Uplink scheduling for joint wireless orthogonal frequency and time division multiple access networks

In this paper, we present a deterministic resource allocation model for a hybrid uplink wireless orthogonal frequency and time division multiple access network. Since the input data of the model may be affected by uncertainty, we further consider a stochastic formulation of the problem which we transform into an equivalent deterministic binary second-order conic program (SOCP). Subsequently, we use this binary SOCP to derive an equivalent integer linear programming formulation. The proposed models are aimed at maximizing the total bandwidth channel capacity subject to user power and sub-carrier assignment constraints while simultaneously scheduling users in time. As such, the models are best suited for non-real-time applications where sub-channel multiuser diversity can be further exploited simultaneously in frequency and time domains. Finally, in view of the large execution times required by CPLEX to solve the proposed models, we propose a variable neighborhood search metaheuristic procedure. Our numerical results show tight bounds and near optimal solutions for most of the instances when compared to the optimal solution of the problem. Moreover, we obtain better feasible solutions than CPLEX in the stochastic case. Finally, these bounds are obtained at a very low computational cost.     

多设备间任务依赖的最佳卸载决策和资源分配

考虑了多个设备的移动边缘计算(mobile edge computing, MEC)与端对端(device-to-device, D2D)技术协作网络, 其中多个无线设备的最终输出作为另一个设备上某个子任务的输入. 为了最小化无线设备的能耗和任务完成时间的加权和, 研究了最优的资源分配(卸载发射功率和本地CPU频率)和任务卸载决策问题. 首先固定卸载决策, 推导出卸载发射功率和本地CPU频率的闭合表达式, 运用凸优化方法求出该问题的解. 然后基于一次爬升策略提出了一种低复杂度线性搜索算法, 该算法可以在线性时间内获得最佳卸载决策. 数值结果表明, 该策略的性能明显优于其他有代表性的基准测试.     

Stochastic and semidefinite optimization for scheduling in orthogonal frequency division multiple access networks

In this paper, we propose stochastic binary quadratic programs for the scheduling resource allocation process of a wireless orthogonal frequency division multiple access network. More precisely, we formulate a two-stage stochastic model, then we further extend the two-stage model by introducing a knapsack probabilistic constrained approach, and finally we propose a multi-stage stochastic program for this problem. The models are aimed at minimizing the total power consumption of the network at each time slot of the scheduling process subject to user bit rates, sub-carrier and modulation linear constraints. In order to compute lower bounds, we derive linear and semidefinite programming relaxations for each of the proposed models. The bounds are also compared with a basic variable neighborhood search metaheuristic approach. Numerical results show tight lower bounds for the semidefinite relaxations when compared to the linear ones and with the metaheuristic. Moreover, near optimal solutions are found with the semidefinite relaxations for the two-stage model without using probabilistic constraints and for the multi-stage program as well.     

A VNS heuristic for escaping local extrema entrapment in normalized cut clustering

Normalized cut is one of the most popular graph clustering criteria. The main approaches proposed for its resolution are spectral clustering methods and a multilevel approach of Dhillon et al. (TPAMI 29:1944–1957, 2007), called graclus. Their aim is to obtain good solutions in a small amount of time for large instances. Metaheuristics are general frameworks for stochastic searches often employed in global optimization to improve the solutions obtained by other heuristics. Variable neighborhood search (VNS) is a metaheuristic which exploits systematically the idea of neighborhood change during the search. In this paper, we propose a VNS heuristic for normalized cut segmentation. Computational experiments show that in most cases this VNS heuristic improves significantly, and in moderate time, the solutions obtained by the current state-of-the-art algorithms, i.e., graclus and a spectral method proposed by Yu and Shi (ICCV, 2003).     

