1. SLA-Oriented Resource Provisioning for Cloud Computing
Challenges, Architecture, and Solutions

2. Author

3. Content Abstract Introduction Challenges and Requirements
SLA-Oriented Cloud Computing Vision
State-of-the-art
System Architecture
SLA Provisioning in Aneka
Performance Evaluation
Future Directions

4. Abstract
Need to offer differentiated services to users and meet their quality expectations.
Existing resource management systems are yet to support SLA-oriented resource allocation.
No work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing.
This paper presents vision, challenges, and architectural elements of SLA-oriented resource management.

5. Introduction
There are dramatic differences between developing software for millions to use as a service versus distributing software for millions to run their PCs
-- Professor David Patterson
New Computing Paradigms
Cloud Computing
Grid Computing
P2P Computing
Utility Computing

6. Challenges and Requirements - 1

7. Challenges and Requirements - 2
Customer-driven Service Management
Computational Risk Management
Autonomic Resource Management
SLA-oriented Resource Allocation Through Virtualization
Service Benchmarking and Measurement
System Modeling and Repeatable Evaluation

8. SLA-Oriented Cloud Computing Vision
The resource provisioning will be driven by market-oriented principles for efficient resource allocation depending on user QoS targets and workload demand patterns.
Support for customer-driven service management based on customer profiles and QoS requirements;
Definition of computational risk management tactics to identify, assess, and manage risks involved in the execution of applications;
Derivation of appropriate market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation;
Incorporation of autonomic resource management models;
Leverage of Virtual Machine technology to dynamically assign resource shares;
Implementation of the developed resource management strategies and models into a real computing server;

9. State-of-the-art
Traditional Resource Management Systems(Condor, LoadLeveler, Load Sharing Facility, Portable Batch System)
adopt system-centric resource allocation approaches that focus on optimizing overall cluster performance
Increase processor throughput and utilization for the cluster
Reduce the average waiting time and response time for jobs
Assume that all job requests are of equal user importance and neglect actual levels of service required by different users.
Virtual Machine management platform solutions(Eucalyptus, OpenStack, Apache VCL, Citrix Essentials)
Main goal is to provide automatic configuration and maintenance of the centers
Market-based resource management
Not considered and incorporated customer-driven service management, computational risk management, and autonomic resource management into market-driven resource management

10. System Architecture
High-level system architectural framework

11. SLA Provisioning in Aneka -1
Aneka architecture

12. SLA Provisioning in Aneka - 2

13. SLA Provisioning in Aneka - 3

14. SLA Provisioning in Aneka - 4

15. Performance Evaluation - 1
Static resource
1 Aneka master - m1.large(7.5GB memory, 4 EC2 compute units, 850GB instance storage, 64bit platform, US0.48 per instance per hour) Windows-based VM
4 Aneka workers – m1.small(1.7GB memory, 1 EC2 compute unit, 160GB instance storage, 32bit platform, US0.085 per instance per hour) Linux-based VM
Dynamic resources
m1.small Linux-based instances

16. Performance Evaluation - 2
CPU-intensive application
SLA is defined in terms of user-defined deadline
execution time of each task was set to 2 minutes
Each job consists of 120 tasks

17. Conclusions and Future Directions
The need for a deeper investigation in SLA- oriented resource allocation strategies that encompass:
Customer-driven service management
Computational risk management
Autonomic management of Clouds
In order to:
Improve the system efficiency
Minimize violation of SLAs
Improve profitability of service providers

18. Thanks !