Making Cloud Spot Instance Interruption Events Visible
Pages 2998 - 3009
Editorial Notes
The authors requested minor, non-substantive changes to the Version of Record (VoR). In accordance with ACM policies, a Corrected Version of Record was published on September 12, 2024. For reference purposes, the VoR may still be accessed via the Supplemental Material section of this citation page.
Abstract
Public cloud computing providers offer a surplus of computing resources at a lower price with a service of a spot instance. Despite the possible great cost savings from using spot instances, sudden resource interruption can occur as resource demand changes. To help users estimate cost savings and the possibility of interruption when using spot instances, vendors provide diverse datasets. However, the effectiveness of using the datasets has not yet been quantitatively evaluated, and many users still rely on the guess when choosing spot instances. To help users lower the chance of interruption of the spot instance for reliable usage, in this paper, we thoroughly analyze various datasets of the spot instance and present the feasibility for value prediction. Then, to measure how the public datasets reflect real-world spot instance interruption events, we conduct real-world experiments for spot instances of AWS, Azure, and Google Cloud. Combining the dataset analysis, modeling, and the real-world spot instance interruption experiment, we present a significant improvement in reducing the possibility of interruption events.
Supplemental Material
MOV File
Supplemental video
- Download
- 35.31 MB
MP4 File
video presentation
- Download
- 1171.60 MB
PDF File - Version of Record
VoR for "Making Cloud Spot Instance Interruption Events Visible" by Kim et al., Proceedings of the ACM Web Conference 2024 (WWW '24).
- Download
- 1.52 MB
References
[1]
Orna Agmon Ben-Yehuda, Muli Ben-Yehuda, Assaf Schuster, and Dan Tsafrir. 2013. Deconstructing Amazon EC2 Spot Instance Pricing. ACM Trans. Econ. Comput. 1, 3, Article 16 (sep 2013), 20 pages. https://doi.org/10.1145/2509413.2509416
[2]
Ahmed Ali-Eldin, Jonathan Westin, Bin Wang, Prateek Sharma, and Prashant Shenoy. 2019. SpotWeb: Running Latency-Sensitive Distributed Web Services on Transient Cloud Servers. In Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing (Phoenix, AZ, USA) (HPDC '19). Association for Computing Machinery, New York, NY, USA, 1--12. https://doi.org/10.1145/3307681.3325397
[3]
Sarah Alkharif, Kyungyong Lee, and Hyeokman Kim. 2018. Time-Series Analysis for Price Prediction of Opportunistic Cloud Computing Resources. In Proceedings of the 7th International Conference on Emerging Databases, Wookey Lee, Wonik Choi, Sungwon Jung, and Min Song (Eds.). Springer Singapore, Singapore, 221--229.
[4]
Pradeep Ambati, David Irwin, Prashant Shenoy, Lixin Gao, Ahmed Ali-Eldin, and Jeannie Albrecht. 2019. Understanding Synchronization Costs for Distributed ML on Transient Cloud Resources. In 2019 IEEE International Conference on Cloud Engineering (IC2E). 145--155. https://doi.org/10.1109/IC2E.2019.00029
[5]
Mariette Awad and Rahul Khanna. 2015. Efficient Learning Machines. Apress. https://doi.org/10.1007/978--1--4302--5990--9
[6]
Matt Baughman, Simon Caton, Christian Haas, Ryan Chard, Rich Wolski, Ian Foster, and Kyle Chard. 2019. Deconstructing the 2017 Changes to AWS Spot Market Pricing. In Proceedings of the 10th Workshop on Scientific Cloud Computing (Phoenix, AZ, USA) (ScienceCloud '19). Association for Computing Machinery, New York, NY, USA, 19--26. https://doi.org/10.1145/3322795.3331465
[7]
George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. 2015. Time series analysis: forecasting and control. John Wiley & Sons.
[8]
Leo Breiman. 2001. Random Forests. Machine Learning 45, 1 (2001), 5--32. https://doi.org/10.1023/A:1010933404324
[9]
Sarah Chasins, Alvin Cheung, Natacha Crooks, Ali Ghodsi, Ken Goldberg, Joseph E Gonzalez, Joseph M Hellerstein, Michael I Jordan, Anthony D Joseph, Michael W Mahoney, et al . 2022. The Sky Above The Clouds. arXiv preprint arXiv:2205.07147 (2022).
