Resource Allocation Problem In Networks Research Articles

Evaluating the impact of delay constraints in network services for intelligent network slicing based on SKM model

Solving the problem of network resource allocation with delay constraint is a significant challenge for realizing future Internet and 5G networks services such as advanced mobile broadband services and Internet of things (IoT), especially under the network slicing scenario. The impact of delay constraints may lead to rejection of demands, resulting in low resource utilization of network resources. This is especially severe when dynamic traffic is considered. Therefore, intelligent resource allocation algorithms are required to use the network resources in delay constrained scenario efficiently. Moreover, these algorithms should guarantee quality of service (QoS) between different priority slices during congestion case. Therefore, in this paper, we analyze the impact of delay constraint on the performance of an online resource allocation algorithm based on an intelligent efficient squatting and kicking model (SKM), proved in other works to be the most effective up to the present time yet. SKM incorporates kicking and squatting of resources as innovative techniques enabling it to achieve 100% resource utilization and acceptance ratio for higher priority slices in scenarios where the other state of art algorithms not able to reach by far in some scenarios. Simulation results showed that incorporating delay constraints has a significant impact on the performance, resulting in up to 10% and 4% reduction in terms of average resource utilization and acceptance ratios respectively.

Joint Network Control and Resource Allocation for Space-Terrestrial Integrated Network Through Hierarchal Deep Actor-Critic Reinforcement Learning

Conventional approaches to network control and resource allocation by allocating dedicated spectrum resources and separate infrastructure for massive Internet of Things (IoT) network are cost-inefficient. The concept of space-terrestrial integrated network (STIN), which is one of the enabling technologies and architectures for future 6 G wireless networks, can provide a solution to the network control and resource allocation problems for massive IoTs. In this paper, a novel STIN based network control and resource allocation problem is proposed for massive IoTs and solved through state of the art hierarchical deep actor-critic networks (H-DAC). The massive IoT networks spread over urban vicinity where cooperation can be possible. This is leveraged to negotiate a joint policy for per unit spectrum which the IoT networks are willing to pay. Deep actor-critic based reinforcement learning (RL) is used in this paper to solve the joint network control and resource allocation problem which is modeled as a utility maximization problem. The RL based algorithms solve the problem of cost per unit spectrum for the federated cloud of IoT networks and the data rate assigned to each IoT network and IoT devices. The algorithm also decides whether to transmit either through the space network or the terrestrial network. We validate performance of our proposed H-DAC scheme by comparing it with results of variants of the actor-critic based RL. We show that through proper system state formulation and reward design, the proposed H-DAC scheme outperforms the reference schemes with different network parameters and metrics.

Standardized cargo network revenue management with dual channels under stochastic and time-dependent demand

We consider the network resource allocation problem, faced by sea freight operators, for standardized cargo transportation networks with time-dependent and stochastic demand. Standardized cargo capacity is sold through allotment contracts and on the spot market. The objective is to decide on the allotment contracts to select at the beginning of the booking horizon, as well as investigate dynamic booking control policies based on which spot market requests are accepted (booked) over the booking horizon before departure. This problem is formulated and solved in two stages: In the first stage, the allotment selection problem is formulated as an integer program in which the objective is to maximize the expected profit generated from the realized utilization of each resource in the network. The optimal solution to the allotment selection problem becomes the input to the spot booking control problem in the second stage. We formulate the spot booking problem as a dynamic program, and provide an efficient heuristic as an alternative to the computationally prohibitive dynamic programming (DP) approach. The DP is solved optimally for a small-size problem and for a variety of numerical cases. For large size problems, heuristics are proposed and tested via simulation. Also extensive sensitivity analysis is performed to test the heuristics, gain managerial insights and provide practical recommendations. We recommend a well-performing heuristic that outperforms the trivial first-come first-serve policy for large-sized problems. This leading heuristic is within 2.75%, on average, of the optimal DP solution for small problems and 3.9% for large problems.

Numerical Methods for the Resource Allocation Problem in a Computer Network

The resource allocation problem in computer networks with a large number of links is considered. The links are used by consumers (users), whose number can also be very large. For the dual problem, numerical optimization methods are proposed, such as the fast gradient method, the stochastic projected subgradient method, the ellipsoid method, and the random gradient extrapolation method. A convergence rate estimate is obtained for each of the methods. Algorithms for distributed computation of steps in the considered methods as applied to computer networks are described. Special attention is given to the primal-dual property of the proposed algorithms.

Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles

The interconnection of vehicles in the future fifth generation (5G) wireless ecosystem forms the so-called Internet of Vehicles (IoV). IoV offers new kinds of applications requiring delay-sensitive, compute-intensive, and bandwidth-hungry services. Mobile edge computing (MEC) and network slicing are two of the key enabler technologies in 5G networks that can be used to optimize the allocation of the network resources and guarantee the diverse requirements of IoV applications. As traditional model-based optimization techniques generally end up with NP-hard and strongly non-convex and nonlinear mathematical programming formulations, in this article, we introduce a model-free approach based on deep reinforcement learning (DRL) to solve the resource allocation problem in MEC-enabled IoV networks based on network slicing. Furthermore, the solution uses non-orthogonal multiple access (NOMA) to enable better exploitation of the scarce channel resources. The considered problem addresses jointly the channel and power allocation, the slice selection, and the vehicle selection (vehicle grouping). We model the problem as a single-agent Markov decision process. Then we solve it using DRL with the well-known deep Q learning (DQL) algorithm. We show that our approach is robust and effective under different network conditions compared to benchmark solutions.

Pricing-based edge caching resource allocation in fog radio access networks

The edge caching resource allocation problem in Fog Radio Access Networks (F-RANs) is investigated. An incentive mechanism is introduced to motivate Content Providers (CPs) to participate in the resource allocation procedure. We formulate the interaction between the cloud server and the CPs as a Stackelberg game, where the cloud server sets nonuniform prices for the Fog Access Points (F-APs) while the CPs lease the F-APs for caching their most popular contents. Then, by exploiting the multiplier penalty function method, we transform the constrained optimization problem of the cloud server into an equivalent non-constrained one, which is further solved by using the simplex search method. Moreover, the existence and uniqueness of the Nash Equilibrium (NE) of the Stackelberg game are analyzed theoretically. Furthermore, we propose a uniform pricing based resource allocation strategy by eliminating the competition among the CPs, and we also theoretically analyze the factors that affect the uniform pricing strategy of the cloud server. We also propose a global optimization-based resource allocation strategy by further eliminating the competition between the cloud server and the CPs. Simulation results are provided for quantifying the proposed strategies by showing their efficiency in pricing and resource allocation.

AI-Chain: Blockchain Energized Edge Intelligence for Beyond 5G Networks

Beyond fifth generation (B5G) networks have recently emerged as the advancement of existing communication systems. With the unprecedented proliferation of artificial intelligence (Ai), more intelligent B5G networks are projected to fuel the continuous development of Ai applications and the efficiency of communication technologies. More recently, the outgrowths of B5G have brought billions of devices throwing zillions of bytes of data to network edges. Training such huge data volumes using a centralized data center proves to be a challenging task, due to prohibitively heavy bandwidth costs, poor time efficiency, and high privacy leakages. These challenges have fueled the revolutionary shift of intelligence applications from a centralized data center to ubiquitous edges, denoted as edge intelligence. However, there is still a long way ahead before edge intelligence is able to fully mature. Governing and sharing the use of learning results efficiently, reliably, and safely are hampered by the heterogeneity and non-confidence among edges. in this article, we propose blockchain energized edge intelligence for B5G networks, called the Ai-Chain, fusing deep learning and blockchain. The Ai-Chain is a distributed and immutable record of learning results that is able to construct a new basis of sharing among edges. Additionally, we consider a novel learning-based consensus protocol in the Ai-Chain, denoted as proof of learning (PoL). in response to the huge computing power waste, PoL treats the training process as a working puzzle, rather than the meaningless hashing in a proof of work protocol. On the other hand, PoL fully unlocks the potential of sharing more advanced intelligence among edges. in order to demonstrate the effectiveness of the Ai-Chain, we employ it to solve a joint resource allocation problem in B5G networks. Experimental results prove the superior performance of the Ai-Chain to the current popular scheme.

