15933152
Homeostatic and tendency-based CPU load predictions
The dynamic nature of a resource-sharing environment means that applications must be able to adapt their behavior in response to changes in system status. Predictions of future system performance can be used to guide such adaptations. In this paper, we present and evaluate several new one-step-ahead and low-overhead time series prediction strategies that track recent trends by giving more weight to recent data. We present results that show that a dynamic tendency prediction model with different ascending and descending behavior performs best among all strategies studied. A comparative study conducted on a set of 38 machine load traces shows that this new predictor achieves average prediction errors that are between 2% and 55% less (36% less on average) than those incurred by the predictors used within the popular Network Weather Service system.
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.
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.
37853546
Impact of device geometry and doping strategy on linearity and RF performance in Si/SiGe MODFETs
Abstract Based on careful calibration in respect of 70 nm n-type strained Si channel Si/SiGe modulation doped FETs (MODFETs) fabricated by Daimler Chrysler, numerical simulations have been used to study the impact of the device geometry and various doping strategies on device performance and linearity. Both the lateral and vertical layer structures are crucial to achieve high RF performance or high linearity. The simulations suggest that gate length scaling helps to achieve higher RF performance, but degrades the linearity. Doped channel devices are found to be promising for high linearity applications. Trade-off design strategies are required for reconciling the demands of high device performance and high linearity simultaneously.
6650910
A predictive, decentralized load balancing approach
The growth of load balancing system raises the issue of scalability, and decentralized load balancing architecture has been proposed to address this issue. In this paper, we investigate how a load balancing architecture can be built on decentralized policies based on CORBA and enhanced by predictive algorithm. The L/sub 2/E predictive filtering model was used to supply workstations with robust cluster load information, which allows them to make more accurate independent allocation decisions. Experimental results showed that our decentralized load balancing approach was able to suppress thrashing and oscillations compared to other load monitoring and prediction techniques, and it was able to achieve a highly balanced system than Sun Grid Engine.
20306091
Fast and exact computation of Cartesian geometric moments using discrete Green's theorem
Abstract Green's theorem evaluates a double integral over a planar object by a simple integration along the boundary of the object. It can be used in fast computation of Cartesian geometric moments of two-dimensional binary objects, since the object shape is totally determined by the boundary. Li and Shen [Pattern Recognition 24, 807–813 (1991)] used an approximation of Green's theorem to deal with discrete objects. This method is fast, but not accurate. In this paper, we present a discrete version of Green's theorem, which evaluates a double sum over a two-dimensional discrete object by a simple summation along the discrete boundary of the object. By using the discrete Green's theorem, we propose a new algorithm for fast computation of the geometric moments. The new algorithm is faster than the previous methods, and gives exactly the same results as if a double sum were used. The importance of the exact computation is discussed by examining the accuracy of Hu's moment invariants and some other moment based shape features, which are frequently used in various shape description and pattern recognition tasks. A fast method for computing moments of regions in gray level images, using the discrete Green's theorem, is also presented. We show that the moments of this type are useful in structural texture analysis.
10496869
Conservative Scheduling: Using Predicted Variance to Improve Scheduling Decisions in Dynamic Environments
In heterogeneous and dynamic environments, efficient execution of parallel computations can require mappings of tasks to processors whose performance is both irregular (because of heterogeneity) and time-varying (because of dynamicity). While adaptive domain decomposition techniques have been used to address heterogeneous resource capabilities, temporal variations in those capabilities have seldom been considered. We propose a conservative scheduling policy that uses information about expected future variance in resource capabilities to produce more efficient data mapping decisions. We first present techniques, based on time series predictors that we developed in previous work, for predicting CPU load at some future time point, average CPU load for some future time interval, and variation of CPU load over some future time interval. We then present a family of stochastic scheduling algorithms that exploit such predictions of future availability and variability when making data mapping decisions. Finally, we describe experiments in which we apply our techniques to an astrophysics application. The results of these experiments demonstrate that conservative scheduling can produce execution times that are both significantly faster and less variable than other techniques.
3016307
Weighted Information Entropy: A Method for Estimating the Complex Degree of Infrared Images' Backgrounds
The validity of using the weighted information entropy to estimate the complex degree of the infrared images' backgrounds is discussed in this paper. A Butterworth high pass filter whose cut-off frequency can be adaptively regulated to meet the different backgrounds is proposed to restrain the different clutters. Since the backgrounds usually have some random change in the video sequences, an adaptive binarisation criterion for small target detection is also presented. Experimental results show the robustness of our method.