Stochastic modeling and quality evaluation of infrastructure as-a-service clouds
1. Stochastic Modeling and Quality Evaluation of Infrastructure-as-a-
Service Clouds
Abstract:
Cloud computing is a recently developed new technology for complex
systems with massive service sharing, which is different from the resource
sharing of the grid computing systems. In a cloud environment, service
requests from users go through numerous provider-specific steps from the
instant it is submitted to when the requested service is fully delivered.
Quality modeling and analysis of clouds are not easy tasks because of the
complexity of the automated provisioning mechanism and dynamically
changing cloud environment. This work proposes an analytical model-
based approach for quality evaluation of Infrastructure-as-a-Service cloud
by considering expected request completion time, rejection probability, and
system overhead rate as key quality metrics. It also features with the
modeling of different warm-up and cool-down strategies of machines and
the ability to identify the optimal balance between system overhead and
performance. To validate the correctness of the proposed model, we obtain
simulative quality-of-service (QoS) data and conduct a confidence interval
analysis. The result can be used to help design and optimize industrial
cloud computing systems.
2. Existing System:
The quality-of-service (QoS) of cloud computing is very critical but hard to
analyze due to its characteristics of massive-scale service sharing, wide-
area network, heterogeneous software/hardware components, and
complicated interactions among them. Hence, prior QoS models for
traditional software/ hardware or conventional networks, e.g., cannot be
directly applied to study the cloud.
Proposed System:
The proposed QoS model in this study, based on our earlier one, can
analytically evaluate the aforementioned QoS metrics. It can also help one
identify the optimal tradeoff between performance and system overhead. It
employs the queuing models as the fundamental mechanism of stochastic
modeling and analysis. It takes several parameters as model inputs, i.e.,
request arrival rate, the number of initial hot/warm/cold PMs, PM
execution rate, PM warming/cooling rate, buffer size, and failure/repair
rates. In evaluating the QoS metrics, we also explore different combinations
of PM warm-up/cool-down strategies and investigate their impact on
system performance and overhead. Discrete-event simulation is used to
obtain simulative data and conduct correctness and confidence interval
analysis of the obtained results.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
3. • Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Windows XP.
• Front End : - JSP
• Back End : - SQL Server
Software Requirements:
• Operating system : - Windows XP.
• Front End : - .Net
• Back End : - SQL Server
