1459531
SmartSaver: Turning Flash Drive into a Disk Energy Saver for Mobile Computers
In a mobile computer the hard disk consumes a considerable amount of energy. Existing dynamic power management policies usually take conservative approaches to save disk energy, and disk energy consumption remains a serious issue. Meanwhile, the flash drive is becoming a must-have portable storage device for almost every laptop user on travel. In this paper, we propose to make another highly desired use of the flash drive - saving disk energy. This is achieved by using the flash drive as a standby buffer for caching and prefetching disk data. Our design significantly extends disk idle times with careful and deliberate consideration of the particular characteristics of the flash drive. Trace-driven simulations show that up to 41% of disk energy can be saved with a relatively small amount of data written to the flash drive
12471348
Look-Ahead Architecture Adaptation to Reduce Processor Power Consumption
An effective approach to reducing processor power consumption is to adaptively activate and deactivate hardware resources. The authors propose a look-ahead scheme that adjusts the processor issue rate triggered by main-memory accesses. This architecture-independent technique is particularly effective for memory-intensive applications. Combined with an existing technique based on IPC values, it also reduces power consumption for computation-intensive applications.
6763495
Memory Hierarchy Considerations for Cost-Effective Cluster Computing
Using off-the-shelf commodity workstations and PCs to build a cluster for parallel computing has become a common practice. The cost-effectiveness of a cluster computing platform for a given budget and for certain types of applications is mainly determined by its memory hierarchy and the interconnection network configurations of the cluster. Finding such a cost-effective solution from exhaustive simulations would be highly time-consuming and predictions from measurements on existing clusters would be impractical. We present an analytical model for evaluating the performance impact of memory hierarchies and networks on cluster computing. The model covers the memory hierarchy of a single SMP, a cluster of workstations/PCs, or a cluster of SMPs by changing various architectural parameters. Network variations covering both bus and switch networks are also included in the analysis. Different types of applications are characterized by parameterized workloads with different computation and communication requirements. The model has been validated by simulations and measurements. The workloads used for experiments are both scientific applications and commercial workloads. Our study shows that the depth of the memory hierarchy is the most sensitive factor affecting the execution time for many types of workloads. However, the interconnection network cost of a tightly coupled system with a short depth in memory hierarchy, such as an SMP, is significantly more expensive than a normal cluster network connecting independent computer nodes. Thus, the essential issue to be considered is the trade-off between the depth of the memory hierarchy and the system cost. Based on analyses and case studies, we present our quantitative recommendations for building cost-effective clusters for different workloads.
8429922
ASAP: an AS-Aware Peer-Relay Protocol for High Quality VoIP
Peer-to-peer (P2P) technology has been successfully applied in Internet telephony or Voice over Internet Protocol (VoIP), such as the Skype system, where P2P is used for both searching clients and relaying voice packets. Selecting one or multiple suitable peers to relay voice packets is a critical factor for the quality, scalability and cost of a VoIP system. In this paper, we first present two sets of intensive Internet measurement results to confirm the benefits gained by peer relays in VoIP, and to investigate the performance of the Skype system. We obtain the following results: (1) many relay peer selections are suboptimal; (2) the waiting time to select a relay node can be quite long; and (3) there are a large number of unnecessary probes, resulting in heavy network traffic to limit scalability of the VoIP system. Our further analysis shows that two main reasons cause these problems. First, the peer selections do not take Autonomous System (AS) topology into consideration, and second, the complex communication relationships among peers are not well utilized. Motivated by our measurements and analysis, we propose an AS-aware peer-relay protocol called ASAP. Our objective is to significantly improve VoIP quality and system scalability with low overhead. Our intensive evaluation by trace-driven simulation shows ASAP is highly effective and easy to implement on the Internet for building large and scalable VoIP systems.
13613044
Low-Cost and Reliable Mutual Anonymity Protocols in Peer-to-Peer Networks
We present several protocols to achieve mutual communication anonymity between an information requester and a provider in a P2P information-sharing environment, such that neither the requester nor the provider can identify each other, and no other peers can identify the two communicating parties with certainty. Most existing solutions achieve mutual anonymity in pure P2P systems without any trusted central controls. Compared with two such representative ones, our protocols improve efficiency in two different ways. First, utilizing trusted third parties and aiming at both reliability and low-cost, we propose a group of mutual anonymity protocols. We show that with some limited central support, our protocols can accomplish the goals of anonymity, efficiency, and reliability. Second, we propose a mutual anonymity protocol which relies solely on self-organizations among peers without any trusted central controls. In this protocol, the returning path can be shorter than the requesting path. This protocol does not need to broadcast the requested file back to the requester so that the bandwidth is saved and efficiency is improved. In addition, this protocol does not need special nodes to keep indices of sharing files, thus eliminating the index maintenance overhead and the potential for inconsistency between index records and peer file contents. We have evaluated our techniques in a browser-sharing environment. We show that the average increase in response time caused by our protocols is negligible, and these protocols show advantages over existing protocols in a P2P system.
