Energy-Oriented Partial Desktop Virtual Machine Migration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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 VMs migrate 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 VM on the consolidation server by copying only VM 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it