Off-line Algorithm Research Articles

On Cost-Driven Collaborative Data Caching: A New Model Approach

In this paper we consider a new caching model that enables data sharing for network services in a cost-effective way. The proposed caching algorithms are characterized by using monetary cost and access information to control the cache replacements, instead of exploiting capacity-oriented strategies as in traditional approaches. In particular, given a stream of requests to a shared data item with respect to a homogeneous cost model, we first propose a fast off-line algorithm using dynamic programming techniques, which can generate an optimal schedule within $O(mn)$ time-space complexity by using cache, migration as well as replication to serve a $n$ -length request sequence in a $m$ -node network, substantially improving the previous results. Furthermore, we also study the online form of this problem, and present an 3-competitive online algorithm by leveraging an idea of anticipatory caching. The algorithm can serve an online request in constant time and is space efficient in $O(m)$ as well, rendering it more practical in reality. We evaluate our algorithms, together with some variants, by conducting extensive simulation studies. Our results show that the optimal cost of the off-line algorithm is changed in a parabolic form as the ratio of caching cost to transfer cost is increased, and the online algorithm is less than 2 times worse in most cases than its optimal off-line counterpart.

A-STC: auction-based spanning tree coverage algorithm formotion planning of cooperative robots

The multi-robot coverage motion planning (MCMP) problem in which every reachable area must be covered is common in multi-robot systems. To deal with the MCMP problem, we propose an efficient, complete, and off-line algorithm, named the “auction-based spanning tree coverage (A-STC)” algorithm. First, the configuration space is divided into mega cells whose size is twice the minimum coverage range of a robot. Based on connection relationships among mega cells, a graph structure can be obtained. A robot that circumnavigates a spanning tree of the graph can generate a coverage trajectory. Then, the proposed algorithm adopts an auction mechanism to construct one spanning tree for each robot. In this mechanism, an auctioneer robot chooses a suitable vertex of the graph as an auction item from neighboring vertexes of its spanning tree by heuristic rules. A bidder robot submits a proper bid to the auctioneer according to the auction vertexes’ relationships with the spanning tree of the robot and the estimated length of its trajectory. The estimated length is calculated based on vertexes and edges in the spanning tree. The bidder with the highest bid is selected as a winner to reduce the makespan of the coverage task. After auction processes, acceptable coverage trajectories can be planned rapidly. Computational experiments validate the effectiveness of the proposed MCMP algorithm and the method for estimating trajectory lengths. The proposed algorithm is also compared with the state-of-the-art algorithms. The comparative results show that the A-STC algorithm has apparent advantages in terms of the running time and the makespan for large crowded configuration spaces.

A Novel Iterative Learning Control Approach Based on Steady-State Kalman Filtering

This paper presents a novel off-line iterative learning control algorithm for multiple-input–multiple-output time-varying discrete stochastic systems. Using the steady-state Kalman filtering method, we provide a novel framework for the selection of optimal/sub-optimal fixed learning gain matrices in real applications, which is convenient for engineers. Meanwhile, this framework considerably decreases the calculation about the operations of inverting matrix by introducing a matrix Riccati equation at every iteration. It is strictly proved that the input error covariance converges to its steady-state value asymptotically in the mean square sense, and accordingly, the tracking error covariance also converges. The numerical simulations verify the theoretical results.

Broadband Frost Adaptive Array Antenna with a Farrow Delay Filter

In a broadband adaptive array antenna with a space-time filter, a delay filter is required before digital beamforming when the Frost algorithm is used to obtain the weight vector. In this paper, we propose a Farrow structure instead of a direct form FIR structure to implement the time delay filter since it can satisfy the demand for real-time update and is very suited for the FPGA platform. Furthermore, a new off-line algorithm to calculate the Farrow filter coefficient is presented if the filter coefficient is symmetric. Finally, simulations are presented to illustrate that the design methodology for the Farrow filter is correct. The simulations also prove that the Frost broadband adaptive antenna effectively mitigates the interferences with a Farrow filter.

Long-Term Power Procurement Scheduling Method for Smart-Grid Powered Communication Systems

With the emergence of smart grids, adopting dynamic energy pricing models has become both possible and desirable. With such a pricing dynamicity, great savings in energy costs can be achieved in telecommunication systems when energy is procured efficiently through carefully designed real-time resource schedulers. Broadly speaking, existing scheduling algorithms can be categorized into two classes: online and off-line. Off-line algorithms are not practical merely because of their need for prior knowledge of future system information. In this paper, we propose an efficient online power procurement and allocation scheduler that maximizes a long-term system utility function without the need for prior knowledge of future system information, where the system utility function is expressed in such a way that the gain coming from serving the users and the cost of the procured energy are traded off for one another. We propose an approach that allows us to derive closed-form instantaneous energy procurement and resource allocations that are functions only of the actual instantaneous system parameters. Our approach computes the optimal power procurement and users’ allocation per time slot in an online fashion with very low computational complexity. Using simulations, we study the efficiency of the proposed approach under various parameters and quantify the energy costs that our approach can potentially save.

