2470832
Statistical data reduction for efficient application performance monitoring
There is a growing need for systems that can monitor and analyze application performance data automatically in order to deliver reliable and sustained performance to applications. However, the continuously growing complexity of high performance computer systems and applications makes this process difficult. We introduce a statistical data reduction method that can be used to guide the selection of system metrics that are both necessary and sufficient to describe observed application behavior, thus reducing the instrumentation perturbation and data volume to be managed. To evaluate our strategy, we applied it to one CPU-bound grid application using cluster machines and GridFTP data transfer in a wide area testbed. A comparative study shows that our strategy produces better results than other techniques. It can reduce the number of system metrics to be managed by about 80%, while still capturing enough information for performance predictions.
8275741
Efficient relational joins with arithmetic constraints on multiple attributes
We introduce and study a new class of queries that we refer to as ACMA (arithmetic constraints on multiple attributes) queries. Such combinatorial queries require the simultaneous satisfaction of arithmetic constraints on three or more attributes from different relations, and thus often involve expensive multi-join operations. Building on techniques from constraint programming, we develop preprocessing methods, algorithms, and a new constrained join operator that allow ACMA queries to be evaluated efficiently within a conventional relational database engine. We present the results of a careful performance evaluation of both our new approach and the conventional nested-loop join algorithm. Measurements of tuples read, intermediate tuples generated, and execution time shows that our approach achieves superior performance for ACMA joins.
2764337
Automated compile-time and run-time techniques to increase usable memory in MMU-less embedded systems
Random access memory (RAM) is tightly-constrained in many embedded systems. This is especially true for the least expensive, lowest-power embedded systems, such as sensor network nodes and portable consumer electronics. The most widely-used sensor network nodes have only 4-10 KB of RAM and do not contain memory management units (MMUs). It is very difficult to implement increasingly complex applications under such tight memory constraints. Nonetheless, price and power consumption constraints make it unlikely that increases in RAM in these systems will keep pace with the requirements of applications.We propose the use of automated compile-time and run-time techniques to increase the amount of usable memory in MMU-less embedded systems. The proposed techniques do not increase hardware cost, and are designed to require few or no changes to existing applications. We have developed a fast compression algorithm well suited to this application, as well as run-time library routines and compiler transformations to control and optimize the automatic migration of application data between compressed and uncompressed memory regions. These techniques were experimentally evaluated on Crossbow TelosB sensor network nodes running a number of data collection and signal processing applications. The results indicate that available memory can be increased by up to 50% with less than 10% performance degradation for most benchmarks.
21010264
Fast Computation of Three-Dimensional Geometric Moments Using a Discrete Divergence Theorem and a Generalization to Higher Dimensions
Abstract The three-dimensional Cartesian geometric moments (for short 3-D moments) are important features for 3-D object recognition and shape description. To calculate the moments of objects in a 3-D image by a straightforward method requires a large number of computing operations. Some authors have proposed fast algorithms to compute the 3-D moments. However, the problem of computation has not been well solved since all known methods require computations of order N 3 , assuming that the object is represented by an N × N × N voxel image. In this paper, we present a discrete divergence theorem which can be used to compute the sum of a function over an n -dimensional discrete region by a summation over the discrete surface enclosing the region. As its corollaries, we give a discrete Gauss's theorem for 3-D discrete objects and a discrete Green's theorem for 2-D discrete objects. Using a fast surface tracking algorithm and the discrete Gauss's theorem, we design a new method to compute the geometric moments of 3-D binary objects as observed in 3-D discrete images. This method reduces the computational complexity significantly, requiring computation of O ( N 2 ). This reduction is demonstrated experimentally on two 3-D objects. We also generalize the method to deal with high-dimensional images. Some 3-D moment invariants and shape features are also discussed.
236427
Efficient XML query processing in mediators
With the advent of XML as the standard for data representation and exchange over the Internet, there are ever increasing applications; E-commerce application in particular, using XML as a mediator model. It raises the issue of how to evaluate XML queries efficiently in mediator systems. The authors address the problem of multiple query optimization in an XML based mediator. Because the XML query language differs from SQL/OQL in its pattern matching and regular path expression constructs, the method of identifying common subexpressions of multiple XML queries is different accordingly. We present the steps to identify common subexpressions. To obtain the global query plan, we propose some heuristics to reduce the whole search space. By using a statistical method to estimate the usage frequency of the common subexpressions in XML queries, a selective caching and materializing of remote data can be accurately made, and this will greatly improve query response time.
5761634
High-performance operating system controlled memory compression
This article describes a new software-based on-line memory compression algorithm for embedded systems and presents a method of adaptively managing the uncompressed and compressed memory regions during application execution. The primary goal of this work is to save memory in disk-less embedded systems, resulting in greater functionality, smaller size, and lower overall cost, without modifying applications or hardware. In comparison with algorithms that are commonly used in on-line memory compression, our new algorithm has a comparable compression ratio but is twice as fast. The adaptive memory management scheme effectively responds to the predicted needs of applications and prevents on-line memory compression deadlock, permitting reliable and efficient compression for a wide range of applications. We have evaluated our technique on an embedded portable device and have found that the memory available to applications can be increased by 150%, allowing the execution of applications with larger working data sets, or allowing existing applications to run with less physical memory
3393163
Dynamic Integration Strategy for Mediation Framework
The trend in the information area is to provide integrated access to diverse and dynamic information sources. We present a modeling and architectural solution to the problem of data integration from heterogeneous data sources based on the use of wrappers and mediators. The mediator supports integrated query processing by the semantic mapping between the global schema type and the local schema types. However, mediation poses extensive security problems. Protecting proprietary data from unauthorized access is recognized as one of the most significant barriers to the mediation systems. Our approach benefits in both flexible strategy of data integration based on semantic mappings and dynamic access control in mediation systems.