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.

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
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19. Thank You