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Service selection is crucial to service composition in determining the composite Quality of Service (QoS). The proliferation of composable services on the Internet and the practical need for timely delivering optimized composite solutions motivate the adoption of population-based algorithms for QoS-aware service selection. However, existing population-based algorithms are generally complicated to use, and often used as a general approach to solving different optimization problems. We propose to develop specialized algorithms for QoS-aware service selection, based on the artificial bee colony algorithm (ABC). ABC is a new and simpler implementation of swarm intelligence, which has proven to be successful in solving many real-world problems, especially the numerical optimization problems. We develop an approximate approach for the neighborhood search of ABC, which enables effective local search in the discrete space of service selection in a way that is analogical to the search in a continuous space. We present three algorithms based on the approach. All the three algorithms are designed to improve the performance and meanwhile preserve the simplicity of ABC. Each algorithm applies a different technique to leverage the unique characteristics of the service selection problem. Experimental results show higher accuracy and convergence speed of the proposed algorithms over the state of the art algorithms. [ABSTRACT FROM AUTHOR]

Attribute subset selection is an important issue in data mining and information processing. However, most automatic methodologies consider only the relevance factor between samples while ignoring the diversity factor. This may not allow the utilization value of hidden information to be exploited. For this reason, we propose a hybrid model named intuitionistic fuzzy (IF) rough set to overcome this limitation. The model combines the technical advantages of rough set and IF set and can effectively consider the above-mentioned statistical factors. First, fuzzy information granules based on IF relations are defined and used to characterize the hierarchical structures of the lower and upper approximations of IF rough set within the framework of granular computing. Then, the computation of IF rough approximations and knowledge reduction in IF information systems are investigated. Third, based on the approximations of IF rough set, significance measures are developed to evaluate the approximation quality and classification ability of IF relations. Furthermore, a forward heuristic algorithm for finding one optimal reduct of IF information systems is developed using these measures. Finally, numerical experiments are conducted on public datasets to examine the effectiveness and efficiency of the proposed algorithm in terms of the number of selected attributes, computational time, and classification accuracy. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) will simplify deployment and management of network and telecommunication services. NFV provides flexibility by virtualizing the network functions and moving them to a virtualization platform. In order to achieve its full potential, NFV is being extended to mobile or wireless networks by considering virtualization of radio functions. A typical network service setup requires the allocation of a virtual network function-forwarding graph (VNF-FG). A VNF-FG is allocated considering the resource constraints of the lower infrastructure. This topic has been well-studied in existing literature, however, the effects of variations of networks over time have not been addressed yet. In this paper, we provide a model of the adaptive and dynamic VNF allocation problem considering also VNF migration. Then we formulate the optimization problem as an integer linear programming (ILP) and provide a heuristic algorithm for allocating multiple VNF-FGs. The idea is that VNF-FGs can be reallocated dynamically to obtain the optimal solution over time. First, a centralized optimization approach is proposed to cope with the ILP-resource allocation problem. Next, a decentralized optimization approach is proposed to deal with cooperative multi-operator scenarios. We adopt AD3, an alternating direction method of multipliers-based algorithm, to solve this problem in a distributed way. The results confirm that the proposed algorithms are able to optimize the network utilization, while limiting the number of reallocations of VNFs which could interrupt network services. [ABSTRACT FROM AUTHOR]

In this paper, we consider autonomous driving of miniature race cars. The viability kernel is used to efficiently generate finite look-ahead trajectories that maximize progress while remaining recursively feasible with respect to static obstacles (e.g., stay inside the track). Together with a low-level model predictive controller, this method makes real-time autonomous racing possible. The viability kernel computation is based on space discretization. To make the calculation robust against discretization errors, we propose a novel numerical scheme based on game theoretical methods, in particular the discriminating kernel. We show that the resulting algorithm provides an inner approximation of the viability kernel and guarantees that, for all states in the cell surrounding a viable grid point, there exists a control that keeps the system within the kernel. The performance of the proposed control method is studied in simulation where we determine the effects of various design choices and parameters and in experiments on an autonomous racing setup maintained at the Automatic Control Laboratory of ETH Zurich. Both simulation and experimental results suggest that the more conservative approximation using the discriminating kernel results in safer driving style at the cost of a small increase in lap time. [ABSTRACT FROM AUTHOR]

The Internet is a gigantic distributed system where the end-to-end (E2E) quality-of-service (QoS) plays an important role. Yet the current inter-domain routing protocol, namely, the Border Gateway Protocol (BGP), cannot provide E2E QoS guarantees. The main reason is that an autonomous system (AS) can only receive guarantees from its first-hop ASes via service level agreements (SLAs). But beyond the first-hop, QoS along the path from a source AS to a destination AS is not within the source AS's control regime. This makes it difficult to provide high quality-of-experience services to many Internet users even when many content providers are willing to pay for such high quality E2E guarantees. In this paper, we investigate the feasibility of providing high QoS-guaranteed E2E transit services by utilizing a (small) set of ASes/IXPs to serve as “brokers” to provide supervision, control and resource negotiation. Finding an optimal set of ASes as brokers can be formulated as a Maximum Coverage with $B-$ dominating path Guarantee (MCBG) problem, and we show that it is in fact NP-hard. To address this problem, we design a $(\frac{1{-}e^{-\!1}}{2}){-}$ approximation algorithm and also an efficient heuristic algorithm when additional constraints (e.g., the path length) are considered. We further analyze the APX-hardness of the MCBG problem to reveal the existence of the best approximation ratio. Based on the current Internet topology, we demonstrate that it is indeed feasible to provide high QoS guarantees for most E2E connections with only a small broker set: with only 0.19, 1.9 or 6.8 percent ASes/IXPs serving as brokers, 53.13, 85.41 or 99.29 percent of all global E2E connections can receive high QoS-guaranteed services. Finally, we provide an economic model to study the behaviours of ASes when cooperating our brokerage scheme with the BGP protocol, and show that there are incentives to form and maintain such a brokerage coalition. [ABSTRACT FROM AUTHOR]

