwidth and power), etc. All these aspects have been discussed individually in previous works. However, none of the existing works has succeeded in proposing an accurate solution to an exact mathematical model that takes into account several of these parameters simultaneously. In this work, we propose a modeling of several network parameters (i.e., base station transmission power and activity, user assignment, bandwidth allocation, interference, and data rate) and their interaction in order to minimize the network energy consumption. An exact solution of the proposed model, which solves the energy optimization problem in an LTE network, is presented afterward. The proposed solution guarantees to satisfy all the users with a minimum quality of service (data rate), assuming a proper call admission mechanism has been put in place. The accurate solution allows energy savings of up to 50\% in a moderately loaded network, which leads to energy savings of up to twice than of the heuristic proposed by Piunti \textit{et al.} (2015). Moreover, a strategy to solve the model using column generation method is proposed and investigated. Various numerical results are presented on hexagonal and randomly generated cellular networks

Discrete location models for content distribution

Cataloged from PDF version of article.The advances in information and computer technology has tremendously eased the way to reach electronic information. This, however, also brought forth many problems regarding the distribution of electronic content. This is especially true in the Internet, where there is a phenomenal growth of demand for any kind of electronic information, placing a high burden on the underlying infrastructure. In this dissertation, we study problems arising in distribution of electronic content. The first problem studied here is related to Content Distribution Networks (CDNs), which have emerged as a new technology to overcome the problems arising on the Internet due to the fast growth of the web-related traffic, such as slow response times and heavy server loads. They aim at increasing the effectiveness of the network by locating identical or partial copies of the origin server(s) throughout the network, which are referred to as proxy servers. In order for such structures to run efficiently, the CDN must be designed such that system resource are properly managed. To this purpose, we develop integer programming models for the problem of designing CDNs and investigate exact and heuristic algorithms for their solution. The second problem considered in this dissertation is Video Placement and Routing, which is related to the so-called Video-on-Demand (VoD) services. Such services are used to deliver programs to the users on request and find many applications in education, entertainment and business. Although bearing similarities with the CDN phenomena, VoD services have special characteristics with respect to the structure of the network and the type of content distributed. We study the problem of Video Placement and Routing for such networks and offer an optimization based solution algorithm for the associated integer programming model. The third problem studied here is the problem of allocating databases in distributed computing systems. In this context, we specifically focus on the well-known multidimensional Knapsack Problem (mKP). The mKP arises as a subproblem in solving the database location problem. We concentrate on the well known cover inequalities that are known to be important for the solution of the mKP. We then propose a novel separation procedure to identify violated cover inequalities and utilize this procedure in a branch-and-cut framework devised for the solution of the mKP.Bektaş, TolgaPh.D

Towards a Virtualized Next Generation Internet

A promising solution to overcome the Internet ossification is network virtualization in which Internet Service Providers (ISPs) are decoupled into two tiers: service providers (SPs), and infrastructure providers (InPs). The former maintain and customize virtual network(s) to meet the service requirement of end-users, which is mapped to the physical network infrastructure that is managed and deployed by the latter via the Virtual Network Embedding (VNE) process. VNE consists of two major components: node assignment, and link mapping, which can be shown to be NP-Complete. In the first part of the dissertation, we present a path-based ILP model for the VNE problem. Our solution employs a branch-and-bound framework to resolve the integrity constraints, while embedding the column generation process to effectively obtain the lower bound for branch pruning. Different from existing approaches, the proposed solution can either obtain an optimal solution or a near-optimal solution with guarantee on the solution quality. A common strategy in VNE algorithm design is to decompose the problem into two sequential sub-problems: node assignment (NA) and link mapping (LM). With this approach, it is inexorable to sacrifice the solution quality since the NA is not holistic and not-reversible. In the second part, we are motivated to answer the question: Is it possible to maintain the simplicity of the Divide-and-Conquer strategy while still achieving optimality? Our answer is based on a decomposition framework supported by the Primal-Dual analysis of the path-based ILP model. This dissertation also attempts to address issues in two frontiers of network virtualization: survivability, and integration of optical substrate. In the third part, we address the survivable network embedding (SNE) problem from a network flow perspective, considering both splittable and non-splittable flows. In addition, the explosive growth of the Internet traffic calls for the support of a bandwidth abundant optical substrate, despite the extra dimensions of complexity caused by the heterogeneities of optical resources, and the physical feature of optical transmission. In this fourth part, we present a holistic view of motivation, architecture, and challenges on the way towards a virtualized optical substrate that supports network virtualization

