14287933
Statistically optimal dynamic power management for streaming data
This paper presents a method that uses data buffers to create long periods of idleness to exploit power management. This method considers the power consumed by the buffers and assigns an energy penalty for buffer underflow. Our approach provides analytic formulas for calculating the optimal buffer sizes without subjective or heuristic decisions. We simulate four different hardware configurations with MPEG-1, MPEG-2, and MPEG-4 formats as a case study. Our results indicate that the optimal buffer size varies significantly for different data formats on different hardware. Simulation results indicate that 16 MB buffers are sufficient for MPEG-1, MPEG-2, and MPEG-4 video streams from a microdrive or a network card, but transfers from an IDE disk require buffer sizes ranging from 16 MB to 176 MB, depending on each video's statistical properties.
7685891
Quantitative comparison of power management algorithms
Dynamic power management saves power by shutting down idle devices. Several management algorithms have been proposed and demonstrated to be effective in certain applications. We quantitatively compare the power saving and performance impact of these algorithms on hard disks of a desktop and notebook computers. This paper has three contributions. First, we build a framework in Windows NT to implement power managers running realistic workloads and directly interacting with users. Second, we define performance degradation that reflects user perception. Finally, we compare power saving and performance of existing algorithms and analyze the difference.
4668328
CoCoA: Coordinated Cooperative Localization for Mobile Multi-Robot Ad Hoc Networks
Mobile robot teams are particularly suited to applications where infrastructure is unavailable or damaged since they can be used to quickly form an infrastructure-less mobile ad hoc network performing tasks in a distributed manner. In such deployment scenarios, localization of individual robots is essential for enabling many applications. In this paper, we propose CoCoA, Coordinated Cooperative localization for mobile multi-robot Ad Hoc Networks, that is low-cost, quickly deployable, energy-efficient, and provides reasonable accuracy for the applications. In CoCoA, only a subset of the robots in the mobile robot team are equipped with external localization devices (e.g. GPS, laser rangers, vision sensors). Subsequently, while robots perform their tasks, they cooperate to help localize each other without requiring static landmarks to be deployed. In addition, CoCoA coordinates the localization operations of robots to reduce energy consumption. Using detailed simulations and localization models calibrated from experimental data, we find that CoCoA is effective in reducing energy consumption while providing good localization accuracy.
5831663
Program counter-based prediction techniques for dynamic power management
Reducing energy consumption has become one of the major challenges in designing future computing systems. This paper proposes a novel idea of using program counters to predict I/O activities in the operating system. It presents a complete design of program-counter access predictor (PCAP) that dynamically learns the access patterns of applications and predicts when an I/O device can be shut down to save energy. PCAP uses path-based correlation to observe a particular sequence of program counters leading to each idle period and predicts future occurrences of that idle period. PCAP differs from previously proposed shutdown predictors in its ability to: 1) correlate I/O operations to particular behavior of the applications and users, 2) carry prediction information across multiple executions of the applications, and 3) attain higher energy savings while incurring lower mispredictions. We perform an extensive evaluation study of PCAP using a detailed trace-driven simulation and an actual Linux implementation. Our results show that PCAP achieves lower average mispredictions and higher energy savings than the simple timeout scheme and the state-of-the-art learning tree scheme
8031661
Requester-aware power reduction
Typically, power reduction is conducted by hardware techniques, such as varying clock frequencies and/or supply voltages. However, hardware devices consume power to serve the requests from software programs. Consequently, it is essential to consider software for power reduction. This paper proposes "requester-aware" power reduction through the collaboration with programs. Experimental results show that this approach can save nearly 70% power with negligible performance degradation.
