Adaptive scheduling in the cloud - SLA for Hadoop job scheduling

Nayak, Deveeshree; Martha, Venkata Swamy; Threm, David; Ramaswamy, Srini; Prince, Summer; Fatimberger, Gunter

doi:10.1109/sai.2015.7237240

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…This work ensures the demand-based adaptive scheduling policy for task scheduling. Further the enhancements of this proposal are presented by Nayak et al [13] utilizing the benefits from cloud-based services for distributed task and resource managements. Yet another direction of these proposals is presented by Xu et al [14] for prioritizing the utilization of the load types with the resources and resource types.…”

Section: Background Of the Load Balancing Strategymentioning

confidence: 99%

A Comprehensive Survey of Load Balancing Strategies Using Hadoop Queue Scheduling and Virtual Machine Migration

Dey

Gunasekhar²

2019

IEEE Access

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The recent growth in the demand for scalable applications from the consumers of the services has motivated the application development community to build and deploy the applications on cloud in the form of services. The deployed applications have significant dependency on the infrastructure available with the application providers. Bounded by the limitations of available resource pools on-premises, many application development companies have migrated the applications to third party cloud environments called data centers. The data center owners or the cloud service providers are entitled to ensure high performance and high availability of the applications and at the same time the desired scalability for the applications. Also, the cloud service providers are also challenging in terms of cost reduction and energy consumption reductions for better manageability of the data center without degrading the performance of the deployed applications. It is to be noted that the performance of the application does not only depend on the responsiveness of the applications rather also must be measured in terms of service level agreements. The violation of the service level agreements or SLA can easily disprove the purpose of application deployments on cloudbased data centers. Thus, the data center owners apply multiple load balancing strategies for maintaining the desired outcomes from the application owners at the minimized cost of data center maintainability. Hence, the demand of the research is to thoroughly study and identify the scopes for improvements in the parallel research outcomes. As the number of applications ranging from small data-centric applications coming with the demand of frequent updates with higher computational capabilities to the big data-centric application as big data analytics applications coming with efficient algorithms for data and computation load managements, the data center owners are forced to think for efficient algorithms for load managements. The algorithms presented by various research attempts have engrossed on application specific demands for load balancing using virtual machine migrations and the solution as the proposed algorithms have become application problem specific. Henceforth, the further demand of the research is a guideline for selecting the appropriate load balancing algorithm via virtual machine migration for characteristics-based specific applications. Hence, this paper presents a comprehensive survey on existing virtual machine migration and selection processes to understand the specific application-oriented capabilities of these strategies with the advantages and bottlenecks. Also, with the understanding of the existing measures for load balancing, it is also important to furnish the further improvement strategies, which can be made possible with a detailed understanding of the parallel research outcomes. Henceforth, this paper also equips the study with guidelines for improvements and for further study. Nonetheless, the study cannot be completed without the mathematical an...

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Section: Background Of the Load Balancing Strategymentioning

confidence: 99%

A Comprehensive Survey of Load Balancing Strategies Using Hadoop Queue Scheduling and Virtual Machine Migration

Dey

Gunasekhar²

2019

IEEE Access

View full text Add to dashboard Cite

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SLA-Based Scheduling of Spark Jobs in Hybrid Cloud Computing Environments

Islam

Karunasekera

et al. 2022

IEEE Trans. Comput.

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SLA Management for Big Data Analytical Applications in Clouds

et al. 2020

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Recent years have witnessed the booming of big data analytical applications (BDAAs). This trend provides unrivaled opportunities to reveal the latent patterns and correlations embedded in the data, and thus productive decisions may be made. This was previously a grand challenge due to the notoriously high dimensionality and scale of big data, whereas the quality of service offered by providers is the first priority. As BDAAs are routinely deployed on Clouds with great complexities and uncertainties, it is a critical task to manage the service level agreements (SLAs) so that a high quality of service can then be guaranteed. This study performs a systematic literature review of the state of the art of SLA-specific management for Cloud-hosted BDAAs. The review surveys the challenges and contemporary approaches along this direction centering on SLA. A research taxonomy is proposed to formulate the results of the systematic literature review. A new conceptual SLA model is defined and a multi-dimensional categorization scheme is proposed on its basis to apply the SLA metrics for an in-depth understanding of managing SLAs and the motivation of trends for future research.

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Adaptive scheduling in the cloud - SLA for Hadoop job scheduling

Cited by 5 publications

References 7 publications

A Comprehensive Survey of Load Balancing Strategies Using Hadoop Queue Scheduling and Virtual Machine Migration

A Comprehensive Survey of Load Balancing Strategies Using Hadoop Queue Scheduling and Virtual Machine Migration

SLA-Based Scheduling of Spark Jobs in Hybrid Cloud Computing Environments

SLA Management for Big Data Analytical Applications in Clouds

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