Resource Matching Algorithm Research Articles

Resource matching algorithm under delay QoS constraint for uplink visible light communication based on supermartingale theory

We propose a supermartingale-based resource matching algorithm to guarantee delay quality of service (QoS) for the visible light communication uplink system. A network queuing model with multiple bursty arrivals is established, and multipacket reception technology is considered to alleviate terminal collisions. We introduce the supermartingale theory to evaluate the delay QoS performance, and a tighter delay-violation probability bound is derived. Relying on the supermartingale analysis, we design a search algorithm to achieve resource matching under a delay QoS constraint. The simulation results prove that the proposed algorithm guarantees delay QoS and saves service resources.

Double Deep Q-Network Based Distributed Resource Matching Algorithm for D2D Communication

Device-to-Device (D2D) communication with short communication distance is an efficient way to improve spectrum efficiency and mitigate interference. To realize the optimal resource configuration including wireless channel matching and power allocation, a distributed resource matching scheme is proposed based on deep reinforcement learning(DRL). The reward is defined as the difference of achieve rate of D2D users and the consumed power, which is limited by the Signal to Interference plus Noise Ratio (SINR) of the other cellular users on the current channel. The proposed algorithm maximizes the D2D throughput and energy efficiency in a distributed manner, without online coordination and message exchange between users. The considered resource allocation problem is formulated as a random non-cooperative game with multiple players (D2D pairs), where each player is a learning agent, whose task is to learn its best strategy based on locally observed information, multi-user communication resource matching algorithm is proposed based on a Double Deep Q-network (DDQN), where the total cellular throughput and user energy efficiency could converge to the Nash equilibrium (NE) under the mixed strategy. Simulation results show that the proposed algorithm can improve the communication rate and energy efficiency of each user by selecting the optimal strategy, and has better convergence performance compared with existing schemes.

Distributed Resource Management Framework for IoS Against Malicious Jamming

The internet of satellites (IoS), containing multiple low earth orbit (LEO) satellite constellations, can support tremendous traffic, massive connectivity, and vast coverage. But it also puts forward higher demands for resource management due to the high dynamics of the IoS networks, especially in the malicious jamming environment, where the jammers launch jamming attacks to reduce the efficiency and reliability. Thus, this paper investigates the problem of resource management in malicious jamming environment for IoS, which is divided into three sub-problems: traffic prediction problem, anti-jamming decision problem and resource matching problem. To solve these problems, we proposed a distributed resource management framework (DRMF), which consists of three sub-algorithms. Firstly, the traffic prediction algorithm (TPA) is proposed to deeply mine and accurately predict the traffic rule. Meanwhile, the dynamic anti-jamming algorithm (DAA) is developed to make anti-jamming decision autonomously. Then, based on the outputs obtained by TPA and DAA, the distributed resource matching algorithm (DRMA) is proposed for IoS, and the satellites with insufficient resource can apply for assistance from neighboring satellites with excess resource, thereby improving the safety and efficiency of the entire IoS network. Finally, experiment results and algorithm analysis verify the proposed scheme has better performance than the existing algorithms.

Dynamic resource allocation strategy for latency-critical and computation-intensive applications in cloud–edge environment

Edge computing is more and more popular due to its low latency and bandwidth-efficient services. Edge computing is mainly applied to the latency-critical and computation-intensive application. However, there are several challenges to the improvement on the quality of service in edge computing environment. For instance, the reduction of server latency, the network transmission efficiency, etc. In this paper, we propose the dynamic resource allocation algorithm for cloud–edge environment. The dynamic resource allocation algorithm consists of the resource scheduling algorithm and the resource matching algorithm. In the resource scheduling algorithm, a resource scheduling problem can be obtained according to the stored penalty of scheduling contents, the value of scheduling contents and the transmission cost of scheduling contents. Then, tabu search algorithm is applied to find the optimal solution to the resource scheduling problem. Furthermore, the resources are scheduled into the edge servers from cloud datacenter with the optimal solution. In the resource matching algorithm, an optimization problem of the resource matching is built with respect to the resource location, the task priorities and the network transmission cost. For addressing this problem, the optimal problem is converted to an optimal matching problem of the weighted bipartite graph. Moreover, an optimal matching problem of the weighted complete bipartite graph is created by adding the spurious containers. Then, the optimal strategy of the resource matching for tasks on the edge servers is achieved. Finally, the performance of the proposed algorithms and some typical resource allocation algorithms is evaluated via extensive experiments. The results indicate that proposed algorithms can effectively reduce network delay and enhance QoS.

Energy efficient resource matching algorithm for multi-homing services in dynamic wireless environment

This paper investigates the matching problem between radio resources and multi-homing services in a dynamic heterogeneous wireless environment. A mobile terminal with a multi-homing service simultaneously transmits data to multiple radio access networks using multiple air interfaces. This paper proposes an energy-efficient resource matching algorithm to statistically guarantee the service rate requirements of each user with the lowest power consumption. First, according to the concept of resource matching and the dynamic characteristics of wireless channels, this paper defines two matching probability metrics and subsequently selects the most appropriate metric to build a resource matching model to realize the maximum energy efficiency of each user while meeting the statistical guarantee constraints. In particular, the Rayleigh fading channel model is introduced in this paper to accurately reflect the dynamics of the wireless channel, and a complete expression of the matching probability metric is deduced. Second, according to the optimal solution characteristics of the optimization model, the original problem is decomposed into the inner power minimization problem and the outer matching probability maximization problem, and the convex optimization and the equality constrained optimization are separately used to solve the problem. Hence, the two-layer distributed solution algorithm is proposed. In particular, this paper conducts rigorous mathematical analyses on the convergence, optimality and complexity of the algorithm and constructs an improved resource matching model by relaxing the statistical guarantee parameters in the case of no solution to the model. Finally, the simulation results show that this algorithm has better performance than the three existing resource matching algorithms, can achieve lower user power consumption, and can realize the goal of the best match between the network resources and service requirements in a dynamic environment.

