Structure aware resource estimation for effective scheduling and execution of data intensive workflows in cloud

Kanagaraj, K.; Swamynathan, S.

doi:10.1016/j.future.2017.09.001

Cited by 20 publications

(14 citation statements)

References 22 publications

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“…Ghobaei‐Arani et al 17 introduce hybrid autonomic resource provisioning based on autonomic computing and machine learning techniques. Kanagaraj and Swamynathan 16 deal with under/over provisioning employing the number of tasks together with their arrangement in the workflow structure to determine VMs required for execution. The algorithms achieves more efficient utilization however does not take into consideration the sensitivity of the individual tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Process mining-controlled scheduling is an option worth pursuing when executing data-intensive applications in the hybrid cloud to comply with some given business constraints and meeting given QoS related deadlines. Related works in scheduling and cloud resource provisioning employ techniques such as load-awareness, 14 parallelism-awareness, 15 structure-awareness 16 to aid in task scheduling, and middle-ware tooling. 17,18 Data-awareness (sensitivity) but not process-awareness is considered in Oktay et al 6 From our best literature search efforts, no work specifically combines data sensitivity and process-awareness in task scheduling.…”

Section: Introductionmentioning

confidence: 99%

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Process mining‐constrained scheduling in the hybrid cloud

Azumah

Sørensen

Montella

et al. 2020

Concurrency and Computation

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Summary Hybrid cloud, typically a combination of public and private cloud deployment models, is a rising paradigm due to the benefits it offers: full control of data and applications in the private cloud and elastic computing resource availability in the public cloud. This combination however brings an extra layer of complexity that can potentially erode the benefits and present serious challenges if not managed well. Among the challenges, ensuring business constraint compliance across the combination of cloud deployment models is a growing concern. Our article brings a sensitive, data‐ and process‐aware framework to bear on task scheduling in hybrid clouds with compliance to business constraints. Our proposed approach utilizes data from a real hybrid cloud‐based hospital billing system that is governed by complex and dynamic data processing rules. Our system successfully employs a process mining controlled algorithm to schedule tasks in the hybrid cloud to comply with the given set of business constraints.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Process mining‐constrained scheduling in the hybrid cloud

Azumah

Sørensen

Montella

et al. 2020

Concurrency and Computation

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“…Heterogeneous resource allocation 17,18 and mapping task in cloud, faces many di±culties due to being directly applied to the Cloud environments. Cloud service providers objective is to optimize the system throughput and allow a fair usage of the resources.…”

Section: Literature Surveymentioning

confidence: 99%

Adaptive Workflow Scheduling Using Evolutionary Approach in Cloud Computing

Jaybhaye

Attar

2020

Vietnam J. Comp. Sci.

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Cloud services are used to achieve diverse computing needs such as cost, security, scalability, and availability. Acceleration evolution in the distributed and cloud domains is common for large and dynamic workflows deployment. Resources and task mapping depend on the user’s objectives such as reduction in cost or execution completion within the stipulated time in consideration with certain quality of services. Multiple virtual machine instances can be launched by defining different configurations such as operating system, server types, and applications. Though workflow scheduling is an NP-Hard problem, variety of decision-making techniques are available for optimum resource allocation. In this research paper, different algorithms are studied and compared with evolutionary approaches. Workflow scheduling using genetic algorithm is implemented and discussed. This paper aims to design a decision-making technique to optimize resources of cloud. It is an adaptive scheduling to maximize profit by reducing execution time. The approach implemented is useful to cloud service providers to maximize profit and resource efficiency in their services.

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“…Structure aware resource estimation: [8] This approach overcomes the problems of over-provisioning and under-provisioning. In this approach the number of tasks in the workflow and the arrangement of tasks in the workflow (structure) are used to determine the number of Virtual Machines required executing the workflow.…”

Section: 2mentioning

confidence: 99%

Enhanced Resource Provisioning Strategies for Scientific Workflows in Cloud Environment: A Survey

Sridevi¹,

Katiravan²

2018

IJCA

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Efficient resource provisioning is the key challenge that brings the best quality of service which is beneficial both to the users and CSPs. Since cloud computing aims at providing adaptive provisioning as pay-per-use basis, dynamic resource provisioning is a critical research issue. The on-demand provisioning and resource availability in cloud computing make it ideal for executing scientific workflow applications. To ensure better performance, there is a need for auto-scaling. The problem of assigning resources to tasks and orchestrating their execution to preserve the dependencies of workflows is NP-complete. [2] Hence, no optimal solution can be found in polynomial time. NP-complete problems are often addressed by using heuristic or meta-heuristics approaches. This survey presents the existing auto-scaling techniques and metaheuristics approaches for VM placements of cloud workflows.

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Structure aware resource estimation for effective scheduling and execution of data intensive workflows in cloud

Cited by 20 publications

References 22 publications

Process mining‐constrained scheduling in the hybrid cloud

Process mining‐constrained scheduling in the hybrid cloud

Adaptive Workflow Scheduling Using Evolutionary Approach in Cloud Computing

Enhanced Resource Provisioning Strategies for Scientific Workflows in Cloud Environment: A Survey

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