Cooperative scheduling mechanism for large-scale peer-to-peer computing systems

Rius, Josep; Cores, Fernando; Solsona, Francesc

doi:10.1016/j.jnca.2013.01.002

Cited by 14 publications

(9 citation statements)

References 27 publications

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“…Планировщик заданий может принимать во внимание граф связей между заданиями для ускорения вычислений, снижения нагрузки на сервер, уменьшения времени коммуникаций и др. [9,10]. В представленной работе рассматриваются наиболее распростра-ненные Desktop Grid, используемые для организации добровольных вычислений.…”

Section: вычислительные системы Desktop Gridunclassified

A survey of task scheduling in Desktop Grid

Chernov¹,

Ivashko²,

Nikitina³

2017

PSTA

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Section: вычислительные системы Desktop Gridunclassified

A survey of task scheduling in Desktop Grid

Chernov¹,

Ivashko²,

Nikitina³

2017

PSTA

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“…From the utility provider's profitability stance (Table 7), some approaches [3,8,57,63,65,103] are aimed at solving the rescheduling challenge affecting the communication cost of tasks across resources. Similarly, other approaches [12,51,52,64,72] have focused on resource management challenges to provide optimal cost-aware solutions which can impact the QoS of the requested services.…”

Section: System Functionality Challenges Form Profitability Aspectmentioning

confidence: 99%

Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities

Alkhanak

Lee

Khan

2015

Future Generation Computer Systems

136

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“…[18] proposes a two-level architecture to solve the scheduling placement problem in large-scale computing systems. The main idea behind, similar to those applied in Clommunity, is to divide the resources in smaller areas controlled by low-level schedulers (LS).…”

Section: Related Workmentioning

confidence: 99%

Exploring local service allocation in Community Networks

Vega

Meseguer

Cabrera

et al. 2014

2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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Community Cloud computing is a new trend on cloud computing that aims to build service infrastructures upon Wireless Community Networks taking advantage of underused community physical resources. Service allocation protocols are a key design challenge that all cloud systems must properly address to optimize resource utilization. They are specially important when cloud services require a Quality of Service (QoS) and network stability or performance (delay, jitter, minimum bandwidth) cannot be guaranteed a-priory. This work presents a study that tries to understand how to address cloud service deployments in such scenario. In particular, we start proposing an allocation algorithm to find optimal solutions when there is a central authority that coordinates the process. These solutions optimize the communication cost in two ways: (1) minimizing the service overlay diameter and, (2) minimizing the coordination cost along the network. Based on the study of the algorithm and the experimental simulations, we study the variables that outcome optimal service allocations to the detriment of other solutions. We verify these findings using data mining techniques. Researchers can take advantage of the simulation results and our observations to design more reliable distributed algorithms able to dynamically self-adapt to network changes.

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Cooperative scheduling mechanism for large-scale peer-to-peer computing systems

Cited by 14 publications

References 27 publications

A survey of task scheduling in Desktop Grid

A survey of task scheduling in Desktop Grid

Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities

Exploring local service allocation in Community Networks

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