Deterministic Logarithmic Completeness in the Distributed Sleeping Model
Leonid Barenboim, Tzalik Maimon

TL;DR
This paper introduces a deterministic method for solving any decidable problem in the distributed sleeping model with logarithmic awake complexity, using a novel structure called Distributed Layered Tree, and establishes lower bounds for this approach.
Contribution
It presents a deterministic scheme achieving $O(\log n)$ awake complexity for all decidable problems in the sleeping model, and introduces the Distributed Layered Tree structure as a key tool.
Findings
Distributed Layered Tree enables constant-round graph information collection.
The $O(\log n)$ awake complexity bound is proven to be optimal.
A scheme for O-LOCAL problems achieves $O(\log \Delta + \log^* n)$ awake rounds.
Abstract
We provide a deterministic scheme for solving any decidable problem in the distributed {sleeping model}. The sleeping model is a generalization of the standard message-passing model, with an additional capability of network nodes to enter a sleeping state occasionally. As long as a vertex is in the awake state, it is similar to the standard message-passing setting. However, when a vertex is asleep it cannot receive or send messages in the network nor can it perform internal computations. On the other hand, sleeping rounds do not count towards {\awake complexity.} Awake complexity is the main complexity measurement in this setting, which is the number of awake rounds a vertex spends during an execution. In this paper we devise algorithms with worst-case guarantees on the awake complexity. We devise a deterministic scheme with awake complexity of for solving any decidable…
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Taxonomy
TopicsOptimization and Search Problems · Complexity and Algorithms in Graphs · Advanced Queuing Theory Analysis
