Improving the Productivity of Volunteer Computing by Using the Most Effective Task Retrieval Policies

Tóth, Dávid; Finkel, David

doi:10.1007/s10723-009-9133-4

Cited by 12 publications

(9 citation statements)

References 8 publications

(22 reference statements)

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“…[29][30][31] The Berkeley Open Infrastructure for Network Computing (BOINC) 4 is a middleware system widely used for volunteer computing. There has been a lot of research toward improving its performance and productivity.…”

Section: Related Workmentioning

confidence: 99%

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Resilient parallel computing on volunteer PC grids

Subhlok

Nguyen

Gabriel

et al. 2018

Concurrency and Computation

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Volunteer PC hosts represent massive computation capacity at a low cost but are challenging to employ for general parallel computing. This paper presents the design, execution model, implementation, and evaluation of the Volpex framework for robust execution of parallel codes on volunteer PC grids characterized by system and network heterogeneity, varying availability, and frequent failures. The communication model is based on one-sided Put/Get calls to an abstract global shared space enhanced to support multiple autonomous instances of the same process at different stages of execution. Our approach customizes and combines the use of replication, checkpointing, and host selection. This presents formidable challenges that are addressed in this work; efficient checkpointing of distributed replicated processes, dynamic management of redundancy, quick restart in a distributed environment, and application specific host selection. The integrated runtime system is shown to effectively execute moderate size, coarse-grain, communicating codes on a worldwide distributed volunteer environment, a new milestone in volunteer computing. Extensive evaluation is conducted with example scientific codes on a pool of around 600 volunteer hosts. The results demonstrate the trade-offs in deploying checkpointing, redundancy, and host selection, and how these methods combine to provide application performance that is close to the ideal failure free performance. INTRODUCTIONDistributed computing is widely deployed for resource hungry applications that need massive computational power, large storage capacity, high processing speed or quick accessibility of data. Grid computing addresses this challenge with geographically distributed networked loosely coupled clusters. Popular grid computing middleware systems include Globus Toolkit 1 and UNICORE 2,3 (Uniform Interface to COmpute REsources). Ordinary PCs have been employed successfully for large scale scientific computing, most commonly using BOINC 4 as middleware. Web browsers can host middleware for distributed computing, a popular example being Weevilscout. 5The BOINC middleware uses volunteered public PCs for scientific applications when idle. It has been remarkably successful, managing as much as 5 Petaflops of aggregate compute power and supporting over 60 scientific research projects. However, this is a tiny fraction of the volunteer compute power that could be exploited. The research presented in this paper aims to enhance the state-of-the-art for parallel computing on volunteer PC grids. We will refer to PCs made available for scientific computing when idle as volunteer nodes (or hosts, or PCs) and an execution environment composed of PCs connected by a LAN or Internet as a volunteer PC grid (VPG); even if the PCs are managed hosts in an organization. Volunteer PCs represent a potentially immense but volatile resource; they are heterogeneous in terms of architecture, networking, and operating system, prone to failure, and their availability to execute guest scientific applications...

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Section: Related Workmentioning

confidence: 99%

“…There has been a lot of research toward improving its performance and productivity. [29][30][31] The Berkeley Open Infrastructure for Network Computing (BOINC) 4 is a middleware system widely used for volunteer computing. Condor, 32 is a workload management system that can effectively harness wasted CPU power.…”

Section: Related Workmentioning

confidence: 99%

Resilient parallel computing on volunteer PC grids

Subhlok

Nguyen

Gabriel

et al. 2018

Concurrency and Computation

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“…In [16] the same aim is considered from another point of view: they re-solve randomly chosen tasks of a parcel to reveal lying nodes. Di erent retrieval methods for better performance are compared in [52]: this is not exactly task parceling, but a related approach.…”

Section: Task Groupingmentioning

confidence: 99%

Task Scheduling in Desktop Grids: Open Problems

2017

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Abstract:We survey the areas of Desktop Grid task scheduling that seem to be insu ciently studied so far and are promising for e ciency, reliability, and quality of Desktop Grid computing. These topics include optimal task grouping, "needle in a haystack" paradigm, gametheoretical scheduling, domain-imposed approaches, special optimization of the nal stage of the batch computation, and Enterprise Desktop Grids.

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“…Any imbalance in the amount of work fetched would either result in wasted CPU cycles and other resources (RAM, disk) caused by missed deadlines or less than optimal utilization of the already scarce shared resources. BOINC Client uses two work fetch policies buffer none and buffer multiple tasks, also a number of other variations have been suggested in [7,9]. As stated earlier, work fetch policies addresses the issues of when to ask for more work, which project to ask work for and how much work to ask for.…”

Section: Evalauating Client Based Work Fetch Policiesmentioning

confidence: 99%

“…Buffer One Task [9] The policy buffers one task so the client always has a task to process, even while it is downloading a new task.…”

Section: Download Early [9]mentioning

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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Improving the Productivity of Volunteer Computing by Using the Most Effective Task Retrieval Policies

Cited by 12 publications

References 8 publications

Resilient parallel computing on volunteer PC grids

Resilient parallel computing on volunteer PC grids

Task Scheduling in Desktop Grids: Open Problems

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

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