GPU Virtualization in Cloud Computing: Enhancing Resource Efficiency

Abstract

GPU virtualization has been approached in many ways—direct pass-through, mediated pass-through, time-slicing, API remoting, para-virtualization and hardware-assisted virtualization—which keep affecting the performance, scalability, resource efficiency?and security aspects of GPU resources in cloud computing. As the demand for high-performance computing?(HPC) and artificial intelligence (AI) workloads continues to grow, efficient management of GPU resources is crucial, yet traditional allocation methods often fall short. GPU virtualization resolves these inefficiencies by?allowing dynamic sharing of GPU resources between virtual machines (VMs). This paper empirically shows that state-of-the-art GPU virtualization technologies can deliver high performance comparable to native settings whilst improving resource utilization inside multi-tenancy?clouds. Standalone time-slice techniques do not satisfy?the restrictions in performance-demanding scenarios like gaming, where dedicated virtual machines with PCIe mediated pass-through are needed to maximize the GPU potential; however, an API remote approach can enhance results by up to 40%, compared to used stand-alone approaches [1]. Moreover,?this study delves into optimization techniques, security issues, and up-and-coming trends like AI-based resource management and edge computing convergence. The results show that, by applying hardware-assisted virtualization and intelligent scheduling algorithms, the GPU can achieve up to a 45% reduction in idle GPU time while?guaranteeing quality of service for compute-intensive workloads. This work highlights the impact of GPU virtualization?on improving cloud computing performance and food for thought on future improvements to optimizing GPU workloads. The optimal balance on all performance parameters is achieved using the hybrid system while proving that Direct Pass-Through is best in terms of latency but?lacks resource sharing abilities.