The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Elasticity in Cloud Computing: State of the Art and Research Challenges
Open accessAbstract
Elasticity is a fundamental property in cloud computing that has recently witnessed major developments. This article reviews both classical and recent elasticity solutions and provides an overview of containerization, a new technological trend in lightweight virtualization. It also discusses major issues and research challenges related to elasticity in cloud computing. We comprehensively review and analyze the proposals developed in this field. We provide a taxonomy of elasticity mechanisms according to the identified works and key properties. Compared to other works in literature, this article presents a broader and detailed analysis of elasticity approaches and is considered as the first survey addressing the elasticity of containers.
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
Cites
Docker: lightweight Linux containers for consistent development and deployment
Merkel, D. (2014) · Linux journal · Cited by 3,191
An updated performance comparison of virtual machines and Linux containers
Felter, W., Ferreira, A., Rajamony, R., & Rubio, J. (2015) · 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) · doi:10.1109/ispass.2015.7095802 · Cited by 1,054
Buyya, R., Ranjan, R., & Calheiros, R. N. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-13119-6_2 · Cited by 1,023
The HiBench benchmark suite: Characterization of the MapReduce-based data analysis
Huang, S., Huang, J., Dai, J., Xie, T., & Huang, B. (2010) · 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010) · doi:10.1109/icdew.2010.5452747 · Cited by 713
A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments
Lorido-Botran, T., Miguel-Alonso, J., & Lozano, J. A. (2014) · Journal of Grid Computing · doi:10.1007/s10723-014-9314-7 · Cited by 701
Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016) · Communications of the ACM · doi:10.1145/2890784 · Cited by 690
Empirical prediction models for adaptive resource provisioning in the cloud
Islam, S., Keung, J., Lee, K., & Liu, A. (2012) · Future Generation Computer Systems · doi:10.1016/j.future.2011.05.027 · Cited by 631
CloudScale: elastic resource scaling for multi-tenant cloud systems
Shen, Z., Subbiah, S., Gu, X., & Wilkes, J. (2011) · Proceedings of the 2nd ACM Symposium on Cloud Computing · doi:10.1145/2038916.2038921 · Cited by 592
Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting
Roy, N., Dubey, A., & Gokhale, A. (2011) · 2011 IEEE 4th International Conference on Cloud Computing · doi:10.1109/cloud.2011.42 · Cited by 591
PRESS: PRedictive Elastic ReSource Scaling for cloud systems
Gong, Z., Gu, X., & Wilkes, J. (2010) · 2010 International Conference on Network and Service Management · doi:10.1109/cnsm.2010.5691343 · Cited by 544
Cited by
Aslanpour, M. S., Gill, S. S., & Toosi, A. N. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100273 · Cited by 289
Ali, S., Wadho, S. A., Yichiet, A., Gan, M. L., & Lee, C. K. (2024) · Egyptian Informatics Journal · doi:10.1016/j.eij.2024.100519 · Cited by 32
Metadata
- Authors
- Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., & Merle, P.
- Venue
- IEEE Transactions on Services Computing
- Published
- 2018-03-01
- Volume
- 11
- Issue
- 2
- Pages
- 430-447
- ISSN
- 1939-1374 (ISSN-L)
- OpenAlex
- W2620934568
- Cited by
- 409
- References
- 79
- FWCI
- 78.96
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Elasticity in Cloud Computing: State of the Art and Research Challenges. https://app.cochat.ai/papers/10.1109/tsc.2017.2711009
- The paper
- Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., & Merle, P. (2018). Elasticity in Cloud Computing: State of the Art and Research Challenges. IEEE Transactions on Services Computing. https://doi.org/10.1109/tsc.2017.2711009
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.
