Energy-aware Task Scheduling using Ant-colony Optimization in cloud
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The document proposes an energy-aware task scheduling algorithm using ant colony optimization for cloud computing. It aims to minimize energy consumption in datacenters by scheduling tasks efficiently across virtual machines and physical hosts. The algorithm uses concepts from ant colony optimization to probabilistically determine good task-to-resource allocations. The results show that the proposed approach reduces energy consumption by 22% compared to a first-come, first-served scheduling approach.
![Task Scheduling
•Scheduling [3] the n tasks (T1, T2,…,Tn) to m
Virtual Machines (VM1, VM2,…,VMm) running
on p Physical hosts (P1, P2,…,Pp) in such a way
that maximum completion time or makespan of
these n tasks will be minimized.
•n>m>p](https://image.slidesharecdn.com/conference-final-140910011909-phpapp01/85/Energy-aware-Task-Scheduling-using-Ant-colony-Optimization-in-cloud-5-320.jpg)
![System Model
•Cloudsim [16]
•CIS registry hold
information about the
resources
•Scheduler (or Broker)
is enhanced to be an
energy-aware
scheduler](https://image.slidesharecdn.com/conference-final-140910011909-phpapp01/85/Energy-aware-Task-Scheduling-using-Ant-colony-Optimization-in-cloud-9-320.jpg)
![System Model
•LP Model (Based on [13])
•ϕ = CPU Utilization
•Pidle = Power when
CPU is idle
•Pmax = Power when
CPU is fully utilized
•RT Model (Based on [12] )
•The Expected
Time to Compute is
given by
•Wi = Workload
•CCj = Computing
Capacity](https://image.slidesharecdn.com/conference-final-140910011909-phpapp01/85/Energy-aware-Task-Scheduling-using-Ant-colony-Optimization-in-cloud-10-320.jpg)
Energy-aware Task Scheduling using Ant-colony Optimization in cloud
- 1. Energy Aware Task Scheduling using Ant Colony Optimization in Cloud Linda . J, Ananthanarayana V.S. NITK Surathkal
- 2. Agenda •Introduction •Need for Task Scheduling & Energy Awareness •Problem Statement & Objective •Proposed Solution & Methodology •Results •Conclusion
- 3. Cloud Computing •“BURST” (Sudden increase or decrease) natured Web application demands affected the business. •Cloud Computing provided the solution via different service models like, •IaaS, PaaS, SaaS •Deployment Models Public, Private & Hybrid Cloud •Cloud computing is a model for enabling ubiquitous, convenient, on-demand access to a shared pool of resources.
- 4. Need for Energy Awareness in Task Scheduling •Many users access Virtual Machines everyday. •Efficient Task Scheduling Algorithms are required to increase profit for the cloud providers. •Consequently, Servers are always ON thereby increasing the Total Energy Consumption. •There arises a need to reduce the energy consumption in datacenters.
- 5. Task Scheduling •Scheduling [3] the n tasks (T1, T2,…,Tn) to m Virtual Machines (VM1, VM2,…,VMm) running on p Physical hosts (P1, P2,…,Pp) in such a way that maximum completion time or makespan of these n tasks will be minimized. •n>m>p
- 6. Requirements of Energy-aware Scheduler •To go through all possible (Task,VM) pairs so as to reduce the makespan. •To go through the Total Energy Consumption in all the hosts.
- 7. Problem Statement •The Problem is to design a Energy-aware Task Scheduling Algorithm. •Objective: –To design a Task scheduling Algorithm. –To add the Energy Awareness factor to the technique. –To compare the Energy-aware Algorithm with existing algorithms.
- 8. Proposed Solution •Ant Colony Optimization for Task Scheduling
- 9. System Model •Cloudsim [16] •CIS registry hold information about the resources •Scheduler (or Broker) is enhanced to be an energy-aware scheduler
- 10. System Model •LP Model (Based on [13]) •ϕ = CPU Utilization •Pidle = Power when CPU is idle •Pmax = Power when CPU is fully utilized •RT Model (Based on [12] ) •The Expected Time to Compute is given by •Wi = Workload •CCj = Computing Capacity
- 11. Methodology Initial Pheromone Task Rule •An ant randomly samples a task node from the list of task nodes yet to visit J’k
- 12. Methodology contd. VM Rule • An ant k positioned at task node r, selects a VM node s, by η(r,s) = inverse of makespan till s, 1/ CTmax Vk = list of visited VM nodes Θ(v) = Completion time of last Job in v β is a parameter which determines the importance of pheromone.
