The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Consolidating virtual machines with dynamic bandwidth demand in data centers
Abstract
Recent advances in virtualization technology have made it a common practice to consolidate virtual machines(VMs) into a fewer number of servers. An efficient consolidation scheme requires that VMs are packed tightly, yet receive resources commensurate with their demands. However, measurements from production data centers show that the network bandwidth demands of VMs are dynamic, making it difficult to characterize the demands by a fixed value and to apply traditional consolidation schemes. In this work, we formulate the VM consolidation into a Stochastic Bin Packing problem and propose an online packing algorithm by which the number of servers required is within (1+∈)(√2+1) of the optimum for any ∈ >; 0. The result can be improved to within (√2+1) of the optimum in a special case. In addition, we use numerical experiments to evaluate the proposed consolidation algorithm and observe 30% server reduction compared to several benchmark algorithms.
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
(1979) · Cited by 43,841
Reducibility Among Combinatorial Problems
Karp, R. M. (2010) · 50 Years of Integer Programming 1958-2008 · doi:10.1007/978-3-540-68279-0_8 · Cited by 2,490
The nature of data center traffic: measurements & analysis
Kandula, S., Sengupta, S., Greenberg, A., Patel, P., & Chaiken, R. (2009) · Proceedings of the 9th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1644893.1644918 · Cited by 1,030
Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement
Meng, X., Pappas, V., & Zhang, L. (2010) · 2010 Proceedings IEEE INFOCOM · doi:10.1109/infcom.2010.5461930 · Cited by 935
Self-citationWorst-Case Performance Bounds for Simple One-Dimensional Packing Algorithms
Johnson, D. S., Demers, A., Ullman, J. D., Garey, M. R., & Graham, R. L. (1974) · SIAM Journal on Computing · doi:10.1137/0203025 · Cited by 911
Approximation algorithms for bin packing: a survey
Coffman, E. G., Garey, M. R., & Johnson, D. S. (1996) · PWS Publishing Co. eBooks · Cited by 862
Dynamic Placement of Virtual Machines for Managing SLA Violations
Bobroff, N., Kochut, A., & Beaty, K. (2007) · 2007 10th IFIP/IEEE International Symposium on Integrated Network Management · doi:10.1109/inm.2007.374776 · Cited by 808
Understanding data center traffic characteristics
Benson, T., Anand, A., Akella, A., & Zhang, M. (2010) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1672308.1672325 · Cited by 548
Fast algorithms for bin packing
Johnson, D. S. (1974) · Journal of Computer and System Sciences · doi:10.1016/s0022-0000(74)80026-7 · Cited by 540
Understanding data center traffic characteristics
Benson, T., Anand, A., Akella, A., & Zhang, M. (2009) · Proceedings of the 1st ACM workshop on Research on enterprise networking · doi:10.1145/1592681.1592692 · Cited by 447
Cited by
A hierarchical edge cloud architecture for mobile computing
Tong, L., Li, Y., & Gao, W. (2016) · IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications · doi:10.1109/infocom.2016.7524340 · Cited by 518
Li, X., Qian, Z., Lu, S., & Wu, J. (2013) · Mathematical and Computer Modelling · doi:10.1016/j.mcm.2013.02.003 · Cited by 186
A Survey of Virtual Machine Placement Techniques in a Cloud Data Center
Usmani, Z., & Singh, S. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.02.093 · Cited by 142
A Joint Power Efficient Server and Network Consolidation approach for virtualized data centers
Marotta, A., Avallone, S., & Kassler, A. (2018) · Computer Networks · doi:10.1016/j.comnet.2017.11.003 · Cited by 19
Metadata
- Authors
- Wang, M., Meng, X., & Zhang, L.
- Venue
- 2011 Proceedings IEEE INFOCOM
- Publisher
- IEEE
- Published
- 2011-04
- Pages
- 71-75
- OpenAlex
- W2110458625
- Cited by
- 339
- References
- 26
- FWCI
- 26.44
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Consolidating virtual machines with dynamic bandwidth demand in data centers. https://app.cochat.ai/papers/10.1109/infcom.2011.5935254
- The paper
- Wang, M., Meng, X., & Zhang, L. (2011). Consolidating virtual machines with dynamic bandwidth demand in data centers. 2011 Proceedings IEEE INFOCOM. https://doi.org/10.1109/infcom.2011.5935254
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.