Greedy randomised adaptive search procedures for topological design of MPLS networks, Journal of Telecommunications and Information Technology, 2002, nr 2

In this paper, the IP/MPLS network cost optimization problem of selecting localisation of nodes and links, combined with link’s dimensioning, is discussed. As the considered problem is hard, we discuss and propose greedy randomized adaptive search procedure (GRASP) based solution method. GRASP is an iterative randomised sampling technique which combines adaptive randomised greedy function in constructing initial solution with local search optimisation. The effectiveness of the method is illustrated by means of a numerical study. We compare the GRASP results with results for both exact and heuristic methods obtained in previous research concerning topological design problem

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

New formulations for the hop-constrained minimum spanning tree problem via Sherali and Driscoll's tightened Miller-Tucker-Zemlin constraints

Given an undirected network with positive edge costs and a natural number p, the hop-constrained minimum spanning tree problem (HMST) is the problem of finding a spanning tree with minimum total cost such that each path starting from a specified root node has no more than p hops (edges). In this paper, the new models based on the Miller-Tucker-Zemlin (MTZ) subtour elimination constraints are developed and computational results together with comparisons against MTZ-based, flow-based, and hop-indexed formulations are reported. The first model is obtained by adapting the MTZ-based Asymmetric Traveling Salesman Problem formulation of Sherali and Driscoll [18] and the other two models are obtained by combining topology-enforcing and MTZ-related constraints offered by Akgün and Tansel (submitted for publication) [20] for HMST with the first model appropriately. Computational studies show that the best LP bounds of the MTZ-based models in the literature are improved by the proposed models. The best solution times of the MTZ-based models are not improved for optimally solved instances. However, the results for the harder, large-size instances imply that the proposed models are likely to produce better solution times. The proposed models do not dominate the flow-based and hop-indexed formulations with respect to LP bounds. However, good feasible solutions can be obtained in a reasonable amount of time for problems for which even the LP relaxations of the flow-based and hop-indexed formulations can be solved in about 2 days. © 2010 Elsevier Ltd. All rights reserved

Many-core Algorithms for Combinatorial Optimization

Combinatorial Optimization is becoming ever more crucial, in these days. From natural sciences to economics, passing through urban centers administration and personnel management, methodologies and algorithms with a strong theoretical background and a consolidated real-word effectiveness is more and more requested, in order to find, quickly, good solutions to complex strategical problems. Resource optimization is, nowadays, a fundamental ground for building the basements of successful projects. From the theoretical point of view, Combinatorial Optimization rests on stable and strong foundations, that allow researchers to face ever more challenging problems. However, from the application point of view, it seems that the rate of theoretical developments cannot cope with that enjoyed by modern hardware technologies, especially with reference to the one of processors industry. In this work we propose new parallel algorithms, designed for exploiting the new parallel architectures available on the market. We found that, exposing the inherent parallelism of some resolution techniques (like Dynamic Programming), the computational benefits are remarkable, lowering the execution times by more than an order of magnitude, and allowing to address instances with dimensions not possible before. We approached four Combinatorial Optimization’s notable problems: Packing Problem, Vehicle Routing Problem, Single Source Shortest Path Problem and a Network Design problem. For each of these problems we propose a collection of effective parallel solution algorithms, either for solving the full problem (Guillotine Cuts and SSSPP) or for enhancing a fundamental part of the solution method (VRP and ND). We endorse our claim by presenting computational results for all problems, either on standard benchmarks from the literature or, when possible, on data from real-world applications, where speed-ups of one order of magnitude are usually attained, not uncommonly scaling up to 40 X factors