Distributed algorithm for resource allocation problems under persistent attacks

Resource allocation problems usually seek to find an optimal allocation of a limited amount of resources to a number of activities. The allocation solutions of different problems usually optimize different objectives under constraints [1, 2]. If the activities and constraints among them are presented as nodes and edges respectively, the resource allocation problem can be modeled as a k-coloring problem with additional optimization objectives [3, 4]. Since the amount of resources is limited, it is common that some of the activities (nodes) cannot obtain a resource (color). Because the importance of the nodes is usually different, let the weight of a node denote the cost if it cannot obtain a resource, then the resource allocation problem can be described by a node-weighted graph G(E,V), where E and V are the edge and node set, respectively. Some of the nodes that cannot obtain a resource will incur cost to the allocation solution. The optimization objective of the resource allocation problem formulated in this paper is to minimize the total cost of all the nodes that do not obtain a resource. If the total cost is zero, the obtained solution is a k-coloring of the graph; otherwise, the obtained solution is a k -coloring of the graph after removing the nodes that do not obtain a resource. So the resource allocation problem is a generalization of the k-coloring problem.

In this research work, we present a combination of a memetic algorithm and an immunological algorithm that we call Memetic Immunological Algorithm - MIA. This algorithm has been designed to tackle the resource allocation problem on a communication network. The aim of the problem is to supply all resource requested on a communication network with minimal costs and using a fixed number of providers, everyone with a limited resource quantity to be supplied. The scheduling of several resource allocations is a classical combinatorial problem that finds many applications in real-world problems. MIA incorporates two deterministic approaches: (1) a local search operator, which is based on the exploration of the neighbourhood; and (2) a deterministic approach for the assignment scheme based on the Depth First Search (DFS) algorithm. The results show that the usage of a local search procedure and mainly the DFS algorithm is an effective and efficient approach to better exploring the complex search space of the problem. To evaluate the performances of MIA we have used 28 different instances. The obtained results suggest that MIA is an effective optimization algorithm in terms of the quality of the solution produced and of the computational effort.

In this paper, the distributed resource allocation problem over undirected networks, considering box constraints and demand–supply constraints, is studied. Furthermore, each agent has one private cost function that is only known by itself. The aim is to minimize the sum of all local cost functions in a distributed manner while meeting the constraints. To solve the dispatch problem, a continuous‐time distributed algorithm with fixed step‐size is proposed. After that, a modified distributed algorithm of discrete‐time multi‐agent system is provided. Moreover, the proposed algorithms obtain the global optimal solution with an exponential convergence speed by the conventional Lyapunov methods. Finally, several simulations of IEEE‐14 bus are presented to validate and illustrate the proposed designed algorithm.

This paper investigates the problem of joint base station selection and resource allocation in an orthogonal frequency division multiple access (OFDMA) heterogeneous cellular network. The original throughput maximization problem is NP-hard and we propose solving it by using game theoretic stochastic learning approaches. To this end, we first transform the original problem into a tractable form, which has a weighted utility function. Then we prove that an exact potential game applies and it exists the best Nash equilibria which is a near optimal solution of the original problem when an efficient solution method of the weights is employed. To obtain the optimal solution, we redesign the utility function by leveraging a state space to formulate the original problem into an ordinal state based potential game, which is proved that it exists a recurrent state equilibrium point that maximizes system throughput. Furthermore, we propose two different variants of Max-logit learning algorithm based on these two games respectively: one is a simultaneous learning algorithm with less information exchange, which achieves the best Nash equilibria point of the exact potential game and the other is an efficient learning algorithm for the ordinal state based potential game, which can converge to the global optimization solution. Finally, numerical results are given to validate that theoretical findings.

