The document discusses various scheduling techniques in cloud computing. It begins with an introduction to scheduling and its importance in cloud computing. It then covers traditional scheduling approaches like FCFS, priority queue, and shortest job first. The document also presents job scheduling frameworks, dynamic and fault-tolerant scheduling, deadline-constrained scheduling, and inter-cloud meta-scheduling. It concludes with the benefits of effective scheduling in improving service quality and resource utilization in cloud environments.

1. Presentation By
Dr. Manjunath Kotari
Professor & Head-CSE
AIET, Moodbidre
Scheduling in Cloud Computing
01/20/191

2. Contents
Introduction
Traditional Cloud Scheduling
Job Scheduling Framework in Clouds
Dynamic Cloud Scheduling
Fault-Tolerant Scheduling in Cloud
Deadline-Constrained Dynamic Scheduling.
Inter-Cloud Meta-Scheduling
Conclusion
References
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3. Introduction
Cloud computing process the huge data.
Scheduling mechanism is essential.
Scheduling is at the heart of Distributed Computing.
PaaS model -> Workflow (job) Scheduling
IaaS model -> Virtual Machines (VM) Scheduling.
Scheduling decides allocation of VMs.
An effective scheduling can
Reduce operational costs
Reduce queue waiting time
Increase resource utilization
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4. Traditional Cloud Scheduling
FCFS
Simplest
Lower Headache
Fit for Small Size of data
No Starvation
Priority Queue Scheduling
Avoid starvation
Round Robin Scheduling
Time quantum is used
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5. Traditional Cloud Scheduling [cont..]
Shortest Job First Scheduling
Basis of shortest execution time
Multi Level Feedback Queue Scheduling
Use multiple queue with RR & FCFS.
Multi Level Queue Scheduling
Uses multiple queues with different scheduling.
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6. Job Scheduling Framework in Clouds
Challenges
Allocating massive job requests
Satisfying user QoS requirements.
Maintaining average response time
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7. Job Scheduling Framework
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8. Steps
User portal -manages job requests.
Job scheduler - routing decisions & selects VM instance.
Management module-
VM Monitor
Job monitor - keeps track of jobs
Job profiling - identify job types
History repository - stores the records of job .
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9. Dynamic Cloud Scheduling
Classification based on Historical data.
Creation of VMs
Matches tasks with suitable VMs dynamically
Minimize task waiting time and executing time.
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10. Cloud task scheduling framework
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11. Fault-Tolerant Scheduling in Cloud
Fault tolerance technique
Enhances Reliability and Availability
Introduce redundancy
Incurs extra overhead.
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12. Fault-tolerant scheduling architecture
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13. Steps
Global Scheduler
 Analyzes information
Makes decisions
Sends the primary/backup copies of the task to different VMs.
Local Scheduler
Rearranging the order of the local queue
Resource Manager
Decides how VMs should be added or migrated
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14. Deadline-Constrained Dynamic Scheduling
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15. Inter-Cloud Meta-Scheduling
Multiple autonomous clouds.
Functions under a single federated management entity.
The algorithm estimates the queue length of neighboring
processors
Reschedules the loads based on estimates.
The method aims to increase the possibilities to gain load
balancing.
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16. Inter-Cloud Meta-Scheduling
Facilitates scalable resource provisioning.
ICMS is based on a novel message exchange
mechanism.
Offers improved flexibility, robustness and
decentralization.
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17. Conclusions
Scheduling and execution improve service quality of the
clouds.
Creates VMs and decrease the failure rate of task
scheduling.
Increase in resource utilizations
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18. References
1. PeiYun Zhang, and MengChu Zhou, Dynamic Cloud Task Scheduling Based on
a Two-Stage Strategy, IEEE TRANSACTIONS ON AUTOMATION
SCIENCE AND ENGINEERING, VOL. 15, NO. 2, APRIL 2018.
2. YI WEI , LI PAN, SHIJUN LIU , LEI WU, AND XIANGXU MENG, DRL-
Scheduling: An Intelligent QoS-Aware Job Scheduling Framework for
Applications in Clouds, IEEE ACCESS, 2018.
3. PENGZE GUO, MING LIU1, JUN WU, ZHI XUE, AND XIANGJIAN HE,
Energy-Efficient Fault-Tolerant Scheduling Algorithm for Real-Time Tasks
in Cloud-Based 5G Networks, IEEE ACCESS ,2018.
4. Jyoti Sahni and Deo Prakash Vidyarthi, A Cost-Effective Deadline-
Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a
Cloud Environment, IEEE TRANSACTIONS ON CLOUD COMPUTING,
VOL. 6, NO. 1, JANUARY-MARCH 2018.
01/20/1918

19. References [cont..]
5. Stelios Sotiriadis , Nik Bessis, Ashiq Anjum, and Rajkumar Buyya, An Inter-
Cloud Meta-Scheduling (ICMS) Simulation Framework: Architecture and
Evaluation, IEEE TRANSACTIONS ON SERVICES COMPUTING, VOL.
11, NO. 1, JANUARY/FEBRUARY 2018.
6. MIAN GUO, QUANSHENG GUAN, AND WENDE KE, Optimal Scheduling
of VMs in Queuing Cloud Computing Systems With a Heterogeneous
Workload, IEEE ACCESS 2018.
7. Ruiting Zhou , Zongpeng Li, and Chuan Wu,Scheduling Frameworks for Cloud
Container Services, IEEE/ACM TRANSACTIONS ON NETWORKING,
VOL. 26, NO. 1, FEBRUARY 2018
8. Arnav Wadhonkar , Deepti Theng ,A Survey on Different Scheduling
Algorithms in Cloud Computing, International Conference on Advances in
Electrical, Electronics, Information, Communication and Bio-Informatics
(AEEICB16).
01/20/1919