This work proposes a unified heuristic algorithm for a large class of earliness-tardiness (E-T) scheduling problems. We consider single/parallel machine E-T problems that may or may not consider some additional features such as idle time, setup times and release dates. In addition, we also consider those problems whose objective is to minimize either the total (average) weighted completion time or the total (average) weighted flow time, which arise as particular cases when the due dates of all jobs are either set to zero or to their associated release dates, respectively. The developed local search-based metaheuristic framework is quite simple, but at the same time relies on sophisticated procedures for efficiently performing local search according to the characteristics of the problem. We present efficient move evaluation approaches for some parallel machine problems that generalize the existing ones for single machine problems. The algorithm was tested in thousands of instances of several E-T problems and particular cases. The results obtained show that our unified heuristic is capable of producing high-quality solutions when compared to the best ones available in the literature that were obtained by specific methods. Moreover, we provide an extensive annotated bibliography on the problems related to those considered in this work, where we not only indicate the approach(es) used in each publication, but we also point out the characteristics of the problem(s) considered. Beyond that, we classify the existing methods in different categories so as to have a better idea of the popularity of each type of solution procedure. [ABSTRACT FROM AUTHOR]

It is challenging to design large and low-cost communication networks. In this paper, we formulate this challenge as the prize-collecting Steiner Tree Problem (PCSTP). The objective is to minimize the costs of transmission routes and the disconnected monetary or informational profits. Initially, we note that the PCSTP is MAX SNP-hard. Then, we propose some post-processing techniques to improve suboptimal solutions to PCSTP. Based on these techniques, we propose two fast heuristic algorithms: the first one is a quasilinear time heuristic algorithm that is faster and consumes less memory than other algorithms; and the second one is an improvement of a state-of-the-art polynomial time heuristic algorithm that can find high-quality solutions at a speed that is only inferior to the first one. We demonstrate the competitiveness of our heuristic algorithms by comparing them with the state-of-the-art ones on the largest existing benchmark instances (169 800 vertices and 338 551 edges). Moreover, we generate new instances that are even larger (1 000 000 vertices and 10 000 000 edges) to further demonstrate their advantages in large networks. The state-of-the-art algorithms are too slow to find high-quality solutions for instances of this size, whereas our new heuristic algorithms can do this in around 6 to 45s on a personal computer. Ultimately, we apply our post-processing techniques to update the best-known solution for a notoriously difficult benchmark instance to show that they can improve near-optimal solutions to PCSTP. In conclusion, we demonstrate the usefulness of our heuristic algorithms and post-processing techniques for designing large and low-cost communication networks. [ABSTRACT FROM AUTHOR]

Coded caching is an effective technique to reduce the redundant traffic in wireless networks. The existing coded caching schemes require the splitting of files into a possibly large number of subfiles, i.e., they perform coded subfile caching. Keeping the files intact during the caching process would actually be appealing, broadly speaking because of its simpler implementation. However, little is known about the effectiveness of this coded file caching in reducing the data delivery rate. In this paper, we propose such a file caching scheme that uses a decentralized algorithm for content placement and either a greedy clique cover or an online matching algorithm for the delivery of missing data. We derive approximations to the expected delivery rates of both schemes using the differential equations method, and show them to be tight through concentration analysis and computer simulations. Our numerical results demonstrate that the proposed coded file caching is significantly more effective than uncoded caching in reducing the delivery rate. We, furthermore, show the additional improvement in the performance of the proposed scheme when its application is extended to subfile caching with a small number of subfiles. [ABSTRACT FROM AUTHOR]

The issue of missing values (MVs) has appeared widely in real-world datasets and hindered the use of many statistical or machine learning algorithms for data analytics due to their incompetence in handling incomplete datasets. To address this issue, several MV imputation algorithms have been developed. However, these approaches do not perform well when most of the incomplete tuples are clustered with each other, coined here as theClustered Missing Values Phenomenon, which attributes to the lack of sufficient complete tuples near an MV for imputation. In this paper, we propose theOrder-Sensitive Imputation for Clustered Missing values(OSICM) framework, in which missing values are imputed sequentially such that the values filled earlier in the process are also used for later imputation of other MVs. Obviously, the order of imputations is critical to the effectiveness and efficiency of OSICM framework. We formulate the searching of the optimal imputation order as an optimization problem, and show its NP-hardness. Furthermore, we devise an algorithm to find the exact optimal solution and propose two approximate/heuristic algorithms to trade off effectiveness for efficiency. Finally, we conduct extensive experiments on real and synthetic datasets to demonstrate the superiority of our OSICM framework. [ABSTRACT FROM AUTHOR]

We consider the problem of decoding a discrete signal of categorical variables from the observation of several histograms of pooled subsets of it. We present an approximate message passing (AMP) algorithm for recovering the signal in the random dense setting where each observed histogram involves a random subset of entries of size proportional to $n$. We characterize the performance of the algorithm in the asymptotic regime where the number of observations $m$ tends infinity proportionally to $n$ by deriving the corresponding state evolution (SE) equations and studying their dynamics. We initiate the analysis of the multi-dimensional SE dynamics by proving their convergence to a fixed point, along with some further properties of the iterates. The analysis reveals sharp phase transition phenomena where the behavior of AMP changes from exact recovery to weak correlation with the signal, as $m/n$ crosses a threshold. We derive formulae for the threshold in some special cases and show that they accurately match experimental behavior. [ABSTRACT FROM AUTHOR]

This paper constructed an optimizing model on multiple vehicles and multiple goods, which is used in a study on the loading problem for railway freight. We take the maximum utilization coefficient of car loading capacity, volume capacity and layout optimal degree as objective functions. Several major factors in railway transportation have been cited as constraints: center of gravity, nonoverlapping freight, transportation circumscription, car marked loading capacity and volume capacity. In order to increase the car utilization coefficient and obtain flat layers in the loading scheme, an improved genetic annealing algorithm is proposed to solve this optimizing model. The proposed algorithm can efficiently obtain a satisfactory loading scheme in railway transportation. At the end, a numerical example shows that the proposed model and algorithm are better than First Fit Algorithm and Neural Network Algorithm in goods loading capacity and volume capacity, the new loading schemes are more flat on each layer. [ABSTRACT FROM AUTHOR]

