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Interdependency Attack-Aware Secure and Performant Virtual Machine Allocation Policies With Low Attack Efficiency and Coverage
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DOI：10.1109/ACCESS.2024.3404949.png)
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Abstract
En 中文
Cloud computing has completely changed IT (information technology) by providing IT resources as services on the internet. However, certain types of attacks, such as interdependency attacks, impede its wide adoption. With the latter, an attacker who succeeds in compromising the VM of a user can traverse the hypervisor to launch an attack on the VM(s) of other users on the same hypervisor. Unfortunately, we note a lack of secure and performant allocation policies against this problem. Existing policies focus on security but ignore other factors, including workload balance and energy consumption, which are vital for commercial cloud platforms. In this context, we propose different allocation policies for choosing the datacenter server to which we allocate a new virtual machine. These policies aim to minimize the interdependence of different users' VMs while keeping the system performant regarding workload balance and/or power consumption. By default, our allocation policies treat all legitimate users as attackers and host their virtual machines according to their efficiency and coverage. We first design a secure and balanced solution that increases workload balance to prevent the servers from being overused. Afterward, we propose an algorithm that addresses security, power consumption, and workload balance objectives simultaneously. Based on our simulation results, our solutions perform better than existing algorithms regarding security, workload balance, and power consumption. The balanced solution reduces the chance of an attacker to zero and increases workload balance linearly. In other words, the workload balance is between [5, 35] , and it utilizes slightly more hosts than existing proposals, with gains between [2, 8]. Although our final proposal is less secure than previous algorithms, it performs better, so it has a good workload balance ([5, 30]) and consumes less energy.
Keywords:
Virtual machining
Resource management
Cloud computing
Security
Servers
Virtual machine monitors
Power demand
Energy consumption
Virtual machine allocation
interdependency attack
security
workload balance
power consumption
hypervisor
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W