Optimizing Task Scheduling and Containers in Cloud Data Centers - Arash GhorbanniaDelavar (22563696) (2025)
Summary: This study introduces TPMCD (Throughput and Cost Optimizing Method for Clustering Tasks and Hybrid Containers in Cloud Data Centers) — a novel approach designed to improve cloud efficiency, reduce costs, and balance workloads. By integrating metrics such as response time, execution accuracy, and sensitivity rate, TPMCD intelligently classifies and re-clusters tasks across virtual machines (VMs) and containers. This hybrid scheduling strategy minimizes redundancy, optimizes energy consumption, and ensures stability under dynamic workloads. Compared to existing algorithms, TPMCD achieved up to 7% cost reduction, 4% throughput improvement, and 9.5% faster real execution time, while also using fewer computational nodes. Context and Innovation: In modern cloud computing, efficient resource allocation and energy optimization are critical challenges. Traditional scheduling often struggles with load imbalance, resource waste, and high management overhead from virtual machines. TPMCD addresses these issues through a synergy of clustering techniques and intelligent resource mapping between VMs and containers. By considering service-level agreements (SLAs) and adaptive thresholds, the method ensures high-quality performance, reliability, and reduced environmental impact. Overall, TPMCD provides a scalable and cost-efficient framework that enhances both performance and sustainability in cloud data centers. Original article DOI: https://doi.org/10.1016/j.jnca.2025.104132