Optimal Resource Allocation Algorithm Research Articles

Cloud Computing Virtualization of Resources Allocation for Distributed Systems

Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques. The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and scheduling algorithms applied for resource allocation within cloud computing through virtualization in the datacenter. The paper also aims to explore the role of virtualization in providing resources effectively based on clients’ requirements. The results of these approaches showed that each proposed approach and scheduling algorithm has an obvious role in utilizing the shared resources of the cloud data center. The paper also explored that virtualization technique has a significant impact on enhancing the network performance, save the cost by reducing the number of Physical Machines (PM) in the datacenter, balance the load, conserve the server’s energy, and allocate resources actively thus satisfying the clients’ requirements. Based on our review, the availability of Virtual Machine (VM) resource and execution time of requests are the key factors to be considered in any optimal resource allocation algorithm. As a results of our analyzing for the proposed approaches is that the requests execution time and VM availability are main issues and should in consideration in any allocating resource approach.

Joint resource allocation algorithm based on multi-objective optimization for wireless sensor networks

With the limitations of the network resources and battery energy of wireless sensors, the competition of resources in the process of communication will increase the network energy consumption and reduce the Quality of Service (QoS), resulting in that the application of Multi-Radio Multi-Channel (MRMC) Wireless Sensor Networks (WSNs) face many challenges. In this paper, we concentrate on the resource allocation of joint time slot assignment, channel allocation and power control for MRMC WSNs. Due to the diversity of research objectives and the computational complexity of the non-convex problem, this paper develops a two-stage resource allocation optimization algorithm by analyzing the interdependence of various resources. Specifically, to exchange information with conflict-free transmission among all sensors, a graph coloring algorithm for time slot assignment is designed firstly. Then based on the first stage of this algorithm, the problem of joint power control and channel allocation is studied and formulated as a multi-objective optimization problem to achieve the trade-off between energy efficiency and network capacity maximization under the constraints of link interference and load balance. Multi-objective hybrid particle swarm optimization is introduced to obtain the Pareto optimal solutions. The simulation results show that the proposed algorithm significantly performs better in terms of achieving the trade-off of multi-performance.

Multiuser MISO UAV Communications in Uncertain Environments With No-Fly Zones: Robust Trajectory and Resource Allocation Design

In this paper, we investigate robust resource allocation algorithm design for multiuser downlink multiple-input single-output (MISO) unmanned aerial vehicle (UAV) communication systems, where we account for the various uncertainties that are unavoidable in such systems and, if left unattended, may severely degrade system performance. We jointly optimize the two-dimensional (2-D) trajectory and the transmit beamforming vector of the UAV for minimization of the total power consumption. The algorithm design is formulated as a non-convex optimization problem taking into account the imperfect knowledge of the angle of departure (AoD) caused by UAV jittering, user location uncertainty, wind speed uncertainty, and polygonal no-fly zones (NFZs). Despite the non-convexity of the optimization problem, we solve it optimally by employing monotonic optimization theory and semidefinite programming relaxation which yields the optimal 2-D trajectory and beamforming policy. Since the developed optimal resource allocation algorithm entails a high computational complexity, we also propose a suboptimal iterative low-complexity scheme based on successive convex approximation to strike a balance between optimality and computational complexity. Our simulation results reveal not only the significant power savings enabled by the proposed algorithms compared to two baseline schemes, but also confirm their robustness with respect to UAV jittering, wind speed uncertainty, and user location uncertainty. Moreover, our results unveil that the joint presence of wind speed uncertainty and NFZs has a considerable impact on the UAV trajectory. Nevertheless, by counteracting the wind speed uncertainty with the proposed robust design, we can simultaneously minimize the total UAV power consumption and ensure a secure trajectory that does not trespass any NFZ.

A Joint Iterative Optimal Resource Allocation Algorithm for Non-Orthogonal Multi-User and Multi-Weight Opportunistic Beamforming Systems

Opportunistic beamforming (OBF) is an attractive research topic in wireless communications, which can provide multi-user diversity with a low complexity. The conventional OBF technique requires a large number of waiting users, and cannot simultaneously support multiple users. In order to solve the mentioned problem, this paper proposes a downlink multi-user multi-weight opportunistic beamforming non-orthogonal multiple access (MW-OBF-NOMA) system. An objective function is formulated to maximize the sum spectrum efficiency (SE) of the proposed system, however, it is a non-convex issue and intractable to derive an optimal solution. To solve this non-convex issue, we divide it into two sub-optimal issues and solve them one-by-one. Based on the solutions of the two sub-optimal issues, a joint iterative resource allocation algorithm is presented. Moreover, we discuss the influences of signal-to-noise ratio (SNR) estimation, and deduce a closed-form expression of bit error ratio (BER). Numerical results show that our proposed system can simultaneously serve multiple users without extra resources, and achieve an improved sum SE with a low complexity.

