无线Ad hoc网络中基于0-1优化的两步骤资源分配算法

为了解决无线Ad hoc网络中节点性能随节点个数增加而下降的问题,利用多射频多信道技术(MR-MC)进行资源分配以及减少网络干扰量已成为优化无线网络性能的重要技术手段。在此基础上,提出了一种基于0-1规划的网络优化模型和两步骤资源分配算法TBCA&LS(Tree Based Channel Assignment&Link Scheduling)。该算法利用分簇重组网络结构,通过分析相邻链路干扰关系构建0-1优化模型,并在此基础上执行将信道分配和链路调度结合的资源分配算法,减少相邻链路冲突,增加并行传输量,进而达到提升网络吞吐量、优化网络资源分配的目的。最后,在Matlab仿真软件中执行两步骤资源分配算法,实验结果表明,与对比算法CCAS和仅利用信道分配的算法相比,所提算法可以有效优化网络性能。     

一种多收发器WiMax mesh网络集中式调度算法

根据IEEE 802.16协议,集中式调度是WiMax mesh网络中的主要带宽调度方式.目前的集中式调度算法只适用于单收发器系统.本文研究多收发器WiMax mesh网络中的调度问题,提出一种集中式调度与信道分配算法.该算法用令牌代表数据包在路由树上的传输,在每个时隙内选择可以并行传输的链路集合并分配信道.模拟实验表明本文提出的算法可以充分利用多收发器系统特性,有效减少总传输时间.     

An optimal scheduling algorithm for an agent-based multicast strategy on irregular networks

This paper describes an agent-based approach for scheduling multiple multicast on wormhole switch-based networks with irregular topologies. Multicast/broadcast is an important communication pattern, with applications in collective communication operations such as barrier synchronization and global combining. Our approach assigns an agent to each subtree of switches such that the agents can exchange information efficiently and independently. The entire multicast problem is then recursively solved with each agent sending message to those switches that it is responsible for. In this way, communication is localized by the assignment of agents to subtrees. This idea can be easily generalized to multiple multicast since the order of message passing among agents can be interleaved for different multicasts. The key to the performance of this agent-based approach is the message-passing scheduling between agents and the destination processors. We propose an optimal scheduling algorithm, called ForwardInSwitch to solve this problem. We conduct extensive experiments to demonstrate the efficiency of our approach by comparing our results with SPCCO, a highly efficient multicast algorithm reported in literature. We found that SPCCO suffers link contention when the number of simultaneous multiple multicast becomes large. On the other hand, our agent-based approach achieves better performance in large cases.     

On-line joint QoS routing and channel assignment in multi-channel multi-radio wireless mesh networks

We study the problem of on-line joint QoS routing and channel assignment for performance optimization in multi-channel multi-radio wireless mesh networks, which is a fundamental issue in supporting quality of service for emerging multimedia applications. To our best knowledge, this is the first time that the problem is addressed. Our proposed solution is composed of a routing algorithm that finds up to k but not necessarily feasible paths for each demand and an on-demand channel (re)assignment algorithm that adapts network resources to maintain feasibility of one of the paths. We also study the problem of obtaining an upper bound on the network performance. First, we consider an artificial version of the problem, in which all demands arrive at the same time, and formulate it as a mixed integer linear programming model. To tackle the complexity of the model, it is relaxed that provides a tight upper bound while improves solution time up to 3.0e+5 times. Then, we model the original problem by extending the relaxed model to consider dynamic demands, it leads to a huge model; thus, we develop another model, which is equivalent to the first one and is decomposable. It is broken down by a decomposition algorithm into subproblems, which are solved sequentially. Our extensive simulations show that the proposed solution has comparable performance to the bound obtained from the decomposition algorithm; it efficiently exploits available channels, and needs very few radios per node to achieve high network performance.     

Hybrid monkey search algorithm for flow shop scheduling problem under makespan and total flow time

This paper addresses a sub-population based hybrid monkey search algorithm to solve the flow shop scheduling problem which has been proved to be non-deterministic polynomial time hard (NP-hard) type combinatorial optimization problems. Minimization of makespan and total flow time are the objective functions considered. In the proposed algorithm, two different sub-populations for the two objectives are generated and different dispatching rules are used to improve the solution quality. To the best of our knowledge, this is the first application of monkey search algorithm to solve the flow shop scheduling problems. The performance of the proposed algorithm has been tested with the benchmark problems addressed in the literature. Computational results reveal that the proposed algorithm outperforms many other heuristics and meta-heuristics addressed in the literature.     

