Collaborative Reuse of Streaming Dataflows in IoT Applications

Distributed Stream Processing Systems (DSPS) like Apache Storm and Spark Streaming enable composition of continuous dataflows that execute persistently over data streams. They are used by Internet of Things (IoT) applications to analyze sensor data from Smart City cyber-infrastructure, and make active utility management decisions. As the ecosystem of such IoT applications that leverage shared urban sensor streams continue to grow, applications will perform duplicate pre-processing and analytics tasks. This offers the opportunity to collaboratively reuse the outputs of overlapping dataflows, thereby improving the resource efficiency. In this paper, we propose \emph{dataflow reuse algorithms} that given a submitted dataflow, identifies the intersection of reusable tasks and streams from a collection of running dataflows to form a \emph{merged dataflow}. Similar algorithms to unmerge dataflows when they are removed are also proposed. We implement these algorithms for the popular Apache Storm DSPS, and validate their performance and resource savings for 35 synthetic dataflows based on public OPMW workflows with diverse arrival and departure distributions, and on 21 real IoT dataflows from RIoTBench.Comment: To appear in IEEE eScience Conference 201

Basic parallel and distributed computing curriculum

International audienceWith the advent of multi-core processors and their fast expansion, it is quite clear that parallel computing is now a genuine requirement in Computer Science and Engineering (and related) curriculum. In addition to the pervasiveness of parallel computing devices, we should take into account the fact that there are lot of existing softwares that are implemented in the sequential mode, and thus need to be adapted for a parallel execution. Therefore, it is required to the programmer to be able to design parallel programs and also to have some skills in moving from a given sequential code to the corresponding parallel code. In this paper, we present a basic educational scenario on how to give a consistent and efficient background in parallel computing to ordinary computer scientists and engineers

On sunlet graphs connected to a specific map on {1,2,…,p−1}\{1,2,\dots,p-1\}

In this article, we study the structure of the graph implied by a given map on the set $S_p=\{1,2,\dots,p-1\}$, where $p$ is an odd prime. The consecutive applications of the map generate an integer sequence, or in graph theoretical context a walk, that is linked to the discrete logarithm problem.Comment: 6 pages, 2 figure

Sensing the Shape of Canine Responses to Cancer

We conducted a short study investigating the pressure patterns produced by cancer detection dogs via a canine-centered interface while searching samples of amyl acetate. We advance previous work by providing further insights into the potential of the approach for supporting and partly automating the practice of cancer detection with dogs

Multivariate Matching Polynomials of Cyclically Labelled Graphs

We consider the matching polynomials of graphs whose edges have been cyclically labelled with the ordered set of t labels {x1, . . ., xt}. We first work with the cyclically labelled path, with first edge label xi, followed by N full cycles of labels {x1, . . ., xt}, and last edge label xj . Let Φi,Nt+j denote the matching polynomial of this path. It satisfies the (τ, Δ)-recurrence: Φi,Nt+j = τΦi,(N−1)t+j−ΔΦi,(N−2)t+j, where τ is the sum of all non-consecutive cyclic monomials in the variables {x1, . . ., xt} and Δ = (−1)t x1 · · ·xt. A combinatorial/algebraic proof and a matrix proof of this fact are given. Let GN denote the first fundamental solution to the (τ, Δ)-recurrence. We express GN (i) as a cyclic binomial using the Symmetric Representation of a matrix, (ii) in terms of Chebyshev polynomials of the second kind in the variables τ and Δ, and (iii) as a quotient of two matching polynomials. We extend our results from paths to cycles and rooted trees