6937136
Dynamic power management using data buffers
This paper presents a method to reduce energy consumption by inserting data buffers. The method determines whether power can be reduced by inserting a buffer between two components and periodically turning off one of them. This method calculates the length of the period and the required buffer size to achieve the optimal energy savings. Our approach can be applied to any applications whose data arrival and departure rates are different and known in advance.
2733921
Joint power management of memory and disk
The paper presents a scheme to combine memory and power management for achieving better energy reduction. Our method periodically adjusts the size of physical memory and the timeout value to shut down a hard disk for reducing the average power consumption. We use Pareto distributions to model the distributions of idle time. The parameters of the distributions are adjusted at run-time for calculating the corresponding timeout value of the disk power management. The memory size is changed based on the inclusion property to predict the number of disk accesses at different memory sizes. Experimental results show more than 50% energy savings compared to a 2-competitive fixed-timeout method.
161273
Dynamic power management for streaming data
This paper presents a method that uses data buffers to smoothen request variations and to create long idleness for 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 and the amount of data to store in the buffers. We use video prefetching as a case study and obtain power savings of more than 74% for MPEG-1 and 34% for MPEG-2 videos.
15594355
Replacing Failed Sensor Nodes by Mobile Robots
Sensor replacement is important for sensor networks to provide continuous sensing services. Existing approaches relocate redundant nodes to fill the holes created by failed sensors and require all or most sensor nodes to have mobility. However, mobility equipment is expensive while technology trends are scaling sensors to be smaller and cheaper. In this paper, we propose to use a small number of mobile robots to replace failed sensors in a large-scale static sensor network. We study algorithms for detecting and reporting sensor failures and coordinating the movement of robots that minimize the motion energy of mobile robots and the messaging overhead incurred to the sensor network. A manager receives failure reports and determines which robot to handle a failure. We study three algorithms: a centralized manager algorithm, a fixed distributed manager algorithm, and a dynamic distributed manager algorithm. Our simulations show that: (a) The centralized and the dynamic distributed algorithms have lower motion overhead than the fixed distributed algorithm. (b) The centralized algorithm is less scalable than the two distributed manager algorithms. (c) The two distributed algorithms have higher messaging cost than the centralized algorithm. Hence, the optimal choice of the coordination algorithm depends on the specific scenarios and objectives being optimized.
9215156
Energy management using buffer memory for streaming data
This work presents a new approach for energy management by inserting data buffers. The buffers are managed using the concept of inventory control by treating energy as cost and computation as production. The data stored in the buffers are considered as merchandise. Our method provides a mathematical framework to develop general strategies managing the energy consumption in computers. The approach can solve a wide range of problems. For example, it can calculate the needed buffer sizes to achieve optimal energy savings. It can derive the conditions when a standby state saves energy. The method also shows the effect of load balancing on energy conservation and can handle the variations of data rates. We use sensor networks as case studies and demonstrate more than 20% energy savings.
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.
1552056
Pama: a Fast String Matching Algorithm
String matching is a fundamental operation in computer science, and its performance has great impact on many applications including database query, text processing, DNA and protein sequence analysis. In this paper, we propose a fast string matching algorithm, PAMA (PAttern MAtching). The shift rule used by PAMA not only subsumes both the bad character rule and the good suffix rule employed by the well-known Boyer-Moore algorithm, but also employs an additional key observation to enable faster shifting during the string matching process. Theoretically, we prove that from the same alignment, the next shift of PAMA will be at least as much as that of the Boyer-Moore algorithm. Experimentally, we show that PAMA indeed significantly outperforms the original Boyer-Moore algorithm in almost all cases, and outperforms other Boyer-Moore variants such as Tuned-BM, Turbo-BM and Horspool for long patterns (length ≥ 128) or for small alphabets (size < 8).
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.
2807541
IDEA:: an infrastructure for detection-based adaptive consistency control in replicated services
In Internet-scale distributed and replicated services, poor consistency results in poor QoS or even monetary loss. Recent research focuses on enforcing a certain consistency level, instead of perfect consistency, to strike a balance between consistency guarantee and system's scalability. In this paper, we argue that it is equally, if not more, important to achieve adaptability. I.e., the system adjusts its consistency level on the fly to suit applications. ongoing need. This paper presents IDEA (an Infrastructure for DEtection-based Adaptive consistency control), a protocol that adaptively controls consistency in replicated services by detecting inconsistency among nodes in a timely manner via an inconsistency detection framework and resolving the detected inconsistencies efficiently when necessary. Through experimentation on Planet-Lab, IDEA is evaluated from two aspects: its adaptive interface and its performance of inconsistency resolution. Results show that IDEA achieves adaptability by adjusting the consistency level according to users. preference on-demand, and it achieves low inconsistency resolution delay and incurs minimal communication cost.
3148116
Modeling of wave behavior of substrate noise coupling for mixed-signal IC design
A new full-wave method is introduced for substrate noise analysis and simulation. The method is based on solution of the wave equation for the magnetic potential and can be implemented using standard circuit simulators. We compare the new method with the standard quasi-static method for typical substrate profiles and investigate the limits of validity of the quasi-static method.
10102267
Parallel Randomized Support Vector Machine
A parallel support vector machine based on randomized sampling technique is proposed in this paper. We modeled a new LP-type problem so that it works for general linear-nonseparable SVM training problems unlike the previous work [2]. A unique priority based sampling mechanism is used so that we can prove an average convergence rate that is so far the fastest bounded convergence rate to the best of our knowledge. The numerical results on synthesized data and a real geometric database show that our algorithm has good scalability.