Risk-Aware Sensitive Property-Driven Resource Management in Cloud Datacenters

TL;DR

This paper introduces a risk-aware resource management mechanism for cloud datacenters that uses information-theoretic measures to protect sensitive data, formulated as an NP-complete optimization problem and addressed with heuristics.

Contribution

It presents a novel, efficient, risk-aware resource assignment approach for cloud datacenters based on sensitive property profiling and heuristic solutions.

Findings

Heuristics improve resource assignment efficiency.

The approach effectively reduces data leakage risk.

Simulation results validate the method's performance.

Abstract

Organizations are increasingly moving towards the cloud computing paradigm, in which an on-demand access to a pool of shared configurable resources is provided. However, security challenges, which are particularly exacerbated by the multitenancy and virtualization features of cloud computing, present a major obstacle. In particular, sharing of resources among potentially untrusted tenants in access controlled cloud datacenters can result in increased risk of data leakage. To address such risk, we propose an efficient risk-aware sensitive property-driven virtual resource assignment mechanism for cloud datacenters. We have used two information-theoretic measures, i.e., KL-divergence and mutual information, to represent sensitive properties in the dataset. Based on the vulnerabilities of cloud architecture and the sensitive property profile, we have formulated the problem as a cost-drive optimization problem. The problem is shown to be NP-complete. Accordingly, we have proposed two heuristics and presented simulation based performance results for cloud datacenters with multiple sensitivity.