Maximizing Revenue Resource Allocation Algorithm for Power Communication Network Service Providers

In order to improve the revenue of power communication network service providers, this paper first constructs a power communication network resource allocation model composed of self-built service providers, third-party service providers and demanders, and models resource allocation problems in power communication networks. Secondly, the Jacobi iteration formula of service capability is derived, and the optimal resource allocation algorithm of service provider is proposed. In the simulation experiment, it is verified that in the competing environment including the self-built service provider and the third-party service provider, the resource allocation of the power communication network has achieved Nash equilibrium in terms of service capability, and when the third-party service provider is introduced, the capital investment of network resources is reduced and the benefits of service providers is increased.

Latency-Aware Dynamic Resource Allocation Scheme for Multi-Tier 5G Network: A Network Slicing-Multitenancy Scenario

In 5G slice networks, the multi-tenant, multi-tier heterogeneous network will be critical in meeting the quality of service (QoS) requirement of the different slice use cases and in reduction of the capital expenditure (CAPEX) and operational expenditure (OPEX) of mobile network operators. Hence, 5G slice networks should be as flexible as possible to accommodate different network dynamics such as user location and distribution, different slice use case QoS requirements, cell load, intra-cluster interference, delay bound, packet loss probability, and service level agreement (SLA) of mobile virtual network operators (MVNO). Motivated by this condition, this paper addresses a latency-aware dynamic resource allocation problem for 5G slice networks in a multi-tenant, multi-tier heterogeneous environment, for efficient radio resource management. The latency-aware dynamic resource allocation problem is formulated as a maximum utility optimisation problem. The optimisation problem is transformed and the hierarchical decomposition technique is adopted to reduce the complexities in solving the optimisation problem. Furthermore, we propose a genetic algorithm (GA) intelligent latency-aware resource allocation scheme (GI-LARE). We compare GI-LARE with the static slicing (SS) resource allocation; the spatial branch and bound-based scheme; and, an optimal resource allocation algorithm (ORA) via Monte Carlo simulation. Our findings reveal that GI-LARE outperformed these other schemes.

Resource Allocation and Node Placement in Multi-Hop Heterogeneous Integrated-Access-and-Backhaul Networks

We consider a heterogeneous MIMO-OFDMA based dense small cell (SC) system in which each macro cell base station (MBS) serves its coverage area with the help of small cell base stations (SBSs) through multi-hop wireless connections. The SBSs act as integrated access and backhaul (IAB) nodes that handle both access and backhaul traffics with wireless links. We first develop an optimal (sum-rate maximization) resource allocation (RA) algorithm which considers subcarriers/spatial subchannels assignment and the associated power allocations. We also present two low-complexity suboptimal RA schemes which, as verified by simulations, incur only minor performance loss in the high SNR region. Our RA algorithms can be applied to other multi-hop networks with general UE association rule and node location distributions. We study the channel aging effect caused by the time lag between the time channel state information (CSI) is measured and that when data transmission occurs. We show the benefit of channel prediction and the limit of a centralized RA approach. The advantages of frequency (channel) reuse and the multi-hop architecture are demonstrated as well. A related but perhaps more important system design issue for an IAB cellular network is the IAB node placement problem. With the given UE association rule and UE location distribution, we present systematic approaches to find the optimal node locations. For two special propagation models, we derive closed-form expressions for the node locations that maximizes a spectral efficiency lower bound. Numerical results validate the accuracy of our estimates based on either numerical evaluations or closed-form solutions.

Hybrid Cloud Resource Provisioning (HCRP) Algorithm for Optimal Resource Allocation Using MKFCM and Bat Algorithm

The basic purpose of resource allocation is to make the most efficient allocation of available resources. It contains resources and the number of tasks. The proposed methodology has two types there are resource discovery and resource allocation. The Multiple Kernel Fuzzy C Means Clustering Algorithm (MKFCM) is utilized for resource discovery process. Depends on the MKFCM algorithm the recommended method is group the available resources. Thereafter the resources are allocated with the help of a hybrid optimization technique. Here, resource provisioning algorithm is hybrid with bat algorithm for hybridization approach. The experimental analysis of the proposed mechanism is evaluated based on cost value, memory utilization and time. The prospective strategies have been experimented using the Cloud simulator with Java as the working platform.