Distributed Resource Allocation Over Dynamic Networks With Uncertainty

Motivated by broad applications in various fields of engineering, we study a network resource allocation problem where the goal is to optimally allocate a fixed quantity of the resources over a network of nodes. We consider large scale networks with complex interconnection structures, thus any solution must be implemented in parallel and based only on local data resulting in a need for distributed algorithms. In this article, we study a distributed Lagrangian method for such problems. By utilizing the so-called distributed subgradient methods to solve the dual problem, our approach eliminates the need for central coordination in updating the dual variables, which is often required in classic Lagrangian methods. Our focus is to understand the performance of this distributed algorithm when the number of resources is unknown and may be time-varying. In particular, we obtain an upper bound on the convergence rate of the algorithm to the optimal value, in expectation, as a function of network topology. The effectiveness of the proposed method is demonstrated by its application to the economic dispatch problem in power systems, with simulations completed on the benchmark IEEE-14 and IEEE-118 bus test systems.

Latency and Mobility–Aware Service Function Chain Placement in 5G Networks

5G networks are expected to support numerous novel services and applications with versatile quality of service (QoS) requirements such as high data rates and low end-to-end (E2E) latency. It is widely agreed that E2E latency can be reduced by moving the computational capability closer to the network edge. The limited amount of computational resources of the edge nodes, however, poses the challenge of efficiently utilizing these resources while, at the same time, satisfying QoS requirements. In this work, we employ mixed-integer linear programming (MILP) techniques to formulate and solve a joint user association, service function chain (SFC) placement, where SFCs are composed of virtualized service functions (VSFs), and resource allocation problem in 5G networks composed of decentralized units (DUs), centralized units (CUs), and a core network (5GC). Specifically, we compare four approaches to solving the problem. The first two approaches minimize, respectively, the E2E latency experienced by users and the service provisioning cost. The other two instead aim at minimizing VSF migrations along with their impact on users’ quality of experience with the last one minimizing also the number of inter-CU handovers. We then propose a heuristic to address the scalability issue of the MILP-based solutions. Simulations results demonstrate the effectiveness of the proposed heuristic algorithm.

Low complexity resource allocation algorithms for chunk based OFDMA multi-user networks with max–min fairness

This paper investigates the resource allocation problem in orthogonal frequency division multiple access networks with multiple users. Different from most works that perform resource allocation based on each subcarrier, we consider the subcarriers are grouped into chunks due to simplicity of implementation. The objective is to maximize the users’ minimal transmission rate by adjusting the transmission power allocation and chunk allocation while taking into account several important constraints. The problem is formulated as a mixed integer nonlinear programming problem, which is generally hard to solve. By binary relaxation, the problem is transformed to be a convex problem. By exploiting the special structure of the relaxed problem, a barrier method based fast algorithm is proposed. To achieve binary chunk allocation, a low complexity suboptimal algorithm is proposed further. When the number of chunks is less than the number of users, it is necessary to consider the joint admission control and resource allocation problem. To solve this problem, a fast algorithm based on bisection search and Hungarian method is designed. Simulations are carried out to validate the superior performance of the proposed algorithms in terms of the users’ minimal transmission rate and running time comparing with benchmark algorithms under different conditions.

Dynamic Auction for Network Resource Allocation

We study network resource allocation under information asymmetry, i.e., users have private information regarding their utility functions. The service provider would like to design an incentive-compatible pricing scheme to maximize the social welfare. It is well known that there is a market mechanism for the network resource allocation achieving the first best under complete information. Under the market mechanism, the service provider only needs to decide on the resource allocation to maximize the aggregate surrogate utility of the users. However, user utility information is typically private in practice. With the information asymmetry, we propose a novel dynamic auction mechanism. Under our dynamic auction, the users must follow the monotone activity rule in the bidding process and the auctioneer updates the prices based on the submitted bids by maximizing the aggregate surrogate utility. Surprisingly, our dynamic auction mechanism is a first-best dominant-strategy incentive compatible mechanism. In particular, the equilibrium outcome is the same as the market equilibrium and budget-balanced. The auctioneer does not need the utility information of the users, i.e., the auction is robust to the user utility information.This is possible by exploiting of the aggregate surrogate utility maximization of the auctioneer and the monotone activity rule we imposed. Thus, our dynamic auction mechanism is practical as it is simple, detail-free and privacy preserving, enriching the mechanism design theory. Finally, our results can be potentially applied to other network resource allocation problems.

