Oasis: Energy proportionality with hybrid server consolidation
Junji Zhi, Nilton Bila, et al.
EuroSys 2016
Modern offices are crowded with personal computers. While studies have shown these to be idle most of the time, they remain powered, consuming up to 60% of their peak power. Hardware-based solutions engendered by PC vendors (e.g., low-power states, Wake-on-LAN) have proved unsuccessful because, in spite of user inactivity, these machines often need to remain network active in support of background applications that maintain network presence. Recent proposals have advocated the use of consolidation of idle desktop Virtual Machines (VMs). However, desktop VMs are often large, requiring gigabytes of memory. Consolidating such VMs creates large network transfers lasting in the order of minutes and utilizes server memory inefficiently. When multiple VMsmigrate concurrently, networks become congested, and the resulting migration latencies are prohibitive.We present partial VM migration, an approach that transparently migrates only the working set of an idle VM. It creates a partial replica of the desktop VMon the consolidation server by copying onlyVM metadata, and it transfers pages to the server on-demand, as the VM accesses them. This approach places desktop PCs in low-power mode when inactive and switches them to running mode when pages are needed by the VM running on the consolidation server. To ensure that desktops save energy, we have developed sleep scheduling and prefetching algorithms, as well as the context-aware selective resume framework, a novel approach to reduce the latency of power mode transition operations in commodity PCs. Jettison, our software prototype of partial VM migration for off-the-shelf PCs, can deliver 44-91% energy savings during idle periods of at least 10 minutes, while providing low migration latencies of about 4 seconds and migrating minimal state that is under an order of magnitude of the VM's memory footprint.
Junji Zhi, Nilton Bila, et al.
EuroSys 2016
Jonathan McChesney, Nan Wang, et al.
SEC 2019
Salman Baset, Sahil Suneja, et al.
Middleware 2017
Sahil Suneja, Canturk Isci, et al.
SIGMETRICS 2014