Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers
Abstract
:1. Introduction
2. Related Work
3. Proposed Sharing with Live Migration (SLM) Model
3.1. Workflow Model
3.2. Energy Model
3.3. The Execution Model
- Users send a number of tasks to the data center.
- The scheduler starts processing tasks in case the needed server is idle.
- Queuing applied to incoming tasks if their needed file is in use.
- A task checkup procedure is performed by the scheduler for each of the queued tasks for each queue. In case any of the tasks need to perform a similar type of process on the available resource, it will grant them permission to start concurrently with the current task without waiting in the queue if the designated host is not overloaded.
- Migrate tasks to the replica underutilized server if the needed main server is overloaded.
- Migrate tasks from the under-loaded replica server to the main server, then set the replica back to sleep mode.
- Apply queuing on the tasks with minimum waiting time in case resources are unavailable.
4. SLM Scheduler Algorithm
Algorithm 1: The Sharing with Live Migration (SLM) algorithm |
Create different types of task |
Submit task to broker for execution |
For each task from task queue |
For each VM from VM list |
Submit task to the VM |
If VM.host.utilization > 80 |
Migrate VMs until host utilization is <80 |
Else |
If VM.host.utilization < 30 |
Migrate to another host and hibernate current host |
If the read/download counter=0 & write/upload lock is in off mode for the current task required resource file |
Submit task for execution |
If the task type is read/download |
Increment the read/download counter |
Else |
Set write/upload lock on |
Else |
If read/download counter ≠0 |
If task type is read/download |
Submit task for execution, increment counter |
Else |
Submit task to waiting queue |
Else |
Submit task to the waiting queue |
After execution of task |
If task has used file for read or download |
Decrement read/download count |
Else |
Set write/upload off |
5. SLM Simulation and Analysis
5.1. Assumptions
5.2. Simulation Environment
5.3. Simulation Scenario
- (1)
- Generating tasks: Set up task types to reading, writing, uploading, and downloading with task lengths of 10,000, 40,000, 80,000, and 200,000. Generate a random number of tasks and set their configuration (type, size, number).
- (2)
- Primary admission control: Data center broker submits tasks to a class called ‘cloudlet’ to manage transferring it to the proper VM without violating any concurrency control rule while there are enough free processing elements (PEs).
- (3)
- Application-specific analysis and ranking: The file execution details describe each task, such as file VM number, file host number, file number, and task type. The SLM scheduling policy assign tasks directly to the first under-loaded VM that has the corresponding required file. The ‘cloudlet’ type represents the type of the task process, which could be either reading, downloading, writing, or uploading, each bearing a corresponding code. The SLM scheduler allows multiple tasks processing at the same time only if the task’s type is ‘read’ or ‘download’ (not if the current task type under processing is either ‘write’ or ‘upload’, due to the overwriting problem). Hence, the SLM scheduler will provide concurrency control.
- (4)
- QoS and SLA control: The proposed SLM simulation model added a continuous observation class to the simulation to detect changes of the QoS and the SLA conditions, and prevent any violation to those agreements.
6. Experimental Results
7. Conclusions
Author Contributions
Conflicts of Interest
References
- Singh, S.; Jeong, Y.; Hyuk, J. A survey on cloud computing security: Issues, threats, and solutions. J. Netw. Comput. Appl. 2016, 75, 200–222. [Google Scholar] [CrossRef]
- Azzouzi, M.; Neri, F. An Introduction to the Special Issue on Advanced Control of Energy Systems. WSEAS Trans. Power Syst. 2013, 8, 103. [Google Scholar]
- Singh, S.; Sharma, P.K.; Moon, S.Y. EH-GC: An Efficient and Secure Architecture of Energy Harvesting Green Cloud Infrastructure. Sustainability 2017, 9, 673. [Google Scholar] [CrossRef]
- Xu, D.; Wang, K. Stochastic Modeling and Analysis with Energy Optimization for Wireless Sensor Networks. Int. J. Distrib. Sens. Netw. 2014, 5, 1–5. [Google Scholar] [CrossRef]
- Patel, S.; Makwana, R.M. Optimized Energy Efficient Virtual Machine Placement Algorithm and Techniques for Cloud Data Centers. J. Comput. Sci. 2016, 12, 1–7. [Google Scholar] [CrossRef]
- Baiboz, A. Energy-Efficient Data Center Concepts under the EXPO-2017 Astana. J. Multidiscip. Eng. Sci. Technol. 2015, 2, 1126–1128. [Google Scholar]
- Awad, A.I.; El-Hefnawy, N.A.; Abdelkader, H.M. Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments. Procedia Comput. Sci. 2015, 65, 920–929. [Google Scholar] [CrossRef]
- Mahmood, A.; Khan, S.A.; Bahlool, R.A. Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm. Computers 2017, 6, 15. [Google Scholar] [CrossRef]
- Ma, T.; Tang, M.; Shen, W.; Jin, Y. Improved FIFO Scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing. Information 2017, 5, 25. [Google Scholar] [CrossRef]
