a Analysing and quantifying the influence of system parameters on virtual machine co-residency in public clouds

Abstract

Public Infrastructure-as-a-Service (IaaS) cloud promises significant efficiency to businesses and organisations. This efficiency is possible by allowing “co-residency” where Virtual Machines (VMs) that belong to multiple users share the same physical infrastructure. With co-residency being inevitable in public IaaS clouds, malicious users can leverage information leakage via side channels to launch several powerful attacks on honest co-resident VMs. Because co-residency is a necessary first step to launching side channel attacks, this motivates this thesis to look into understanding the co-residency probability (i.e. the probability that a given VM receives a co-resident VM). This thesis aims to analyse and quantify the influence of cloud parameters (such as the number of hosts and users) on the co-residency probability in four commonly used Placement Algorithms (PAs). These PAs are First Fit, Next Fit, Power Save and Random. This analysis then helps to identify the cloud parameters’ settings that reduce the coresidency probability in four PAs. Because there are many cloud parameters and parameters’ settings to consider, this forms the main challenge in this thesis. In order to overcome this challenge, fractional factorial design is used to reduce the number of required experiments to analyse and quantify the parameters’ influence in various settings. This thesis takes a quantitative experimental simulation and analytical prediction approach to achieve its aim. Using a purpose-built VM Co-residency simulator, (i) the most influential cloud parameters affecting co-residency probability in four PAs have been identified. Identifying the most influential parameters has helped to (ii) explore the best settings of these parameters that reduce the co-residency probability under the four PAs. Finally, analytical estimation, with the coexistence of different populations of attackers, has been derived to (iii) find the probability that a new co-residing VM belongs to an attacker. This thesis identifies the number of hosts to be the most influential cloud parameters on the coresidency probability in the four PAs. Also, this thesis presents evidence that VMs hosted in IaaS clouds that use Next Fit or Random are more resilient against receiving co-resident VMs compared to when First Fit or Power Save are used. Further, VMs in IaaS clouds with a higher number of hosts are less likely to exhibit co-residency. This thesis generates new insights into the potential of co-residency reduction to reduce the attack surface for side channel attacks. The outcome of this thesis is a plausible blueprint for IaaS cloud providers to consider the influence on the co-residency probability as an important selection factor for cloud settings and PAs.