2618375
Server-Level Power Control
We present a technique that controls the peak power consumption of a high-density server by implementing a feedback controller that uses precise, system-level power measurement to periodically select the highest performance state while keeping the system within a fixed power constraint. A control theoretic methodology is applied to systematically design this control loop with analytic assurances of system stability and controller performance, despite unpredictable workloads and running environments. In a real server we are able to control power over a 1 second period to within 1 W. Additionally, we have observed that power over an 8 second period can be controlled to within 0.1 W. We believe that we are the first to demonstrate such precise control of power in a real server. Conventional servers respond to power supply constraint situations by using simple open-loop policies to set a safe performance level in order to limit peak power consumption. We show that closed-loop control can provide higher performance under these conditions and test this technique on an IBM BladeCenter HS20 server. Experimental results demonstrate that closed-loop control provides up to 82% higher application performance compared to open-loop control and up to 17% higher performance compared to a widely used ad-hoc technique.
5867346
Feedback utilization control in distributed real-time systems with end-to-end tasks
An increasing number of distributed real-time systems face the critical challenge of providing quality of service guarantees in open and unpredictable environments. In particular, such systems often need to enforce utilization bounds on multiple processors in order to avoid overload and meet end-to-end deadlines even when task execution times are unpredictable. While recent feedback control real-time scheduling algorithms have shown promise, they cannot handle the common end-to-end task model where each task is comprised of a chain of subtasks distributed on multiple processors. This paper presents the end-to-end utilization control (EUCON) algorithm that adaptively maintains desired CPU utilization through performance feedbacks loops. EUCON is based on a model predictive control approach that models utilization control on a distributed platform as a multivariable constrained optimization problem. A multi-input-multi-output model predictive controller is designed based on a difference equation model that describes the dynamic behavior of distributed real-time systems. Both control theoretic analysis and simulations demonstrate that EUCON can provide robust utilization guarantees when task execution times deviate from estimation or vary significantly at runtime.
15273314
Feedback control real-time scheduling in ORB middleware
Existing real-time ORB middleware standards such as RT-CORBA do not adequately address the challenges of 1) providing robust performance guarantees portably across different platforms, and 2) managing unpredictable workload. To overcome this limitation, we have developed software called FCS/nORB that integrates a Feedback Control real-lime Scheduling (FCS) service with the nORB small-footprint real-time ORB designed for networked embedded systems. FCS/nORB features feedback control loops that provide real-time performance guarantees by automatically adjusting the rate of remote method invocations transparently to an application. FCS/nORB thus enables real-time applications to be truly portable in terms of real-time performance as well as functionality, without the need for hand tuning. This paper presents the design, implementation, and evaluation of FCS/nORB. Our extensive experiments on a Linux testbed demonstrate that FCS can provide deadline miss ratio and utilization guarantees in face of changes in the platform and task execution times, while introducing a small amount of overhead.
1347401
Decentralized utilization control in distributed real-time systems
Many real-time systems must control their CPU utilizations in order to meet end-to-end deadlines and prevent overload. Utilization control is particularly challenging in distributed real-time systems with highly unpredictable workloads and a large number of end-to-end tasks and processors. This paper presents the decentralized end-to-end utilization control (DEUCON) algorithm that can dynamically enforce desired utilizations on multiple processors in such systems. In contrast to centralized control schemes adopted in earlier work, DEUCON features a novel decentralized control structure that only requires localized coordination among neighbor processors. DEUCON is systematically designed based on recent advances in distributed model predictive control theory. Both control-theoretic analysis and simulations show that DEUCON can provide robust utilization guarantees and maintain global system stability despite severe variations in task execution times. Furthermore, DEUCON can effectively distribute the computation and communication cost to different processors and tolerate considerable communication delay between local controllers. Our results indicate that DEUCON can provide scalable and robust utilization control for large-scale distributed real-time systems executing in unpredictable environments
5719252
End-to-end utilization control in distributed real-time systems
An increasing number of distributed real-time systems face the critical challenge of providing end-to-end quality of service (QoS) guarantees in open and unpredictable environments. In particular, such systems often need to guarantee the CPU utilization on multiple processors in order to achieve overload protection and meet end-to-end deadlines while task execution times are unpredictable. While the recently developed feedback control real-time scheduling algorithms have shown promise, they cannot handle the common end-to-end task model in distributed systems where each task is comprised of a chain of subtasks distributed on multiple processors. We present the end-to-end utilization control (EUCON) algorithm that features a distributed feedback loop that dynamically enforces desired CPU utilization bounds on multiple processors based on online performance measurements EUCON is based on a model predictive control approach that models the utilization control problem on a distributed platform as a multivariable constrained optimization problem. A multiinput-multioutput model predictive controller is designed based on a difference equation model that describes the dynamic behavior of distributed real-time systems. Both control theoretic analysis and simulations demonstrate that EUCON can provide robust utilization guarantees even when task execution times deviate from the estimation or vary significantly at run-time.