Approximate triple modular redundancy (ATMR) is sought for logic masking of soft errors while effectuating lower area overhead than conventional TMR through the introduction of approximate modules. However, the use of approximate modules instigates reduced fault coverage in ATMR. In this work, we target better design tradeoffs in ATMR by proposing a heuristic method that effectively utilizes a threshold for unprotected input vectors to generate good enough combinations of approximate modules for ATMR, which accomplishes higher fault coverage and reduced area overhead compared with previously proposed approaches. The key concept is to employ logic optimization techniques of prime implicant (PI) expansion and reduction for successively obtaining approximate modules such that the combination of three approximate modules appropriately functions as an ATMR. For an ATMR to function appropriately, blocking is used to ensure that at each input vector, through the prime implicant (PI) expansion and reduction technique, only one approximate module differ from the original circuit. For large circuits, clustering is utilized and comparative analysis indicates that higher fault coverage is attained through the proposed ATMR scheme while preserving the characteristic feature of reduced area overhead. With a small percentage of unprotected input vectors, we achieved substantial decrease in transistor count and greater fault detection, i.e., an improvement of up to 26.1% and 42.1%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the incentive mechanism design for a vehicle-based, nondeterministic crowdsensing system. In this crowdsensing system, vehicles move along their trajectories and perform corresponding sensing tasks with different probabilities. Each task may be performed by multiple vehicles jointly so as to ensure a high probability of success. Designing an incentive mechanism for such a crowdsensing system is challenging since it contains a non-trivial set cover problem. To solve this problem, we propose a truthful, reverse-auction-based incentive mechanism that includes an approximation algorithm to select winning bids with a nearly minimum social cost and a payment algorithm to determine payments for all participants. Moreover, we extend the problem to a more complex case in which the Quality of sensing Data (QoD) of each vehicle is taken into consideration. For this problem, we propose a QoD-aware incentive mechanism, which consists of a QoD-aware winning-bid selection algorithm and a QoD-aware payment determination algorithm. We prove that the proposed incentive mechanisms have truthfulness, individual rationality, and computational efficiency. Moreover, we analyze the approximation ratios of the winning-bid selection algorithms. The simulations, based on a real vehicle trace, also demonstrate the significant performances of our incentive mechanisms. [ABSTRACT FROM AUTHOR]

We consider the problem of link scheduling in multihop wireless networks subject to interference constraints. We consider a graph-based representation of wireless networks where scheduled links adhere to the $K$ -hop link interference model. For this scheduling problem, we develop an asynchronous and distributed greedy heuristic. Furthermore, we show that this distributed heuristic computes the exact same schedule as the well-known centralized greedy link scheduling algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the fair scheduling problem for dense wireless networks with access point cooperation and multiple-input-multiple-output (MIMO) links. The problem is to maximize the aggregate throughput subject to a fairness constraint that is general enough to capture many different fairness objectives. We formally specify a non-convex optimization problem that captures all aspects of the problem setting, and we propose two algorithms to approximate its solution. The first algorithm jointly optimizes the selection of user sets, MIMO precoders, and assignment of user sets to time slots. The second algorithm separately optimizes first user sets and MIMO precoders and second assignment of user sets to time slots. The first algorithm guarantees perfect fairness and produces a local optimum or a saddle point for aggregate throughput at a fairly high computational cost. The second algorithm also guarantees perfect fairness and produces optimal aggregate throughput for a given set of (possibly non-optimal) user sets while having lower computational complexity. The second algorithm also has a parameter that allows throughput and fairness to be traded off for situations where maximizing throughput is critical and approximate fairness is acceptable. Analyses are complemented by simulation results, which show that: 1) the first algorithm produces significantly higher aggregate throughput than known approaches with a running time that is practical for scenarios with up to 50 users and 2) the second algorithm produces aggregate throughput that is very close to existing heuristics while having significantly lower running time. [ABSTRACT FROM AUTHOR]

The connected vertex cover (CVC) problem, which has many important applications, is a variant of the vertex cover problem, such as wireless network design, routing, and wavelength assignment problem. A good algorithm for the problem can help us improve engineering efficiency, cost savings, and resources consumption in industrial applications. In this work, we present an efficient algorithm GRASP-CVC (Greedy Randomized Adaptive Search Procedure for Connected Vertex Cover) for CVC in general graphs. The algorithm has two main phases, i.e., construction phase and local search phase. In the construction phase, to construct a high quality feasible initial solution, we design a greedy function and a restricted candidate list. In the local search phase, the configuration checking strategy is adopted to decrease the cycling problem. The experimental results demonstrate that GRASP-CVC is better than other comparison algorithms in terms of effectivity and efficiency. [ABSTRACT FROM AUTHOR]

With the proliferation of spatial-textual data such as location-based services and geo-tagged websites, spatial keyword queries are ubiquitous in real life. One example of spatial-keyword query is the so-called collective spatial keyword query (CoSKQ) which is to find for a given query consisting a query location and several query keywords a set of objects which covers the query keywords collectively and has the smallest cost wrt the query location. In the literature, many different functions were proposed for defining the ${cost}$ and correspondingly, many different approaches were developed for the CoSKQ problem. In this paper, we study the CoSKQ problem systematically by proposing a unified cost function and a unified approach for the CoSKQ problem (with the unified cost function). The unified cost function includes all existing cost functions as special cases and the unified approach solves the CoSKQ problem with the unified cost function in a unified way. Experiments were conducted on both real and synthetic datasets which verified our proposed approach. [ABSTRACT FROM AUTHOR]

