Robust task scheduling for volunteer computing systems

Lee, Young Choon; Zomaya, Albert Y.; Siegel, Howard Jay

doi:10.1007/s11227-009-0326-1

Cited by 18 publications

(5 citation statements)

References 24 publications

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“…An interesting comparison of two dynamic approaches appears in [22]: replicated allocation of chores vs. deadlinetriggered reallocation. Other sources have analyzed the reliability of scheduling DAGs under execution-time uncertainty in execution-and/or communication-time [15], [25], [26]. One finds in [1] a framework for minimizing makespan when processors proceed asynchronously on DAGs having unit-time chores.…”

Section: Related Workmentioning

confidence: 99%

An AREA-Oriented Heuristic for Scheduling DAGs on Volatile Computing Platforms

Cordasco¹,

Chiara

Rosenberg

2015

IEEE Trans. Parallel Distrib. Syst.

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Many modern computing platforms-notably clouds and desktop grids-exhibit dynamic heterogeneity: the availability and computing power of their constituent resources can change unexpectedly and dynamically, even in the midst of a computation. We introduce a new quality metric, AREA, for schedules that execute computations having interdependent constituent chores (jobs, tasks, etc.) on such platforms. AREA measures the average number of chores that a schedule renders eligible for execution at each step of a computation. Even though the definition of AREA does not mention any properties of host platforms (such as volatility), intuition suggests that rendering chores eligible at a faster rate will have a benign impact on the performance of volatile platforms. We report on simulation experiments that support this intuition. Earlier work has derived the basic properties of the AREA metric and has shown how to efficiently craft AREA-maximizing (A-M) schedules for several classes of significant computations. Even though A-M schedules always exist for every computation, it is not always known how to derive such schedules efficiently. In response, the current study develops an efficient algorithm that produces AREA-Oriented (A-O) schedules, which aim to efficiently approximate the AREAs of A-M schedules on arbitrary computations. The simulation experiments reported on here suggest that, in common with A-M schedules, A-O schedules complete computations on volatile heterogeneous platforms faster than a variety of heuristics that range from lightweight ones to computationally intensive ones-albeit not to the same degree as A-M schedules do. Our experiments suggest that schedules having larger AREAs have smaller completion times-but no proof of that yet exists.

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Section: Related Workmentioning

confidence: 99%

An AREA-Oriented Heuristic for Scheduling DAGs on Volatile Computing Platforms

Cordasco¹,

Chiara

Rosenberg

2015

IEEE Trans. Parallel Distrib. Syst.

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“…Research has been done on several of the problems underlying volunteer computing, such as validating results [36] [37] [38], job runtime estimation [39], accelerating the completion of batches and DAGs of jobs [40] [41], and optimizing replication policies [42] [43]. Many of these ideas could and perhaps will be implemented in BOINC.…”

Section: Related Workmentioning

confidence: 99%

BOINC: A Platform for Volunteer Computing

Anderson

2019

J Grid Computing

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Volunteer computing" is the use of consumer digital devices for high-throughput scientific computing. It can provide large computing capacity at low cost, but presents challenges due to device heterogeneity, unreliability, and churn. BOINC, a widely-used open-source middleware system for volunteer computing, addresses these challenges. We describe its features, architecture, and implementation.Volunteer computing (VC) is the use of consumer digital devices, such as desktop and laptop computers, tablets, and smartphones, for high-throughput scientific computing. Device owners participate in VC by installing a program that downloads and executes jobs from servers operated by science projects. There are currently about 30 VC projects in many scientific areas and at many institutions. The research enabled by VC has resulted in numerous papers in Nature, Science, PNAS, Physical Review, Proteins, PloS Biology, Bioinformatics, J. of Mol. Biol., J. Chem. Phys, and other top journals [1].About 700,000 devices are actively participating in VC projects. These devices have about 4 million CPU cores and 560,000 GPUs, and collectively provide an average throughput of 93 PetaFLOPS. The devices are primarily modern, high-end computers: they average 16.5 CPU GigaFLOPS and 11.4 GB of RAM, and most have a GPU capable of general-purpose computing using OpenCL or CUDA.In terms of power consumption, data-center nodes are more efficient (i.e. have greater FLOPS/watt) than many consumer devices. However, to compare net energy usage, two other factors must be considered. First, data centers use air conditioning to remove the heat generated by hardware; consumer devices generally do not. Second, in cool climates, the heat generated by consumer devices contributes to ambient heating, so the net energy cost of computing may be zero. Thus it's not clear whether VC is globally more or less energy efficient than data-center computing. In any case, this does not affect VC's cost to the scientist, since the energy is paid for by volunteers.VC is best suited to high-throughput computing (HTC): workloads consisting of large groups or streams of jobs where the goal is high rate of job completion, rather than low job turnaround time. VC is less suited to workloads that have extreme memory or storage requirements, or for which the ratio of network communication (i.e. input and output file size) to computing is extremely high.VC differs from other forms of HTC, such as grids and clouds, in several ways: The computers are anonymous and untrusted.  The computers are heterogeneous in many hardware and software dimensions; for example, of desktops and laptops participating in VC, 85% are Windows, 7% are Macs, and 7% are Linux. Job turnaround times are uncertain because of sporadic host availability.  There is significant "device churn", i.e. arrival and disappearance of computers. Creating the resource pool requires volunteer recruitment and retention, and the client software must be easy for consumers to install and run. The scale of VC is larger: ...

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“…Scheduling mechanism that performs most of below mentioned points is taken as better mechanism. Though none of these factors are considered collectively in the literature but few of them can be found in [2,3,5,6,7,14,15,22,25,26].…”

Section: A Key Performance Factorsmentioning

confidence: 99%

Scheduling in Desktop Grid Systems: Theoretical Evaluation of Policies & Frameworks

Khan¹,

Mahmood²,

Hyder³

2017

ijacsa

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Abstract-Desktop grid systems have already established their identity in the area of distributed systems. They are well suited for High Throughput Computing especially for Bag-ofTasks applications. In desktop grid systems, idle processing cycles and memory of millions of users (connected through internet or through any other communication mechanism) can be utilized but the workers / hosts machines not under any centralized administrative control that result in high volatility. This issue is countered by applying various types of scheduling policies that not only ensure task assignments to better workers but also takes care of fault tolerance through replication and other mechanism. In this paper, we discussed leading desktop grid systems framework and performed a comparative analysis of these frameworks. We also presented a theoretical evaluation of server and client based scheduling policies and identified key performance indicators to evaluate these policies.

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Robust task scheduling for volunteer computing systems

Cited by 18 publications

References 24 publications

An AREA-Oriented Heuristic for Scheduling DAGs on Volatile Computing Platforms

An AREA-Oriented Heuristic for Scheduling DAGs on Volatile Computing Platforms

BOINC: A Platform for Volunteer Computing

Scheduling in Desktop Grid Systems: Theoretical Evaluation of Policies & Frameworks

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