Distributed Mechanism Design for Network Resource Allocation Problems

In the standard mechanism design framework, agents’ messages are gathered at a central point and allocation/tax functions are calculated in a centralized manner, i.e., as functions of all network agents’ messages. This requirement may cause communication and computation overhead and necessitates the design of mechanisms that alleviate this bottleneck. We consider a scenario where message transmission can only be performed locally so that the mechanism allocation/tax functions can be calculated in a decentralized manner. Each agent transmits messages to her local neighborhood, as defined by a given message-exchange network, and her allocation/tax functions are only functions of the available neighborhood messages. This scenario gives rise to a novel research problem that we call “distributed mechanism design.”In this paper, we propose two distributed mechanisms for network utility maximization problems that involve private and public goods with competition and cooperation between agents. As a concrete example, we use the problems of rate allocation in networks with either unicast or multirate multicast transmission protocols. The proposed mechanism for each of the protocols fully implements the optimal allocation in Nash equilibria, and its message space dimensionality scales linearly with respect to the number of agents in the network.

Network resource optimization configuration in edge computing environment

Edge computing helps break through the resource limitations of the terminal, providing powerful computing power and improving the user experience. Time cost and energy cost are the main considerations for end users to perform edge calculations, so a reasonable edge calculation strategy can ensure that user spending is minimized. This paper first studies the service deployment and edge computing of multi-terminal single-base station scenarios and proposes a resource coordination strategy for multi-base station scenarios. Secondly, the network resource allocation problem is modeled by linear programming. The objective function of the model is to maximize the remaining processing resources of the host with the most processing tasks allocated in the whole network, that is, the load balancing target, and at the same time take into account the routing efficiency; there are three main types of constraints: bandwidth constraint, traffic balancing constraints and processing. The linear optimization tool is used to solve and analyze the optimal configuration results of network resources in a specific network topology. Finally, through the simulation work, it is verified that our scheme is effective for network resource allocation, and the impact of network traffic speed on resource allocation scheme is tested.

Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting.

The progress of science and technology and the expansion of the Internet of Things make the information transmission between communication infrastructure and wireless sensors become more and more convenient. For the power-limited wireless sensors, the life time can be extended through the energy-harvesting technique. Additionally, wireless sensors can use the unauthored spectrum resource to complete certain information transmission tasks based on cognitive radio. Harvesting enough energy from the environments, the wireless sensors, works as the second users (SUs) can lease spectrum resource from the primary user (PU) to finish their task and bring additional transmission cost to themselves. To minimize the overall cost of SUs and to maximize the spectrum profit of the PU during the information transmission period, we formulated a differential game model to solve the resource allocation problem in the cognitive radio wireless sensor networks with energy harvesting, considering the SUs as the game players. By solving the proposed resource allocation game model, we found the open loop Nash equilibrium solutions and feedback Nash equilibrium solutions for all SUs as the optimal control strategies. Ultimately, series numerical simulation experiments have been made to demonstrate the rationality and effectiveness of the game model.

Decentralized hierarchical constrained convex optimization

This paper proposes a decentralized optimization algorithm for the triple-hierarchical constrained convex optimization problem of minimizing a sum of strongly convex functions subject to a paramonotone variational inequality constraint over an intersection of fixed point sets of nonexpansive mappings. The existing algorithms for solving this problem are centralized optimization algorithms using all the information in the problem, and these algorithms are effective, but only under certain additional restrictions. The main contribution of this paper is to present a convergence analysis of the proposed algorithm in order to show that the proposed algorithm using incremental gradients with diminishing step-size sequences converges to the solution to the problem without any additional restrictions. Another contribution of this paper is the elucidation of the practical applications of hierarchical constrained optimization in the form of network resource allocation and optimal control problems. In particular, it is shown that the proposed algorithm can be applied to decentralized network resource allocation with a triple-hierarchical structure.