[10]
Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD '16). Association for Computing Machinery, New York, NY, USA, 785--794. https://doi.org/10.1145/2939672.2939785
[11]
Navraj Chohan, Claris Castillo, Mike Spreitzer, Malgorzata Steinder, Asser Tantawi, and Chandra Krintz. 2010. See Spot Run: Using Spot Instances for MapReduce Workflows. In 2nd USENIX Workshop on Hot Topics in Cloud Computing (Hot-Cloud 10). USENIX Association, Boston, MA. https://www.usenix.org/conference/hotcloud-10/see-spot-run-using-spot-instances-mapreduce-workflows
[12]
Jeffrey Dean and Sanjay Ghemawat. 2004. MapReduce: Simplified Data Processing on Large Clusters. In Proceedings of the 6th Conference on Symposium on Operating Systems Design & Implementation - Volume 6 (San Francisco, CA) (OSDI'04). USENIX Association, Berkeley, CA, USA, 10--10. http://dl.acm.org/citation.cfm?id=1251254.1251264
[13]
Nnamdi Ekwe-Ekwe and Adam Barker. 2018. Location, Location, Location: Exploring Amazon EC2 Spot Instance Pricing Across Geographical Regions. In 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). 370--373. https://doi.org/10.1109/CCGRID.2018.00059
[14]
Shereen Elsayed, Daniela Thyssens, Ahmed Rashed, Lars Schmidt-Thieme, and Hadi Samer Jomaa. 2021. Do We Really Need Deep Learning Models for Time Series Forecasting? CoRR abs/2101.02118 (2021). arXiv:2101.02118 https://arxiv.org/abs/2101.02118
[15]
Javier Fabra, Joaquín Ezpeleta, and Pedro Álvarez. 2019. Reducing the price of resource provisioning using EC2 spot instances with prediction models. Future Generation Computer Systems 96 (2019), 348--367. https://doi.org/10.1016/j.future.2019.01.025
[16]
Apache Software Foundation. 2004. Apache Hadoop. http://hadoop.apache.org/
[17]
Weichao Guo, Kang Chen, Yongwei Wu, and Weimin Zheng. 2015. Bidding for Highly Available Services with Low Price in Spot Instance Market. In Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing (Portland, Oregon, USA) (HPDC '15). Association for Computing Machinery, New York, NY, USA, 191--202. https://doi.org/10.1145/2749246.2749259
[18]
Hårek Haugerud, Jonas Krüger Svensson, and Anis Yazidi. 2020. Autonomous Provisioning of Preemptive Instances in Google Cloud for Maximum Performance Per Dollar. In 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech). 1--8. https://doi.org/10.1109/CloudTech49835.2020.9365879
[19]
Xin He, Prashant Shenoy, Ramesh Sitaraman, and David Irwin. 2015. Cutting the Cost of Hosting Online Services Using Cloud Spot Markets. In Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing (Portland, Oregon, USA) (HPDC '15). Association for Computing Machinery, New York, NY, USA, 207--218. https://doi.org/10.1145/2749246.2749275
[20]
David Irwin, Prashant Shenoy, Pradeep Ambati, Prateek Sharma, Supreeth Shastri, and Ahmed Ali-Eldin. 2019. The Price Is (Not) Right: Reflections on Pricing for Transient Cloud Servers. In 2019 28th International Conference on Computer Communication and Networks (ICCCN). 1--9. https://doi.org/10.1109/ICCCN.2019.8846933
[21]
Bahman Javadi, Ruppa K. Thulasiram, and Rajkumar Buyya. 2013. Characterizing spot price dynamics in public cloud environments. Future Generation Computer Systems 29, 4 (2013), 988--999. https://doi.org/10.1016/j.future.2012.06.012 Special Section: Utility and Cloud Computing.