Efficient link scheduling with joint power control and successive interference cancellation in wireless networks无线网络中联合功率控制和串行干扰消除的高效链路调度算法

Existing works have addressed the interference mitigation by any two of the three approaches: link scheduling, power control, and successive interference cancellation (SIC). In this paper, we integrate the above approaches to further improve the spectral efficiency of the wireless networks and consider the max-min fairness to guarantee the transmission demand of the worst-case link. We formulate the link scheduling with joint power control and SIC (PCSIC) problem as a mixed-integer non-linear programming (MINLP), which has been proven to be NP-complete. Consequently, we propose an iterative algorithm to tackle the problem by decomposing it into a series of linear subproblems, and then the analysis shows that the algorithm has high complexity in the worst case. In order to reduce the computational complexity, we have further devised a two-stage algorithm with polynomial-time complexity. Numerical results show the performance improvements of our proposed algorithms in terms of the network throughput and power consumption compared with the link scheduling scheme only with SIC.     

Links organization for channel assignment in multi-radio wireless mesh networks

It is one key issue in the wireless mesh networks to provide various scenarios such as multimedia and applications. Links in the network can be organized and assigned to orthogonal channels so as to minimize the co-channel interference. In this paper we focus on the channel assignment problem for links in the mesh networks and aim at minimizing the overall network interference. The problem is proved to be NP-hard. We have first formulated an approach based on the Particle Swarm Optimization (PSO) algorithm which can be used to find the approximate optimized solution in small-size networks and as a baseline that other algorithms can be compared with. We also have proposed a centralized heuristic as well as a distributed heuristic algorithm for the channel assignment problem. Extensive simulation results have demonstrated that our schemes have good performance in both dense and sparse networks compared with related works.     

A distributed channel assignment control for QoS support in mobile ad hoc networks

Recently, one of the most critical issues in mobile ad hoc networks (MANETs) is providing quality of service (QoS) through routing, access/admission control, resource reservation, and mobility management. However, most existing solutions do not provide QoS effectively due to the interference arising from mobility. In this paper, we refer to interference as a quasi-exposed node problem. To solve this problem, a new algorithm, named a distributed channel assignment control, is proposed that focuses on performance enhancements related to QoS and mathematical analysis techniques for the channel bandwidth. This novel algorithm uses channel assignment control with a power control to reduce the negative effects induced by the quasi-exposed node problem, and then the channels are adaptively negotiated to allow communication in the interference region. The proposed algorithm has been evaluated via extensive simulations, and the results show that it can successfully guarantee QoS by maintaining good throughput, reducing control message overhead, and enhancing delay.     

Adaptive scheduling for integrated traffic on WDM optical networks

One of the important issues in the design of future generation of high-speed networks is to provide differentiated service to different types of traffic with various time constraints. In this paper, we study the problem of providing real-time service to either hard or soft real-time messages and normal transmission service to variable-length messages without time constraints in WDM optical networks. We propose an adaptive scheduling algorithm for scheduling message transmissions in order to improve the network performance when both real-time and non real-time messages are transmitted in one topology. We have analyzed the complexity of the algorithm to show its feasibility. We have conducted extensive discrete-event simulations to evaluate the performance of the proposed algorithm. The study suggests that when scheduling message transmission in WDM networks differentiated services should be considered in order to meet time constraints of real-time messages while non real-time messages are being served so that the overall performance of the network could be improved.     

A memetic algorithm for minimizing total weighted tardiness on parallel batch machines with incompatible job families and dynamic job arrival

This paper addresses a scheduling problem motivated by scheduling of diffusion operations in the wafer fabrication facility. In the target problem, jobs arrive at the batch machines at different time instants, and only jobs belonging to the same family can be processed together. Parallel batch machine scheduling typically consists of three types of decisions—batch forming, machine assignment, and batch sequencing. We propose a memetic algorithm with a new genome encoding scheme to search for the optimal or near-optimal batch formation and batch sequence simultaneously. Machine assignment is resolved in the proposed decoding scheme. Crossover and mutation operators suitable for the proposed encoding scheme are also devised. Through the experiment with 4860 problem instances of various characteristics including the number of machines, the number of jobs, and so on, the proposed algorithm demonstrates its advantages over a recently proposed benchmark algorithm in terms of both solution quality and computational efficiency.     

