Stochastic modeling and performance analysis of migration enabled and error-prone clouds
1. Stochastic Modeling and Performance Analysis of Migration-Enabled
and Error-Prone Clouds
Abstract:
Cloud computing is a promising paradigm capable of rationalizing the use
of computational resources by means of outsourcing and virtualization.
Virtualization allows to instantiate virtual machines (VMs) on top of fewer
physical systems managed by a VM manager. Performance evaluation of
clouds is required to evaluate and quantify the cost-benefit of a strategy
portfolio and the quality of service (QoS) experienced by end-users. Such
evaluation is not feasible by means of simulation or on-the-field
measurement, due to the great scale of parameter spaces that have to be
traversed. In this study, we present a stochastic-queuing network-based
approach to performance analysis of migration enabled clouds in error-
prone environment. Several performance metrics are defined and
evaluated: utilization, expected task completion time, and task rejection
rate under different load conditions and error intensities. To validate the
proposed approach, we obtain experimental performance data through a
real-world cloud and conduct a confidence-interval analysis. The analysis
results suggest the perfect coverage of theoretical performance results by
corresponding experimental confidence intervals.
2. Existing System:
Through the use of virtualization, clouds promise to address with the same
shared set of physical resources, i.e., PMs, the different needs of numerous
users. This mechanism allows one to instantiate multiple VMs on top of
fewer PMs managed by a VM manager (VMM). However, virtualization
may induce significant performance penalties [1] when facing highly
demanding workloads.
Proposed System:
The main objective of this study is therefore to analytically evaluate the
impacts of migration activities and error-recovery on the performance of
error-prone clouds. For this purpose, a queuing-network-based model is
proposed and a non state based approach to evaluate system performance
is developed. To validate the proposed approach, experimental
performance data through a real-world cloud, i.e., the Course-Selection-
and-Management-Cloud of Chongqing University, are obtained and used.
A confidence-interval analysis shows the perfect coverage of theoretical
performance results by corresponding 90◦ experimental confidence-
intervals and suggests the correctness of the proposed model. Finally,
performance results under different resource, error-intensity, and load
conditions are investigated through the proposed performance model.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
3. • 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
![Existing System:
Through the use of virtualization, clouds promise to address with the same
shared set of physical resources, i.e., PMs, the different needs of numerous
users. This mechanism allows one to instantiate multiple VMs on top of
fewer PMs managed by a VM manager (VMM). However, virtualization
may induce significant performance penalties [1] when facing highly
demanding workloads.
Proposed System:
The main objective of this study is therefore to analytically evaluate the
impacts of migration activities and error-recovery on the performance of
error-prone clouds. For this purpose, a queuing-network-based model is
proposed and a non state based approach to evaluate system performance
is developed. To validate the proposed approach, experimental
performance data through a real-world cloud, i.e., the Course-Selection-
and-Management-Cloud of Chongqing University, are obtained and used.
A confidence-interval analysis shows the perfect coverage of theoretical
performance results by corresponding 90◦ experimental confidence-
intervals and suggests the correctness of the proposed model. Finally,
performance results under different resource, error-intensity, and load
conditions are investigated through the proposed performance model.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)