[22]
JCS Kadupitige, Vikram Jadhao, and Prateek Sharma. 2020. Modeling The Temporally Constrained Preemptions of Transient Cloud VMs. In Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing (Stockholm, Sweden) (HPDC '20). Association for Computing Machinery, New York, NY, USA, 41--52. https://doi.org/10.1145/3369583.3392671
[23]
JCS Kadupitiya, Vikram Jadhao, and Prateek Sharma. 2022. SciSpot: Scientific Computing On Temporally Constrained Cloud Preemptible VMs. IEEE Transactions on Parallel and Distributed Systems 33, 12 (2022), 3575--3588. https://doi.org/10.1109/TPDS.2022.3157272
[24]
E. L. Kaplan and Paul Meier. 1958. Nonparametric Estimation from Incomplete Observations. J. Amer. Statist. Assoc. 53, 282 (1958), 457--481. https://doi.org/10.1080/01621459.1958.10501452 arXiv:https://www.tandfonline.com/doi/pdf/10.1080/01621459.1958.10501452
[25]
V. Khandelwal, A. Chaturvedi, and C. P. Gupta. 2017. Amazon EC2 Spot Price Prediction using Regression Random Forests. IEEE Transactions on Cloud Computing (2017), 1--1. https://doi.org/10.1109/TCC.2017.2780159
[26]
Mikhail Khodak, Liang Zheng, Andrew S. Lan, Carlee Joe-Wong, and Mung Chiang. 2018. Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies. In IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. 2762--2770. https://doi.org/10.1109/INFOCOM.2018.8486291
[27]
KyungHwan Kim and Kyungyong Lee. 2024. Making Cloud Spot Instance Interruption Events Visible Artifact Dataset. https://doi.org/10.5281/zenodo.10633186
[28]
KyungHwan Kim and Kyungyong Lee. 2024. Making Cloud Spot Instance Interruption Events Visible Artifact Source Code. https://doi.org/10.5281/zenodo.10652941
[29]
Kyunghwan Kim, Subin Park, Jaeil Hwang, Hyeonyoung Lee, Seokhyeon Kang, and Kyungyong Lee. 2023. Public Spot Instance Dataset Archive Service. In Companion Proceedings of the ACM Web Conference 2023 (Austin, TX, USA) (WWW Association for Computing Machinery, New York, NY, USA, 69--72. https://doi.org/10.1145/3543873.3587314
[30]
K. Lee and M. Son. 2017. DeepSpotCloud: Leveraging Cross-Region GPU Spot Instances for Deep Learning. In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). 98--105. https://doi.org/10.1109/CLOUD.2017.21
[31]
S. Lee, J. Hwang, and K. Lee. 2022. SpotLake: Diverse Spot Instance Dataset Archive Service. In 2022 IEEE International Symposium on Workload Characterization (IISWC). IEEE Computer Society, Los Alamitos, CA, USA, 242--255. https://doi.org/10.1109/IISWC55918.2022.00029
[32]
Markus Lumpe, Mohan Baruwal Chhetri, Quoc Bao Vo, and Ryszard Kowalcyk. 2017. On Estimating Minimum Bids for Amazon EC2 Spot Instances. In 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). 391--400. https://doi.org/10.1109/CCGRID.2017.76
[33]
Aniruddha Marathe, Rachel Harris, David Lowenthal, Bronis R. de Supinski, Barry Rountree, and Martin Schulz. 2014. Exploiting Redundancy for Cost- Effective, Time-Constrained Execution of HPC Applications on Amazon EC2. In Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing (Vancouver, BC, Canada) (HPDC '14). Association for Computing Machinery, New York, NY, USA, 279--290. https://doi.org/10.1145/2600212.2600226
[34]
Ishai Menache, Ohad Shamir, and Navendu Jain. 2014. On-demand, Spot, or Both: Dynamic Resource Allocation for Executing Batch Jobs in the Cloud. In 11th International Conference on Autonomic Computing (ICAC 14). USENIX Association, Philadelphia, PA, 177--187. https://www.usenix.org/conference/icac14/technical-sessions/presentation/menache
[35]
Bruce D. Meyer. 1990. Unemployment Insurance and Unemployment Spells. Econometrica 58, 4 (1990), 757--782. http://www.jstor.org/stable/2938349
[36]
Danielle Movsowitz Davidow, Orna Agmon Ben-Yehuda, and Orr Dunkelman. 2023. Deconstructing Alibaba Cloud's Preemptible Instance Pricing. In Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing (Orlando, FL, USA) (HPDC '23). Association for Computing Machinery, New York, NY, USA, 253--265. https://doi.org/10.1145/3588195.3593001
[37]
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12 (2011), 2825--2830.
[38]
Dana Petcu. 2013. Multi-Cloud: Expectations and Current Approaches. In Proceedings of the 2013 International Workshop on Multi-Cloud Applications and Federated Clouds (Prague, Czech Republic) (MultiCloud '13). Association for Computing Machinery, New York, NY, USA, 1--6. https://doi.org/10.1145/2462326.2462328
[39]
Thanh-Phuong Pham, Sasko Ristov, and Thomas Fahringer. 2018. Performance and Behavior Characterization of Amazon EC2 Spot Instances. In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). 73--81. https://doi.org/10.1109/CLOUD.2018.00017
[40]
Gustavo Portella, Genaina N. Rodrigues, Eduardo Nakano, and Alba C.M.A. Melo. 2019. Statistical analysis of Amazon EC2 cloud pricing models. Concurrency and Computation: Practice and Experience 31, 18 (2019), e4451. https://doi.org/10.1002/cpe.4451 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.4451e4451 cpe.4451.
