Research
My research focuses on distributed systems, with special interest in virtual machine fault tolerance, anomaly detection in cloud computing environments, and cloud resource allocation optimization algorithms.
Current Research Projects
Virtual Machine Placement for Live Gang Migrations in Cloud Environments
May 2025 - Present
This research aims to find suitable destination hosts for live migration of co-located Virtual Machines in cloud datacenters, which is proven to be an NP-Hard problem. Existing work on VM Gang Migration assumes static destination mapping, which is not suitable for dynamic cloud environments. The destination suitability will be calculated considering multiple constraints such as, resource utilization, migration bandwidth and SLA requirements.
Anomaly Detection in Containerized Environments
May 2025 - Present
This study aims to identify anomalies within containerized environments, such as Kubernetes, which generate extensive multivariate time series data, including logs and resource metrics, to enhance system reliability and security. We aim to tackle the unique challenges associated with anomaly detection in containers having an ephemeral lifespan, such as the cold start problem, normality drift, and the replication noise.
Optimal Destination Node Selection in Live Virtual Machine Migration
June 2024 - May 2025
This research addresses the NP-Hard problem of finding optimal destination hosts for Virtual Machines in Cloud Datacenters. By utilizing an Ant Colony Optimization algorithm, we aim to develop a solution that efficiently places VMs while considering multiple constraints including resource utilization, energy efficiency, and SLA requirements.
Virtual Machine Proactive Fault Tolerance using Log-based Anomaly Detection
March 2023 - April 2024
This research focused on identifying potential Virtual Machine failures due to hardware/software faults in real-time using an anomaly detection approach based on our improved Matrix Profile algorithm. The system analyzes system logs to detect patterns indicative of impending failures, allowing for proactive migration before service disruption occurs.