The problem of scheduling malleable tasks with precedence constraints is one of the most important strongly $\mathcal {NP}$ -hard problems, given $m$ identical processors and $n$ tasks. A malleable task is one that runs in parallel on a varying number of processors. In addition, the processing sequences of tasks are constrained by the precedence constraints. The goal is to find a feasible schedule that minimizes the makespan (maximum completion time). This article presents an iterative method for improving the performance ratio of scheduling malleable tasks. The proposed algorithm achieves an approximation ratio of 4.4841 after 2 iterations. This improves the so far best-known factor of 4.7306 due to Jansen and Zhang. For a large number of iterations ( $>100$ ), the approximation ratio of the proposed algorithm is tends toward $2+\sqrt{2}\approx 3.4143$ . [ABSTRACT FROM AUTHOR]

Demand response program with real-time pricing can encourage electricity users toward scheduling their energy usage to off-peak hours. A user needs to schedule the energy usage of his appliances in an online manner since he may not know the energy prices and the demand of his appliances ahead of time. In this paper, we study the users’ long-term load scheduling problem and model the changes of the price information and load demand as a Markov decision process, which enables us to capture the interactions among users as a partially observable stochastic game. To make the problem tractable, we approximate the users’ optimal scheduling policy by the Markov perfect equilibrium (MPE) of a fully observable stochastic game with incomplete information. We develop an online load scheduling learning (LSL) algorithm based on the actor-critic method to determine the users’ MPE policy. When compared with the benchmark of not performing demand response, simulation results show that the LSL algorithm can reduce the expected cost of users and the peak-to-average ratio in the aggregate load by 28% and 13%, respectively. When compared with the short-term scheduling policies, the users with the long-term policies can reduce their expected cost by 17%. [ABSTRACT FROM AUTHOR]

Optimal road side unit (RSU) deployment is of crucial importance to vehicular ad hoc networks. The RSU deployment (RD) problem models in the literature usually lack the ability to describe curve-shaped roads and roads nonuniform statistics. We had proposed a more realistic RD problem model taking these features into consideration. However, our understanding of the RD problem with the new model is still seriously limited. In this paper, we try to deepen our understanding of the one-dimensional RD (D1RD) problem with the new model by analyzing its properties and designing optimal and efficient approximate algorithms for it. We first analyze the properties of optimal solutions of the D1RD problem. Next, suspecting that the D1RD problem with $n$ RSUs of different coverage radii is intractable, we propose two greedy-based algorithms (named as Greedy2P3 and Greedy2P3E) and show that Greedy2P3E's approximation ratio is at least $1-(\frac{n-1}{n})^2$. Then, by exploiting the properties of the D1RD problem, we propose an optimal algorithm named OptGreDyn by combining greedy idea and dynamic programming, and prove its optimality. At last, we proved that, if applied to the RD problem with $n$ RSUs of identical radii, the approximation ratios of Greedy2P3 and Greedy2P3E are at least $\frac{2}{3}$ , and it is tight for Greedy2P3 when $n=3i$ , where $i$ is a positive integer. Simulation results verify the optimality of OptGreDyn, meanwhile show that Greedy2P3 and Greedy2P3E usually return near-optimal solutions with profit more than 98% of the optimal solutions, and they are preferable than the existing approximate algorithms. [ABSTRACT FROM AUTHOR]

Crowdsourcing has been shown to be effective in a wide range of applications, and is seeing increasing use. A large-scale crowdsourcing task often consists of thousands or millions of atomic tasks, each of which is usually a simple task such as binary choice or simple voting. To distribute a large-scale crowdsourcing task to limited crowd workers, a common practice is to pack a set of atomic tasks into a task bin and send to a crowd worker in a batch. It is challenging to decompose a large-scale crowdsourcing task and execute batches of atomic tasks, which ensures reliable answers at a minimal total cost. Large batches lead to unreliable answers of atomic tasks, while small batches incur unnecessary cost. In this paper, we investigate a general crowdsourcing task decomposition problem, called the Smart Large-scAle task DE composer (SLADE) problem, which aims to decompose a large-scale crowdsourcing task to achieve the desired reliability at a minimal cost. We prove the NP-hardness of the SLADE problem and propose solutions in both homogeneous and heterogeneous scenarios. For the homogeneous SLADE problem, where all the atomic tasks share the same reliability requirement, we propose a greedy heuristic algorithm and an efficient and effective approximation framework using an optimal priority queue (OPQ) structure with provable approximation ratio. For the heterogeneous SLADE problem, where the atomic tasks can have different reliability requirements, we extend the OPQ-based framework leveraging a partition strategy, and also prove its approximation guarantee. Finally, we verify the effectiveness and efficiency of the proposed solutions through extensive experiments on representative crowdsourcing platforms. [ABSTRACT FROM AUTHOR]

Although various methods of controlling mobile robots have been studied, the distributed tracking control problem for uncertain nonholonomic mobile multirobot (NMMR) systems in an optimal manner with disturbance rejection for both kinematics and dynamics has not been thoroughly solved. This paper, therefore, devotes a novel method to solve the problem with application to real NMMR systems equipped with omnidirectional vision sensors, of which parameters are unknown or uncalibrated. First, the distributed optimal tracking control problem of a separate system in the presence of kinematic and dynamic disturbances is transformed into the equivalent optimal regulation with disturbance rejection of an integrated system. Then, the theory of differential games is utilized to formulate the integrated system into coupled Hamilton–Jacobi–Isaac equations, of which the solutions are approximated in real time by designed algorithms. By the Lyapunov theory, it is proven that the algorithms converge, and the closed-loop systems are stable. Finally, compared simulations and experiments for a group of three robots are provided to show the effectiveness of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

We consider a model of competition on complex networks, in which two competitors are fixed to opposite states while other agents, called normal agents, adjust their states according to a distributed consensus protocol. Suppose that one of the competitors could enhance its influence by creating new links. A natural question is, when the number of new links is limited due to the limited resource, how to add these links so as to maximize the influence of the given competitor over the other one (called competitiveness). We consider two competitiveness maximization problems: Problem 1 tries to maximize the number of supporters of the competitor, while Problem 2 tries to maximize the total supporting degree of normal agents toward the competitor. We prove that Problem 1 is NP-hard. We also show that the objective function of Problem 2 is monotonous and submodular, and hence there exists a polynomial-time greedy algorithm (GA) approximately solving Problem 2. Several centrality-based heuristic algorithms of less computational burden are also designed to provide approximate solutions to these two problems. We carry out extensive simulations to check the performances of these algorithms in six real networks. We find that GA always provides the best approximate solution to Problem 2, while for Problem 1, GA only has the best performance in directed networks. Furthermore, among those heuristic algorithms, an algorithm based on centrality in descending order is better than its counterpart in ascending order in solving Problem 2 in statistical sense. But for Problem 1, the performance of the centrality-based heuristic algorithms is more sensitive to the network structure and the locations of competitors. [ABSTRACT FROM AUTHOR]