Multiuser Orthogonal Frequency Division Multiple Access (OFDMA) networks, such as Long Term Evolution networks, use the frequency reuse-1 model to face the tremendous increase of mobile traffic demands, and to increase network capacity. However, inter-cell interference problems are generated, and they have a negative impact on cell-edge users performance. Resource and power allocation should be managed in a manner that alleviates the negative impact of inter-cell interference on system performance. In this paper, we formulate a novel centralized multi-cell resource and power allocation problem for multiuser OFDMA networks. The objective is to maximize system throughput while guaranteeing a proportional fair rate for all the users. We decompose the joint problem into two independent problems: a resource allocation problem and a power allocation problem. We prove that each of these problems is a convex optimization problem, and that their optimal solution is also an optimal solution to the original joint problem. Lagrange duality theory and subgradient projection method are used to solve the centralized power allocation problem. We study the convergence of our centralized approach, and we find out that it reduces inter-cell interference, and increases system throughput and spectral efficiency in comparison with the frequency reuse-1 model, reuse-3 model, fractional frequency reuse, and soft frequency reuse techniques.

In this paper, we propose a multi-objective optimization framework to address the joint mobile association and resource allocation problem in a video transmitted wireless heterogeneous network. We consider user quality of experience (QoE) as one of the design objectives together with two other performance metrics to characterize the design tradeoffs among perceived video quality, power consumption, and network resource consumption. In order to find the Pareto optimal solutions for a multi-objective optimization problem, we first apply weighted Tchebycheff approach to aggregate multiple objectives and minimize the Tchebycheff distance between the optimal solution and utopia solution. Dual decomposition technique is then introduced to decompose the above mentioned problem into a series of similar subproblems. By using linear relaxation and variable transformation, standard convex optimization method is applied to solve these subproblems efficiently and the optimal mobile association and resource allocation are obtained. System level simulation studies numerically demonstrate the tradeoffs among three design objectives and provide an insightful understanding on the performance compromise under multi-objective conditions.

Mobile Edge Computing (MEC) is a promising computing paradigm, which can provide mobile devices with powerful computing capability and low delay by offloading resource-intensive tasks to the MEC Server. However, due to the varying wireless channel conditions and different task delay requirements, the resource allocation decision made by MEC Server cannot always achieve the lowest system energy consumption. In this paper, we consider resource allocation problem in a multi-user MEC system. The objective is to achieve minimum system energy consumption under delay constraint by suitably allocating communication and computing resources. We solve the communication resources (subcarrier) allocation problem through regression algorithm, make use of three regression algorithms to predict subcarrier allocation. And then calculate the computing resources (CPU frequency) allocation and system energy consumption. Simulation results show that machine learning can achieve near-optimal system energy consumption, where the system energy consumption of the ANN(Artificial Neural Network), Xgboost (Extreme Gradient Boosting), and RF(Random Forest) algorithms is 1.30%, 7.49%, and 23.29% higher than the optimal system energy consumption respectively. In the view of prediction loss, ANN and Xgboost perform better than RF.

This paper presents a new method for solving a linear programming problem, which is an extended version of the one previously presented by the author. The optimal solution of a linear programming problem is composed of some inequality constraints in their equality form. Then, it is possible to recognize the problem of finding the equality constraints which constitute the optimal solution. Some index is introduced to estimate the equality constraints among the inequality conditions. The estimated constraints give some solution which may be optimal or not optimal but feasible solution, or infeasible solution. Then the remains of the algorithm are the methods to transfer the infeasible/feasible solution to a feasible/optimal solution. Some candidate algorithm with a new optimality criterion is proposed in this paper and is confirmed its validity by many examples. The algorithm is also applied for solving a traveling salesperson problem.

Considering the proliferation in the number of cloud users on an everyday basis, the task of resource provisioning in order to support all these users becomes a challenging problem. When resource allocation is non-optimal, users may face high costs or performance issues. So, in order to maximize profit and resource utilization while satisfying all client requests, it is essential for Cloud Service Providers to come up with ways to allocate resources adaptively for diverse conditions. This is a constrained optimization problem. Each client that submits a request to the cloud has its own best interests in mind. But each of these clients competes with other clients in the quest to obtain required quantum of resources. Hence, every client is a participant in this competition. So, a preliminary analysis of the problem reveals that it can be modelled as a game between clients. A game theoretic modelling of this problem provides us an ability to find an optimal resource allocation by employing game theoretic concepts. Resource allocation problems are NP-Hard, involving VM allocation and migration within and possibly, among data centres. Owing to the dynamic nature and number of requests, static methods fail to surmount race conditions. Using a Min-Max Game approach, we propose an algorithm that can overcome the problems mentioned. We propose to employ a utility maximization approach to solve the resource provisioning and allocation problem. We implement a new factor into the game called the utility factor which considers the time and budget constraints of every user. Resources are provisioned for tasks having the highest utility for the corresponding resource.

