Profiling and optimization of software-based network-analysis applications

A large set of tools for network monitoring and accounting, security, traffic analysis and prediction - more broadly, for network operation and management - require direct and efficient real-time access to data traveling on the network. Software tools are often preferred because of their low cost and high versatility. However, these tools are often considered to suffer from performance problems on high-speed networks. We demonstrate that, despite the common belief, the performance limits for software real-time network analysis tools are still far from being reached and it can even be improved with limited hardware support. We analyze the performance of a widely used library for network analysis, WinPcap, highlight its bottlenecks, and propose some solutions that almost double the overall speed, thus enabling the deployment of software-based tools on high speed networks

Profiling and optimization of software-based network-analysis applications

A large set of tools for network monitoring and accounting, security, traffic analysis and prediction - more broadly, for network operation and management - require direct and efficient real-time access to data traveling on the network. Software tools are often preferred because of their low cost and high versatility. However, these tools are often considered to suffer from performance problems on high-speed networks. We demonstrate that, despite the common belief, the performance limits for software real-time network analysis tools are still far from being reached and it can even be improved with limited hardware support. We analyze the performance of a widely used library for network analysis, WinPcap, highlight its bottlenecks, and propose some solutions that almost double the overall speed, thus enabling the deployment of software-based tools on high speed networks

Synapse: Synthetic Application Profiler and Emulator

We introduce Synapse motivated by the needs to estimate and emulate workload execution characteristics on high-performance and distributed heterogeneous resources. Synapse has a platform independent application profiler, and the ability to emulate profiled workloads on a variety of heterogeneous resources. Synapse is used as a proxy application (or "representative application") for real workloads, with the added advantage that it can be tuned at arbitrary levels of granularity in ways that are simply not possible using real applications. Experiments show that automated profiling using Synapse represents application characteristics with high fidelity. Emulation using Synapse can reproduce the application behavior in the original runtime environment, as well as reproducing properties when used in a different run-time environments

TANGO: Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation

The paper is concerned with the issue of how software systems actually use Heterogeneous Parallel Architectures (HPAs), with the goal of optimizing power consumption on these resources. It argues the need for novel methods and tools to support software developers aiming to optimise power consumption resulting from designing, developing, deploying and running software on HPAs, while maintaining other quality aspects of software to adequate and agreed levels. To do so, a reference architecture to support energy efficiency at application construction, deployment, and operation is discussed, as well as its implementation and evaluation plans.Comment: Part of the Program Transformation for Programmability in Heterogeneous Architectures (PROHA) workshop, Barcelona, Spain, 12th March 2016, 7 pages, LaTeX, 3 PNG figure

D-SPACE4Cloud: A Design Tool for Big Data Applications

The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their core business activities, nonetheless there are no tools and techniques to support the design of the underlying hardware configuration backing such systems. In particular, the focus in this report is set on Cloud deployed clusters, which represent a cost-effective alternative to on premises installations. We propose a novel tool implementing a battery of optimization and prediction techniques integrated so as to efficiently assess several alternative resource configurations, in order to determine the minimum cost cluster deployment satisfying QoS constraints. Further, the experimental campaign conducted on real systems shows the validity and relevance of the proposed method