Energy-aware Task Scheduling using Ant-colony Optimization in cloud

Energy Aware Task Scheduling using Ant
Colony Optimization in Cloud
Linda . J, Ananthanarayana V.S.
NITK Surathkal

Agenda
•Introduction
•Need for Task Scheduling & Energy Awareness
•Problem Statement & Objective
•Proposed Solution & Methodology
•Results
•Conclusion

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.

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.

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

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.

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.

Proposed Solution
•Ant Colony Optimization for Task Scheduling

System Model
•Cloudsim [16]
•CIS registry hold
information about the
resources
•Scheduler (or Broker)
is enhanced to be an
energy-aware
scheduler

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

Methodology
Initial Pheromone
Task Rule
•An ant randomly samples a task node
from the list of task nodes yet to visit J’k

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.

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.

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

Results
•22%
improvement
over FCFS
Energy in
kWh
Job Mix

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.

References
1. Kun Li, Gaochao Xu, Guangyu Zhao, Yushuang Dong, Dan Wang, Cloud Task scheduling 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, Ã Ã±Igo 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

Energy-aware Task Scheduling using Ant-colony Optimization in cloud

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