1182896
Power Reduction of Multiple Disks Using Dynamic Cache Resizing and Speed Control
This paper presents an energy-conservation method for multiple disks and their cache memory. Our method periodically resizes the cache memory and controls the rotation speeds under performance constraints. The cache memory stores the data from the disks for reuse. Enlarging the cache memory reduces disk accesses and disk utilization. This allows the disks to reduce their speeds and conserve energy because the disks' power consumption is quadratic to their speeds. However, the cache memory itself consumes power to retain data. Shrinking cache memory can save memory power while increasing disk accesses and degrading performance. Choosing proper cache sizes and rotation speeds can reduce the energy consumption of both memory and disks with satisfactory performance. We model cache resizing and speed setting as an optimization problem with minimizing the power consumption as objective and limiting disk utilization as constraints. We compare our method with the methods resizing cache based on request rates. The simulation results show that our method achieves better energy-savings while limiting disk access latency
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.
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.
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.
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.
7682350
Incremental genetic K-means algorithm and its application in gene expression data analysis
BackgroundIn recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.ResultsIn this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm.ConclusionsOur experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.
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.
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.
7735246
Identifying cycling genes by combining sequence homology and expression data
MOTIVATION
The expression of genes during the cell division process has now been studied in many different species. An important goal of these studies is to identify the set of cycling genes. To date, this was done independently for each of the species studied. Due to noise and other data analysis problems, accurately deriving a set of cycling genes from expression data is a hard problem. This is especially true for some of the multicellular organisms, including humans.


RESULTS
Here we present the first algorithm that combines microarray expression data from multiple species for identifying cycling genes. Our algorithm represents genes from multiple species as nodes in a graph. Edges between genes represent sequence similarity. Starting with the measured expression values for each species we use Belief Propagation to determine a posterior score for genes. This posterior is used to determine a new set of cycling genes for each species. We applied our algorithm to improve the identification of the set of cell cycle genes in budding yeast and humans. As we show, by incorporating sequence similarity information we were able to obtain a more accurate set of genes compared to methods that rely on expression data alone. Our method was especially successful for the human dataset indicating that it can use a high quality dataset from one species to overcome noise problems in another.


AVAILABILITY
C implementation is available from the supporting website: http://www.cs.cmu.edu/~lyongu/pub/cellcycle/.