Solving large <Emphasis Type="Italic">p</Emphasis>-median clustering problems by primal–dual variable neighborhood search

Data clustering methods are used extensively in the data mining literature to detect important patterns in large datasets in the form of densely populated regions in a multi-dimensional Euclidean space. Due to the complexity of the problem and the size of the dataset, obtaining quality solutions within reasonable CPU time and memory requirements becomes the central challenge. In this paper, we solve the clustering problem as a large scale p-median model, using a new approach based on the variable neighborhood search (VNS) metaheuristic. Using a highly efficient data structure and local updating procedure taken from the OR literature, our VNS procedure is able to tackle large datasets directly without the need for data reduction or sampling as employed in certain popular methods. Computational results demonstrate that our VNS heuristic outperforms other local search based methods such as CLARA and CLARANS even after upgrading these procedures with the same efficient data structures and local search. We also obtain a bound on the quality of the solutions by solving heuristically a dual relaxation of the problem, thus introducing an important capability to the solution process.     

On the wireless local area network design problem with performance guarantees

In this paper, we propose a model for the wireless local area network (WLAN) design problem with performance guarantees. This problem consists of selecting the location of the access points (APs) as well as the power and the channel of each AP. The performance guarantees we refer to are coverage and minimum bandwidth guarantees. Since this problem is -hard, we propose a tabu search algorithm to find solutions for real-size instances of the problem. Finally, numerical results are presented. The results show that good solutions can be found with the proposed algorithm in a reasonable amount of time.     

Simulated annealing algorithm for solving the single machine early/tardy problem

Recently, several studies have been conducted regarding the application of the simulated annealing (SA) method to solve combinatorial optimization problems. Most of the previous reports have shown that SA has been used to obtain reasonable solutions that are better than some heuristic algorithms and in comparable CPU time. A single machine early/tardy problem is selected in this paper to demonstrate the usefulness of SA. Firstly, based on previous studies, this research uses the factorial experiment to analyse the factors that are critical to the efficiency of SA. Secondly, SA, tuned by the previous step, is compared with branch-and-bound and neighbourhood search methods by solving some test problems. The results show that SA is very sensitive to the cooling schedule, generation mechanism, acceptance criteria and stopping criteria. When SA is used to solve the single machine problem with n ≤ 15, it can converge to the optimal solution quickly. For the single machine problem where n ≥ 30, the branch-and-bound algorithm is not feasible while SA can provide a much better solution than the neighbourhood search method.     

Parallel machine total tardiness scheduling with a new hybrid metaheuristic approach

This work proposes a hybrid metaheuristic (HMH) approach which integrates several features from tabu search (TS), simulated annealing (SA) and variable neighbourhood search (VNS) in a new configurable scheduling algorithm. In particular, either a deterministic or a random candidate list strategy can be used to generate the neighbourhood of a solution, both a tabu list mechanism and the SA probabilistic rule can be adopted to accept solutions, and the dimension of the explored neighbourhood can be dynamically modified. The considered class of scheduling problems is characterized by a set of independent jobs to be executed on a set of parallel machines with non-zero ready times and sequence dependent setups. In particular, the NP-hard generalized parallel machine total tardiness problem (GPMTP) recently defined by Bilge et al. [A tabu search algorithm for parallel machine total tardiness problem. Computers & Operations Research 2004;31:397–414], is faced. Several alternative configurations of the HMH have been tested on the same benchmark set used by Bilge et al. The results obtained highlight the appropriateness of the proposed approach.     

A polar-based guided multi-objective evolutionary algorithm to search for optimal solutions interested by decision-makers in a logistics network design problem

In practical multi-objective optimization problems, respective decision-makers might be interested in some optimal solutions that have objective values closer to their specified values. Guided multi-objective evolutionary algorithms (guided MOEAs) have been significantly used to guide their evolutionary search direction toward these optimal solutions using by decision makers. However, most guided MOEAs need to be iteratively and interactively evaluated and then guided by decision-makers through re-formulating or re-weighting objectives, and it might negatively affect the algorithms performance. In this paper, a novel guided MOEA that uses a dynamic polar-based region around a particular point in objective space is proposed. Based on the region, new selection operations are designed such that the algorithm can guide the evolutionary search toward optimal solutions that are close to the particular point in objective space without the iterative and interactive efforts. The proposed guided MOEA is tested on the multi-criteria decision-making problem of flexible logistics network design with different desired points. Experimental results show that the proposed guided MOEA outperforms two most effective guided and non-guided MOEAs, R-NSGA-II and NSGA-II.     

