mj1.at Michael Jaros' Techblog

16Oct/110

From Tuple Spaces to Tweetflows

Posted by mj

The coordination of asynchronous actors has always been a major problem in distributed systems and parallel computing. About 20 years ago, David Gelernter and Nicholas Carriero have introduced the coordination language Linda. The most important difference to existing approaches was that they treated the coordination problem seperately from the computation problem.

Linda implementations offer the four basic operations in, out, rd, and eval for access to a virtual shared memory called the tuple space. Developers do not have to worry about network protocols or message formats because coordination is achieved just by reading data from and writing data to the tuple space. This is facilitated by the facts that read operations block by default until data is available and data can be consumed when read.

Today, another worldwide space connects millions of asynchronous actors: The Twitter network has some features very similar to a tuple space, and it supports basic operations as well. One major difference is that Twitter users do not use the system for coordination in a uniform way so far. Tweetflows could be a solution that not only coordinates human actors, but also integrates computer-provided services.

Another interesting difference between Linda implementations and Tweetflows lies in the type of coupling provided by each system and the resulting semantics: As mentioned before, Linda's actors are only coupled by the data put into the space -- Tweetflows on the other hand implement some kind of message-passing.

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1Sep/110

Crowdsourcing Workflow Execution

Posted by mj

Service-oriented architectures map business processes to software components both inside and across organizations. Processes are described as workflows and define the coordination of activities performed by potentially distributed actors.

In my work I examine the requirements and implications of crowdsourced workflow execution in social networks. The traditional workflow approaches are well established in many domains. However they are not suitable for an increasing number of applications due to lack of flexibility, scalability and availability as well as need for simplicity and loose coupling.

Building upon scientific work by Martin Treiber and others (Tweetflows), I try to find a prediction model for the execution probability of crowdsourced lightweight workflows.

Abstract

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