Joint UAV Hovering Altitude and Power Control for Space-Air-Ground IoT Networks

Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications. Equipped with diverse communication payloads, UAVs cooperating with satellites and base stations constitute a space-air-ground three-tier heterogeneous network, which are beneficial in terms of both providing the seamless coverage as well as of improving the capacity for increasingly prosperous Internet of Things networks. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks when sharing the same spectrum. The power association problem in satellite, UAV and macrocell three-tier networks becomes a critical issue. In this paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem in UAV networks considering the inevitable cross-tier interference from space-air-ground heterogeneous networks. Furthermore, Lagrange dual decomposition and concave-convex procedure method are used to solve this problem, followed by a low-complexity greedy search algorithm. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network’s total throughput.

An Information Analysis of Iterative Algorithms for Network Utility Maximization and Strategic Games

A variety of resource allocation problems on networked systems, for example, those in cyber-physical systems or Internet-of-things applications, require distributed solution methods. Modern distributed algorithms usually require bandwidth-limited digital communication between the system and its users, who are often modeled as independent decision makers with individual preferences. This paper presents a quantitative information flow and knowledge gain analysis of decentralized iterative algorithms with bounded trajectories in the context of convex network utility maximization problems and strategic games with a unique Nash equilibrium solution. First, a novel generic framework is introduced to quantify knowledge gain in network resource allocation problems using entropy by taking into account priors in the solution space. Second, a general result is presented on the interplay between quantization of information and distributed algorithm performance both for linear and sublinear convergence. Third, information flow in distributed algorithms is studied and a lower bound is derived on the total amount of information exchanged for convergence under uniform quantization. The well-known primal-dual decomposition algorithm is used as an example to illustrate the results. Finally, convergence guarantees for distributed algorithms with estimation are investigated. This paper establishes specific links between information concepts and iterative algorithms in addition to building a foundation for integrating learning schemes into distributed optimization.

Application of the conditional gradient method to a network resource allocation problem with several classes of users

We consider a general problem of optimal allocation of a limited resource in a telecommunication network. The users are divided into several different groups (or classes), which correspond to different levels of service. The network manager has to satisfy users requirements and maximize the network profit, which gives a convex optimization problem with balance and capacity constraints. We suggest to solve this problem with a two-level iterative method where the conditional gradient method is used at its upper level. Then we can solve the lower level problem with simple decomposition algorithms. The computational experiments show rather satisfactory convergence of the suggested method.

Efficient and Fair Resource Allocation Scheme for Cognitive Satellite-Terrestrial Networks

In this paper, we consider the resource allocation problem in uplink cognitive satellite-terrestrial networks, where the cognitive satellite users reuse the frequency band of terrestrial cellular networks. In this scenario, the joint radio resource allocation strategy has to be reasonably designed to satisfy the interference constraint required by the incumbent terrestrial network (ITN). We formulate the optimization model of joint resource allocation with the perspective of efficiency and fairness, where the interference constraint is defined as the maximum tolerable interference outage probability of the ITN. Then, we simplify the proposed optimization problem into a tractable convex optimization form by some necessary mathematical simplification, wherein the simplification of the probability constraint takes the outdated channel state information into account. By analyzing the conditions of the optimal solution for the joint resource allocation problem in detailed, we derive the closed-form expression for both power allocation and subchannel allocation. Besides, we design an iterative algorithm for the joint resource allocation based on these closed-form solutions. Finally, some supporting results are given to verify the correctness and efficiency of the proposed algorithm.

Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach

Recently, there is the widespread use of mobile devices and sensors, and rapid emergence of new wireless and networking technologies, such as wireless sensor network, device-to-device (D2D) communication, and vehicular ad hoc networks. These networks are expected to achieve a considerable increase in data rates, coverage, and the number of connected devices with a significant reduction in latency and energy consumption. Because there are energy resource constraints in user’s devices and sensors, the problem of wireless network resource allocation becomes much more challenging. This leads to the call for more advanced techniques in order to achieve a tradeoff between energy consumption and network performance. In this paper, we propose to use reinforcement learning, an efficient simulation-based optimization framework, to tackle this problem so that user experience is maximized. Our main contribution is to propose a novel non-cooperative and real-time approach based on deep reinforcement learning to deal with the energy-efficient power allocation problem while still satisfying the quality of service constraints in D2D communication.