A variable neighborhood search for the multi-depot vehicle routing problem with loading cost

The purpose of this paper is to propose a variable neighbourhood search (VNS) for solving the multi-depot vehicle routing problem with loading cost (MDVRPLC). The MDVRPLC is the combination of multi-depot vehicle routing problem (MDVRP) and vehicle routing problem with loading cost (VRPLC) which are both variations of the vehicle routing problem (VRP) and occur only rarely in the literature. In fact, an extensive literature search failed to find any literature related specifically to the MDVRPLC. The proposed VNS comprises three phases. First, a stochastic method is used for initial solution generation. Second, four operators are randomly selected to search neighbourhood solutions. Third, a criterion similar to simulated annealing (SA) is used for neighbourhood solution acceptance. The proposed VNS has been test on 23 MDVRP benchmark problems. The experimental results show that the proposed method provides an average 23.77% improvement in total transportation cost over the best known results based on minimizing transportation distance. The results show that the proposed method is efficient and effective in solving problems.     

Toward low CPU usage and efficient DPDK communication in a cluster

In recent years, DPDK (Data Plane Development Kit, a data plane development tool set provided by Intel, focusing on high-performance processing of data packets in network applications), one of the high-performance packet I/O frameworks, is widely used to improve the efficiency of data transmission in the cluster. But, the busy polling used in DPDK will not only waste a lot of CPU cycles and cause certain power consumption, but also the high CPU usage will have a great impact on the performance of other applications in the host. Although some technologies, such as DVFS (dynamic voltage and frequency scaling, which is to dynamically adjust the operating frequency and voltage of the chip according to the different needs of the computing power of the application running on the chip, so as to achieve the purpose of energy saving) and LPI (low power idle, a technology that saves power by turning off the power of certain supporting circuits when the CPU core is idle), can reduce power consumption by adjusting CPU voltage and frequency, they can also cause performance degradation in other applications. Using thread sleep technology is a promising method to reduce the CPU usage and power consumption. However, it is challenging because the appropriate thread sleep duration cannot be obtained accurately. In this paper, we propose a model that finds the optimal thread sleep duration to solve the above challenges. From the model, we can balance the thread CPU usage and transmission efficiency to obtain the optimal sleep duration called the transmission performance threshold. Experiments show that the proposed models can significantly reduce the thread CPU usage. Generally, while the communication performance is slightly reduced, the CPU utilization is reduced by about 80%.

     

Variable neighborhood search for the pharmacy duty scheduling problem

In this paper, we study on the Pharmacy Duty Scheduling (PDS) problem, where a subset of pharmacies should be on duty on national holidays, at weekends and at nights in order to be able to satisfy the emergency drug needs of the society. PDS problem is a multi-period p-median problem with special side constraints and it is an NP-Hard problem. We propose four Variable Neighborhood Search (VNS) heuristics. The first one is the basic version, BVNS. The latter two, Variable Neighborhood Decomposition Search (VNDS) and Variable Neighborhood Restricted Search (VNRS), aim to obtain better results in less computing time by decomposing or restricting the search space. The last one, Reduced VNS (RVNS), is for obtaining good initial solutions rapidly for BVNS, VNDS and VNRS. We test BVNS, VNRS and VNDS heuristics on randomly generated instances and report the computational test results. We also use VNS heuristics on real data for the pharmacies in central İzmir and obtain significant improvements.     