Robust asymptotic stability of parametric switched linear systems with dwell time

Switched linear systems with dwell time constraints constitute a very popular class of switched systems. In this study, impact of uncertain parameters on the stability of Parametrised switched linear (SL) systems with dwell time is investigated. Sufficient conditions are first proposed to analyse the global asymptotic stability of SL systems with dwell time. The conditions are then generalised to the case with parametric uncertainties to determine upper bounds on parametric uncertainties such that robust asymptotic stability is assured under dwell time constraints. Finally, an off-line computational algorithm is proposed to enlarge the estimated stability intervals. Effectiveness and simplicity of the computations are shown by a numerical example.

Near MAP Dynamical Delay Estimator and Bayesian CRB for Coded QAM Signals

This paper presents an off-line algorithm for dynamical time delay recovery for which the whole observation block is used. The time offset varies over the observation interval following a random walk model. The proposed synchronizer applies to data-aided (DA), non-data-aided (NDA), and code-aided (CA) modes. Theoretical performance of the off-line technique is derived and compared with simulation results. The Bayesian Cramer-Rao Bound (BCRB) is also evaluated for DA, NDA, and CA modes and for both the off-line and on-line scenarios. Simulation results show the improvement brought by the off-line and the CA schemes. The presented algorithm outperforms the conventional on-line estimator, which only considers the current and previous observations, and its mean square error approaches the BCRB.

Using semantic graphs to detect overlapping target events and story lines from newspaper articles

Event detection from text data is an active area of research. While the emphasis in the literature has been on event identification and labeling using a single data source, this work considers event and story line detection when using a large number of data sources. In this setting, it is natural for different events in the same domain, e.g., violence, sports, politics, to occur at the same time and for different story lines about the same event to emerge. To capture events in this setting, we propose an Offline algorithm that detects events and story lines about events for a target domain given a news article collection. Our algorithm leverages a multi-relational sentence-level semantic graph and well-known graph properties to identify overlapping events and story lines within the events. We then extend this algorithm for an Online setting. Both the Offline and Online approaches are evaluated using two large data sets containing millions of news articles from a large number of sources. Our empirical analysis shows that methods using the proposed semantic graph beat the state of the art in terms of precision and recall while providing more complete event summaries.

Solving a class of simulation-based optimization problems using “optimality in probability”

We consider a class of simulation-based optimization problems using optimality in probability, an approach which yields what is termed a “champion solution”. Compared to the traditional optimality in expectation, this approach favors the solution whose actual performance is more likely better than that of any other solution; this is an alternative complementary approach to the traditional optimality sense, especially when facing a dynamic and nonstationary environment. Moreover, using optimality in probability is computationally promising for a class of simulation-based optimization problems, since it can reduce computational complexity by orders of magnitude compared to general simulation-based optimization methods using optimality in expectation. Accordingly, we have developed an “Omega Median Algorithm” in order to effectively obtain the champion solution and to fully utilize the efficiency of well-developed off-line algorithms to further facilitate timely decision making. An inventory control problem with nonstationary demand is included to illustrate and interpret the use of the Omega Median Algorithm, whose performance is tested using simulations.

Method to increase the working frequency of digital carrier synchronization module for high-speed digital coherent optical receivers

A digital carrier synchronization module with high working frequency is indispensable for high-speed digital coherent optical receivers to recover the transmitted symbols. We proposed a method to increase the working frequency of the digital carrier synchronization (DCS) module based on the commonly used M’th power algorithms. Parallel architecture can increase the throughput of digital signal processing (DSP) modules for a given working frequency. pipelined architecture (PA) leads to a reduction in the critical path, and thus it can be exploited to increase the throughput of DSP modules by increasing the working frequency. It is demonstrated that in PA the working frequency is not limited by the computation time of the M’th power subfunction with the highest complexity because it is feedforward and thus pipelining registers can be introduced to reduce the critical path inside it. Instead, the phase unwrapping subfunction (PUS) becomes the bottleneck of the working frequency because it requires the immediately preceding result and cannot be implemented in PA, which results in the longest critical path among the DCS module. To solve this problem, we propose a feedforward look-up-table-based PUS design that can greatly reduce the critical path and increase the working frequency. Experimental DCS implementation in a Xilinx Virtex7 field programmable gate array shows that with this method the working frequency of the DCS module for quadrature phase-shift keying (QPSK) signals can be increased by 63.8%. Furthermore, using experimental and simulation data, it is demonstrated that the performance of the DCS module with increased working frequency is close to that of the off-line DCS algorithms for QPSK signals.

