Naplus: a software distributed shared memory for virtual clusters in the cloud
Bibliographic record
Abstract
Summary Virtual clusters (VCs) have exhibited various advantages over traditional cluster computing platforms by virtue of their extensibility, reconfigurability, and maintainability. As such, they have become a major execution environment for cloud‐based cluster applications. However, compared with traditional clusters, their distributed‐memory programming paradigm still remains largely unchanged, which implies that cluster applications cannot be efficiently deployed in VCs, especially when virtual machines (VMs) are running in different physical hosts. Recently, some efforts have been made to improve inter‐VM communication, resulting in many studies on how cluster applications could take advantages of VCs. However, most of them mainly focus on the situation that the VMs are all coresident on the same physical machine where the message passing mechanism is usually optimized away by exploiting the host's shared memory. In this paper, we present a design and implementation of Naplus, a kernel‐based virtual machine approach to the inter‐VM communications that are across different physical hosts. Naplus is based on Nahanni, a mechanism for shared‐memory communication in virtual environments. As such, it not only inherits the major merits of Nahanni with respect to flexible data structures and efficient synchronization but also achieves a shared‐memory paradigm among VMs. With Naplus, we enable the size of shared space to be maximized as large as the sum of each machine's local memory to accommodate cluster applications with large memory footprints. We prototype Naplus in a dual‐host system where an empirical study is conducted to show the effectiveness of the Naplus approach in achieving distributed shared memory for VCs in data centers. Copyright © 2017 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".