A population-based variable neighborhood search for the single machine total weighted tardiness problem

This paper investigates the single machine total weighted tardiness problem, in which a set of independent jobs with distinct processing times, weights, and due dates are to be scheduled on a single machine to minimize the sum of weighted tardiness of all jobs. This problem is known to be strongly NP-hard, and thus provides a challenging area for metaheuristics. A population-based variable neighborhood search (PVNS) algorithm is developed to solve it. This algorithm differs from the basic variable neighborhood search (VNS). First, the PVNS consists of a number of iterations of the basic VNS, and in each iteration a population of solutions is used to simultaneously generate multiple trial solutions in a neighborhood so as to improve the search diversification. Second, the PVNS adopts a combination of path-relinking, variable depth search and tabu search to act as the local search procedure so as to improve the search intensification. Computational experiments show that the proposed PVNS algorithm can obtain the optimal or best known solutions within a reasonable computation time for all standard benchmark problem instances from the literature.     

Exact and heuristic algorithms for the aerial refueling parallel machine scheduling problem with due date-to-deadline window and ready times

The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for fighter aircrafts (jobs) on multiple tankers (machines) to minimize the total weighted tardiness. ARSP can be modeled as a parallel machine scheduling with ready times and due date-to-deadline window to minimize total weighted tardiness. ARSP assumes that the jobs have different ready times and a due date-to-deadline window between refueling due date and a deadline to return without refueling. In this paper, we first formulate the ARSP as a mixed integer programming model. The objective function is a piece-wise tardiness cost that takes into account due date-to-deadline windows and job priorities. Since ARSP is NP-hard, two heuristics are proposed to obtain solutions in reasonable computation times, namely (1) modified ATC rule (MATC), (2) a simulated annealing method (SA). The proposed heuristic algorithms are tested in terms of solution quality and CPU time through computational experiments with data randomly generated to represent aerial refueling operations of an in-theater air operation. Solutions provided by both algorithms were compared to optimal solutions for problems with up to 12 jobs and to each other for larger problems with up to 60 jobs. The results show that, MATC is more likely to outperform SA especially when the problem size increases, although it has significantly worse performance than SA in terms of deviation from optimal solution for small size problems. Moreover CPU time performance of MATC is significantly better than SA in both cases.     

无线传感器网络的改进GASA优化设计

无线传感器网络由大量传感器节点构成,因此对网络整体造价特别敏感．优化设计传感器网络构成,可以在满足监测精度的同时最小化网络造价．本文提出了一种GA和SA结合的改进GASA优化设计方法,解决由异类、多级传感器组成的无线传感器网络的优化设计问题．该方法采用特殊设计的排序组合算子提高GA的并行搜索能力．降低异类、多级传感器带来的复杂性;通过最优可行化处理加速搜索过程;利用SA的概率突跳特性避免陷入局部极小值,提高局部搜索能力．仿真实验表明,改进的GASA方法可以快速、有效地解决异类、多级传感器优化问题．     

Static scheduling techniques for dependent tasks on dynamically reconfigurable devices

Dynamically reconfigurable hardware not only has high silicon reusability, but it can also deliver high performance for computation-intensive tasks. Advanced features such as run-time reconfiguration allow multiple tasks to be mapped onto the same device either simultaneously or multiplexed in time domain. These tasks need to be scheduled optimally or near optimally in order to efficiently utilize the device. It is a NP-hard problem, because task scheduling, allocation and configuration prefetching all need to be considered. In this paper, we target dependent task models and propose three static schedulers that use different problem solving strategies. The first is a heuristic approach developed from traditional list-based schedulers. It presents high efficiency but the least accuracy. The second is based on a full-domain search using constraint programming. It can guarantee to produce optimal solutions but requires significant searching effort. The last is a guided random search technique based on a genetic algorithm, which shows reasonable efficiency and much better accuracy than the heuristic approach.     

Optimization models and methods for planning wireless mesh networks

In this paper novel optimization models are proposed for planning Wireless Mesh Networks (WMNs), where the objective is to minimize the network installation cost while providing full coverage to wireless mesh clients. Our mixed integer linear programming models allow to select the number and positions of mesh routers and access points, while accurately taking into account traffic routing, interference, rate adaptation, and channel assignment. We provide the optimal solutions of three problem formulations for a set of realistic-size instances (with up to 60 mesh devices) and discuss the effect of different parameters on the characteristics of the planned networks. Moreover, we propose and evaluate a relaxation-based heuristic for large-sized network instances which jointly solves the topology/coverage planning and channel assignment problems. Finally, the quality of the planned networks is evaluated under different traffic conditions through detailed system level simulations.     

