154,757 research outputs found
Big Data for Traffic Engineering in Software-Defined Networks
Software-defined networking overcomes the limitations of traditional networks by splitting the control plane from the data plane. The logic of the network is moved to a component called the controller that manages devices in the data plane. To implement this architecture, it has become the norm to use the OpenFlow (OF) protocol, which defines several counters maintained by network devices. These counters are the starting point for Traffic Engineering (TE) activities. TE monitors several network parameters, including network bandwidth utilization. A great challenge for TE is to collect and generate statistics about bandwidth utilization for monitoring and traffic analysis activities. This becomes even more challenging if fine-grained monitoring is required. Network management tasks such as network provisioning, capacity planning, load balancing, and anomaly detection can benefit from this fine-grained monitoring. Because the counters are updated for every packet that crosses the switch, they must be retrieved in a streaming fashion. This scenario suggests the use of Big Data streaming techniques to collect and process counter values. Therefore, this paper proposes an approach based on a fine-grained Big Data monitoring method to collect and generate traffic statistics using counter values. This research work can significantly leverage TE. The approach can provide a more detailed view of network resource utilization because it can deliver individual and aggregated statistical analyses of bandwidth consumption. Experimental results show the effectiveness of the proposed method
Assessing the joint impact of DNAPL source-zone behavior and degradation products on the probabilistic characterization of human health risk
The release of industrial contaminants into the subsurface has led to a rapid degradation of groundwater resources. Contamination caused by Dense Non-Aqueous Phase Liquids (DNAPLs) is particularly severe owing to their limited solubility, slow dissolution and in many cases high toxicity. A greater insight into how the DNAPL source zone behavior and the contaminant release towards the aquifer impact human health risk is crucial for an appropriate risk management. Risk analysis is further complicated by the uncertainty in aquifer properties and contaminant conditions. This study focuses on the impact of the DNAPL release mode on the human health risk propagation along the aquifer under uncertain conditions. Contaminant concentrations released from the source zone are described using a screening approach with a set of parameters representing several scenarios of DNAPL architecture. The uncertainty in the hydraulic properties is systematically accounted for by high-resolution Monte Carlo simulations. We simulate the release and the transport of the chlorinated solvent perchloroethylene and its carcinogenic degradation products in randomly heterogeneous porous media. The human health risk posed by the chemical mixture of these contaminants is characterized by the low-order statistics and the probability density function of common risk metrics. We show that the zone of high risk (hot spot) is independent of the DNAPL mass release mode, and that the risk amplitude is mostly controlled by heterogeneities and by the source zone architecture. The risk is lower and less uncertain when the source zone is formed mostly by ganglia than by pools. We also illustrate how the source zone efficiency (intensity of the water flux crossing the source zone) affects the risk posed by an exposure to the chemical mixture. Results display that high source zone efficiencies are counter-intuitively beneficial, decreasing the risk because of a reduction in the time available for the production of the highly toxic subspecies.Peer ReviewedPostprint (author's final draft
The RD53 Collaboration's SystemVerilog-UVM Simulation Framework and its General Applicability to Design of Advanced Pixel Readout Chips
The foreseen Phase 2 pixel upgrades at the LHC have very challenging
requirements for the design of hybrid pixel readout chips. A versatile pixel
simulation platform is as an essential development tool for the design,
verification and optimization of both the system architecture and the pixel
chip building blocks (Intellectual Properties, IPs). This work is focused on
the implemented simulation and verification environment named VEPIX53, built
using the SystemVerilog language and the Universal Verification Methodology
(UVM) class library in the framework of the RD53 Collaboration. The environment
supports pixel chips at different levels of description: its reusable
components feature the generation of different classes of parameterized input
hits to the pixel matrix, monitoring of pixel chip inputs and outputs,
conformity checks between predicted and actual outputs and collection of
statistics on system performance. The environment has been tested performing a
study of shared architectures of the trigger latency buffering section of pixel
chips. A fully shared architecture and a distributed one have been described at
behavioral level and simulated; the resulting memory occupancy statistics and
hit loss rates have subsequently been compared.Comment: 15 pages, 10 figures (11 figure files), submitted to Journal of
Instrumentatio
Compiler analysis for trace-level speculative multithreaded architectures
Trace-level speculative multithreaded processors exploit trace-level speculation by means of two threads working cooperatively. One thread, called the speculative thread, executes instructions ahead of the other by speculating on the result of several traces. The other thread executes speculated traces and verifies the speculation made by the first thread. In this paper, we propose a static program analysis for identifying candidate traces to be speculated. This approach identifies large regions of code whose live-output values may be successfully predicted. We present several heuristics to determine the best opportunities for dynamic speculation, based on compiler analysis and program profiling information. Simulation results show that the proposed trace recognition techniques achieve on average a speed-up close to 38% for a collection of SPEC2000 benchmarks.Peer ReviewedPostprint (published version
Performance Debugging and Tuning using an Instruction-Set Simulator
Instruction-set simulators allow programmers a detailed level of insight into,
and control over, the execution of a program, including parallel programs and
operating systems. In principle, instruction set simulation can model any
target computer and gather any statistic. Furthermore, such simulators are
usually portable, independent of compiler tools, and deterministic-allowing
bugs to be recreated or measurements repeated. Though often viewed as being
too slow for use as a general programming tool, in the last several years
their performance has improved considerably.
We describe SIMICS, an instruction set simulator of SPARC-based
multiprocessors developed at SICS, in its rĂ´le as a general programming tool.
We discuss some of the benefits of using a tool such as SIMICS to support
various tasks in software engineering, including debugging, testing, analysis,
and performance tuning. We present in some detail two test cases, where we've
used SimICS to support analysis and performance tuning of two applications,
Penny and EQNTOTT. This work resulted in improved parallelism in, and
understanding of, Penny, as well as a performance improvement for EQNTOTT of
over a magnitude. We also present some early work on analyzing SPARC/Linux,
demonstrating the ability of tools like SimICS to analyze operating systems
HALLS: An Energy-Efficient Highly Adaptable Last Level STT-RAM Cache for Multicore Systems
Spin-Transfer Torque RAM (STT-RAM) is widely considered a promising
alternative to SRAM in the memory hierarchy due to STT-RAM's non-volatility,
low leakage power, high density, and fast read speed. The STT-RAM's small
feature size is particularly desirable for the last-level cache (LLC), which
typically consumes a large area of silicon die. However, long write latency and
high write energy still remain challenges of implementing STT-RAMs in the CPU
cache. An increasingly popular method for addressing this challenge involves
trading off the non-volatility for reduced write speed and write energy by
relaxing the STT-RAM's data retention time. However, in order to maximize
energy saving potential, the cache configurations, including STT-RAM's
retention time, must be dynamically adapted to executing applications' variable
memory needs. In this paper, we propose a highly adaptable last level STT-RAM
cache (HALLS) that allows the LLC configurations and retention time to be
adapted to applications' runtime execution requirements. We also propose
low-overhead runtime tuning algorithms to dynamically determine the best
(lowest energy) cache configurations and retention times for executing
applications. Compared to prior work, HALLS reduced the average energy
consumption by 60.57% in a quad-core system, while introducing marginal latency
overhead.Comment: To Appear on IEEE Transactions on Computers (TC
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