The DOI matches its Crossref record.
A Performance Study on the VM Startup Time in the Cloud
Abstract
One of many advantages of the cloud is the elasticity, the ability to dynamically acquire or release computing resources in response to demand. However, this elasticity is only meaningful to the cloud users when the acquired Virtual Machines (VMs) can be provisioned in time and be ready to use within the user expectation. The long unexpected VM startup time could result in resource under-provisioning, which will inevitably hurt the application performance. A better understanding of the VM startup time is therefore needed to help cloud users to plan ahead and make in-time resource provisioning decisions. In this paper, we study the startup time of cloud VMs across three real-world cloud providers -- Amazon EC2, Windows Azure and Rackspace. We analyze the relationship between the VM startup time and different factors, such as time of the day, OS image size, instance type, data center location and the number of instances acquired at the same time. We also study the VM startup time of spot instances in EC2, which show a longer waiting time and greater variance compared to on-demand instances.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds
Rodriguez, M. A., & Buyya, R. (2014) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2014.2314655 · Cited by 686
Containerization and the PaaS Cloud
Pahl, C. (2015) · IEEE Cloud Computing · doi:10.1109/mcc.2015.51 · Cited by 451
A Survey on Cloud Computing Elasticity
Galante, G., & de Bona, L. C. E. (2012) · 2012 IEEE Fifth International Conference on Utility and Cloud Computing · doi:10.1109/ucc.2012.30 · Cited by 270
A Framework and Algorithm for Energy Efficient Container Consolidation in Cloud Data Centers
Piraghaj, S. F., Dastjerdi, A. V., Calheiros, R. N., & Buyya, R. (2015) · 2015 IEEE International Conference on Data Science and Data Intensive Systems · doi:10.1109/dsdis.2015.67 · Cited by 118
A Survey of Performance Optimization for Mobile Applications
Hort, M., Kechagia, M., Sarro, F., & Harman, M. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3071193 · Cited by 91
Metadata
- Authors
- Mao, M., & Humphrey, M.
- Venue
- 2012 IEEE Fifth International Conference on Cloud Computing
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 423-430
- ISSN
- 2159-6190 (ISSN-L)
- OpenAlex
- W2125262761
- Cited by
- 526
- References
- 0
- FWCI
- 39.67
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.