Resiliency of interactive distributed tasks (extended abstracts)

1991 ◽  
Author(s):  
Benny Chor ◽  
Lee-Bath Nelson
Keyword(s):  
Distributed Tasks

Scheduling of distributed tasks for survivability of the application

1997 ◽  
Vol 97 (1-2) ◽  
pp. 179-198 ◽  
Author(s):  
Sophie Chabridon ◽  
Erol Gelenbe
Keyword(s):  
Distributed Tasks

Randomized Local Network Computing: Derandomization Beyond Locally Checkable Labelings

2021 ◽  
Vol 8 (4) ◽  
pp. 1-25
Author(s):  
Laurent Feuilloley ◽  
Pierre Fraigniaud
Keyword(s):  
Monte Carlo ◽  
Distributed Algorithms ◽  
Deterministic Algorithm ◽  
Monte Carlo Algorithm ◽  
Local Network ◽  
Network Computing ◽  
Seminal Paper ◽  
Distributed Tasks

We carry on investigating the line of research questioning the power of randomization for the design of distributed algorithms. In their seminal paper, Naor and Stockmeyer [STOC 1993] established that, in the context of network computing in which all nodes execute the same algorithm in parallel, any construction task that can be solved locally by a randomized Monte-Carlo algorithm can also be solved locally by a deterministic algorithm. This result, however, holds only for distributed tasks such that the correctness of their solutions can be locally checked by a deterministic algorithm. In this article, we extend the result of Naor and Stockmeyer to a wider class of tasks. Specifically, we prove that the same derandomization result holds for every task such that the correctness of their solutions can be locally checked using a 2-sided error randomized Monte-Carlo algorithm.

Decentralized Control for Swarm Robots That Can Effectively Execute Spatially Distributed Tasks

2020 ◽  
Vol 26 (2) ◽  
pp. 242-259 ◽  
Author(s):  
Takeshi Kano ◽  
Eiichi Naito ◽  
Takenobu Aoshima ◽  
Akio Ishiguro
Keyword(s):  
Decentralized Control ◽  
Internal State ◽  
Search And Rescue ◽  
Multiple Robots ◽  
Rescue Operation ◽  
Control Scheme ◽  
Swarm Robots ◽  
Multiple Tasks ◽  
Spatially Distributed ◽  
Distributed Tasks

A swarm robotic system is a system in which multiple robots cooperate to fulfill a macroscopic function. Many swarm robots have been developed for various purposes. This study aims to design swarm robots capable of executing spatially distributed tasks effectively, which can be potentially used for tasks such as search-and-rescue operation and gathering scattered garbage in rooms. We propose a simple decentralized control scheme for swarm robots by extending our previously proposed non-reciprocal-interaction-based model. Each robot has an internal state, called its workload. Each robot first moves randomly to find a task, and when it does, its workload increases, and then it attracts its neighboring robots to ask for their help. We demonstrate, via simulations, that the proposed control scheme enables the robots to effectively execute multiple tasks in parallel under various environments. Fault tolerance of the proposed system is also demonstrated.

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