10102756
Semi-Empirical Multiprocessor Performance Predictions
This paper presents a multiprocessor performance prediction methodology supported by experimental measurements, which predicts the execution time of large application programs on large parallel architectures based on a small set of sample data. We propose a graph model to describe application program behavior. In order to precisely abstract an architecture model for the prediction, important and implicit architecture parameters are obtained by experiments. We focus on performance predictions of application programs in shared-memory and data-parallel architectures. Real world applications are implemented using the shared-memory model on the KSR-1 and using the data-parallel model on the CM-5 for performance measurements and prediction validation. We show that experimental measurements provide strong support for performance predictions on multiprocessors with implicit communications and complex memory systems, such as shared-memory and data-parallel systems, while analytical techniques partially applied in the prediction significantly reduce computer simulation and measurement time.
17528755
Significance of Gene Ranking for Classification of Microarray Samples
Many methods for classification and gene selection with microarray data have been developed. These methods usually give a ranking of genes. Evaluating the statistical significance of the gene ranking is important for understanding the results and for further biological investigations, but this question has not been well addressed for machine learning methods in existing works. Here, we address this problem by formulating it in the framework of hypothesis testing and propose a solution based on resampling. The proposed r-test methods convert gene ranking results into position p-values to evaluate the significance of genes. The methods are tested on three real microarray data sets and three simulation data sets with support vector machines as the method of classification and gene selection. The obtained position p-values help to determine the number of genes to be selected and enable scientists to analyze selection results by sophisticated multivariate methods under the same statistical inference paradigm as for simple hypothesis testing methods
8448018
Efficient Frequent Pattern Mining on Web Logs
Mining frequent patterns from Web logs is an important data mining task. Candidate-generation-and-test and pattern-growth are two representative frequent pattern mining approaches. We have conducted extensive experiments on real world Web log data to analyse the characteristics of Web logs and the behaviours of these two approaches on Web logs. To improve the performance of current algorithms on mining Web logs, we propose a new algorithm – Combined Frequent Pattern Mining (CFPM) to cater for Web log data specifically. We use heuristics to prune search space and reduce costs in mining so that better efficiency is achieved. Experimental results show that CFPM significantly improves the performance of the pattern-growth approach by 1.2–7.8 times on mining frequent patterns from Web logs.
6359793
Integration of cluster ensemble and EM based text mining for microarray gene cluster identification and annotation
In this paper, we design and develop a unified system GE-Miner (Gene Expression Miner) to integrate cluster ensemble, text clustering and multi document summarization and provide an environment for comprehensive gene expression data analysis. We present a novel cluster ensemble approach to generate high quality gene cluster. In our text summarization module, given a gene cluster, our Expectation Maximization (EM) based algorithm can automatically identify subtopics and extract most probable terms for each topic. Then, the extracted top k topical terms from each subtopic are combined to form the biological explanation of each gene cluster. Experimental results demonstrate that our system can obtain high quality clusters and provide informative key terms for the gene clusters.
17533834
Efficiently Computing the Top N Averages in Iceberg Cubes
Data cubes are an enabling approach for efficient on-line analytical processing (OLAP) systems. Iceberg cubes are special cubes consisting of the aggregates of multi-dimensional groups that satisfy user-specified thresholds. When there are a relatively large number of dimensions, the number of groups in an iceberg cube is huge. End users cannot fully understand the aggregate results or directly use them to make a decision. Approaches for the efficient computation of the top n groups have been proposed and have been shown to work well on iceberg cubes with simple aggregate functions such as COUNT and SUM. In this paper, we study the efficient computation of the top n groups with the complex aggregate function AVERAGE. As the average of a sub-group does not increase/decrease monotonically with its super-group, AVERAGE constraints cannot be used directly for pruning. We propose a new technique upper-bounding average which is anti-monotonic and can be used for low-cost effective pruning. Based on a tree structure representing groups, search and pruning techniques are developed, and an algorithm is proposed to compute the top n averages efficiently.
35361263
Reducing Overfitting in Predicting Intrinsically Unstructured Proteins
Intrinsically unstructured or disordered proteins are proteins that lack fixed 3-D structure globally or contain long disordered regions. Predicting disordered regions has attracted significant research recently. In developing a decision tree based disordered region predictor, we note that many previous predictors applying 20 amino acid compositions as training parameter tend to overfit the data. In this paper we propose to alleviate overfitting in prediction of intrinsically unstructured proteins by reducing input parameters. We also compare this approach with the random forest model, which is inherently tolerant to overfitting. Our experiments suggest that reducing 20 amino acid compositions into 4 groups according to amino acid property can reduce the overfitting in decision tree model. Alternatively, ensemble-learning techniques like random forest is inherently more tolerant to this kind of overfitting and can be a promising candidate in disordered region prediction.