Supporting Deadline Constrained Distributed Computations on Grids

2011 ◽  
Author(s):  
Xinghui Zhao ◽  
Nadeem Jamali
Keyword(s):  
Distributed Computations

scholarly journals On the Fly Testing of Regular Patterns in Distributed Computations

1994 ◽  
Author(s):  
Eddy Fromentin ◽  
Michel Raynal ◽  
Vijay Garg ◽  
Alex Tomlinson
Keyword(s):  
Distributed Computations ◽  
Regular Patterns

scholarly journals Even Simple Processes of π-calculus are Hard for Analysis

2018 ◽  
Vol 25 (6) ◽  
pp. 589-606
Author(s):  
Marat M. Abbas ◽  
Vladimir A. Zakharov
Keyword(s):  
Information Security ◽  
Mathematical Models ◽  
Cryptographic Protocols ◽  
Bounded Number ◽  
Distributed Computations ◽  
Security Properties ◽  
Mobile Processes ◽  
Π Calculus ◽  
Np Complete ◽  
Turing Complete

Mathematical models of distributed computations, based on the calculus of mobile processes (π-calculus) are widely used for checking the information security properties of cryptographic protocols. Since π-calculus is Turing-complete, this problem is undecidable in general case. Therefore, the study is carried out only for some special classes of π-calculus processes with restricted computational capabilities, for example, for non-recursive processes, in which all runs have a bounded length, for processes with a bounded number of parallel components, etc. However, even in these cases, the proposed checking procedures are time consuming. We assume that this is due to the very nature of the π -calculus processes. The goal of this paper is to show that even for the weakest model of passive adversary and for relatively simple protocols that use only the basic π-calculus operations, the task of checking the information security properties of these protocols is co-NP-complete.

scholarly journals Interpreting the Data: Parallel Analysis with Sawzall

2005 ◽  
Vol 13 (4) ◽  
pp. 277-298 ◽  
Author(s):  
Rob Pike ◽  
Sean Dorward ◽  
Robert Griesemer ◽  
Sean Quinlan
Keyword(s):  
Programming Language ◽  
Regular Structure ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Parallel ◽  
Web Document ◽  
Distributed Computations ◽  
Procedural Programming ◽  
Two Phases

Very large data sets often have a flat but regular structure and span multiple disks and machines. Examples include telephone call records, network logs, and web document repositories. These large data sets are not amenable to study using traditional database techniques, if only because they can be too large to fit in a single relational database. On the other hand, many of the analyses done on them can be expressed using simple, easily distributed computations: filtering, aggregation, extraction of statistics, and so on. We present a system for automating such analyses. A filtering phase, in which a query is expressed using a new procedural programming language, emits data to an aggregation phase. Both phases are distributed over hundreds or even thousands of computers. The results are then collated and saved to a file. The design – including the separation into two phases, the form of the programming language, and the properties of the aggregators – exploits the parallelism inherent in having data and computation distributed across many machines.

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