Data‐aware task scheduling on heterogeneous hybrid memory multiprocessor systems

In this paper, we propose a method about task scheduling and data assignment on heterogeneous hybrid memory multiprocessor systems for real‐time applications. In a heterogeneous hybrid memory multiprocessor system, an important problem is how to schedule real‐time application tasks to processors and assign data to hybrid memories. The hybrid memory consists of dynamic random access memory and solid state drives when considering the performance of solid state drives into the scheduling policy. To solve this problem, we propose two heuristic algorithms called improvement greedy algorithm and the data assignment according to the task scheduling algorithm, which generate a near‐optimal solution for real‐time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm, which is commonly used to solve heterogeneous task scheduling problem. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance and demonstrate that considering data allocation in task scheduling is significant for saving energy. We conduct experiments on two heterogeneous multiprocessor systems. Copyright © 2016 John Wiley & Sons, Ltd.     

A topology control approach for utilizing multiple channels in multi-radio wireless mesh networks

We consider the channel assignment problem in a multi-radio wireless mesh network that involves assigning channels to radio interfaces for achieving efficient channel utilization. We present a graph–theoretic formulation of the channel assignment guided by a novel topology control perspective, and show that the resulting optimization problem is NP-complete. We also present an ILP formulation that is used for obtaining a lower bound for the optimum. We then develop a new greedy heuristic channel assignment algorithm (termed CLICA) for finding connected, low interference topologies by utilizing multiple channels. Our evaluations show that the proposed CLICA algorithm exhibits similar behavior and comparable performance relative to the optimum bound with respect to interference and capacity measures. Moreover, our extensive simulation studies show that it can provide a large reduction in interference even with a small number of radios per node, which in turn leads to significant gains in both link layer and multihop performance in 802.11-based multi-radio mesh networks.     

Improving densely deployed wireless network performance in unlicensed spectrum through hidden-node aware channel assignment

It is well known that a wireless local area network (WLAN) based on the IEEE 802.11 standard suffers from interference and scalability problems due to the limited number of non-overlapping channels. In order to mitigate the interference problem, channel assignment algorithms has been a popular research topic in recent years. It has been shown that such algorithms can greatly reduce the interference among wireless access points. However, in this paper, we show that previously proposed channel assignment algorithms may lead to an increased number of hidden nodes in dense network deployments. We also show that this can significantly decrease the performance of the network. Furthermore, we present results from experiments showing that the Request to send (RTS)/Clear to send (CTS) mechanism is unable to solve the hidden node problem in infrastructure WLANs, and therefore careful consideration needs to be taken when choosing channel assignment strategies in densely deployed wireless networks. To this end, we propose both a centralized channel assignment algorithm and a distributed channel assignment algorithm. Using a simulation study, we show that the proposed algorithms can outperform traditional channel assignment in densely deployed scenarios, in terms of QoS sensitive VoIP support without compromising the aggregate throughput, and that they are therefore a better performing alternative in such settings.     

Scheduling multimedia services in a low-power MAC for wireless andmobile ATM networks

This paper describes the design and analysis of the scheduling algorithm for energy conserving medium access control (EC-MAC), which is a low-power medium access control (MAC) protocol for wireless and mobile ATM networks. We evaluate the scheduling algorithms that have been proposed for traditional ATM networks. Based on the structure of EC-MAC and the characteristics of wireless channel, we propose a new algorithm that can deal with the burst errors and the location-dependent errors. Most scheduling algorithms proposed for either wired or wireless networks were analyzed with homogeneous traffic or multimedia services with simplified traffic models. We analyze our scheduling algorithm with more realistic multimedia traffic models based on H.263 video traces and self-similar data traffic. One of the key goals of the scheduling algorithms is simplicity and fast implementation. Unlike the time-stamped based algorithms, our algorithm does not need to sort the virtual time, and thus, the complexity of the algorithm is reduced significantly     

Sequence of linear programming for transmission of fine-scalable coded content in bandwidth-limited environments

In this paper, we propose an optimal peer assignment algorithm on peer-to-peer networks. This algorithm is designed to maximize the quality of transmitting fine-scalable coded content by exploiting the embedding property of scalable coding. To be more realistic, we assume that the requesting peer has a delay constraint to display the content within a certain delay bound, and it also has limited incoming bandwidth. We first use a simple example to illustrate the peer assignment problem, and then formulate this problem as a linear programming problem, followed by a nonlinear programming problem. To efficiently solve the second nonlinear problem, we transform it into a sequence of linear programming problems. Finally, we apply our proposed algorithm to both image and video transmissions in bandwidth-limited environments. Extensive experiments have been carried out to evaluate the complexity and performance of our approach by comparing it with both nonlinear formulation and two heuristic schemes. The results have verified the superior performance of our proposed algorithm.     

