Network-wide Configuration Synthesis

Computer networks are hard to manage. Given a set of high-level requirements (e.g., reachability, security), operators have to manually figure out the individual configuration of potentially hundreds of devices running complex distributed protocols so that they, collectively, compute a compatible forwarding state. Not surprisingly, operators often make mistakes which lead to downtimes. To address this problem, we present a novel synthesis approach that automatically computes correct network configurations that comply with the operator's requirements. We capture the behavior of existing routers along with the distributed protocols they run in stratified Datalog. Our key insight is to reduce the problem of finding correct input configurations to the task of synthesizing inputs for a stratified Datalog program. To solve this synthesis task, we introduce a new algorithm that synthesizes inputs for stratified Datalog programs. This algorithm is applicable beyond the domain of networks. We leverage our synthesis algorithm to construct the first network-wide configuration synthesis system, called SyNET, that support multiple interacting routing protocols (OSPF and BGP) and static routes. We show that our system is practical and can infer correct input configurations, in a reasonable amount time, for networks of realistic size (> 50 routers) that forward packets for multiple traffic classes.Comment: 24 Pages, short version published in CAV 201

Automatic Derivation of Statistical Data Analysis Algorithms: Planetary Nebulae and Beyond

AUTOBAYES is a fully automatic program synthesis system for the data analysis domain. Its input is a declarative problem description in form of a statistical model; its output is documented and optimized C/C++ code. The synthesis process relies on the combination of three key techniques. Bayesian networks are used as a compact internal representation mechanism which enables problem decompositions and guides the algorithm derivation. Program schemas are used as independently composable building blocks for the algorithm construction; they can encapsulate advanced algorithms and data structures. A symbolic-algebraic system is used to find closed-form solutions for problems and emerging subproblems. In this paper, we describe the application of AUTOBAYES to the analysis of planetary nebulae images taken by the Hubble Space Telescope. We explain the system architecture, and present in detail the automatic derivation of the scientists’ original analysis [1] as well as a refined analysis using clustering models. This study demonstrates that AUTOBAYES is now mature enough so that it can be applied to realistic scientific data analysis tasks

A Performance Comparison of Virtual Backbone Formation Algorithms for Wireless Mesh Networks

Currently wireless networks are dominant by star topology paradigm. Its natural the evolution is towards wireless mesh multi-hop networks. This article compares the performance of several algorithms for virtual backbone formation in ad hoc mesh networks both theoretically and through simulations. Firstly, an overview of the algorithms is given. Next, the results of the algorithm simulations made with the program Dominating Set Simulation Suite (DSSS) are described and interpreted. We have been extended the simulator to simulate the Mobile Backbone Network Topology Synthesis Algorithm. The results show that this algorithm has the best combination of performance characteristics among the compared algorithms

LiveSNN: a new ecosystem for HEENS architecture

This project proposal and development of a several tools, a communication protocol and an embedded program for giving a neural network called HEENS that it is currently developed by the group ISSET from UPC the capability of being controlled remotely, and to extract the neural . This will provide a faster development, user friendly tools for the development and analysis of spiking neural networks for emulation and verification of biological neural networks and neural models. As such, three new pieces of software are developed called: LiveSNN protocol, which communicates the HEENS architecture to a remote PC for Supervisory Control And Data Acquisition (SCADA) operations, LiveSNN program, which manages the conections and supervises the activity of the HEENS, and HEENS Toolchain Suite (HTS), which is an upgrade of the previous synthesis and assembler tools in order to allow the implementation of more sophisticated neural networks being easy and be capable of optimise the assembler model of the neuron for the architecture. With those developments, the time to generate spiking neural networks, to debug them, and emulate them is increased in addition to the reduction of possible human errors due to the increased automation workflow that those programs give to the end user