Optimal Resource Allocation Model and Algorithm for Elastic Enterprise Applications Migration to the Cloud

Cloud computing has been widely used in various industries in recent years. However, when migrating enterprise applications into the cloud, enterprise users face a problem with minimizing migration time and cloud resource providers face a dilemma of resource allocation problem, with the objective of maximizing the migration utility of enterprise users while minimizing the cost of cloud resource providers. In order to achieve them, this paper considered cloud migration objectives including cloud migration time, cloud migration utility, and cloud data center cost, and proposed a resource allocation model for enterprise applications migration into the cloud. The model is divided into two stages: the bandwidth allocation for enterprise applications migration to the cloud and the physical resource allocation of cloud resource providers for enterprise applications deployment into the cloud. In the first stage, we aim to minimize the cloud migration time for enterprise applications, and propose a scheme of bandwidth allocation for each component of applications. In the second stage, we present the resource allocation of cloud resource providers and propose a gradient-based algorithm which can achieve optimal resource allocation. Finally, we give some numerical simulation results to illustrate the performance of the proposed algorithm.

Uplink resource allocation for multiple access computational offloading

The mobile edge computing framework offers the opportunity to reduce the energy that devices must expend to complete computational tasks. The extent of that energy reduction depends on the nature of the tasks, and on the choice of the multiple access scheme. In this paper, we first address the uplink communication resource allocation for offloading systems that exploit the full capabilities of the multiple access channel (FullMA). For indivisible tasks we provide a closed-form optimal solution of the energy minimization problem when a given set of users with different latency constraints are offloading, and a tailored greedy search algorithm for finding a good set of offloading users. For divisible tasks we develop a low-complexity algorithm to find a stationary solution. To highlight the impact of the choice of multiple access scheme, we also consider the TDMA scheme, which, in general, cannot exploit the full capabilities of the channel, and we develop low-complexity optimal resource allocation algorithms for indivisible and divisible tasks under that scheme. The energy reduction facilitated by FullMA is illustrated in our numerical experiments. Further, those results show that the proposed algorithms outperform existing algorithms in terms of energy consumption and computational cost.

Optimal Rate-Adaptive Data Dissemination in Vehicular Platoons

In intelligent transportation systems, wireless connected vehicles moving in platoons can improve roads’ throughput. For managing driving status of the platoon, a lead vehicle transmits driving information to following autonomous vehicles by using multi-hop data dissemination. We study a novel data dissemination protocol which investigates a chain-based transmit rate control to reduce data dissemination latency. The optimal resource allocation algorithm is formulated to minimize the total dissemination latency of the platoon under guaranteed bit error rates, and can be judiciously reformulated and solved using standard optimization techniques. A novel dynamic programming algorithm is presented to solve the platooning resource allocation optimization, which uses backward induction to significantly reduce the resource allocation complexity. In addition, we interpret the vehicular platoon as one-dimensional Markov chain, and derive a closed form of dissemination latency. Simulations are carried out to evaluate the performance of the proposed dynamic programming algorithm. The numerical results show that our algorithm achieves optimal solutions with cutting off the complexity by orders of magnitude, while improving dissemination rate in the vehicular platoon.

Price-Based Resource Allocation in Wireless Power Transfer-Enabled Massive MIMO Networks.

This paper considers the price-based resource allocation problem for wireless power transfer (WPT)-enabled massive multiple-input multiple-output (MIMO) networks. The power beacon (PB) can transmit energy to the sensor nodes (SNs) by pricing their harvested energy. Then, the SNs transmit their data to the base station (BS) with large scale antennas by the harvesting energy. The interaction between PB and SNs is modeled as a Stackelberg game. The revenue maximization problem of the PB is transformed into the non-convex optimization problem of the transmit power and the harvesting time of the PB by backward induction. Based on the equivalent convex optimization problem, an optimal resource allocation algorithm is proposed to find the optimal price, energy harvesting time, and power allocation for the PB to maximize its revenue. Finally, simulation results show the effectiveness of the proposed algorithm.