Rematch: a highly reliable scheduling algorithm on heterogeneous wireless mesh network

In highly dynamic and heterogeneous wireless mesh networks (WMN), link quality will seriously affect network performance. Two challenges hinder us from achieving a highly efficient WMN. One is the channel dynamics. As in real network deployment, channel qualities are changing over time, which would seriously affect network bandwidth and reliability. Existing works are limited to the assumption that link quality values are fixed, and optimal scheduling algorithms are working on the fixed values, which would inevitably suffer from the link quality dynamics. Another challenge is the channel diversity. In single channel wireless networks, channel assignment and scheduling are NP\mathcal{NP} -hard. And in multichannel wireless networks, it could be even harder for higher throughput and efficient scheduling. In this study, we firstly characterize the stochastic behavior on wireless communications in a Markov process, which is based on statistical methodology. Secondly, on exploiting the stochastic behavior on wireless channels, we propose a stochastic programming model in achieving maximized network utilization. Considering the NP\mathcal{NP} -hardness, we propose a heuristic solution for it. The key idea in the proposed algorithm is a two-stage matching process named “Rematch.” Indeed, our solution to the stochastic network scheduling is a cross-layer approach. Also, we have proved that it is 2-approximate to the optimal result. Moreover, extensive simulations have been done, showing the efficiency of “Rematch” in highly dynamic and distributed wireless mesh networks.     

A genetic algorithm approach to the multiple machine tool selection problem

A number of earlier researches have emphasized the on-the-job scheduling problems that occur with a single flexible machine. Two solutions to the problem have generally been considered; namely minimization of tool switches and minimization of tool switching instances. Methods used to solve the problems have included KTNS heuristic, dual-based relaxation heuristic, and non-LP-based branch-and-bound methods. However, scant literature has considered the case of job scheduling on multiple parallel machines which invokes another problem involving machine assignment. This paper addresses the problem of job scheduling and machine assignment on a flexible machining workstation (FMW) equipped with multiple parallel machines in a tool-sharing environment. Under these circumstances, the authors have attempted to model the problem with the objective of simultaneously minimizing both the number of tool switches and the number of tool switching instances. Furthermore, a set of realistic constraints has been included in the investigation. A novel genetic algorithm (GA) heuristic has been developed to solve the problem, and performance results show that GA is an appropriate solution.     

Job shop scheduling heuristics for sequence dependent setups

In this paper we develop a more realistic algorithm which addresses the job shop scheduling problem with sequence dependent setup times, and some complex constraints such as multiple machines, multiple resources, and alternate routes; then, we evaluate the performance of the algorithm relative to the performance criteria used in the real world shops. To illustrate the potential impact of addressed problem, we simulate the scheduling jobs using three different approaches under a variety of conditions. Experimental designs are used to find the difference among these approaches, and to justify the value of the more realistic approach.     

采用任意播方式的机会数据聚集算法

数据收集是无线传感网络研究的关键问题,是诸多无线传感网络应用的基础.降低数据聚集的延迟是数据聚集研究中的重点问题.现有的面向延迟的数据聚集算法,多是通过在树型网络结构上设计无冲突的节点调度算法,来降低数据聚集的延迟,没有考虑到无线网络数据易丢失的特性,不能达到期望的延迟效果.本文针对上述问题,提出一种采用任意播(anycast)方式的机会数据收集算法(OA算法).该算法利用机会传输(opportunistic transmission)的思想,用任意播方式传输数据,通过减少数据聚集中重传数据包的数目,来降低数据聚集的延迟.实验表明,与SPT(Shortest path tree)上的数据聚集延迟相比,该方法的发包数目减少了15%,延迟降低了10%.     

基于任务调度的无线网贪婪信道分配算法

针对无线网络链路干扰问题,综合借鉴多处理器任务调度算法提出了一种贪婪信道分配算法,为所访问的无线网链路甄选出干扰最小的信道,并且证明了本算法的近似比率为2-1/k,其中为k为可用的正交信道数,算法复杂度为O（|E|2）。为了验证本文算法的可行性和有效性,将本文所提出的贪婪算法与随机信道分配算法和按序信道分配算法进行了实验对比。仿真结果表明：本文所提出的贪婪算法的整体性能优于其他两种算法,并且贪婪算法得到的最大干扰和平均干扰归一化值随着可用正交信道数的变化趋势较其他两种算法稳定。从而验证了本文算法能有效的降低链路干扰,一定程度上可以提升网络吞吐量。