Resource Allocation In Communication Networks Research Articles

Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks

Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks

Blockchain-Based Educational Management and Secure Software-Defined Networking in Smart Communities

As network multimedia technology has advanced rapidly, video communication has made enormous strides and has been used in many educational industries. The use of educational video communication is a simple, effective, and convenient method of communication. A fast and energy-efficient blockchain clustering algorithm is proposed to pick the desired cluster head in software-defined networking (SDN). This research suggests using an adaptive resource allocation method and secure communication networks to improve the quality of instructional video transmission. To better educational management resources (EMR), this research suggests using a particle swarm algorithm (PSA). Using the PSA-EMR, an extended particle swarm optimization model suggests a low-complexity solution for adaptive central and distributed resource allocation in communication networks. The method improves the productivity and accuracy of the teaching team. Finally, a cost-effective digital framework for collecting and transmitting high-quality instructional content was developed to conclude the research.

Compositional Design and Verification of Large-Scale Systems Using Dissipativity Theory: Determining Stability and Performance from Subsystem Properties and Interconnection Structures

A major problem for safety-critical engineering systems is to certify the required stability and performance properties using analytical and computational models of a physical system. The methods for such certification are severely limited in their scalability, that is, their ability to cope with a large number of physical components and the complexity of their interactions. Rather than tackling the system model as a whole, this article advocates a compositional approach that derives system-level guarantees from dissipativity properties of the subsystems and their interconnection structure. This method is particularly suitable when the subsystems are amenable to standard analytical and computational methods. However, a monolithic model of the interconnection is beyond the reach of these techniques. The results reviewed here were motivated by several application problems, such as resource allocation in communication networks, motion coordination in multiagent systems, and dynamical analysis of biochemical reaction networks. Several examples are drawn from these applications to illustrate the results.

Resource allocation in communication networks with large number of users: the dual stochastic gradient method

Рассматривается сеть связи с фиксированным набором соединений, используемых большим числом пользователей. Распределение ресурсов сети производится на основе максимизации суммарной полезности в соответствии с популярным подходом, предложенным в 1998 г. Ф. Келли с соавторами. Задача состоит в определении механизма назначения цен на скорость передачи данных с целью стимулирования оптимального использования имеющихся ресурсов. В отличие от обычного подхода, предлагаемый алгоритм не использует информацию о суммарном трафике на каждом соединении. Его входные данные: общее число пользователей $N$, пропускные способности соединений и оптимальные близорукие реакции случайно выбранных пользователей на текущие цены. Динамическая схема назначения цен основана на двойственном стохастическом методе проекции градиента. Для специального класса функций полезности $u_i$ получены верхние оценки для невязок в ограничениях и для отклонения целевой функции от оптимального значения. Эти оценки равномерны по $N$ и имеют порядок $O(T^{-1/4})$ по числу $T$ измеренных реакций пользователей. Приведены результаты компьютерных экспериментов для квадратичных функций $u_i$, представляющих собой разности между линейной полезностью, индивидуальной для каждого пользователя, и квадратичным штрафом, назначаемым сетью.

Resource Allocation in Communication Networks with Large Number of Users: The Dual Stochastic Gradient Method

We consider a communication network with a fixed set of links that are shared by a large number of users. The resource allocation is performed on the basis of the aggregate utility maximization in ...

Optimization-based resource allocation in communication networks

The continuously growing number of applications competing for resources in current communication networks highlights the necessity for efficient resource allocation mechanisms to maximize user satisfaction. Optimization Theory can provide the necessary tools to develop such mechanisms that will allocate network resources optimally and fairly among users. The aim of this paper is to provide a starting point for researchers interested in applying optimization techniques in the resource allocation problem for current communication networks. To achieve that we, first, describe the fundamental optimization theory tools necessary to design optimal resource allocation algorithms. Then, we describe the Network Utility Maximization (NUM) framework, a framework that has already found numerous applications in network optimization, along with some recent advancements of the initial NUM framework. Finally, we summarize some of our recent work in the area and discuss some of the remaining research challenges towards the development of a complete optimization-based resource allocation protocol.

Logarithmic laws in service quality perception: where microeconomics meets psychophysics and quality of experience

Utility functions, describing the value of a good or a resource from an end user's point of view, are widely used as an important ingredient for all sorts of microeconomic models. In the context of resource allocation in communication networks, a logarithmic version of utility usually serves as the standard example due to its simplicity and mathematical tractability. In this paper we argue that indeed there are much more (and better) reasons to consider logarithmic utilities as really paradigmatic, at least when it comes to characterizing user experience with specific telecommunication services. We justify this claim with the help of recent results from Quality of Experience (QoE) research, and demonstrate that, especially for Voice-over-IP and mobile broadband scenarios, there is increasing evidence that user experience and satisfaction follows logarithmic laws. Finally, we go even one step further and put these results into the broader context of the Weber-Fechner Law, a key principle in psychophysics describing the general relationship between the magnitude of a physical stimulus and its perceived intensity within the human sensory system.