Computational analysis of the structure of intra-cellular molecular interaction networks can suggest novel therapeutic approaches for systemic diseases like cancer. Recent research in the area of network science has shown that network control theory can be a powerful tool in the understanding and manipulation of such bio-medical networks. In 2011, Liu et al. developed a polynomial time algorithm computing the size of the minimal set of nodes controlling a linear network. In 2014, Gao et al. generalized the problem for target control, minimizing the set of nodes controlling a target within a linear network. The authors developed a Greedy approximation algorithm while leaving open the complexity of the optimization problem. We prove here that the target controllability problem is NP-hard in all practical setups, i.e., when the control power of any individual input is bounded by some constant. We also show that the algorithm provided by Gao et al. fails to provide a valid solution in some special cases, and an additional validation step is required. We fix and improve their algorithm using several heuristics, obtaining in the end an up to 10-fold decrease in running time and also a decrease in the size of solutions. [ABSTRACT FROM AUTHOR]

Demand response (DR) management has become one of the key enabling technologies for smart grids. Motivated by the increasing DR incentives offered by service operators, more customers are subscribing to various DR programs. However, with growing customer participation, the problem of determining the optimal loads to be curtailed in a microgrid (MG) during contingencies within a feasible time frame becomes computationally hard. This paper proposes an efficient approximation algorithm for event-based DR management in MGs. In event-based management, it is important to curtail loads as fast as possible to maintain the stability of an MG during the islanded mode in a scalable manner. A simple greedy approach is presented that can rapidly determine a close-to-optimal load curtailment scheme to maximize the aggregate customer utility in milliseconds for a large number of customers. This paper further derives a novel theoretical guarantee of the gap between the proposed efficient algorithm and the optimal solution (that may be computationally hard to obtain). The performance of algorithm is corroborated extensively by simulations with up to thousands of customers. For the sake of practicality, the proposed event-based DR management algorithm is applied to a feeder from the Canadian benchmark distribution system. The simulation results demonstrate that the proposed approach efficiently optimizes MG operation during islanded mode while maintaining appropriate voltage levels and network constrains. [ABSTRACT FROM AUTHOR]

We present the first sample compression algorithm for nearest neighbors with non-trivial performance guarantees. We complement these guarantees by demonstrating almost matching hardness lower bounds, which show that our performance bound is nearly optimal. Our result yields new insight into margin-based nearest neighbor classification in metric spaces and allows us to significantly sharpen and simplify existing bounds. Some encouraging empirical results are also presented. [ABSTRACT FROM AUTHOR]

The design of a protein folding approximation algorithm is not straightforward even when a simplified model is used. The folding problem is a combinatorial problem, where approximation and heuristic algorithms are usually used to find near optimal folds of proteins primary structures. Approximation algorithms provide guarantees on the distance to the optimal solution. The folding approximation approach proposed here depends on two-dimensional cellular automata to fold proteins presented in a well-studied simplified model called the hydrophobic-hydrophilic model. Cellular automata are discrete computational models that rely on local rules to produce some overall global behavior. One-third and one-fourth approximation algorithms choose a subset of the hydrophobic amino acids to form H-H contacts. Those algorithms start with finding a point to fold the protein sequence into two sides where one side ignores H’s at even positions and the other side ignores H’s at odd positions. In addition, blocks or groups of amino acids fold the same way according to a predefined normal form. We intend to improve approximation algorithms by considering all hydrophobic amino acids and folding based on the local neighborhood instead of using normal forms. The CA does not assume a fixed folding point. The proposed approach guarantees one half approximation minus the H-H endpoints. This lower bound guaranteed applies to short sequences only. This is proved as the core and the folds of the protein will have two identical sides for all short sequences. [ABSTRACT FROM AUTHOR]

This paper presents a computationally efficient method of generating state-to-state trajectories for fully actuated multirotor vehicles. The approach consists of computing translational and rotational motion primitives that guide the vehicle from any initial state, defined by position, velocity, and attitude, to any end state in a given time, and subsequently verifying the motion primitives’ feasibility. Computationally lightweight motion primitives for which closed-form solutions exist are presented and an efficient method to test their feasibility is derived. The algorithm is shown to be able to generate trajectories and verify their feasibility within a few microseconds and can thus be used as an implicit feedback law or in high-level path planners that involve evaluating a large number of possible trajectories to achieve some high-level goal. The algorithm's performance is analyzed by comparing it with time-optimal trajectories. An experimental demonstration that requires the computation of trajectories for a large set of end states in real time is used to evaluate the approach. [ABSTRACT FROM AUTHOR]

Content delivery networks (CDNs) cache and serve a majority of the user-requested content on the Internet. Designing caching algorithms that automatically adapt to the heterogeneity, burstiness, and non-stationary nature of real-world content requests is a major challenge and is the focus of our work. While there is much work on caching algorithms for stationary request traffic, the work on non-stationary request traffic is very limited. Consequently, most prior models are inaccurate for non-stationary production CDN traffic. We propose two TTL-based caching algorithms that provide provable performance guarantees for request traffic that is bursty and non-stationary. The first algorithm called d-TTL dynamically adapts a TTL parameter using stochastic approximation. Given a feasible target hit rate, we show that d-TTL converges to its target value for a general class of bursty traffic that allows Markov dependence over time and non-stationary arrivals. The second algorithm called f-TTL uses two caches, each with its own TTL. The first-level cache adaptively filters out non-stationary traffic, while the second-level cache stores frequently-accessed stationary traffic. Given feasible targets for both the hit rate and the expected cache size, f-TTL asymptotically achieves both targets. We evaluate both d-TTL and f-TTL using an extensive trace containing more than 500 million requests from a production CDN server. We show that both d-TTL and f-TTL converge to their hit rate targets with an error of about 1.3%. But, f-TTL requires a significantly smaller cache size than d-TTL to achieve the same hit rate, since it effectively filters out non-stationary content. [ABSTRACT FROM AUTHOR]

