customer driven sla in cloud based systems
TRANSCRIPT
Customer Driven SLA in
Cloud Based Systems
Dr Mydhili K. Nair
Associate Professor,
Dept. of ISE, MSRIT
Balaji Soundararajan
Manager, Software QA
McAfee Software (India) Pvt. Ltd.
Varun M Deshpande
Software QA Engineer,
McAfee Software (India) Pvt. Ltd.
&
PhD Student,
Dept. of CSE, Jain University
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Agenda Background
Problem Domain
Literature Survey
Design and Analysis
Results
Conclusion and Future Scope
Bibliography
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Background Gartner says..
The total public cloud services market size in 2011 was $91.4 billion, and it will
grow to $206.6 billion in 2016.
Gartner expects the highest regional growth rates in emerging markets of
Asia/Pacific regions
ResearchOnChina.com writes..
Barely 14% of Chinese companies have adopted virtualization as compared to that
of 74% in the U.S.
The cloud computing market of China is poised to demonstrate a robust growth of
77.5% CAGR during 2010-15
China’s cloud computing market stands to capitalize upon the untapped potential in
47 million domestic SMB’s.
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Background contd..
Service Delivery
Requirements
Customer Service Provider
Cloud Service Model – Customer requests for service. Service provider provides the same
But each customer is different. They have different needs.
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Problem Domain
Enterprise Customers
Highest Availability
Lowest Downtime
24/7 Technical Support
Quickest Resolution Time
Cost is not an Issue
SMB Customers
High Availability
Low Downtime
On Demand Technical Support
Quick Resolution Time
Cost should be within Budget
Start-up and Individual Developers
High Availability during Spikes
Scheduled Downtime is fine
Technical Support after Issue reported
Resolution Time depends on Issue
Cost is a Concern
Each Class of Customer has their own concerns
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Problem Domain Contd..
0
2000
4000
6000
8000
10000
12000
Number of Hits
Number of Hits
Sample Request Variation
for Start-up Application
Requirements
On an average, I need to support
2000 hits per day
Only in specific weeks, I need to
support 10000+ hits
I need highest Quality of Service
(QoS) during these 2 weeks. I
am ready to pay more.
Other 6 weeks, I can do with
moderate QoS. I want to pay
less during this time.
Start-up Customer
Similarly, SMB / Enterprise
Customers may also have
similar constraints
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Literature Survey
Rajkumar Buyya et al. [4] highlighted the point -With growing demand of delivering services to a large number of users, cloud service providers need to offer differentiated services to users and meet their quality expectations.
Few of the approaches which they discussed to realize of this research vision consist of following:
Support for customer driven service management based on customer profiles and QoS requirements
Incorporation of autonomic resource management models that effectively self-manage changes in service requirements to satisfy both new service demands and existing service obligations
Leverage VM technology to dynamically assign resource shares according to service requirements
They envisioned the need for a deeper investigation in SLA-oriented resource allocation strategies that encompass customer driven service management.
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Literature Survey Contd..
Siddesh G.M. and Srinivasa K.G. in their work[5], have concentrated
on resource management problem in heterogeneous workload
environments which is the key challenge faced by cloud service
providers. They focus on dynamic resource allocation with risk analysis
by meeting SLA.
The discussed framework aims in designing, dynamic capacity
forecasting with risk analysis on clouds in scheduling the resources with
the following features:
Dynamic Resource Provision
Heterogeneous Work loads
Advanced Reservation
Dynamic capacity forecasting with risk analysis
SLA based resource scheduling/ rescheduling
Experimental results suggest that dynamic resource management is
preferable while compared to traditional static configuration.
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Literature Survey Contd..
Zhen Xiao et al. In their work[6], attempt to study the problem: How can
a cloud service provider best multiplex its virtual resources onto physical
hardware.
Studies have shown that many of existing data centers are often
underutilized due to over provisioning for the peak demand. This cloud
model is expected to make such practice unnecessary by offering
automatic scale up and down in response to the load variation.
They discuss a design of an automated resource management system
that achieves a good balance between the two goals. Some of their
contributions are
Developed a resource allocation system that can avoid overload the system
effectively while minimizing total number of servers used.
Reduce uneven utilization of servers. Improve overall utilization of servers.
Design load prediction algorithm that can capture future resource usages of
applications accurately
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Design and Analysis
Profiles inbuilt in the Cloud Architecture
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Customer of cloud service
should be able to choose
different QoS profiles at
different times during the
contract.
There are other
quantitative parameters
like hard disk space, RAM
size, network bandwidth
etc, which can also be
choose as a function of
time.
Cloud service provider is
required to setup cloud
profiles which are
maintained at different
levels of quality of service.
Number of different types
of profiles may not be
fixed.
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Design and Analysis..
Cloud Profiles are maintained at different QoS
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Design and Analysis..
Dynamic Resource Management Work Flow Diagram
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Design and Analysis..
User Flow Model
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Simulation and Testing..
Assumed pricing values
for Simulation
Sample Requirements input
for Simulation
Parameter Requirements
Bandwidth (GB) 50
Machine
Architecture
x86
Number of VMs 2
HDD Space (GB) 100
DB Space (GB) 3
RAM (GB) 4
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Results Customer Class Request
Week 1 Week 2 Week 3 Week 4
Enterprise Best Better Best Best
SMB Better Better Best Better
Start-up / Individual
Developer
Good Best Good Good
Sample Profile Requirements
input for Simulation
Simulation
Results
Even if number of Start-up and SMB
customers increase by 1000, revenue
would increase by Rs 37.3 Lakh per
Month (4 Week)
Start-up customer would be saving
20% of their expenditure
SMB customer would be saving 13%
of their expenditure
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0
500
1000
1500
2000
2500
Start-up SMB Enterprise
Actual Cost
Savings
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Novel QoS based Cloud System using dynamic
resource management and cloud profiling strategies
Cloud Services would be $200+ Billion industry with majority of demand coming
from developing regions
Customers can judiciously decide upon their custom
needs and avail the service which they intend to use
By providing flexibility of choosing the resources, it
would attract more customers to adopt cloud computing increasing market capital.
Customer Driven SLA in Cloud Based Systems
Conclusion 16
Benefits of Current Work
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Future Scope
Customer driven SLA is need of the hour in cloud computing
and further investigation is necessary
Work towards standardization of the cloud profiles.
Although Cloud Services are proprietary, it would be important to
have some standard template for maintaining and publishing various
cloud profiles which can be provided by the cloud service provider.
Gather real world data of cloud subscribers and run simulations
on the data in order to test and improve proposed system.
Analyze security implications of developing such a system like
VM migration issues, information security etc.
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Bibliography
Daryl C. Plummer, Thomas J. Bittman, Tom Austin, David W. Cearley and David Mitchell Smith, “Cloud Computing: Defining and Describing an EmergingPhenomenon,” Gartner Research- ID G00156220, June 2008
Barrie Sosinsky, “Cloud Computing Bible,” Published by John Wiley & Sons, 2011
Stefano Ferretti, Vittorio Ghini, Fabio Panzieri, Michele Pellegrini and Elisa Turrini, “QoS-aware Clouds,” IEEE 3rd International Conference on Cloud Computing, 2010
Rajkumar Buyya, Saurabh Kumar Garg and Rodrigo N. Calheiros, “SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture and Solutions,” International Conference on Cloud and Service Computing, 2011
Siddesh G.M and Srinivasa K.G, “SLA- Driven Dynamic Resource Allocation on Clouds,” ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
Zhen Xiao, Weijia Song, and Qi Chen, “Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment”, IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, June 2013
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Thank You
For Your Time
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