Network Games: Theory, Models, and Dynamics

Traditional network optimization focuses on a single control objective in a network populated by obedient users and limited dispersion of information. However, most of today's networks are large-scale with lack of access to centralized information, consist of users with diverse requirements, and are subject to dynamic changes. These factors naturally motivate a new distributed control paradigm, where the network infrastructure is kept simple and the network control functions are delegated to individual agents which make their decisions independently (selfishly). The interaction of multiple independent decision-makers necessitates the use of game theory, including economic notions related to markets and incentives. This monograph studies game theoretic models of resource allocation among selfish agents in networks. The first part of the monograph introduces fundamental game theoretic topics. Emphasis is given to the analysis of dynamics in game theoretic situations, which is crucial for design and control of networked systems. The second part of the monograph applies the game theoretic tools for the analysis of resource allocation in communication networks. We set up a general model of routing in wireline networks, emphasizing the congestion problems caused by delay and packet loss. In particular, we develop a systematic approach to characterizing the inefficiencies of network equilibria, and highlight the effect of autonomous service providers on network performance. We then turn to examining distributed power control in wireless networks. We show that the resulting Nash equilibria can be efficient if the degree of freedom given to end-users is properly designed.

Optimisation problems for control of distributed resources

We consider a two-level optimisation problem of resource allocation in communication networks, which is based on profit maximisation of the network subject to capacity constraints. The cost function of the upper level problem involves a sum of non-differentiable functions whose values are computed algorithmically. The corresponding solution methods utilise duality theory and decomposition technique for optimisation problems.

A Framework for Evaluation of Communication Bandwidth Market Models

The article presents a method of analysis of marketbased models for resource allocation in communication networks. It consists of several stages: classification of a market model, generation of input data, data adaptation to a tested model, test calculations and, finally, presentation and interpretation of results. A set of general criteria to assess various models has been proposed. Tests are run using dedicated computer applications, data is stored in open XML-based format originated in the multicommodity market model. Network topologies are derived from the SNDlib library.

Stability of primal–dual gradient dynamics and applications to network optimization

This paper considers dynamic laws that seek a saddle point of a function of two vector variables, by moving each in the direction of the corresponding partial gradient. This method has old roots in the classical work of Arrow, Hurwicz and Uzawa on convex optimization, and has seen renewed interest with its recent application to resource allocation in communication networks. This paper brings other tools to bear on this problem, in particular Krasovskii’s method to find Lyapunov functions, and recently obtained extensions of the LaSalle invariance principle for hybrid systems. These methods are used to obtain stability proofs of these primal–dual laws in different scenarios, and applications to cross-layer network optimization are exhibited.

Particle Swarm Optimization for Adaptive Resource Allocation in Communication Networks

A generalized model of particle swarm optimization (PSO) technique is proposed as a low complexity method for adaptive centralized and distributed resource allocation in communication networks. The proposed model is applied to adaptive multicarrier cooperative communications (MCCC) technique which utilizes the subcarriers in deep fade using a relay node in order to improve the bandwidth efficiency. Centralized PSO, based on virtual particles (VPs), is introduced for single layer and cross-layer subcarrier allocation to improve the bit error rate performance in multipath frequency selective fading channels. In the single layer strategy, the subcarriers are allocated based on the channel gains. In the cross-layer strategy, the subcarriers are allocated based on a joint measure of channel gains and distance provided by the physical layer and network layer to mitigate the effect of path loss. The concept of training particles in distributed PSO is proposed and then is applied for relay node selection. The computational complexity and traffic of the proposed techniques are investigated, and it is shown that using PSO for subcarrier allocation has a lower complexity than the techniques in the literature. Significant reduction in the traffic overhead of PSO is demonstrated when using trained particles in distributed optimizations.

A Framework for Evaluation of Communication Bandwidth Market Models

The article presents a method of analysis of marketbased models for resource allocation in communication networks. It consists of several stages: classification of a market model, generation of input data, data adaptation to a tested model, test calculations and, finally, presentation and interpretation of results. A set of general criteria to assess various models has been proposed. Tests are run using dedicated computer applications, data is stored in open XML-based format originated in the multicommodity market model. Network topologies are derived from the SNDlib library.