Although the read disturb spin-transfer torque RAM approximation technique improves performance, it may consist of an approximate read plus an approximate write both at the same time. So it may degrade the application quality of result (QoR) considerably. On the other hand, the incorrect read decision approximation technique improves power without corrupting the stored data. We adopt an opportunity study for instruction-based distinction of read implementation to take advantage of both of the approximation techniques, while enhancing application’s QoR. We evaluated the proposed method using a set of state-of-the-art benchmarks. The experimental results show that our method allows to increase application’s QoR substantially (i.e., by up to about 24 dB) compared to QoR-oblivious use of the read approximation techniques. [ABSTRACT FROM PUBLISHER]

We focus on a dense cellular network, in which a limited-size cache is available at every base station (BS). In order to optimize the overall performance of the system in such scenario, where a significant fraction of the users is covered by several BSs, a tight coordination among nearby caches is needed. To this end, this paper introduces a class of simple and fully distributed caching policies, which require neither direct communication among BSs nor a priori knowledge of content popularity. Furthermore, we propose a novel approximate analytical methodology to assess the performance of interacting caches under such policies. Our approach builds upon the well-known characteristic time approximation and provides predictions that are surprisingly accurate (hardly distinguishable from the simulations) in most of the scenarios. Both synthetic and trace-driven results show that our caching policies achieve an excellent performance (in some cases provably optimal). They outperform state-of-the-art dynamic policies for interacting caches, and, in some cases, also the greedy content placement, which is known to be the best performing polynomial algorithm under static and perfectly known content popularity profiles. [ABSTRACT FROM AUTHOR]

Feature selection based on fuzzy rough sets is an effective approach to select a compact feature subset that optimally predicts a given decision label. Despite being studied extensively, most existing methods of fuzzy rough set based feature selection are restricted to computing the whole dataset in batch, which is often costly or even intractable for large datasets. To improve the time efficiency, we investigate the incremental perspective for fuzzy rough set based feature selection assuming data can be presented in sample subsets one after another. The key challenge for the incremental perspective is how to add and delete features with the subsequent arrival of sample subsets. We tackle this challenge with strategies of adding and deleting features based on the relative discernibility relations that are updated as subsets arrive sequentially. Two incremental algorithms for fuzzy rough set based feature selection are designed based on the strategies. One updates the selected features as each sample subset arrives, and outputs the final feature subset where no sample subset is left. The other updates the relative discernibility relations but only performs feature selection where there is no further subset arriving. Experimental comparisons suggest our incremental algorithms expedite fuzzy rough set based feature selection without compromising performance. [ABSTRACT FROM AUTHOR]

Bandwidth reservation has been increasingly used in high-performance networks to provide quality of service for various applications ranging from real-time multimedia communication in early years to big data transfer more recently. In this paper, we consider multiple bandwidth reservation requests in a batch awaiting to be scheduled in a dedicated network, and formulate two scheduling maximization problems: 1) maximize the amount of data to be transferred and 2) maximize the number of requests to be scheduled. We prove both problems are NP-complete and very difficult to approximate. We then design two heuristic algorithms and evaluate their performance against a scheduling algorithm widely used in real production networks through extensive simulation-based experiments. The experimental results show that the proposed heuristic algorithms achieve significantly better overall scheduling performance than the existing algorithm. [ABSTRACT FROM AUTHOR]

Caching popular files at the user equipments (UEs) provides an effective way to alleviate the burden of the backhaul networks. Generally, popularity-based caching is not a system-wide optimal strategy, especially for user mobility scenarios. Motivated by this observation, we consider optimal caching with the presence of mobility. A cost-optimal caching problem (COCP) for device-to-device (D2D) networks is modeled, in which the impact of user mobility, cache size, and total number of encoded segments are all taken into account. The hardness of the problem is proved via a reduction from the satisfiability problem. Next, a lower-bounding function of the objective function is derived. By the function, an approximation of COCP (ACOCP) achieving linearization is obtained, which features two advantages. First, the ACOCP approach can use an off-the-shelf integer linear programming algorithm to obtain the global optimal solution, and it can effectively deliver solutions for small-scale and medium-scale system scenarios. Second, and more importantly, based on the ACOCP approach, one can derive a lower bound of global optimum of COCP, thus enabling performance benchmarking of any sub-optimal algorithm. To tackle large scenarios with low complexity, we first prove that the optimal caching placement of one user, giving other users’ caching placements, can be derived in polynomial time. Then, based on this proof, a mobility aware multi-user algorithm is developed. Simulation results verify the effectivenesses of the two approaches by comparing them to the lower bound of global optimum and conventional caching algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, the transmission optimization of a dual-hop decode-and-forward relaying system is investigated, where the relay capable of energy harvesting from ambient environment can work in hybrid half-duplex (HD) and/or full-duplex (FD) mode. To maximize the throughput from source to destination, the relay’s working mode is optimized under the constraint of random energy arrival. In particular, upon the availability of channel state information (CSI), two cases are sequentially studied: one is that CSI is unavailable to the transmitter and the other means CSI is available to the transmitter. In the former case, a dynamic programming (DP) algorithm is proposed to find the optimal working mode of the relay; moreover, to reduce the computational complexity, a linear programming (LP)-based heuristic algorithm is developed, which performs similar to the DP algorithm. In the latter case, the optimal mode of the relay is also obtainable by the DP algorithm and an approximate DP algorithm is further developed for lower computational complexity. Simulation results demonstrate that the hybrid mode outperforms pure HD and FD modes given that self-interference is efficiently suppressed. [ABSTRACT FROM PUBLISHER]