Robust Direct Power Control Schemes of DFIGs Driven by Variable-speed Wind Turbines

Despite its several advantages, a classic direct power control (DPC) technique of doubly fed induction generators (DFIGs) driven by variable-speed wind turbines has some drawbacks such as high power ripples and variable switching frequency. In this paper, two robust controllers are designed to improve the classical DPC performance without complicating the overall scheme. First, an integral sliding mode controller (ISMC) is designed to regulate the stator active and reactive powers. Two integral switching functions are selected for controlling stator active and reactive powers. The idea of total sliding mode controller is selected to avoid reaching phase stability problem. Second, a diagonal recurrent neural network (DRNN) controller is designed and trained based on DPC. The DRNN has several advantages compared to the classical static neural networks such as recurrence and simple construction. Simple off-line back-propagation algorithm is proposed to train the proposed DRNN. The stability of the proposed ISMC and DRNN controller is proved using the Lyapunov stability theorem. The grid side converter is controlled based on the DPC principle to ensure both constant DC-link voltage and grid side reactive power. The feasibility of the proposed DPC schemes is validated by simulation studies on a 1.5-MW wind power generation system. The performance of the proposed schemes is compared with a conventional DPC scheme under different operating conditions.

On Service Migrations in the Cloud for Mobile Accesses

We study the problem of dynamically migrating a service in the cloud to satisfy an online sequence of mobile batch-request demands in a cost-effective way. The service may have single or multiple replicas, each running on a virtual machine. As the origin of mobile accesses frequently changes over time, this problem is particularly important for time-bounded services to achieve enhanced Quality of Service and cost effectiveness. Moving the service closer to the client locations not only reduces the service access latency but also minimizes the network costs for service providers. However, these benefits are not free. The migration comes at a cost of bulk-data transfer and service disruption, and hence, increasing the overall service costs. To gain the benefits of service migration while minimizing the caused monetary costs, we propose an efficient search-based algorithm Dmig to migrate a single server, and then extend it as a scalable algorithm, called mDmig , to the multi-server situation, a more general case in the cloud. Both algorithms are fully distributed, symmetric, and characterized by the effective use of historical access information to conduct virtual migration so that the limitations of local search in the cost reduction can be overcome. To evaluate the algorithms, we compared them with some existing algorithms and an off-line algorithm. Our simulation results showed that the proposed algorithms exhibit better performance in service migration by adapting to the changes of mobile access patterns in a cost-effective way.

Designing fuzzy rule base using Spider Monkey Optimization Algorithm in cooperative framework

The paper focusses on the implementation of cooperative Spider Monkey Optimization Algorithm (SMO) to design and optimize the fuzzy rule base. Spider Monkey Optimization Algorithm is a fission-fusion based Swarm Intelligence algorithm. Cooperative Spider Monkey Algorithm is an off-line algorithm used to optimize all the free parameters in a fuzzy rule base. The Spider Monkeys are divided into various groups the solution from each group represents a fuzzy rule. These groups work in a cooperative way to design the whole fuzzy rule base. Simulation on fuzzy rules of two nonlinear controllers is done with a parametric study to verify the performance of the algorithm. It is observed that the root mean square error (RMSE) is least in the case of SMO than the other evolutionary algorithms applied in the literature to solve the problem of fuzzy rule designs like Particle Swarm Optimization (PSO), Ant Colony Optimization algorithm (ACO) algorithms.

A Two-Dimensional DOA Tracking Algorithm Using PAST with L-Shape Array

In this paper, we proposed a 2D-DOA tracking algorithm using projection approximation and subspace tracking (PAST) with L-shape Array. The proposed algorithm obtains signal subspace by PAST, without any eigen-value decomposition (EVD) of the covariance matrix, Compared with off-line algorithms, PAST algorithm has a much lower computational complexity. Finally we use ESPRIT algorithm to achieve automatically paired 2D-DOA estimation.

Data-driven control of nonlinear systems: An on-line direct approach

A data-driven method to design reference tracking controllers for nonlinear systems is presented. The technique does not derive explicitly a model of the system, rather it delivers directly a time-varying state-feedback controller by combining an on-line and an off-line scheme. Like in other on-line algorithms, the measurements collected in closed-loop operation are exploited to modify the controller in order to improve the tracking performance over time. At the same time, a predictable closed-loop behavior is guaranteed by making use of a batch of available data, which is a feature of off-line algorithms. The feedback controller is parameterized with kernel functions and the design approach exploits results in set membership identification and learning by projections. Under the assumptions of Lipschitz continuity and stabilizability of the system’s dynamics, it is shown that if the initial batch of data is informative enough, then the resulting closed-loop system is guaranteed to be finite gain stable. In addition to the main theoretical properties of the approach, the design algorithm is demonstrated experimentally on a water tank system.