- Yadav, A.K.; Rathod, S.B. Study of Scheduling Techniques in Cloud Computing Environment. Int. J. Comput. Trends Technol. 2015, 29, 69–73. [Google Scholar] [CrossRef]
- Yadav, A.K.; Mandoria, H.L. Study of Task Scheduling Algorithms in the Cloud Computing Environment: A Review. Int. J. Comput. Sci. Inf. Technol. 2017, 8, 462–468. [Google Scholar]
- Tang, Z.; Qi, L.; Cheng, Z.; Li, K.; Khan, S. An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment. J. Grid Comput. 2015, 14, 55–74. [Google Scholar] [CrossRef]
- Wu, C.; Chang, R.; Chan, H. A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Future Gener. Comput. Syst. 2014, 37, 141–147. [Google Scholar] [CrossRef]
- Ali, S.A.; Islamia, J.M. A Relative Study of Task Scheduling Algorithms in Cloud Computing Environment. In Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, India, 14–17 December 2016. [Google Scholar] [CrossRef]
- Zhao, Q.; Xiong, C.; Yu, C.; Zhang, C.; Zhao, X. A new energy-aware task scheduling method for data-intensive applications in the cloud. J. Netw. Comput. Appl. 2016, 59, 14–27. [Google Scholar] [CrossRef]
- Lin, C.; Syu, Y.; Chang, C.; Wu, J.; Liu, P.; Cheng, P.; Hsu, W. Energy-efficient task scheduling for multi-core platforms with per-core DVFS. J. Parallel Distrib. Comput. 2015, 86, 71–81. [Google Scholar] [CrossRef]
- Ding, Y.; Qin, X.; Liu, L.; Wang, T. Energy efficient scheduling of virtual machines in cloud with deadline constraint. Future Gener. Comput. Syst. 2015, 50, 62–74. [Google Scholar] [CrossRef]
- Pavithra, B.; Ranjana, R. Energy efficient resource provisioning with dynamic VM placement using energy aware load balancer in cloud. In Proceedings of the 2016 International Conference on Information Communication and Embedded Systems (ICICES), Chennai, India, 25–26 February 2016. [Google Scholar] [CrossRef]
- AlIsmail, S.M.; Kurdi, H.A. Green algorithm to reduce the energy consumption in cloud computing data centers. In Proceedings of the 2016 SAI Computing Conference (SAI), London, UK, 13–15 July 2016; pp. 557–561. [Google Scholar]
- Ziqian, D.; Liu, N.; Rojas-Cessa, R. Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers. J. Cloud Comput. Adv. Syst. Appl. 2015, 4, 5. [Google Scholar] [CrossRef]
- Intel’s Cloud Computing 2015 Vision. Available online: http://www.intel.com/content/www/us/en/cloud-computing/cloudcomputing-intel-cloud-2015-vision.html (accessed on 25 July 2018).
- Ismaila, L.; Fardoun, A. EATS: Energy-Aware Tasks Scheduling in Cloud Computing Systems. In Proceedings of the 6th International Conference on Sustainable Energy Information Technology (SEIT 2016), Madrid, Spain, 23–26 May 2016; pp. 870–877. [Google Scholar]
- Tian, W.; Xu, M.; Chen, A.; Li, G.; Wang, X.; Chen, Y. Open-Source Simulators for Cloud Computing: Comparative Study and Challenging Issues. Simul. Model. Pract. Theory 2015, 1, 239–254. [Google Scholar] [CrossRef]
- Kaur, P.; Rani, A. Virtual Machine Migration in Cloud Computing. Int. J. Grid Distrib. Comput. 2015, 8, 337–342. [Google Scholar] [CrossRef]
- Alshathri, S. Towards an Energy Optimization Framework for Cloud Computing Data Centers. In Proceedings of the Eleventh International Network Conference (INC 2016), Frankfurt am Main, Germany, 19–21 July 2016; pp. 9–12. [Google Scholar]
- Beloglazov, A. Energy-Efficient Management of Virtual Machines in Data Centers for Cloud Computing. Ph.D. Thesis, The University of Melbourne, Melbourne, Australia, February 2013. [Google Scholar]
- Beloglazov, A.; Buyya, R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Concurr. Comput. Pract. Exp. 2012, 24, 1397–1420. [Google Scholar] [CrossRef]
- Shu, W.; Wang, W.; Wang, Y. A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing. EURASIP J. Wirel. Commun. Netw. 2014, 2014, 64. [Google Scholar] [CrossRef] [Green Version]
- Han, G.; Que, W.; Jia, G.; Shu, L. An Efficient Virtual Machine Consolidation Scheme for Multimedia Cloud Computing. Sensors 2016, 16, 246. [Google Scholar] [CrossRef] [PubMed]
- Github. Available online: https://github.com/Cloudslab/cloudsim/releases/tag/cloudsim-3.0.3 (accessed on 25 July 2018).
No. of Tasks | Basic Work Model | Energy Consumption in the SLM Model | ||
---|---|---|---|---|
Energy Consumption (kwh) | Makespan (s) | Energy Consumption (kwh) | Makespan (s) | |
100 | 0.16 | 1098 | 0.07 | 450 |
200 | 0.24 | 1590 | 0.11 | 730.1 |
300 | 0.3 | 2178 | 0.17 | 1260 |
400 | 0.32 | 2290 | 0.21 | 1350.1 |
500 | 0.39 | 2518 | 0.27 | 1920 |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Alshathri, S.; Ghita, B.; Clarke, N. Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers. Future Internet 2018, 10, 86. https://doi.org/10.3390/fi10090086
Alshathri S, Ghita B, Clarke N. Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers. Future Internet. 2018; 10(9):86. https://doi.org/10.3390/fi10090086
Chicago/Turabian StyleAlshathri, Samah, Bogdan Ghita, and Nathan Clarke. 2018. "Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers" Future Internet 10, no. 9: 86. https://doi.org/10.3390/fi10090086