QoS-aware resource matching and recommendation for cloud computing systems

Resource matching and recommendation is an important topic in the field of cloud computing. While a lot of cloud resource discovery and negotiation models have been proposed, resource matching and recommendation issues have often been neglected, such as the utilization of attribute weights and the collaborative application of empirical data, price utility and so on. To cope with this challenge, we focus on designing a novel resource recommendation method which can regulate multi-attribute matching between provider solutions and customer demands in this paper. At first, we describe a resource matching algorithm that considers both functional requirements and QoS attributes. Then, we propose a resource recommendation method for cloud computing system that integrates price utility, multi-attribute matching metric and group customer evaluation. Finally, the extensive simulation results demonstrate that our proposed method is effective in various simulated scenarios. Current results are of high significance to design an efficient resource matching and recommendation with guaranteed QoS requirements under the realistic cloud computing circumstances.

Research on ATS Device Resources Matching Algorithm Based on Minimum Weighted Distance

Aimed at solving difficulties of managing test routing and matching resource ,this paper brings out an instrument resource matching algorithm based on minimum weighted distance .The algorithm adopts weighted distance to describe similarity between signals , while weight value is determined by experts' experience with consideration of actual condition ,which can describe similarity more reasonable .At the same time the strategy and algorithm of resource matching are given ,serial and parallel testing are optimized with simpler and rapider criterion. Application example tests the method possess better routing management capability ,strengthens system reliability and improves resource matching and routing management efficiency on the basis of testing demand models and instrument resource models.

A multidimensional resource model for dynamic resource matching in internet of things

SummaryWith the development of Internet of Things (IoT), many middleware solutions have been proposed for the integration of physical world with the Web. However, most leading middleware solutions provide only limited resource matching and selection functionality. When significant amounts of resources are available, selecting the appropriate resources becomes challenging and time‐consuming. This paper addresses the growing issues of resource matching and selection in IoT solutions. In this paper, we propose a novel resource model to describe the IoT resources in a multidimensional manner. Based on the resource model, a resource matching algorithm that selects the well‐matched resources by matching the similarity between resources is proposed. Moreover, we constructed a combined resource set and used it and OWLS‐TC to evaluate our work comprehensively. Experimental results show that the proposed resource matching approach is more effective and efficient than existing approaches. Copyright © 2013 John Wiley & Sons, Ltd.

An Adaptive Scheduler Framework for Complex Workflow Jobs on Grid Systems

Grid Computing provides sharing of geographically distributed resources among large scale complex applications. Due to dynamic nature of resources in grid, there is a need of highly efficient job scheduling and resource management policies in grid. A novel Grid Resource Scheduler (GRS) is proposed to effectively utilize the available resources in Grid. Proposed GRS contributes, an optimal job scheduling algorithm on Job Rank-Backfilling policy and a resource matching algorithm based on ranking of resources with best fit allocation model. Performance of GRS is measured by considering a web based BLAST algorithm – a bioinformatics application. GRS aims in reducing; Makespan of the job workflow, execution time of varied size jobs, response time of the submitted jobs and overhead of using the system. It also considers improving the utilization factor and throughput of the available heterogeneous resources in grid. The experimental results prove that proposed grid scheduler framework performs better when evaluated against widely used First Come First Serve (FCFS), Shortest Job First (SJF) and Minimum Time to Due Date (MTTD) scheduling strategies.

Resource virtualization methodology for on-demand allocation in cloud computing systems

The resources' heterogeneity and unbalanced capability, together with the diversity of resource requirements in cloud computing systems, have produced great contradictions between resources' tight coupling characteristics and user's multi-granularities requirements. We propose a resource virtualization model and its on-demand allocation oriented infrastructure mainly providing computing services to solve that problem. A loosely coupled resource environment centered on resource users is created to complete a mapping from physical view of resources to logic view of resources. Heuristic resource combination algorithm (HRCA) is proposed to transform physical resources to logic resources, which meets two requirements: randomness in combination and fluctuation control to the size of resources granularities. On the basis of the appraisal indexes presented for the on-demand allocation, resource matching algorithm (RMA), targeting at resource satisfaction with the highest resource utilization, is designed to reuse resources. RMA can satisfy users' requirement in limited time and keep resource satisfaction in the highest level in the condition of logic resources granularities being less than their required size. Resource reconfiguration algorithm (RRA) is presented to implement resource matching in the condition that virtual computing resource pool cannot match granularities of resource requirements. RRA assures the lowest resource refusal rate and the greatest resource satisfaction. We verify the effectiveness, performance and accuracy of algorithms in implementing the goal of resource virtualization centered on resource users and on-demand allocation.

Research of Resource Matching Algorithm Based on Incentive Mechanism and Penalty Mechanism

Existing incentive mechanisms mainly describe the fuzzy index such as local credit with integer, which can’t avoid distortion of the description. Because Vague set can describe and process fuzzy information very well, this paper uses credit, that is represented by Vague set, to describe the resource nodes’ credibility, and introduces corresponding incentive mechanism and penalty mechanism to update the credit of resource nodes, in order to decrease the vicious nodes’ harm to resource discovery and encourage more good resource nodes to join in the grid. Finally, this paper improves the resource matching algorithm which is based on Vague set with incentive mechanism and penalty mechanism. Simulation test demonstrates that the introduction of incentive mechanism and penalty mechanism increases the resource matching algorithm’s capability of discovering resource obviously.