269391
Integrated coverage and connectivity configuration in wireless sensor networks
An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes, while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully, the active nodes must maintain both sensing coverage and network connectivity. Furthermore, the network must be able to configure itself to any feasible degrees of coverage and connectivity in order to support different applications and environments with diverse requirements. This paper presents the design and analysis of novel protocols that can dynamically configure a network to achieve guaranteed degrees of coverage and connectivity. This work differs from existing connectivity or coverage maintenance protocols in several key ways: 1) We present a Coverage Configuration Protocol (CCP) that can provide different degrees of coverage requested by applications. This flexibility allows the network to self-configure for a wide range of applications and (possibly dynamic) environments. 2) We provide a geometric analysis of the relationship between coverage and connectivity. This analysis yields key insights for treating coverage and connectivity in a unified framework: this is in sharp contrast to several existing approaches that address the two problems in isolation. 3) Finally, we integrate CCP with SPAN to provide both coverage and connectivity guarantees. We demonstrate the capability of our protocols to provide guaranteed coverage and connectivity configurations, through both geometric analysis and extensive simulations.
11647652
Analysis of complex bursting in cortical pyramidal neuron models
Abstract Burst firing is a prominent feature of cortical pyramidal cells and is thought to have significant functional roles in reliable signaling and synaptic plasticity. Modeling studies have successfully elucidated possible biophysical mechanisms underlying complex bursting in pyramidal cells. Based on these results (Pinsky, Rinzel, J. Comput. Neurosci. 1 (1994) 39–60), we have built a simplified two-compartment burst model. Using the fast- and slow-variable analysis method, we show that complex bursting is an instance of square-wave bursting, where the dendritic slow potassium conductance is the single slow variable. The coupling parameters between the two compartments change the topological class of bursting thereby altering the firing patterns of the neuron. These results explain the diverse set of firing patterns seen with different dendritic morphologies (Mainen, Sejnowski, Nature 382 (1996) 363–366).
18230248
Impact of Sparse Pixel Vectorization Algorithm Parameters on Line Segmentation Performance
We present the impact of parameters in the Sparse Pixel Vectorization algorithm on the performance of the algorithm, which is evaluated using a pixel level performance evaluation protocol. Both theoretical and experimental analyses are presented, which show that the experimental results are well in accordance with the theoretical analyses. Both prove the robustness of the algorithm. Meanwhile the parameter values for the best performance of the algorithm are also found.
3433910
Residual-Based Measurement of Peer and Link Lifetimes in Gnutella Networks
Existing methods of measuring lifetimes in P2P systems usually rely on the so-called create-based method (CBM), which divides a given observation window into two halves and samples users "created" in the first half every Delta time units until they die or the observation period ends. Despite its frequent use, this approach has no rigorous accuracy or overhead analysis in the literature. To shed more light on its performance, we flrst derive a model for CBM and show that small window size or large Delta may lead to highly inaccurate lifetime distributions. We then show that create-based sampling exhibits an inherent tradeoff between overhead and accuracy, which does not allow any fundamental improvement to the method. Instead, we propose a completely different approach for sampling user dynamics that keeps track of only residual lifetimes of peers and uses a simple renewal-process model to recover the actual lifetimes from the observed residuals. Our analysis indicates that for reasonably large systems, the proposed method can reduce bandwidth consumption by several orders of magnitude compared to prior approaches while simultaneously achieving higher accuracy. We finish the paper by implementing a two-tier Gnutella network crawler equipped with the proposed sampling method and obtain the distribution of ultrapeer lifetimes in a network of 6.4 million users and 60 million links. Our experimental results show that ultrapeer lifetimes are Pareto with shape a alpha ap 1.1; however, link lifetimes exhibit much lighter tails with alpha ap 1.9.
52804588
Cryptanalysis of the Full HAVAL with 4 and 5 Passes
HAVAL is a cryptographic hash function with variable digest size proposed by Zheng, Pieprzyk and Seberry in 1992. It has three variants, 3-, 4-, and 5-pass HAVAL. Previous results on HAVAL suggested only practical collision attacks for 3-pass HAVAL. In this paper, we present collision attacks for 4 and 5 pass HAVAL. For 4-pass HAVAL, we describe two practical attacks for finding 2-block collisions, one with 243 computations and the other with 2 36 computations. In addition, we show that collisions for 5-pass HAVAL can be found with about 2 123 computations, which is the first attack more efficient than the birthday attack.
1290713
NMPP: a user-customized NimbleGen microarray data processing pipeline
UNLABELLED
NMPP package is a bundle of user-customized tools based on established algorithms and methods to process self-designed NimbleGen microarray data. It features a command-line-based integrative processing procedure that comprises five major functional components, namely the raw microarray data parsing and integrating module, the array spatial effect smoothing and visualization module, the probe-level multi-array normalization module, the gene expression intensity summarization module and the gene expression status inference module.


AVAILABILITY
http://plantgenomics.biology.yale.edu/nmpp