Abstract
For efficient management of resources and economic benefits, organizations are increasingly moving towards the paradigm of “cloud computing” by which they are allowed on-demand delivery of hardware, software and data as services. However, there are many security challenges which are particularly exacerbated by the multitenancy and virtualization features of cloud computing that allow sharing of resources among potentially untrusted tenants in access controlled cloud datacenters which can result in increased risk of data leakage. To address this risk vulnerability, we propose an efficient risk-aware virtual resource assignment mechanism for cloud’s multitenant environment. In particular, we have proposed a global property/knowledge driven profile model for an RBAC policy. For this propose we have used two properties based on KL-divergence and mutual information extracted from check-in dataset. Based on the vulnerabilities of cloud architecture and the knowledge profile, we have proposed resource scheduling problem based on the optimization pertaining to risk management. The problem is shown to be NP-complete. Accordingly, we have proposed two heuristics and presented their simulation based performance results for HSD and LSD datacenters.
