17606134
Operating-system directed power reduction
this paper presents a new approach for power reduction by taking a global, software-centric view. It analyzes the sources of power consumption: tasks that require services from hardware components. When a component is not used by any task, it can enter a sleeping state to save power. Operating systems have detailed information about tasks; therefore, OS is the best place for identifying hardware idleness and shutting down unused components. We implement this technique in Linux and show that it can save more than 50% power compared to traditional hardware-centric shutdown techniques.
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.
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.
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.
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.
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.
651975
Wyner–Ziv Switching Scheme for Multiple Bit-Rate Video Streaming
This paper proposes a Wyner-Ziv (WZ) switching scheme for multiple bit-rate (MBR) video streaming over networks. Identical video content is encoded into a set of normal streams, which are generated by conventional hybrid video coding with multiple bit rates, so that streaming can dynamically switch among these normal streams according to available bandwidth. At encoder side, the WZ codec generates a switching stream by compressing the reconstructed frames of a certain normal stream that will be switched to, no matter which normal stream it switches from. At decoder side, for switching to the same frame, the same WZ switching stream is used to reconstruct the switching-to frame by taking the switching-from frame as the side information. The number of required WZ bits depends on the inherent mutual correlation between two frames switching to and from. Since the WZ switching streams are generated independently of the normal switching-from streams, given normal streams that can switch from any one to another, the proposed scheme reduces the number of switching streams from to . Furthermore, switching streams do not deteriorate the coding efficiency of normal streams when no switching occurs. However a big problem here, similar to requesting bits in distributed video coding, is how many WZ bits should be transmitted when a switching happens because the streaming scenario does not tolerate too much extra delay caused by the requests back and forth. Therefore, a Laplacian model, which is proved in the simplified case, is proposed to characterize the correlation between switching-to and switching-from frames. It can be used to accurately estimate the number of WZ bits at the server side.
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.
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).
16579446
Mining Correlation between Motifs and Gene Expression
One of the major challenges in the post-genomic era is to determine all DNA-binding transcription factors (TFs) and their regulatory binding sites (motifs) within the genomes. To discover the relationship between the motifs and changes in gene expression, we propose a new algorithm, co-miner (correlation miner). Correlation rules are generated based on the expression profiles of genes with significant expression change through the time course of gene expression. Thus, we may consider the change in gene expression to be causatively associated with the transcription binding sites in the upstream sequences. In addition, we introduce partition and constraint pushing techniques to improve the performance and demonstrate their effectiveness by our experiments. By applying co-miner to a yeast dataset, the relationships between motifs and gene expression revealed by co-miner are confirmed in the literature.
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.