Implementation and evaluation of scheduling algorithm based on PSO HC for elastic cluster criteria

Škrinárová, Jarmila

doi:10.2478/s13537-014-0216-3

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…This paper is based on our previous work related to the proposal of models for parallel tasks in workflow [13] and design of algorithms to support grid or elastic cluster scheduling [9,14,19,20,21]. We have experiences with the simulators GridSim, CloudSim, and systems Moab or Torque which are described in [1,4,5,8].…”

Section: Related Workmentioning

confidence: 99%

GPGPU based job scheduling simulator for hybrid high-performance computing systems

Škrinárová

Povinsky

2015

2015 IEEE 13th International Scientific Conference on Informatics

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This work is focused on the issue of job scheduling in a high performance computing systems such as grid, cloud or systems with hybrid environments. The goal is based on the analysis of scheduling models of tasks in grid and cloud, design and implementation of the simulator on the base of GPGPU. The simulator is verified by our own proposed model of job scheduling. The simulator consists of a scheduler that is using GPGPU to process large amounts of data by parallel way. For design of the scheduler we take into account that computing resources are used and enable the transfer of files between tasks. We consider a system with non-preemptive tasks. In order to ensure the optimization of the scheduling process we have implemented a simulated annealing algorithm. GPGPU model was compared to the CPU when the number of machines is changing from 32 to 512. Improving the implementation based on GPGPU had a significant impact on the system with 512 machines and with an increasing number of machines further accelerates in comparison with sequential algorithm. The outcome is that the proposed implementation of the GPGPU is relevant in job scheduling for high-performance computing.

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Section: Related Workmentioning

confidence: 99%

GPGPU based job scheduling simulator for hybrid high-performance computing systems

Škrinárová

Povinsky

2015

2015 IEEE 13th International Scientific Conference on Informatics

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“…Sj denotes to start processing time of task Jj, and Cj is time of task Jj completion. The most commonly used criteria for optimization of the schedule φ, that are needed to be minimized [18]:…”

Section: Scheduling Criteria Suitable For Elastic Clustermentioning

confidence: 99%

Parallel Simulation of Tasks Scheduling and Scheduling Criteria in High-performance Computing Systems

Škrinárová

Povinsky

2019

JIOS

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This work is focused on the issue of job scheduling in a high performance computing systems. The goal is based on the analysis of scheduling models of tasks in grid and cloud, design and implementation of the simulator on the base of GPGPU. The simulator is verified by our own proposed model of job scheduling. The simulator consists of a centralized scheduler that is using GPGPU to process large amounts of data by parallel way. In order to ensure the optimization of the scheduling process we have implemented a simulated annealing algorithm. GPGPU model was compared to the CPU when the number of nodes from 32 to 2048. Improving the implementation based on GPGPU had a significant impact on the system with 512 nodes and with an increasing number of nodes further accelerates in comparison with sequential algorithm. In this work are designed new scheduling criteria which are experimentally evaluated.

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“…This paper is also partially related to the proposal of models for parallel tasks in workflow, designed in our previous work [10], [11].…”

Section: Related Workmentioning

confidence: 99%

Construction Of Cloud Based E-Learning Platform With Privacy Preserved Public Data Verification Services

2017

IJMTER

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Abstract-The Cloud Service Provider (CSP) provides application level services and storage level services to the users. The learners are interacted with the system using the user interface tool. All the trainer and learner information are managed under the cloud server. The trainer and learner communication tasks are carried out through the user interface application. The learning materials and evaluation documents are maintained under the storage in the cloud server. Authentication, confidentiality and data integrity operations are carried out under the security solutions. The storage data integrity is verified with public and private data verifiers. The cloud based e-learning system is constructed with privacy preserved public data verification mechanism. Third Party Auditors (TPA) are employed to verify the cloud storage data values. The multi user verification is achieved with batch and data dynamism auditing mechanism. The storage data privacy is ensured with Homomorphic linear authenticator and random masking techniques. The system is also protected with malicious TPA and server activities. All the data verification operations are carried out through the cloud resources.The storage services are improved with version management and traceability features.

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Implementation and evaluation of scheduling algorithm based on PSO HC for elastic cluster criteria

Cited by 6 publications

References 10 publications

GPGPU based job scheduling simulator for hybrid high-performance computing systems

GPGPU based job scheduling simulator for hybrid high-performance computing systems

Parallel Simulation of Tasks Scheduling and Scheduling Criteria in High-performance Computing Systems

Construction Of Cloud Based E-Learning Platform With Privacy Preserved Public Data Verification Services

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