[41]
D. M. W. Powers. 2011. Evaluation: From precision, recall and f-measure to roc., informedness, markedness & correlation. Journal of Machine Learning Technologies 2, 1 (2011), 37--63.
[42]
A. Sarah, K. Lee, and H. Kim. 2018. LSTM Model to Forecast Time Series for EC2 Cloud Price. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). 1085--1088. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00067
[43]
Prateek Sharma, Tian Guo, Xin He, David Irwin, and Prashant Shenoy. 2016. Flint: Batch-Interactive Data-Intensive Processing on Transient Servers. In Proceedings of the Eleventh European Conference on Computer Systems (London, United Kingdom) (EuroSys '16). Association for Computing Machinery, New York, NY, USA, Article 6, 15 pages. https://doi.org/10.1145/2901318.2901319
[44]
Prateek Sharma, David Irwin, and Prashant Shenoy. 2016. How Not to Bid the Cloud. In 8th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 16). USENIX Association, Denver, CO. https://www.usenix.org/conference/hotcloud16/workshop-program/presentation/sharma
[45]
Supreeth Subramanya, Tian Guo, Prateek Sharma, David Irwin, and Prashant Shenoy. 2015. SpotOn: A Batch Computing Service for the Spot Market. In Proceedings of the Sixth ACM Symposium on Cloud Computing (Kohala Coast, Hawaii) (SoCC '15). Association for Computing Machinery, New York, NY, USA, 329--341. https://doi.org/10.1145/2806777.2806851
[46]
ShaoJie Tang, Jing Yuan, and Xiang-Yang Li. 2012. Towards Optimal Bidding Strategy for Amazon EC2 Cloud Spot Instance. In 2012 IEEE Fifth International Conference on Cloud Computing. 91--98. https://doi.org/10.1109/CLOUD.2012.134
[47]
Sean J. Taylor and Benjamin Letham. 2018. Forecasting at Scale. The American Statistician 72, 1 (2018), 37--45. https://doi.org/10.1080/00031305.2017.1380080 arXiv:https://doi.org/10.1080/00031305.2017.1380080
[48]
John Thorpe, Pengzhan Zhao, Jonathan Eyolfson, Yifan Qiao, Zhihao Jia, Minjia Zhang, Ravi Netravali, and Guoqing Harry Xu. 2023. Bamboo: Making Pre-emptible Instances Resilient for Affordable Training of Large DNNs. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23). USENIX Association, Boston, MA, 497--513. https://www.usenix.org/conference/nsdi23/presentation/thorpe
[49]
P. Varshney and Y. Simmhan. 2019. AutoBoT: Resilient and Cost-Effective Scheduling of a Bag of Tasks on Spot VMs. IEEE Transactions on Parallel & Distributed Systems 30, 07 (jul 2019), 1512--1527. https://doi.org/10.1109/TPDS.2018.2889851
[50]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
[51]
Cheng Wang, Qianlin Liang, and Bhuvan Urgaonkar. 2017. An Empirical Analysis of Amazon EC2 Spot Instance Features Affecting Cost-Effective Resource Procurement. In Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering (L'Aquila, Italy) (ICPE '17). Association for Computing Machinery, New York, NY, USA, 63--74. https://doi.org/10.1145/3030207.3030210
[52]
Rich Wolski, John Brevik, Ryan Chard, and Kyle Chard. 2017. Probabilistic Guarantees of Execution Duration for Amazon Spot Instances. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (Denver, Colorado) (SC '17). Association for Computing Machinery, New York, NY, USA, Article 18, 11 pages. https://doi.org/10.1145/3126908.3126953
[53]
Ying Yan, Yanjie Gao, Yang Chen, Zhongxin Guo, Bole Chen, and Thomas Moscibroda. 2016. TR-Spark: Transient Computing for Big Data Analytics. In Proceedings of the Seventh ACM Symposium on Cloud Computing (Santa Clara, CA, USA) (SoCC '16). Association for Computing Machinery, New York, NY, USA, 484--496. https://doi.org/10.1145/2987550.2987576
[54]