Variable neighborhood search for the linear ordering problem

Given a matrix of weights, the linear ordering problem (LOP) consists of finding a permutation of the columns and rows in order to maximize the sum of the weights in the upper triangle. This NP-complete problem can also be formulated in terms of graphs, as finding an acyclic tournament with a maximal sum of arc weights in a complete weighted graph. In this paper, we first review the previous methods for the LOP and then propose a heuristic algorithm based on the variable neighborhood search (VNS) methodology. The method combines different neighborhoods for an efficient exploration of the search space. We explore different search strategies and propose a hybrid method in which the VNS is coupled with a short-term tabu search for improved outcomes. Our extensive experimentation with both real and random instances shows that the proposed procedure competes with the best-known algorithms in terms of solution quality, and has reasonable computing-time requirements.Variable neighborhood search (VNS) is a metaheuristic method that has recently been shown to yield promising outcomes for solving combinatorial optimization problems. Based on a systematic change of neighborhood in a local search procedure, VNS uses both deterministic and random strategies in search for the global optimum.In this paper, we present a VNS implementation designed to find high quality solutions for the NP-hard LOP, which has a significant number of applications in practice. The LOP, for example, is equivalent to the so-called triangulation problem for input–output tables in economics. Our implementation incorporates innovative mechanisms to include memory structures within the VNS methodology. Moreover we study the hybridization with other methodologies such as tabu search.     

An advanced GA–VNS combination for multicriteria route planning in public transit networks

Nowadays, passengers in urban public transport systems do not only seek a short-time travel, but they also ask for optimizing other criteria such as cost and effort. Therefore, an efficient routing system should incorporate a multiobjective analysis into its search process. Several algorithms have been proposed to optimally compute the set of nondominated journeys while going from one place to another such as the generalization of the algorithm of Dijkstra. However, such approaches become less performant or even inapplicable when the size of the network becomes very large or when the number of criteria considered is very important. Therefore, we propose in this paper an advanced heuristic approach whereby a Genetic Algorithm (GA) is combined with a Variable Neighborhood Search (VNS) to solve the Multicriteria Shortest Path Problem (MSPP) in multimodal networks. As transportation modes, we focus on railway, bus, tram and pedestrian. As optimization criteria, we consider travel time, monetary cost, number of transfers and the total walking time. The proposed approach is compared with the exact algorithm of Dijkstra, as well as, with a standard GA and a pure VNS. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that the proposed combination GA–VNS represents the best approach in terms of computational time and solutions quality for a real world routing system.     

A simulated annealing algorithm for router nodes placement problem in Wireless Mesh Networks

Mesh router nodes placement is a central problem in Wireless Mesh Networks (WMNs). An efficient placement of mesh router nodes is indispensable for achieving network performance in terms of both network connectivity and user coverage. Unfortunately the problem is computationally hard to solve to optimality even for small deployment areas and a small number of mesh router nodes. As WMNs are becoming an important networking infrastructure for providing cost-efficient broadband wireless connectivity, researchers are paying attention to the resolution of the mesh router placement problem through heuristic approaches in order to achieve near optimal, yet high quality solutions in reasonable time. In this work we propose and evaluate a simulated annealing (SA) approach to placement of mesh router nodes in WMNs. The optimization model uses two maximization objectives, namely, the size of the giant component in the network and user coverage. Both objectives are important to deployment of WMNs; the former is crucial to achieve network connectivity while the later is an indicator of the QoS in WMNs. The SA approach distinguishes for its simplicity yet its policy of neighborhood exploration allows to reach promising areas of the solution space where quality solutions could be found. We have experimentally evaluated the SA algorithm through a benchmark of generated instances, varying from small to large size, and capturing different characteristics of WMNs such as topological placements of mesh clients. The experimental results showed the efficiency of the annealing approach for the placement of mesh router nodes in WMNs.