Cache contention aware virtual machine placement and mitigation using adaptive ABC algorithm

Cache contention in cloud computing can degrade system performance and lead to security vulnerabilities like side-channel attacks. Virtual Machine (VM) migration is a practical solution to mitigate cache contention by relocating affected VMs to optimal Physical Machines (PMs). The proposed Adaptive Artificial Bee Colony (AABC) algorithm for cache contention-aware VM migration improves upon the traditional Artificial Bee Colony (ABC) algorithm by introducing self-adaptive parameter tuning to enhance efficiency. Upon detecting a cache-based side-channel attack, the algorithm identifies the optimal PM for migration, ensuring minimal contention. The migration process is repeated iteratively until the cloud system achieves a contention-free state. The AABC algorithm evaluates metrics such as the total number of misses, computation time, and cache access, providing a comprehensive view of VM performance. By optimizing the migration process with a focus on these critical factors, it ensures minimal computational overhead and optimal memory performance. This innovative approach not only improves system performance by addressing cache contention but also enhances the security of cloud environments. It guarantees reliable VM allocation, providing a robust solution for secure and efficient cloud operations.