- 13. Methodology contd. Global Updation Rule •Once all ants have built their tours, pheromone is updated on all edges according to, α is a pheromone decay parameter. Δτ(r,s)= 1/Lk , Lk is the length of the tour performed by ant k and m is the no. of ants. Local Updation Rule •While building a solution (i.e., a tour), ants visit edges and change their pheromone level by applying the local updating rule shown below.
- 14. Energy-awareness VM Rule Where ω(r,s)= inverse of total power consumed in hosts μ(h) = power consumed in host h γ is a parameter which determines the importance of power consumption
- 15. Algorithm
- 16. Results •22% improvement over FCFS Energy in kWh Job Mix
- 17. Conclusion •In this project, a new task scheduling algorithm using Ant-colony optimization that reduces the power consumption for cloud is proposed. •The proposed method outperforms the existing method by 22% under the experimental conditions.
- 18. References 1. Kun Li, Gaochao Xu, Guangyu Zhao, Yushuang Dong, Dan Wang, Cloud Task schedul- ing based on Load Balancing Ant Colony Optimization, IEEE, 2011. 2. Marco Dorigo, Luca Maria Gambardella, Ant Colony System: A Cooperative Learning Approach to the Travelling Salesman Problem, IEEE Transactions, April 1997. 3. GU Srikanth, VU Maheswari, AP Shanthi, A Siromoney, Tasks Scheduling Using Ant Colony Optimization, Journal of Computer Science, 2012 4. Alberto Colorni, Marco Dorigo, Vittorio Maniezzo, Marco Trubian, Ant System for Job-shop Scheduling, Belgian Journal of Operations Research, 1994. 5. Mohsen Amini Salehi, P. Radha Krishna, Krishnamurty Sai Deepak and Rajkumar Buyya, Preemption-aware Energy Management in Virtualized DataCenters, IEEE, 2012. 6. Ying Chang-tian, Yi Juong, Energy Aware Task Scheduling using Genetic Algorithms, IEEE, 2012. 7. Eugen Feller, Louis Rilling, Christine Morin, Energy-Aware Ant Colony Based Work- load Placement in Clouds, INRIA, IEEE/ACM Conference on Grid Computing, May 2011. 8. ODC Alliance Carbon Footprints Values 9. Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, C ́esar A. F. De Rose and Rajkumar Buyya, CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, IEEE, 2010. 10. JimBlythe, SonalJain, EwaDeelman, Yolanda Gil, KaranVahi, TaskSchedulingStrategies for Workflow-based Applications in Grids, IEEE, 2005. 11. A. Belaglazov and R. Buyya, “Optimal Online deterministic algorithms and adapative heuristics for energy and performance efficient dynamic consolidation of Virtual Machines in Cloud Datacenters”, Concurrency and Computation: Practice and Experience, 2011. 12. Ali, S., Siegel, H.J., Maheswaran, M., and Hensgen, D.: “Task execution time modeling for heterogeneous computing systems”, Proceedings of Heterogeneous Computing Workshop, pp. 185–199, 2000. 13. T. Guerot, Thiery Monteil, Georges Da Costa, Rodrigo Neves Calheiros, Rajkumar Buyya, Mihai Alexandro, Energy-Aware Simulation Using Dvfs,Simulation Modelling Practice And Theory, Elsevier 2013. 14. Josep Ll. Berral, à ñIgo Goiri, Ramã³N Nou, Ferran Juliã , Jordi Guitart, Ricard Gavaldã , Jordi Torres, Towards Energy- Aware Scheduling In Data Centers Using Machine Learning, In Proceedings Of The First International Conference Oon Energy-Efficient Computing And Networking, Acm 2010. 15. Armel Esnault, Eugen Feller, Christine Morin, Energy-Aware Distributed Ant Colony Based Vm Consolidation In Iaas Cloud,Simulation Modelling Practice And Theory, Elsevier 2013. 16. Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose, and Rajkumar Buyya, “CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms”, Software: Practice and Experience, Volume 41, Number 1, Pages: 23-50, ISSN: 0038-0644, Wiley Press, New York, USA, January 2011. 17. http://www.energy.wsu.edu/Documents/Data%20Center%20Energy%20Savings_Feb2013.pdf at 2.19 pm IST, April 30, 2014
- 19. Thank You