Today is an era where multiprocessor technology plays a major role in designs of modern computer architecture. While multiprocessor systems offer extra computing power, it also opens a new range of opportunities to improve fault-robustness. This paper focuses on a problem of achieving fault-tolerance using replications in real-time, multiprocessor systems. In the problem, multiple replicas, or copies, of a computing task are executed on distinct processors to resist potential processor failures and computing faults. Two greedy, approximation heuristics, named Worst Fit Increasing K-Replication and First Fit Increasing K-Replication, are studied to maximise the number of real-time tasks assigned on a system with identical processors, respecting to the tasks’ replicating and timely requirements. Worst case performance is analysed by using an approximation ratio between the algorithms and an optimal solution. We mathematically prove that the ratios of using both algorithms are infinitely close to 2. Simulations are performed on a large set of testing cases which can be used to bring to light the average performance of using the algorithms in practice. The results show that both heuristic algorithms provide simple but fast and effective solutions to solve the problem. Assigning real-time tasks to a multiprocessor system with replications. [ABSTRACT FROM PUBLISHER]

Finding an equilibrium state of the traffic assignment plays a significant role in the design of transportation networks. We adapt the path finding mathematical model of slime mold Physarum polycephalum to solve the traffic equilibrium assignment problem. We make three contributions in this paper. First, we propose a generalized Physarum model to solve the shortest path problem in directed and asymmetric graphs. Second, we extend it further to resolve the network design problem with multiple source nodes and sink nodes. At last, we demonstrate that the Physarum solver converges to the user-optimized (Wardrop) equilibrium by dynamically updating the costs of links in the network. In addition, convergence of the developed algorithm is proved. Numerical examples are used to demonstrate the efficiency of the proposed algorithm. The superiority of the proposed algorithm is demonstrated in comparison with several other algorithms, including the Frank-Wolfe algorithm, conjugate Frank-Wolfe algorithm, biconjugate Frank-Wolfe algorithm, and gradient projection algorithm. [ABSTRACT FROM PUBLISHER]

The dynamic frame sizing algorithm is a throughput-optimal algorithm that can achieve maximum network throughput without the knowledge of arrival rates. Motivated by the need for energy-efficient communication in wireless networks, in this paper, we propose a new dynamic frame sizing algorithm, called the Greenput algorithm, that takes power allocation into account. In our Greenput algorithm, time is partitioned into frames, and the frame size of each frame is determined based on the backlogs presented at the beginning of a frame. To obtain a good delay-energy efficiency tradeoff, the key insight of our Greenput algorithm is to reduce transmit power to save energy when the backlogs are low so as not to incur too much packet delay. For this, we define a threshold parameter T\max (for the minimum time to empty the backlogs with maximum power allocation), and the Greenput algorithm enters the (mixed) power-saving mode when the backlogs are below the threshold. Using a large deviation bound, we prove that our Greenput algorithm is still throughput optimal. In addition to the stability result, we also perform a fluid approximation analysis for energy efficiency and average packet delay when T\max is very large. To show the delay-energy efficiency tradeoff, we conduct extensive computer simulations by using the Shannon formula as the channel model in a wireless network. Our simulation results show that both energy efficiency and average packet delay are quite close to their fluid approximations even when T\max is moderately large. [ABSTRACT FROM AUTHOR]

We study the optimal placement of content over a network of caches, a problem naturally arising in several networking applications. Given a demand of content request rates and paths followed, we wish to determine the content placement that maximizes the expected caching gain, i.e., the reduction of routing costs due to intermediate caching. The offline version of this problem is NP-hard and, in general, the demand and topology may be a priori unknown. Hence, a distributed, adaptive approximation algorithm for placing contents into caches is desired. We show that path replication, a simple algorithm frequently encountered in literature, can be arbitrarily suboptimal when combined with traditional eviction policies. We propose a distributed, adaptive algorithm that performs stochastic gradient ascent on a concave relaxation of the expected caching gain, and constructs a probabilistic content placement within a $1-1/e$ factor from the optimal, in expectation. Motivated by our analysis, we also propose a novel greedy eviction policy to be used with path replication, and show through numerical evaluations that both algorithms significantly outperform path replication with traditional eviction policies over a broad array of network topologies. [ABSTRACT FROM AUTHOR]

Randomized algorithms play a central role in low rank approximations of large matrices. In this paper, the scheme of the randomized SVD is extended to a randomized LU algorithm. Several error bounds are introduced, that are based on recent results from random matrix theory related to subgaussian matrices. The bounds also improve the existing bounds of already known randomized SVD algorithm. The algorithm is fully parallelized and thus can utilize efficiently GPUs without any CPU–GPU data transfer. Numerical examples, which illustrate the performance of the algorithm and compare it to other decomposition methods, are presented. [ABSTRACT FROM AUTHOR]

In this paper, the problem of optimal assignment of trailer-mounted steam generators for cyclic steam stimulation (CSS) of petroleum wells is formulated as a parallel uniform machines scheduling (PMS) problem with release dates. The total weighed tardiness is used as the goal of the optimization process. The distinctive features of the proposed PMS formulation include: jobs with variable weights, variable machine setup time, constraint capacity of drilling pads (where machines are allocated), and modified tardiness criterion. A two-stage scheduling algorithm combining heuristic and genetic algorithms is proposed for solving it. A chromosome representation, crossover and mutation operators generating only feasible solutions and thus avoiding the use of any repair mechanism are discussed. The performance of the algorithm is tested on a real-world data set from the oilfield asset located in the coastal swamps of the Gulf of Mexico. The experiments indicate that the proposed approach gives good results in optimization of the operational costs and petroleum recovery. The algorithm is implemented as a part of the software platform for optimization of CSS and currently is in use by oilfield engineers. [ABSTRACT FROM AUTHOR]

