How to Allocate Tasks Asynchronously

Asynchronous task allocation is a fundamental problem in distributed computing in which p asynchronous processes must execute a set of m tasks. Also known as write-all or do-all, this problem been studied extensively, both independently and as a key building block for various distributed algorithms. In this paper, we break new ground on this classic problem: we introduce the To-Do Tree concurrent data structure, which improves on the best known randomized and deterministic upper bounds. In the presence of an adaptive adversary, the randomized To-Do Tree algorithm has O(m+plogplog2m) work complexity. We then show that there exists a deterministic variant of the To-Do Tree algorithm with work complexity O(m+p log5 m log2 max(m, p)). For all values of m and p, our algorithms are within log factors of the O(m + p log p) lower bound for this problem. The key technical ingredient in our results is a new approach for analyzing concurrent executions against a strong adaptive scheduler. This technique allows us to handle the complex dependencies between the processes' coin flips and their scheduling, and to tightly bound the work needed to perform subsets of the tasks.

Authors

• Dan Alistarh
• Michael A. Bender
• Seth Gilbert
• Rachid Guerraoui

Keywords

• Resource management
• Registers
• Complexity theory
• Bismuth
• Data structures
• Measurement
• Radiation detectors
• Upper Bound
• Fundamental Problem
• Head And Tail
• New Ground
• Distributed Algorithm
• Task Allocation
• Break New Ground
• Permutation
• Task Order
• Beginning Of Phase
• Probabilistic Method
• Algorithm For Problem
• Total Work
• Child Nodes
• Random Choice
• Reference Tree
• Random Bits
• Left Child
• Logarithmic Factor
• Deterministic Solution
• Real Tree
• distributed computing
• do-all
• randomized algorithms
• deterministic algorithms