Optimal 3D-Trajectory Design and Resource Allocation for Solar-Powered UAV Communication Systems

In this paper, we investigate the resource allocation algorithm design for multicarrier solar-powered unmanned aerial vehicle (UAV) communication systems. In particular, the UAV is powered by the solar energy enabling sustainable communication services to multiple ground users. We study the joint design of the 3D aerial trajectory and the wireless resource allocation for maximization of the system sum throughput over a given time period. As a performance benchmark, we first consider an off-line resource allocation design assuming non-causal knowledge of the channel gains. The algorithm design is formulated as a mixed-integer non-convex optimization problem taking into account the aerodynamic power consumption, solar energy harvesting, a finite energy storage capacity, and the quality-of-service requirements of the users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization to obtain the optimal 3D-trajectory and the optimal power and subcarrier allocation policy. Subsequently, we focus on the online algorithm design that only requires real-time and statistical knowledge of the channel gains. The optimal online resource allocation algorithm is motivated by the off-line scheme and entails a high computational complexity. Hence, we also propose a low-complexity iterative suboptimal online scheme based on the successive convex approximation. Our simulation results reveal that both the proposed online schemes closely approach the performance of the benchmark off-line scheme and substantially outperform two baseline schemes. Furthermore, our results unveil the tradeoff between solar energy harvesting and power-efficient communication. In particular, the solar-powered UAV first climbs up to a high altitude to harvest a sufficient amount of solar energy and then descends again to a lower altitude to reduce the path loss of the communication links to the users it serves.

Energy Efficient Resource Allocation in Hybrid Non-Orthogonal Multiple Access Systems

By blending the concepts of non-orthogonal multiple access (NOMA) and orthogonal frequency division multiplexing, in this paper, a novel hybrid scheme is conceived for supporting diverse services in future wireless systems. Motivating to maximize energy efficiency (EE), the joint resource management of user clustering (UC) and power allocation is investigated for the downlink hybrid NOMA systems. Under two different power consumption cases, the optimal resource allocation (Opt-RA) algorithm is developed with the help of converting the original mixed integer non-linear programming (MINLP) problem to the tractable decoupled problems. For practical implementation, the heuristic resource allocation (Heur-RA) algorithm is also proposed, and it includes a low-complexity UC algorithm based on the candidate search-and-allocation approach. Our simulation results show that, both the Opt-RA and Heur-RA algorithms achieve significantly higher EE performance than other existing algorithms. Further, the results also prove that, the hybrid NOMA conceived is able to exploit the advantages of NOMA scheme, and is superior to conventional orthogonal multiple access (OMA) in terms of EE, as well as achieving higher flexibility for system configuration than NOMA.

Second-Order Continuous-Time Algorithm for Optimal Resource Allocation in Power Systems

In this paper, based on differential inclusions and the saddle point dynamics, a novel second-order continuous-time algorithm is proposed to solve the optimal resource allocation problem in power systems. The considered cost function is the sum of all local cost functions with a set of affine equality demand constraints and an inequality constraint on generating capacity of the generator. In virtue of nonsmooth analysis, geometric graph theory, and Lyapunov stability theory, all generators achieve consensus on the Lagrange multipliers associated with a set of affine equality constraints while the proposed algorithm converges exponentially to the optimal solution of the resource allocation problem starting from any initial states over an undirected and connected graph. Moreover, the obtained results can be further extended to the optimal resource allocation problem in case of switching communication topologies. Finally, two numerical examples involving a smart grid system composed of five generators and the IEEE 30-bus system demonstrate the effectiveness and the performance of the theoretical results.

Resource Allocation for Performance Enhancement in Mobile Ad Hoc Networks

The low-cost and easy-deployable mobile ad hoc networks (MANETs) have great potential to serve as the underlay network architecture for implementing lots of critical applications. In order to support different demands in such applications, understanding the fundamental performance of the MANETs and exploring their performance enhancement are of great importance. To this end, this paper, for the first time, investigates the performance enhancement of the MANETs by appropriately allocating the network resource. Specifically, we consider both the transmission resource and storage resource of a network node and use a two-tuple to depict a general resource allocation configuration. First, for a MANET with any given resource allocation configuration, we establish an analytical framework to model the network dynamics as queuing processes. With the help of the network modeling, we then derive the expressions of fundamental performance metrics, including per node throughput, expected an end-to-end delay, and throughput capacity. Based on these results, we further develop optimal resource allocation algorithms to determine the optimal setting of resource allocation, for enhancing network performance. Finally, we provide extensive simulation and numerical results to verify the efficiency of our theoretical modeling and illustrate the effects of resource allocation on network performance.