Software testing Resource allocation and release time decisions are vital for the software systems. The objective behind such critical decisions may differ from firm to firm. The motive of the firm may be maximization of software reliability or maximization of number of faults to be removed from each module or it may be minimization of number of faults remaining in the software or minimization of testing resources. Taking into consideration these different aims, various authors have investigated the problem of resource allocation and release time problem. In this paper we investigate various software release policies and resource allocation problem, for example, policies based on the dual constraints of cost and reliability.

Spatial conservation planning under uncertainty using modern portfolio theory and Nash bargaining solution

This paper considers the nonlinear fractional knapsack problem and demonstrates how its solution can be effectively applied to two resource allocation problems dealing with the World Wide Web. The novel solution involves a "team" of deterministic learning automata (LA). The first real-life problem relates to resource allocation in web monitoring so as to "optimize" information discovery when the polling capacity is constrained. The disadvantages of the currently reported solutions are explained in this paper. The second problem concerns allocating limited sampling resources in a "real-time" manner with the purpose of estimating multiple binomial proportions. This is the scenario encountered when the user has to evaluate multiple web sites by accessing a limited number of web pages, and the proportions of interest are the fraction of each web site that is successfully validated by an HTML validator. Using the general LA paradigm to tackle both of the real-life problems, the proposed scheme improves a current solution in an online manner through a series of informed guesses that move toward the optimal solution. At the heart of the scheme, a team of deterministic LA performs a controlled random walk on a discretized solution space. Comprehensive experimental results demonstrate that the discretization resolution determines the precision of the scheme, and that for a given precision, the current solution (to both problems) is consistently improved until a nearly optimal solution is found--even for switching environments. Thus, the scheme, while being novel to the entire field of LA, also efficiently handles a class of resource allocation problems previously not addressed in the literature.

A fuzzy max-T location problem is considered. The fuzzy max-T location problem is a generalization of a fuzzy maximin location problem by allowing in the objective function arbitrary triangular norms instead of the triangular norm defined by the minimum operation. Then we give conditions for the existence of its optimal solutions, and derive a relationship between its optimal solutions and efficient solutions of a fuzzy multicriteria location problem. Furthermore, we give some properties of triangular norms, and for triangular norms, we investigate the stability of optimal solutions of the fuzzy max-T location problem. 1 Introduction and preliminaries In a general continuous location model, finitely many points called demand points in R n , modeling existing facilities or customers, are given. Let di ∈ R n , i =1 , 2, ··· , � (≥ 2) be demand points. We put I ≡{ 1, 2, ··· , � }. Then a problem to locate a new facility in R n is called a single facility location problem. If one prefers the location of the facility near demand points, then the problem is formulated as follows: (1) min x∈Rn f (γ1(x − d1) ,γ 2(x − d2), ··· ,γ � (x − d� ))

An Optimal Solution to a Resource Allocation Problem under COVID-19 Financial Constraints

In this paper, a simulation-based optimization approach, named NHOCBA, for a typical resource allocation problem is presented. The hybrid algorithm based on neighborhood algorithm is applied to explore toward optimal direction in design space. For increasing efficiency, an optimal computing budget allocation (OCBA) is adopted to compute the optimal number of replications and to provide reliable evaluation of variance. In addition, we deal with the resource allocation problem which exist multiple global optima in design space. Therefore, a trim procedure which prevents to allocate extra numbers of replication to local optima has been proposed to enhance efficiency. Then, we use confidence interval at the end of algorithm procedure to find an optimal set instead of an optimal solution from design space. Finally, we compare the NHOCBA with different algorithms by experimentation study which shows that NHOCBA approach can perform better than the other algorithms under certain conditions.