14,858 research outputs found
Toward a Unified Performance and Power Consumption NAND Flash Memory Model of Embedded and Solid State Secondary Storage Systems
This paper presents a set of models dedicated to describe a flash storage
subsystem structure, functions, performance and power consumption behaviors.
These models cover a large range of today's NAND flash memory applications.
They are designed to be implemented in simulation tools allowing to estimate
and compare performance and power consumption of I/O requests on flash memory
based storage systems. Such tools can also help in designing and validating new
flash storage systems and management mechanisms. This work is integrated in a
global project aiming to build a framework simulating complex flash storage
hierarchies for performance and power consumption analysis. This tool will be
highly configurable and modular with various levels of usage complexity
according to the required aim: from a software user point of view for
simulating storage systems, to a developer point of view for designing, testing
and validating new flash storage management systems
Validating a timing simulator for the NGMP multicore processor
Timing simulation is a key element in multicore systems design. It enables a fast and cost effective design space exploration, allowing to simulate new architectural improvements without requiring RTL abstraction levels. Timing simulation also allows software developers to perform early testing of the timing behavior of their software without the need of buying the actual physical board, which can be very expensive when the board uses non-COTS technology. In this paper we present the validation of a timing simulator for the NGMP multicore processor, which is a 4 core processor being developed to become the reference platform for future missions of the European Space Agency.The research leading to these results has received funding from the European Space Agency under contract NPI 4000102880 and the Ministry of Science and Technology of
Spain under contract TIN-2015-65316-P. Jaume Abella has been partially supported by the Ministry of Economy and Competitiveness under Ramon y Cajal postdoctoral fellowship
number RYC-2013-14717.Peer ReviewedPostprint (author's final draft
An Interaction Model for Simulation and Mitigation of Cascading Failures
In this paper the interactions between component failures are quantified and
the interaction matrix and interaction network are obtained. The quantified
interactions can capture the general propagation patterns of the cascades from
utilities or simulation, thus helping to better understand how cascading
failures propagate and to identify key links and key components that are
crucial for cascading failure propagation. By utilizing these interactions a
high-level probabilistic model called interaction model is proposed to study
the influence of interactions on cascading failure risk and to support online
decision-making. It is much more time efficient to first quantify the
interactions between component failures with fewer original cascades from a
more detailed cascading failure model and then perform the interaction model
simulation than it is to directly simulate a large number of cascades with a
more detailed model. Interaction-based mitigation measures are suggested to
mitigate cascading failure risk by weakening key links, which can be achieved
in real systems by wide area protection such as blocking of some specific
protective relays. The proposed interaction quantifying method and interaction
model are validated with line outage data generated by the AC OPA cascading
simulations on the IEEE 118-bus system.Comment: Accepted by IEEE Transactions on Power System
Third Conference on Artificial Intelligence for Space Applications, part 2
Topics relative to the application of artificial intelligence to space operations are discussed. New technologies for space station automation, design data capture, computer vision, neural nets, automatic programming, and real time applications are discussed
Toward Contention Analysis for Parallel Executing Real-Time Tasks
In measurement-based probabilistic timing analysis, the execution conditions imposed to tasks as measurement scenarios, have a strong impact to the worst-case execution time estimates. The scenarios and their effects on the task execution behavior have to be deeply investigated. The aim has to be to identify and to guarantee the scenarios that lead to the maximum measurements, i.e. the worst-case scenarios, and use them to assure the worst-case execution time estimates.
We propose a contention analysis in order to identify the worst contentions that a task can suffer from concurrent executions. The work focuses on the interferences on shared resources (cache memories and memory buses) from parallel executions in multi-core real-time systems. Our approach consists of searching for possible task contenders for parallel executions, modeling their contentiousness, and classifying the measurement scenarios accordingly. We identify the most contentious ones and their worst-case effects on task execution times. The measurement-based probabilistic timing analysis is then used to verify the analysis proposed, qualify the scenarios with contentiousness, and compare them. A parallel execution simulator for multi-core real-time system is developed and used for validating our framework.
The framework applies heuristics and assumptions that simplify the system behavior. It represents a first step for developing a complete approach which would be able to guarantee the worst-case behavior
Computing server power modeling in a data center: survey,taxonomy and performance evaluation
Data centers are large scale, energy-hungry infrastructure serving the
increasing computational demands as the world is becoming more connected in
smart cities. The emergence of advanced technologies such as cloud-based
services, internet of things (IoT) and big data analytics has augmented the
growth of global data centers, leading to high energy consumption. This upsurge
in energy consumption of the data centers not only incurs the issue of surging
high cost (operational and maintenance) but also has an adverse effect on the
environment. Dynamic power management in a data center environment requires the
cognizance of the correlation between the system and hardware level performance
counters and the power consumption. Power consumption modeling exhibits this
correlation and is crucial in designing energy-efficient optimization
strategies based on resource utilization. Several works in power modeling are
proposed and used in the literature. However, these power models have been
evaluated using different benchmarking applications, power measurement
techniques and error calculation formula on different machines. In this work,
we present a taxonomy and evaluation of 24 software-based power models using a
unified environment, benchmarking applications, power measurement technique and
error formula, with the aim of achieving an objective comparison. We use
different servers architectures to assess the impact of heterogeneity on the
models' comparison. The performance analysis of these models is elaborated in
the paper
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