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A Distributed Platform for the Volunteer Execution of Workflows on a Local Area Network
Albatroz Engineering has developed a framework for over-head power lines inspec- tion data acquisition and analysis, which includes hardware and software. The frame- work’s software components include inspection data analysis and reporting tools, com- monly known as PLMI2 application/platform. In PLMI2, the analysis of over-head power line maintenance inspection data consists of a sequence of Automatic Tasks (ATs) interleaved with Manual Tasks (MTs). An AT consists of a set of algorithms that receives as input one or more datasets, processes them and returns new datasets. In turn, an MT enables human supervisors (also known as operators) to correct, improve and validate the results of ATs. ATs run faster than MTs and in the overall work cycle, ATs take less than 10% of total processing time, but still take a few minutes. There is data flow dependency between tasks, which can be modelled with a workflow and if MTs are omitted from this workflow, it is possible to carry the sequence of ATs, postponing MTs. In fact, if the computing cost and waiting time are negligible, it may be advantageous to run ATs earlier in the workflow, prior to validation. To address this opportunity, Alba- troz Engineering has invested in a new procedure to stream the data through all ATs fully unattended. Considering these scenarios, it could be useful to have a system capable of detecting available workstations at a given instant and subsequently distribute the ATs to them. In this way, operators could schedule the execution of future ATs for a given inspection data, while they are performing MTs of another. The requirements of the system to implement fall within the field Volunteer Comput- ing Systems and we will address some of the challenges posed by these kinds of systems, namely the hosts volatility and failures. Volunteer Computing is a type of distributed computing which exploits idle CPU cycles from computing resources donated by volun- teers and connected through the Internet/Intranet to compute large-scale simulations. This thesis proposes and designs a new distributed task scheduling system in the context of Volunteer Computing Systems, able to schedule the ATs of PLMI2 and exploit idle CPU cycles from workstations within the company’s local area network (LAN) to accelerate the data analysis, being aware of their inter-dependencies and priorities. To evaluate the proposed system, a prototype has been implemented, and the simulations results have shown that it is scalable and that it supports fault-tolerance of tasks execution, by employing the rescheduling mechanism.
Start Date: 2013-03-01
End Date: 2014-06-20
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