Deadline prediction scheduling based on benefits

Palanca, Javier; Navarro, Martí; García-Fornes, Ana; Julián, Vicente

doi:10.1016/j.future.2012.05.007

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…The Local Managers need to predict when a service execution is going to be finished in order to know if the temporal commitment proposed by the SLA Manager is going to be fulfilled. To do this, their scheduling algorithm implements the Deadline Prediction Scheduler [32]. This scheduling algorithm uses resource booking to ensure that a temporal commitment is accomplished.…”

Section: Executionmentioning

confidence: 99%

Real-time agreement and fulfilment of SLAs in Cloud Computing environments

Prieta

Heras

Palanca

et al. 2015

AIC

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A Cloud Computing system must readjust its resources by taking into account the demand for its services. This raises the need for designing protocols that provide the individual components of the Cloud architecture with the ability to self-adapt and to reach agreements in order to deal with changes in the services demand. Furthermore, if the Cloud provider has signed a Service Level Agreement (SLA) with the clients of the services that it offers, the appropriate agreement mechanism has to ensure the provision of the service contracted within a specified time. This paper introduces real-time mechanisms for the agreement and fulfilment of SLAs in Cloud Computing environments. On the one hand, it presents a negotiation protocol inspired by the standard WSAgreement used in web services to manage the interactions between the client and the Cloud provider to agree the terms of the SLA of a service. On the other hand, it proposes the application of a real-time argumentation framework for redistributing resources and ensuring the fulfilment of these SLAs during peaks in the service demand.

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Section: Executionmentioning

confidence: 99%

Real-time agreement and fulfilment of SLAs in Cloud Computing environments

Prieta

Heras

Palanca

et al. 2015

AIC

Self Cite

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“…Palanca et al proposed a scheduling algorithm that composes a scheduling plan having the capability to predict the completion time of the arriving tasks by using CPU booking performed in Distributed Goal-Oriented Computing paradigm. This prediction can be employed to establish a temporal commitment with the client that invokes the execution of the task (Palanca et al, 2013). Bohlouli et al proposed a prediction model that predicts the resource requirements using statistic terms such as linear regression or average by employing historical information.…”

Section: Related Workmentioning

confidence: 99%

An Analytical Model for Resource Characterization and Parameter Estimation for DAG-Based Jobs for Homogeneous Systems

Sajid

Raza

2015

International Journal of Distributed Systems and Technologies

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High Performance Computing (HPC) systems demand and consume a significant amount of resources (e.g. server, storage, electrical energy) resulting in high operational costs, reduced reliability, and sometimes leading to waste of scarce natural resources. On one hand, the most important issue for these systems is achieving high performance, while on the other hand, the rapidly increasing resource costs appeal to effectively predict the resource requirements to ensure efficient services in the most optimized manner. The resource requirement prediction for a job thus becomes important for both the service providers as well as the consumers for ensuring resource management and to negotiate Service Level Agreements (SLAs), respectively, in order to help make better job allocation decisions. Moreover, the resource requirement prediction can even lead to improved scheduling performance while reducing the resource waste. This work presents an analytical model estimating the required resources for the modular job execution. The analysis identifies the number of processors required and the maximum and minimum bounds on the turnaround time and energy consumed. Simulation study reveals that the scheduling algorithms integrated with the proposed analytical model helps in improving the average throughput and the average energy consumption of the system. As the work predicts the resource requirements, it can even play an important role in Service-Oriented Architectures (SOA) like Cloud computing or Grid computing.

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“…Esto se va a conseguir gracias al mecanismo de reserva de CPU. Este trabajo ha sido publicado además en [PNGFJ13].…”

Section: Planificación Con Predicción Del Deadline Basada En Beneficiosunclassified

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Deadline prediction scheduling based on benefits

Cited by 6 publications

References 21 publications

Real-time agreement and fulfilment of SLAs in Cloud Computing environments

Real-time agreement and fulfilment of SLAs in Cloud Computing environments

An Analytical Model for Resource Characterization and Parameter Estimation for DAG-Based Jobs for Homogeneous Systems

Computación distribuida basada en objetivos

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