An efficient method to evaluate intersections on big data sets

Set intersections are important in computer science. Especially, intersection of inverted lists is a fundamental operation in information retrieval for text databases and Web search engines. In this paper, we discuss an efficient and effective way to implement this operation in the context of very big data sets. The main idea behind it is to do a binary search over sorted interval sequences, each of which corresponds to an inverted list and is constructed by establishing a trie over the sequences of set identifiers as well as a kind of tree encoding, by which each node in the trie is assigned an interval. In many cases, an interval sequence is much shorter than its corresponding inverted list. In particular, the lowest common ancestors of intervals in a trie can be utilized to control a binary search to skip over useless interval containment checks, which enables us to reach an optimal off-line algorithm to do the task, and is theoretically better than any traditional on-line methods (at cost of more space). Experiments have been conducted, showing that the trade-off of space for time is worthwhile.

Mapping filtered forwarding-based trajectory tracking control

Forwading-based immersion and invariance approaches have been applied to control the high-order nonlinear systems, whereas it is an off-line algorithm and needs to directly compute analytic derivatives of the mappings. In our study, a new tracking control algorithm is developed by introducing a second-order filter at each step of the design. This recursive algorithm does not only overcome the problem of “explosion of complexity” to improve computational efficiency, but also suppresses the high-frequency noise arising from time derivatives of states and virtual controls. Unlike other existing control methodologies, it does not require the knowledge of a Lyapunov function in principle. The boundedness of all mappings and their analytic derivatives can be ultimately guaranteed by using the internal stability of filters. Also, this bottom-up algorithm is able to provide the modularized design of the controller, where real-time applications can be to some extent realized. In this work, a quadrotor helicopter is used to demonstrate the controller performances via various simulations.

Wavelength channel minimization-based energy-aware mechanism in a multichannel EPON

We propose an energy-aware mechanism (EAM) applicable to the multichannel Ethernet Passive Optical Network that can minimize the number of wavelength channels used and save energy. Wavelength channel minimization is processed by collecting the information such as request message, allocated grant, and start time of each optical network unit (ONU) transmission in the previously elapsed scheduling cycles and comparing it with the buffer occupancy and packet delay conditions of the ONUs required by the user's quality-of-service requirement. They are exchanged between the optical line terminal (OLT) and the ONUs via the multipoint control protocol. In this way, at the beginning of each scheduling cycle, the ONU's buffer occupancy and packet delay conditions can be evaluated, and then, the OLT decides the smallest number of wavelength channels to be used in the current scheduling cycle. By turning off the OLT receivers corresponding to the unused wavelength channels, the OLT can save energy. The performance of the proposed EAM was evaluated through simulations using nonjoint off-line dynamic bandwidth allocation and dynamic wavelength assignment algorithms. The results showed that the OLT receivers' power consumption could be reduced by 48 % on average.

On the competitiveness of scheduling dynamically injected tasks on processes prone to crashes and restarts

To identify the tradeoffs between efficiency and fault-tolerance in dynamic cooperative computing, we initiate the study of a task performing problem under dynamic processes’ crashes/restarts and task injections. The system consists of n message-passing processes which, subject to dynamic crashes and restarts, cooperate in performing tasks that are continuously and dynamically injected to the system. Tasks are not known a priori to the processes. This problem abstracts todays Internet-based computations, such as Grid computing and cloud services, where tasks are generated dynamically and different tasks may become known to different processes. We measure performance in terms of the number of pending tasks, and as such it can be directly compared with the optimum number obtained under the same crash–restart–injection pattern by the best off-line algorithm. Hence, we view the problem as an online problem and we pursue competitive analysis. We propose several deterministic algorithmic solutions to the considered problem under different information models and correctness criteria, and we argue that their performance is close to the best possible offline solutions. We also prove negative results that open interesting research directions.

Scheduling Mixed-Criticality Real-Time Tasks in a Fault-Tolerant System

Enabling computer tasks with different levels of criticality running on a common hardware platform has been an increasingly important trend in the design of real-time and embedded systems. On such systems, a real-time task may exhibit different WCETs (Worst Case Execution Times) in different criticality modes. It is well-known that traditional real-time scheduling methods are not applicable to ensure the timely requirement of the mixed-criticality tasks. In this paper, the authors study a problem of scheduling real-time, mixed-criticality tasks with fault tolerance. An optimal, off-line algorithm is designed to guarantee the most tasks completing successfully when the system runs into the high-criticality mode. A formal proof of the optimality is given. Also, a novel on-line slack-reclaiming algorithm is proposed to recover from computing faults before the tasks' deadline during the run-time. Simulations show that an improvement of about 30% in performance is obtained by using the slack-reclaiming method.