11944586
Comparing System-Level Power Management Policies
Reducing power consumption is a challenge to system designers. Portable systems, such as laptop computers and personal digital assistants (PDAs), draw power from batteries, so reducing power consumption extends their operating times. For desktop computers or servers, high power consumption raises temperature and deteriorates performance and reliability. Soaring energy prices and rising concern about the environmental impact of electronics systems further highlight the importance of low power consumption. Power reduction techniques can be classified as static and dynamic. Static techniques, such as synthesis and compilation for low power, are applied at design time. In contrast, dynamic techniques use runtime behavior to reduce power when systems are serving light workloads or are idle. These techniques are known as dynamic power management (DPM). DPM can be achieved in different ways; for example, dynamic voltage scaling (DVS) changes supply voltage at runtime as a method of power management. Here, we use DPM specifically for shutting down unused I/O devices. We built an experimental environment on a laptop computer running Microsoft Windows. We implemented existing power management policies and quantitatively compared their effects on power saving and performance degradation.
27140699
Differential Algebraic Method for Aberration Analysis of Electron Optical Systems
Differential algebraic method is a powerful technique in computer numerical analysis. It presents a straightforward method for computing arbitrary order derivatives of functions with extreme high accuracy limited only by the machine error. When applied to nonlinear dynamics systems, the arbitrary high order transfer properties of the system can be derived directly. In this paper, the principle of differential algebraic method is applied to calculate high order aberrations of electron optical systems. As an example, an electrostatic lens with an analytical expression has been calculated using this method. Relative errors of the Gaussian properties and spherical aberration coefficient of the lens compared with the analytic solutions are of the order 10− 11 or smaller. It is proved that differential algebraic aberration method is very helpful with high accuracy for high order aberration analysis and computation of electron optical systems.
15882697
Coupling feature selection and machine learning methods for navigational query identification
It is important yet hard to identify navigational queries in Web search due to a lack of sufficient information in Web queries, which are typically very short. In this paper we study several machine learning methods, including naive Bayes model, maximum entropy model, support vector machine (SVM), and stochastic gradient boosting tree (SGBT), for navigational query identification in Web search. To boost the performance of these machine techniques, we exploit several feature selection methods and propose coupling feature selection with classification approaches to achieve the best performance. Different from most prior work that uses a small number of features, in this paper, we study the problem of identifying navigational queries with thousands of available features, extracted from major commercial search engine results, Web search user click data, query log, and the whole Web's relational content. A multi-level feature extraction system is constructed.Our results on real search data show that 1) Among all the features we tested, user click distribution features are the most important set of features for identifying navigational queries. 2) In order to achieve good performance, machine learning approaches have to be coupled with good feature selection methods. We find that gradient boosting tree, coupled with linear SVM feature selection is most effective. 3) With carefully coupled feature selection and classification approaches, navigational queries can be accurately identified with 88.1% F1 score, which is 33% error rate reduction compared to the best uncoupled system, and 40% error rate reduction compared to a well tuned system without feature selection.
9192961
Counterexample-Guided Abstraction Refinement
The main practical problem in model checking is the combinatorial explosion of system states commonly known as the state explosion problem. Abstraction methods attempt to reduce the size of the state space by employing knowledge about the system and the specification in order to model only relevant features in the Kripke structure. Counterexample-guided abstraction refinement is an automatic abstraction method where, starting with a relatively small skeletal representation of the system to be verified, increasingly precise abstract representations of the system are computed. The key step is to extract information from false negatives ("spurious counterexamples") due to over-approximation.
42676253
Synchronization of large sequential circuits by partial reset
In this paper, we present a technique to synchronize large synchronous sequential circuits, which uses partial reset to break feedback loops in the circuits such that they become synchronizable. The method also provides a synchronizing sequence. Experimental results for ISCAS'89 benchmark circuits are presented, including the largest circuits, which were not synchronized previously.
158009
Threshold optimization algorithm for weak signal in distributed-sensor fusion system
Distributed order statistics constant false alarm rate (OS-CFAR) detection techniques are important for nonstationary observation with varying weak narrowband random signals. However, it is very difficult to choose system parameters to obtain optimal threshold values at the fusion center because of the nonlinear property of distributed OS-CFAR detection system. This paper provides a novel solution based on an effective and flexible genetic algorithm. Using this approach, all system parameters are directly coded in decimal chromosomes and they can be optimized simultaneously. Furthermore, we also derive the more general model by implementing the K fusion rules. The simulation results show that applying the proposed approach one can achieve comparable performances with the reported methods and results.