High Availability Management for Applications Services in the Cloud Container-Based Platform
Bibliographic record
Abstract
Cloud is a popular and attractive paradigm for providing online computing services to the end users. Recently many of the users move their business applications to the cloud and become tenants for the cloud service providers. Some tenants expect their applications that are provided as services to be highly available (HA) at any time. Managing HA of applications services in the cloud is a big challenge due to the dynamic nature and the huge number of the provision services in the cloud. Limited number of solutions address the HA of services in the cloud platforms that use containers instead of Virtual Machines (VMs). In addition, HA measurements are still missing by the proposed solutions in the literature. Therefore, in this article we propose a framework to incorporate the HA feature for the applications that are deployed in cloud platforms that use the containers. The framework depends on the novel idea of integrating HA middlewares OpenSAF and Pacemaker with the containers to manage HA of the applications services. As a proof of concept, we build a prototype for our framework using our private cloud Container-based platform. For the evaluation purposes, we compare the same framework using our cloud VM-based platform. The measurements show the ability of the proposed framework to manage HA of different services using the containers with faster service recovery time and shorter service outage time than using the VMs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".