Utility max–min fair resource allocation for communication networks with multipath routing

This paper considers the flow control and resource allocation problem as applied to the generic multipath communication networks with heterogeneous applications. We propose a novel distributed algorithm, show and prove that among all the sources with positive increasing and bounded utilities (no need to be concave) in steady state, the utility max–min fairness is achieved, which is essential for balancing Quality of Service (QoS) for different applications. By combining the first order Lagrangian method and filtering mechanism, the adopted approach eliminates typical oscillation behavior in multipath networks and possesses a rapid convergence property. In addition, the algorithm is capable of deciding the optimal routing strategy and distributing the total traffic evenly out of the available paths. The performance of our utility max–min fair flow control algorithm is evaluated through simulations under two representative case studies, as well as the real implementation issues are addressed deliberately for the practical purpose.

Path problems in generalized stars, complete graphs, and brick wall graphs

Path problems such as the maximum edge-disjoint paths problem, the path coloring problem, and the maximum path coloring problem are relevant for resource allocation in communication networks, in particular all-optical networks. In this paper, it is shown that maximum path coloring can be solved optimally in polynomial time for bidirected generalized stars, even in the weighted case. Furthermore, the maximum edge-disjoint paths problem is proved NP-hard for complete graphs (undirected or bidirected), a constant-factor off-line approximation algorithm is presented for the weighted case, and an on-line algorithm with constant competitive ratio is given for the unweighted case. Finally, an open problem concerning the existence of routings that simultaneously minimize the maximum load and the number of colors is solved: an example for a graph and a set of requests is given such that any routing that minimizes the maximum load requires strictly more colors for path coloring than a routing that minimizes the number of colors.

Resource allocation in communication networks using market-based agents

This work describes a system that allocates end-to-end bandwidth, in a switched meshed communications network. The solution makes use of market-based software agents that compete in a number of decentralised marketplaces to buy and sell bandwidth resources. Agents perform a distributed depth first search with decentralised markets in order to allocate routes for calls. The approach relies on a resource reservation and commit mechanism in the network. Initial results show that under a light network load, the system sets up a high percentage of calls which is comparable to the optimum value and that, under all network loads, it performs significantly better than a random strategy.

Resource allocation in communication networks using abstraction and constraint satisfaction

The fundamental issue of quality-of-service (QoS) routing has triggered a lot of research during the last few years. However, the proposed algorithms attempt to route communication demands only on a call by call basis, without taking into account future traffic. There are nonetheless cases where the traffic profile is known. In this paper, we address this related problem to QoS routing, more specifically, the off-line planning of bandwidth allocation to demands known in advance. Shortest-path routing is the traditional technique applied to this problem. However, this can lead to poor network utilization and even congestion. We show how an abstraction technique combined with systematic search algorithms and heuristics derived from artificial intelligence make it possible to solve this problem more efficiently and in much tighter networks, in terms of bandwidth usage. In addition, this abstraction technique also allows to explain during search why some allocation problems are indeed infeasible. Then, the network regions between which bandwidth must be added are then identified.

Analysis of State-Dependent Probabilistic Server Interruptions in Discrete-Time Queues

This letter considers a problem that arises in the context of statistical, state-dependent resource allocation in communication networks. Specifically, the problem concerns a single-server discrete-time queue with state-dependent probabilistic server interruptions. The system is analyzed for two forms of probabilistic behavior: exponential and hyperbolic forms. The analysis yields several performance metrics of interest, including average system occupancy, average delay, steady-state occupancy distribution.

A simple optimal algorithm for scheduling variable-sized requests

In this paper, an optimal algorithm is found for the following scheduling problem: given N parallel channels that can each accommodate one request at a time and a set of requests that require a channel at a specific start time and for a particular holding time, what is the best way to schedule these requests in order to accommodate the most requests? A proof of optimality is given. The algorithm is conceptually and computationally simple. This scheduling problem has application in resource allocation for communication networks.

Dynamic bandwidth allocation for ATM switches

We explore a dynamic approach to the problems of call admission and resource allocation for communication networks with connections that are differentiated by their quality of service requirements. In a dynamic approach, the amount of spare resources is estimated on-line based on feedbacks from the network's quality of service monitoring mechanism. The schemes we propose remove the dependence on accurate traffic models and thus simplify the tasks of supplying traffic statistics required of network users. In this paper we present two dynamic algorithms. The objective of these algorithms is to find the minimum bandwidth necessary to satisfy a cell loss probability constraint at an asynchronous transfer mode (ATM) switch. We show that in both schemes the bandwidth chosen by the algorithm approaches the optimal value almost surely. Furthermore, in the second scheme, which determines the point closest to the optimal bandwidth from a finite number of choices, the expected learning time is finite.