scholarly journals Globally Scheduling Volunteer Computing

2021 ◽  
Vol 13 (9) ◽  
pp. 229
Author(s):  
David P. Anderson
Keyword(s):  
High Throughput ◽  
Data Center ◽  
Scientific Computing ◽  
Software Requirements ◽  
Volunteer Computing ◽  
Scheduling Problem ◽  
Laptop Computers ◽  
Scheduling Policy ◽  
Science Projects ◽  
Application Programs

Volunteer computing uses millions of consumer computing devices (desktop and laptop computers, tablets, phones, appliances, and cars) to do high-throughput scientific computing. It can provide Exa-scale capacity, and it is a scalable and sustainable alternative to data-center computing. Currently, about 30 science projects use volunteer computing in areas ranging from biomedicine to cosmology. Each project has application programs with particular hardware and software requirements (memory, GPUs, VM support, and so on). Each volunteered device has specific hardware and software capabilities, and each device owner has preferences for which science areas they want to support. This leads to a scheduling problem: how to dynamically assign devices to projects in a way that satisfies various constraints and that balances various goals. We describe the scheduling policy used in Science United, a global manager for volunteer computing.

scholarly journals BOINC: A Platform for Volunteer Computing

2019 ◽  
Vol 18 (1) ◽  
pp. 99-122 ◽  
Author(s):  
David P. Anderson
Keyword(s):  
Open Source ◽  
High Throughput ◽  
Scientific Computing ◽  
Low Cost ◽  
Volunteer Computing ◽  
Digital Devices ◽  
Computing Capacity

Abstract“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 BOINC’s features, architecture, implementation, and algorithms.

scholarly journals Traffic-Independent Multi-Path Routing for High-Throughput Data Center Networks

2020 ◽  
Vol E103.D (12) ◽  
pp. 2471-2479
Author(s):  
Ryuta KAWANO ◽  
Ryota YASUDO ◽  
Hiroki MATSUTANI ◽  
Michihiro KOIBUCHI ◽  
Hideharu AMANO
Keyword(s):  
High Throughput ◽  
Data Center ◽  
Data Center Networks ◽  
High Throughput Data

scholarly journals Dynamic Task Scheduling Algorithm with Deadline Constraint in Heterogeneous Volunteer Computing Platforms

2019 ◽  
Vol 11 (6) ◽  
pp. 121 ◽  
Author(s):  
Ling Xu ◽  
Jianzhong Qiao ◽  
Shukuan Lin ◽  
Wanting Zhang
Keyword(s):  
Distributed Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Volunteer Computing ◽  
Scheduling Problem ◽  
Dynamic Task ◽  
Use Of Resources ◽  
Task Scheduling Algorithm ◽  
Deadline Constraint ◽  
Dynamic Task Scheduling

Volunteer computing (VC) is a distributed computing paradigm, which provides unlimited computing resources in the form of donated idle resources for many large-scale scientific computing applications. Task scheduling is one of the most challenging problems in VC. Although, dynamic scheduling problem with deadline constraint has been extensively studied in prior studies in the heterogeneous system, such as cloud computing and clusters, these algorithms can’t be fully applied to VC. This is because volunteer nodes can get offline whenever they want without taking any responsibility, which is different from other distributed computing. For this situation, this paper proposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint, called deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the nearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic task scheduling problem with deadline constraint. To make full use of resources and maximize the number of completed tasks before the deadline constraint, on the basis of the DPDS algorithm, improved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we conducted experiments, and the results show that the proposed algorithms can effectively solve the dynamic task assignment problem with deadline constraint in VC.

scholarly journals Large-scale virtual high-throughput screening for the identification of new battery electrolyte solvents: computing infrastructure and collective properties

2015 ◽  
Vol 17 (5) ◽  
pp. 3394-3401 ◽  
Author(s):  
Tamara Husch ◽  
Nusret Duygu Yilmazer ◽  
Andrea Balducci ◽  
Martin Korth
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Molecular Structures ◽  
Volunteer Computing ◽  
Battery Electrolyte ◽  
Computing Approach ◽  
Computing Infrastructure

A volunteer computing approach is presented for the purpose of screening a large number of molecular structures with respect to their suitability as new battery electrolyte solvents.

scholarly journals The INFN scientific computing infrastructure: present status and future evolution

2019 ◽  
Vol 214 ◽  
pp. 03001
Author(s):  
T. Boccali ◽  
G. Carlino ◽  
L. dell’Agnello
Keyword(s):  
Data Center ◽  
Scientific Computing ◽  
General View ◽  
Scientific Communities ◽  
Future Evolution ◽  
Tier 1 ◽  
Software And Hardware ◽  
Access Policies ◽  
Near Future ◽  
Computing Infrastructure

The INFN scientific computing infrastructure is composed of more than 30 sites, ranging from CNAF (Tier-1 for LHC and main data center for nearly 30 other experiments) and nine LHC Tier-2s, to ∼ 20 smaller sites, including LHC Tier-3s and not-LHC experiment farms. A comprehensive review of the installed resources, together with plans for the near future, has been collected during the second half of 2017, and provides a general view of the infrastructure, its costs and its potential for expansions; it also shows the general trends in software and hardware solutions utilized in a complex reality as INFN. As of the end of 2017, the total installed CPU power exceeded 800 kHS06 (∼ 80,000 cores) while the total storage net capacity was over 57 PB on disk and 97 PB on tape: the vast majority of resources (95% of cores and 95% of storage) are concentrated in the 16 largest centers. Future evolutions are explored and are towards the consolidation into big centers; this has required a rethinking of the access policies and protocols in order to enable diverse scientific communities, beyond LHC, to fruitfully exploit the INFN resources. On top of that, such an infrastructure will be used beyond INFN experiments, and will be part of the Italian infrastructure, comprising other research institutes, universities and HPC centers.

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