How to Allocate Tasks Asynchronously

Alistarh, Dan; Bender, Michael A.; Gilbert, Seth; Guerraoui, Rachid

doi:10.1109/focs.2012.41

Cited by 14 publications

(18 citation statements)

References 29 publications

(57 reference statements)

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“…Macarthur et al [32] introduced a dynamic and distributed algorithm for multi-agent task allocation problems, based on fast-max-sum algorithm combined with a branch-and-bound technique, in order to reduce the execution time in the allocation of task to its respective agent. Alistarh et al [27,33] present a decentralized task allocation, static and dynamic, based on to-do trees, where decisions on every inner node are based on a probability function.…”

Section: Task Allocation Problemmentioning

confidence: 99%

“…To illustrate the techniques, this work considers a tree structure and supposes that the set of tasks and the set of processes are fixed and defined before the task allocation is performed as in Ref. [27]. The proposed CPN modeling method represents the behavior of a set of processors, or working threads, that traverse a tree structure from the root to a single leaf in order to acquire a task.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Simulation of Task Allocation with Colored Petri Nets

Alcaraz-Mejia¹,

Campos-Rodríguez²,

MarcoCaballero-Gutierrez³

2017

Computer Simulation

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The task allocation problem is a key element in the solution of several applications from different engineering fields. With the explosion of the amount of information produced by the today Internet-connected solutions, scheduling techniques for the allocation of tasks relying on grids, clusters of computers, or in the cloud computing, is at the core of efficient solutions. The task allocation is an important problem within some branch of the computer sciences and operations research, where it is usually modeled as an optimization of a combinatorial problem with the inconvenience of a state explosion problem. This chapter proposes the modeling of the task allocation problem by the use of Colored Petri nets. The proposed methodology allows the construction of compact models for task scheduling problems. Moreover, a simulation process is possible within the constructed model, which allows the study of some performance aspects of the task allocation problem before any implementation stage.

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Section: Task Allocation Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Task Allocation with Colored Petri Nets

Alcaraz-Mejia¹,

Campos-Rodríguez²,

MarcoCaballero-Gutierrez³

2017

Computer Simulation

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“…Structurally, the dynamic to-do tree is relatively simple, and borrows ideas from other shared-memory constructions such as the poly-logarithmic snapshots of Aspnes et al [6] and the one-shot task allocation algorithm of Alistarh et al [4]. Progress trees are a natural idea for task allocation and variants have been analyzed previously, e.g.…”

mentioning

confidence: 99%

“…Progress trees are a natural idea for task allocation and variants have been analyzed previously, e.g. [4,11,24], however this is the first instance of a dynamic progress tree, which supports concurrent insert and remove operations. Instead of using a single progress tree (sufficient for one-shot algorithms) we combine two dual progress trees: one for tracking insertions, and one for tracking removals.…”

mentioning

confidence: 99%

“…[4,5,[11][12][13]16,19,[22][23][24], has focused on algorithms and lower bounds for the asynchronous version of task allocation, also known as do-all [17], or write-all [19], where processes move at arbitrary speeds and are crash-prone. Task allocation is closely connected to many other fundamental distributed problems, such as mutual exclusion [10], distributed clocks [8], and shared-memory collect [3].…”

mentioning

confidence: 99%

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Dynamic Task Allocation in Asynchronous Shared Memory

Alistarh¹,

Aspnes²,

Bender³

et al. 2013

Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms

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Task allocation is a classic distributed problem in which a set of p potentially faulty processes must cooperate to perform a set of tasks. This paper considers a new dynamic version of the problem, in which tasks are injected adversarially during an asynchronous execution. We give the first asynchronous shared-memory algorithm for dynamic task allocation, and we prove that our solution is optimal within logarithmic factors. The main algorithmic idea is a randomized concurrent data structure called a dynamic to-do tree, which allows processes to pick new tasks to perform at random from the set of available tasks, and to insert tasks at random empty locations in the data structure. Our analysis shows that these properties avoid duplicating work unnecessarily. On the other hand, since the adversary controls the input as well the scheduling, it can induce executions where lots of processes contend for a few available tasks, which is inefficient. However, we prove that every algorithm has the same problem: given an arbitrary input, if OPT is the worst-case complexity of the optimal algorithm on that input, then the expected work complexity of our algorithm on the same input is O(OPT log 3 m), where m is an upper bound on the number of tasks that are present in the system at any given time.

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Online Parallel Scheduling of Non-uniform Tasks: Trading Failures for Energy

Anta

Georgiou

Kowalski

et al. 2013

Fundamentals of Computation Theory

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Consider a system in which tasks of different execution times arrive continuously and have to be executed by a set of processors that are prone to crashes and restarts. In this paper we model and study the impact of parallelism and failures on the competitiveness of such an online system. In a fault-free environment, a simple Longest-in-System scheduling policy, enhanced by a redundancy-avoidance mechanism, guarantees optimality in a long-term execution. In the presence of failures though, scheduling becomes a much more challenging task. In particular, no parallel deterministic algorithm can be competitive against an offline optimal solution, even with one single processor and tasks of only two different execution times. We find that when additional energy is provided to the system in the form of processor speedup, the situation changes. Specifically, we identify thresholds on the speedup under which such competitiveness cannot be achieved by any deterministic algorithm, and above which competitive algorithms exist. Finally, we propose algorithms that achieve small bounded competitive ratios when the speedup is over the threshold.

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How to Allocate Tasks Asynchronously

Cited by 14 publications

References 29 publications

Modeling and Simulation of Task Allocation with Colored Petri Nets

Modeling and Simulation of Task Allocation with Colored Petri Nets

Dynamic Task Allocation in Asynchronous Shared Memory

Online Parallel Scheduling of Non-uniform Tasks: Trading Failures for Energy

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