The minimum vertex cover problem (MVCP) and minimum weighted vertex cover problem (MWVCP) have been used in a variety of applications. This paper focuses on a view of test-cost-sensitive rough set for MWVCP. We first provide a method to convert a minimum weight vertex cover of a graph into a minimal test cost attribute reduct of a test-cost-sensitive decision table. Then, an induced test-cost-sensitive decision table from an undirected weighted graph is established. On the foundation of the induced decision table, an improved heuristic algorithm for finding minimum weight vertex covers is proposed, it can avoid a mass of redundant computation. Furthermore, to improve efficiency, a quantum-behaved particle swarm optimization with immune mechanism is presented, which can avoid the phenomenon of premature, improve the global searching ability, and enhance the convergence speed. The results of the experiment show the advantages and limitations of the proposed algorithms compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The minimum weight dominating set problem (MWDSP) is an NP-hard problem with a lot of real-world applications. Several heuristic algorithms have been presented to produce good quality solutions. However, the solution time of them grows very quickly as the size of the instance increases. In this paper, we propose a binary particle swarm optimization (FBPSO) for solving the MWDSP approximately. Based on the characteristic of MWDSP, this approach designs a new position updating rule to guide the search to a promising area. An iterated greedy tabu search is used to enhance the solution quality quickly. In addition, several stochastic strategies are employed to diversify the search and prevent premature convergence. These methods maintain a good balance between the exploration and the exploitation. Experimental studies on 106 groups of 1 060 instances show that FBPSO is able to identify near optimal solutions in a short running time. The average deviation between the solutions obtained by FBPSO and the best known solutions is 0.441%. Moreover, the average solution time of FBPSO is much less than that of other existing algorithms. In particular, with the increasing of instance size, the solution time of FBPSO grows much more slowly than that of other existing algorithms. [ABSTRACT FROM AUTHOR]

Read trimming is a fundamental first step of the analysis of next generation sequencing (NGS) data. Traditionally, it is performed heuristically, and algorithmic work in this area has been neglected. Here, we address this topic and formulate three optimization problems for block-based trimming (truncating the same low-quality positions at both ends for all reads and removing low-quality truncated reads). We find that all problems are NP-hard. Hence, we investigate the approximability of the problems. Two of them are NP-hard to approximate. However, the non-random distribution of quality scores in NGS data sets makes it tempting to speculate that quality constraints for read positions are typically satisfied by fulfilling quality constraints for reads. Thus, we propose three relaxed problems and develop efficient polynomial-time algorithms for them including heuristic speed-up techniques and parallelizations. We apply these optimized block trimming algorithms to 12 data sets from three species, four sequencers, and read lengths ranging from 36 to 101 bp and find that (i) the omitted constraints are indeed almost always satisfied, (ii) the optimized read trimming algorithms typically yield a higher number of untrimmed bases than traditional heuristics, and (iii) these results can be generalized to alternative objective functions beyond counting the number of untrimmed bases. [ABSTRACT FROM PUBLISHER]

Reconstruction of ancestral relationships among genera, species, and populations is a core task in evolutionary biology. At the population level, pedigrees have been commonly used. Reconstruction of pedigree is required in practice due to legal or medical reasons. Pedigrees are very important to geneticists for inferring haplotype segments, recombination, and allele sharing status with which disease loci can be identified. Evaluating reconstruction methods requires comparing the inferred pedigree and the known pedigrees. Moreover, comparison of pedigrees is required in studying relationships among crops such as maize, wheat and barley, etc. In this paper, we discuss three models for comparison of pedigrees, the maximum pedigree isomorphism problem, the maximum paternal-path-preserved mapping problem, and the minimum edge-cutting mapping problem. For the maximum pedigree isomorphism problem, we prove that the problem is NP-hard and give a fixed-parameter algorithm for the problem. For the maximum paternal-path-preserved mapping problem, we give a dynamic-programming algorithm to find the mapping that preserves the maximum number of paternal paths between the two input pedigrees. For the minimum edge-cutting mapping problem, we prove that the problem is NP-hard and give a fixed-parameter algorithm with running time O(n(1+\sqrt2)^k) , where $n$ is the number of vertices in the two input pedigrees and $k$ is the number of edges to be cut. This algorithm is useful in practice when comparing two similar pedigrees. [ABSTRACT FROM PUBLISHER]

We consider the problem of scheduling a number of jobs on m identical processors sharing a continuously divisible resource. Each job j comes with a resource requirement . The job can be processed at full speed if granted its full resource requirement. If receiving only an x-portion of rj, it is processed at an x-fraction of the full speed. Our goal is to find a resource assignment that minimizes the makespan (i.e., the latest completion time). Variants of such problems, relating the resource assignment of jobs to their processing speeds, have been studied under the term discrete–continuous scheduling. Known results are either very pessimistic or heuristic in nature. In this article, we suggest and analyze a slightly simplified model. It focuses on the assignment of shared continuous resources to the processors. The job assignment to processors and the ordering of the jobs have already been fixed. It is shown that, even for unit size jobs, finding an optimal solution is NP-hard if the number of processors is part of the input. Positive results for unit size jobs include a polynomial-time algorithm for any constant number of processors. Since the running time is infeasible for practical purposes, we also provide more efficient algorithm variants: an optimal algorithm for two processors and a -approximation algorithm for m processors. [ABSTRACT FROM AUTHOR]

Cache policies to minimize the content retrieval cost have been studied through competitive analysis when the miss costs are additive and the sequence of content requests is arbitrary. More recently, a cache utility maximization problem has been introduced, where contents have stationary popularities and utilities are strictly concave in the hit rates. This paper bridges the two formulations, considering linear costs and content popularities. We show that minimizing the retrieval cost corresponds to solving an online knapsack problem, and we propose new dynamic policies inspired by simulated annealing, including DynqLRU, a variant of qLRU. We prove that DynqLRU asymptotically asymptotic converges to the optimum under the characteristic time approximation. In a real scenario, popularities vary over time and their estimation is very difficult. DynqLRU does not require popularity estimation, and our realistic, trace-driven evaluation shows that it significantly outperforms state-of-the-art policies, with up to 45% cost reduction. [ABSTRACT FROM PUBLISHER]