Optimal resource allocation algorithm for OFDMA-based WiMAX network using stochastic fish swarm optimization

Generally, slot allocation is followed by power allocation for sub-channels in that slot. The power can be allocated equally to all sub-channels which an optimal power allocation over the sub-channels are used. However, since there is no explicit method to calculate the sub-channels level which should be determined for every symbol period over the sub-channels, it will be a heavy computational burden for the scheduler. This problem is considered as a stochastic problem. Another major motivating factor behind this work is that the 802.16 draft standard does not specify the exact scheduling 12 technique to be used in the system. To overcome problems by the proposed optimal resource allocation (ORA) algorithm is incorporate with the stochastic fish swarm optimization (SFSO) to optimize the constraints to perform resource allocation. The ORA-SFSO algorithm can also maximize the network throughput and satisfy QoS requirements. The simulation results show that the proposed ORA-SFSO algorithm performs better than the existing algorithms.

COMPOSITE APPLICATION DISTRIBUTION METHODS MODELING

The subject of consideration are algorithms for optimal distribution of existing pool of computing resources between composite applications and algorithm of utilization of resources on computing blocks. The purpose of the article is to analyze the results of simulation and mathematical modeling of the resource allocation process between composite applications, depending on the distribution option. Results The efficiency of existing dynamic planning algorithms that are related to the greedy algorithm class is considered. They find a locally optimal solution at each step. The boundary of effective planning of algorithms based on clustering approach is revealed. The efficiency of using ant colony optimization algorithm and algorithms of cluster approach using ant colony optimization algorithm is shown. The simulation of the distribution of the composite application is carried out, depending on the complexity of the graph construction. The dependence of the execution time of the composite application on utilization of resources on the calculated blocks is obtained. Using the resource utilization function, the quality of the distribution of composite application resources is analyzed, depending on the amount of data transferred to the calculations. Conclusions. Data on the quality of resource allocation is obtained, depending on such parameters as the time of implementation of the composite application, the volume of transmitted data, the complexity of the graph construction. A method for choosing the optimal resource allocation algorithm between composite applications depending on the listed parameters is proposed. This will allow you to quickly dispose of distributed computing blocks that are occupied by calculating a distributed task, which will speed up the computation of distributed tasks on an existing pool of computing blocks.

User-centric quality of experience optimized resource allocation algorithm in VLC network with multi-color LED.

Visible light communication (VLC) can provide indoor illumination while achieving high network throughput. In order to mitigate the performance degradation caused by inter-cell interference (ICI) and support multiple users, this paper utilizes the white LED synthesized by multi-color light sources as indoor illumination and signal transmissions. Compared with ordinary LEDs, multi-color LEDs not only has excellent color rendering index, but also realize multi-channel parallel transmission, which greatly improves the transmission speed of VLC. Meanwhile, we propose a user-centric (UC) quality of experience (QoE) optimization scheduling scheme for the VLC down-link system. In contrast to the traditional network-centric (NC) design, the UC scheme is based on the user-centric dynamic construction and adjustment of the network model. Furthermore, in order to further analyze the performances of the illuminations and signal transmissions of the VLC system with multi-color LED, we consider the system model under two scenarios of 3-color and 4-color synthetic white LEDs. For these two different LED composition and optimization problems, we design a new greedy algorithm to allocate optical bandwidth, and dynamically searched for the optimal access point user equipment (AP-UE) link based on optimization of the UEs' QoE values. In order to analyze the robustness of the algorithm, we further consider the influences of UEs on the transmission performance under different UEs' spatial distributions, e.g., uniform and Poisson distributions. The simulation results illustrate that the proposed scheme guarantees the UEs' QoE while offering illumination quality. Meanwhile, compared to ordinary LEDs and the traditional network-centric (NC) design, our proposed scheme can schedules more UEs.

Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading

By offloading intensive computation tasks to the edge cloud located at the cellular base stations, mobile-edge computation offloading (MECO) has been regarded as a promising means to accomplish the ambitious millisecond-scale end-to-end latency requirement of the fifth-generation networks. In this paper, we investigate the latency-minimization problem in a multi-user time-division multiple access MECO system with joint communication and computation resource allocation. Three different computation models are studied, i.e., local compression, edge cloud compression, and partial compression offloading. First, closed-form expressions of optimal resource allocation and minimum system delay for both local and edge cloud compression models are derived. Then, for the partial compression offloading model, we formulate a piecewise optimization problem and prove that the optimal data segmentation strategy has a piecewise structure. Based on this result, an optimal joint communication and computation resource allocation algorithm is developed. To gain more insights, we also analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution of the piecewise optimization problem can be derived. Our proposed algorithms are finally verified by numerical results, which show that the novel partial compression offloading model can significantly reduce the end-to-end latency.