A self-adapting latency/power tradeoff model for replicated search engines

For many search settings, distributed/replicated search engines deploy a large number of machines to ensure efficient retrieval. This paper investigates how the power consumption of a replicated search engine can be automatically reduced when the system has low contention, without compromising its efficiency. We propose a novel self-adapting model to analyse the trade-off between latency and power consumption for distributed search engines. When query volumes are high and there is contention for the resources, the model automatically increases the necessary number of active machines in the system to maintain acceptable query response times. On the other hand, when the load of the system is low and the queries can be served easily, the model is able to reduce the number of active machines, leading to power savings. The model bases its decisions on examining the current and historical query loads of the search engine. Our proposal is formulated as a general dynamic decision problem, which can be quickly solved by dynamic programming in response to changing query loads. Thorough experiments are conducted to validate the usefulness of the proposed adaptive model using historical Web search traffic submitted to a commercial search engine. Our results show that our proposed self-adapting model can achieve an energy saving of 33% while only degrading mean query completion time by 10 ms compared to a baseline that provisions replicas based on a previous day's traffic

Strategic policies and growth: An applied model of R&D-driven endogenous growth

Cataloged from PDF version of article.We introduce and explore a general equilibrium model with R&D-driven endogenous growth, whose antecedents are the models of Romer (1990) [Romer, P.M., 1990. Endogenous technological change. Journal of Political Economy, 98, S71-102] and Grossman and Helpman (1991) [Grossman, G.M., Helpman E., 1991. Innovation and Growth in the Global Economy, The MIT Press, Cambridge]. Utilizing evidence front recent econometric studies on sources of growth, the model also accounts explicitly for cross-border technological spillovers. The model is specified and calibrated to data from Japan, and is solved to obtain both the transitional and the steady-state equilibria. We explore the effects of selective trade and R&D promotion policies on long-run growth and social welfare. The model results suggest that while a strategic trade policy has little effect on re-allocating resources into domestic R&D activities, it can significantly affect the cross-border spillovers of technological knowledge, which, in turn, stimulates growth. We find that trade liberalization may cause the growth rate to fall and lead to a loss of social welfare in the long-run, although it improves welfare in the short-run. R&D promotion policies stimulate growth by inducing private agents to allocate more resources to domestic R&D, as well as to take greater advantage of global R&D spillovers. Here, we find significantly high growth effects together with sizable gains in social welfare at low incidence to tax payers. (C) 1999 Elsevier Science B.V. All rights reserved

Dynamic Adaptation of Temporal Event Correlation Rules

Temporal event correlation is essential to realizing self-managing distributed systems. Autonomic controllers often require that events be correlated across multiple components using rule patterns with timer-based transitions, e.g., to detect denial of service attacks and to warn of staging problems with business critical applications. This short paper discusses automatic adjustment of timer values for event correlation rules, in particular compensating for the variability of event propagation delays due to factors such as contention for network and server resources. We describe a corresponding Management Station architecture and present experimental studies on a testbed system that suggest that this approach can produce results at least as good as an optimal fixed setting of timer values

Dynamic Adaptation of Rules for Temporal Event Correlation in Distributed Systems

Event correlation is essential to realizing self-managing distributed systems. For example, distributed systems often require that events be correlated from multiple systems using temporal patterns to detect denial of service attacks and to warn of problems with business critical applications that run on multiple servers. This paper addresses how to specify timer values for temporal patterns so as to manage the trade-off between false alarms and undetected alarms. A central concern is addressing the variability of event propagation delays due to factors such as contention for network and server resources. To this end, we develop an architecture and an adaptive control algorithm that dynamically compensate for variations in propagation delays. Our approach makes Management Stations more autonomic by avoiding the need for manual adjustments of timer values in temporal rules. Further, studies we conducted of a testbed system suggest that our approach produces results that are at least as good as an optimal fixed setting of timer values

A Control Theory Foundation for Self-Managing Computing Systems

The high cost of operating large computing installations has motivated a broad interest in reducing the need for human intervention by making systems self-managing. This paper explores the extent to which control theory can provide an architectural and analytic foundation for building self-managing systems. Control theory provides a rich set of methodologies for building automated self-diagnosis and self-repairing systems with properties such as stability, short settling times, and accurate regulation. However, there are challenges in applying control theory to computing systems, such as developing effective resource models, handling sensor delays, and addressing lead times in effector actions. We propose a deployable testbed for autonomic computing (DTAC) that we believe will reduce the barriers to addressing research problems in applying control theory to computing systems. The initial DTAC architecture is described along with several problems that it can be used to investigate

Longitudinal age-dependent effect on systolic blood pressure

Age-dependent genetic effects on susceptibility to hypertension have been documented. We present a novel variance-component method for the estimation of age-dependent genetic effects on longitudinal systolic blood pressure using 57,827 Affymetrix single-nucleotide polymorphisms (SNPs) on chromosomes 17-22 genotyped in 2,475 members of the Offspring Cohort of the Framingham Heart Study. We used the likelihood-ratio test statistic to test the main genetic effect, genotype-by-age interaction, and simultaneously, main genetic effect and genotype-by-age interactions (2 degrees of freedom (df) test) for each SNP. Applying Bonferroni correction, three SNPs were significantly associated with longitudinal blood pressure in the analysis of main genetic effects or in combined 2-df analyses. For the associations detected using the simultaneous 2-df test, neither main effects nor genotype-by-age interaction p-values reached genome-wide statistical significance. The value of the 2-df test for screening genetic interaction effects could not be established in this study