In recent years, since scale-up machines are not economical and may not be affordable for small businesses, scale-out has become the standard answer to data analysis, machine learning, and many other fields. However, these frameworks introduce complex programming models that put a burden on developers. Therefore, Single System Image (SSI), which means a cluster of machines that appears to be one single system, has been proposed to hide the complexity of distributed systems. Unfortunately, due to the mature ecosystem of current mainstream Operating Systems (OSes), it might be non-trivial and even unaffordable to modify the current OS to implement SSI. With the wide use of virtualization, we believe that it is appealing to support SSI at the hypervisor, without modifying guest OSes.

Scale-out has become the standard answer to data analysis, machine learning and many other fields. Contrary to common belief, scale-up machines can outperform scale-out clusters for a considerable portion of tasks. However, those scale-up machines are not economical and may not be affordable for small businesses. This paper presents GiantVM, a distributed hypervisor that aggregates resources from multiple physical machines, providing the guest OS with a uniform hardware abstraction. We propose techniques to deal with the challenges of CPU, Memory, and I/O virtualization in distributed environments.