Minimization of Service Compromise Probability Using Resource Reallocation Strategies in a Cloud Computing Environment
Date Issued
2015
Date
2015
Author(s)
Kao, Yen-Fen
Abstract
In the last few years, we have seen a dramatic growth in IT investments, and a new term has come on the surface which is cloud computing. Cloud Computing has been highly adopted by many enterprises and organizations. However, there are still a number of technical barriers that may prevent cloud computing from becoming a truly ubiquitous service. Especially where the customer has strict or complex requirements over the security of an infrastructure. The latest cyber-attacks on high profile firms (Amazon, Google and Sony’s PlayStation) and the predictions of more cyberattacks on cloud infrastructure are threatening to slow the take-off of cloud computing. The numbers of cyber-attacks are now extremely large and their sophistication so great, that many organizations are having trouble determining which new threats and vulnerabilities pose the greatest risk and how resources should be allocated to ensure that the most probable and damaging attacks are dealt first. In contrast, the network defense mechanism is diverse development, so there have more defense alternative for defender to protect the network from external threats. The resource reallocation is the method to allocate the large-scale task to the available resource. The method considers a network state on the virtualization environments. When the service predicted high risk level, a VM will be switch off and withdraw the VM resources to strengthen defense capabilities. Hence, we help the service provider to allocate their defense resource, in order to find the most efficient way against external attacks. In this thesis, we focus on resource reallocation to increasing the network survivability. And we use Monte Carlo to simulate the model of the network attack-defense scenario. Finally, the ultimate goal is to figure out the optimal defense strategy.
Subjects
Resource Reallocation
Mathematical Programming
Network Survivability
Cloud Computing
Monte Carlo Simulation
Optimization
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-104-R02725016-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):89e85d0a667343546c74481c0c84dc36