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2021 | OriginalPaper | Chapter

Analyzing the Concept of Priority Queue for Scheduling in Cloud Scenario

Authors: Rukaiya Naim, Nisha Chaurasia

Published in: Advances in Interdisciplinary Research in Engineering and Business Management

Publisher: Springer Singapore

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Abstract

Nowadays, cloud computing is rigorously used by academicians as well as by researchers due to its excellent applicability. Most significant feature of cloud computing is Live Migration of Virtual Machines (VMs). However, it demands large amount of migration time to transfer data from source to destination. Migration time of VMs can be mitigated by several techniques available in study. In migration, scheduling also plays a vital role which handles the processes, task, resources, etc. FCFS (first come, first serve), RR (round robin), and SJF (shortest job first) are among the several scheduling algorithms in CPU scheduling, utilized for minimization of makespan. In this paper, a more optimized scheduling algorithm known as MLFQ (multilevel feedback queue) is utilized. MLFQ helps in minimizing the makespan and overall workload with the help of feedback scheduler and by categorizing the load as under load and overload, resulting in effective migration.
Literature
1.
go back to reference Noshy, M., Ibrahim, A., & Ali, H. A. (2018). Optimization of live virtual machine migration in cloud computing: A survey and future directions. Journal of Network and Computer Applications. Noshy, M., Ibrahim, A., & Ali, H. A. (2018). Optimization of live virtual machine migration in cloud computing: A survey and future directions. Journal of Network and Computer Applications.
2.
go back to reference Zhang, W., Tan, S., Lu, Q., Liu, X., & Gong, W. (2015). A genetic-algorithm-based approach for task migration in pervasive clouds. International Journal of Distributed Sensor Networks, 11(8), 463230. CrossRef Zhang, W., Tan, S., Lu, Q., Liu, X., & Gong, W. (2015). A genetic-algorithm-based approach for task migration in pervasive clouds. International Journal of Distributed Sensor Networks, 11(8), 463230. CrossRef
3.
go back to reference Elmougy, S., Sarhan, S., & Joundy, M. (2017). A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique. Journal of Cloud Computing, 6(1), 12. CrossRef Elmougy, S., Sarhan, S., & Joundy, M. (2017). A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique. Journal of Cloud Computing, 6(1), 12. CrossRef
5.
go back to reference Behzad, S., Fotohi, R., & Effatparvar, M. (2013). Queue based job scheduling algorithm for cloud computing. International Research Journal of Applied and Basic Sciences ISSN, 37853790. Behzad, S., Fotohi, R., & Effatparvar, M. (2013). Queue based job scheduling algorithm for cloud computing. International Research Journal of Applied and Basic Sciences ISSN, 37853790.
6.
go back to reference Siahaan, A. P. U. (2016). Comparison analysis of CPU scheduling: FCFS, SJF and Round Robin. International Journal of Engineering Development and Research, 4(3), 124–132. Siahaan, A. P. U. (2016). Comparison analysis of CPU scheduling: FCFS, SJF and Round Robin. International Journal of Engineering Development and Research, 4(3), 124–132.
7.
go back to reference Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2011). CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience, 41(1), 23–50. Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2011). CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience, 41(1), 23–50.
8.
go back to reference Beloglazov, A., & Buyya, R. (2013). Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Transactions on Parallel and Distributed Systems, 24(7), 1366–1379. CrossRef Beloglazov, A., & Buyya, R. (2013). Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Transactions on Parallel and Distributed Systems, 24(7), 1366–1379. CrossRef
9.
go back to reference Raheja, S., Dadhich, R., & Rajpal, S. (2016). Designing of vague logic based multilevel feedback queue scheduler. Egyptian Informatics Journal, 17(1), 125–137. CrossRef Raheja, S., Dadhich, R., & Rajpal, S. (2016). Designing of vague logic based multilevel feedback queue scheduler. Egyptian Informatics Journal, 17(1), 125–137. CrossRef
10.
go back to reference EffatParvar, M., Faez, K., EffatParvar, M., Zarei, M., & Safari, S. (2006, October). An Intelligent MLFQ Scheduling Algorithm (IMLFQ) with Fault Tolerant Mechanism. In Intelligent Systems Design and Applications, 2006. ISDA’06. Sixth International Conference on (Vol. 3, pp. 80–85). IEEE. EffatParvar, M., Faez, K., EffatParvar, M., Zarei, M., & Safari, S. (2006, October). An Intelligent MLFQ Scheduling Algorithm (IMLFQ) with Fault Tolerant Mechanism. In Intelligent Systems Design and Applications, 2006. ISDA’06. Sixth International Conference on (Vol. 3, pp. 80–85). IEEE.
11.
go back to reference Thombare, M., Sukhwani, R., Shah, P., Chaudhari, S., & Raundale, P. (2016, March). Efficient implementation of Multilevel Feedback Queue Scheduling. In Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference on (pp. 1950–1954). IEEE. Thombare, M., Sukhwani, R., Shah, P., Chaudhari, S., & Raundale, P. (2016, March). Efficient implementation of Multilevel Feedback Queue Scheduling. In Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference on (pp. 1950–1954). IEEE.
12.
go back to reference Dai, Y., Lou, Y., & Lu, X. (2015, August). A task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm with multi-QoS constraints in cloud computing. In Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2015 7th International Conference on (Vol. 2, pp. 428–431).IEEE. Dai, Y., Lou, Y., & Lu, X. (2015, August). A task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm with multi-QoS constraints in cloud computing. In Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2015 7th International Conference on (Vol. 2, pp. 428–431).IEEE.
13.
go back to reference Azad, P., & Navimipour, N. J. (2017). An energy-aware task scheduling in the cloud computing using a hybrid cultural and ant colony optimization algorithm. International Journal of Cloud Applications and Computing (IJCAC), 7(4), 20–40. CrossRef Azad, P., & Navimipour, N. J. (2017). An energy-aware task scheduling in the cloud computing using a hybrid cultural and ant colony optimization algorithm. International Journal of Cloud Applications and Computing (IJCAC), 7(4), 20–40. CrossRef
Metadata
Title
Analyzing the Concept of Priority Queue for Scheduling in Cloud Scenario
Authors
Rukaiya Naim
Nisha Chaurasia
Copyright Year
2021
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-16-0037-1_8