Fangkai Yang, Bowen Pang, Jue Zhang, Bo Qiao, Lu Wang, Camille Couturier, Chetan Bansal, Soumya Ram, Si Qin, Zhen Ma, Íñigo Goiri, Eli Cortez, Senthil Baladhandayutham, Victor Rühle, Saravan Rajmohan, Qingwei Lin, and Dongmei Zhang. 2022. Spot Virtual Machine Eviction Prediction in Microsoft Cloud. In Companion Proceedings of the Web Conference 2022 (Virtual Event, Lyon, France) (WWW '22). Association for Computing Machinery, New York, NY, USA, 152--156. https://doi.org/10.1145/3487553.3524229
[55]
Fangkai Yang, Lu Wang, Zhenyu Xu, Jue Zhang, Liqun Li, Bo Qiao, Camille Couturier, Chetan Bansal, Soumya Ram, Si Qin, Zhen Ma, Íñigo Goiri, Eli Cortez, Terry Yang, Victor Rühle, Saravan Rajmohan, Qingwei Lin, and Dongmei Zhang. 2023. Snape: Reliable and Low-Cost Computing with Mixture of Spot and On-Demand VMs. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3 (Vancouver, BC, Canada) (ASPLOS 2023). Association for Computing Machinery, New York, NY, USA, 631--643. https://doi.org/10.1145/3582016.3582028
[56]
Zongheng Yang, Zhanghao Wu, Michael Luo, Wei-Lin Chiang, Romil Bhardwaj, Woosuk Kwon, Siyuan Zhuang, Frank Sifei Luan, Gautam Mittal, Scott Shenker, and Ion Stoica. 2023. SkyPilot: An Intercloud Broker for Sky Computing. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23). USENIX Association, Boston, MA, 437--455. https://www.usenix.org/conference/nsdi23/presentation/yang-zongheng
[57]
Murtaza Zafer, Yang Song, and Kang-Won Lee. 2012. Optimal Bids for Spot VMs in a Cloud for Deadline Constrained Jobs. In 2012 IEEE Fifth International Conference on Cloud Computing. 75--82. https://doi.org/10.1109/CLOUD.2012.59
[58]
Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauly, Michael J. Franklin, Scott Shenker, and Ion Stoica. 2012. Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. In Presented as part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12). USENIX, San Jose, CA, 15--28.
[59]
Chengliang Zhang, Minchen Yu, Wei Wang, and Feng Yan. 2022. Enabling Cost- Effective, SLO-Aware Machine Learning Inference Serving on Public Cloud. IEEE Transactions on Cloud Computing 10, 3 (2022), 1765--1779. https://doi.org/10.1109/TCC.2020.3006751
[60]
Liang Zheng, Carlee Joe-Wong, Chee Wei Tan, Mung Chiang, and Xinyu Wang. 2015. How to Bid the Cloud. In Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication (London, United Kingdom) (SIGCOMM '15). Association for Computing Machinery, New York, NY, USA, 71--84. https://doi.org/10.1145/2785956.2787473
[61]
Amelie Chi Zhou, Jianming Lao, Zhoubin Ke, Yi Wang, and Rui Mao. 2022. FarSpot: Optimizing Monetary Cost for HPC Applications in the Cloud Spot Market. IEEE Transactions on Parallel and Distributed Systems 33, 11 (2022), 2955--2967. https://doi.org/10.1109/TPDS.2021.3134644
Index Terms
- Making Cloud Spot Instance Interruption Events Visible
Index terms have been assigned to the content through auto-classification.
Recommendations
Improving Hadoop Monetary Efficiency in the Cloud Using Spot Instances
CLOUDCOM '14: Proceedings of the 2014 IEEE 6th International Conference on Cloud Computing Technology and ScienceInfrastructure-as-a-Service (IaaS) cloud providers offer many elasticities and flexibilities for users to run their systems in the cloud. The monetary cost issues of running those systems in the cloud are hardly ignored and there is less work discussing ...
Self-managed cost-efficient virtual elastic clusters on hybrid Cloud infrastructures
In this study, we describe the further development of Elastic Cloud Computing Cluster (EC3), a tool for creating self-managed cost-efficient virtual hybrid elastic clusters on top of Infrastructure as a Service (IaaS) clouds. By using spot instances and ...
A survey on optimal utilization of preemptible VM instances in cloud computing
In this article, an extensive survey on optimal utilization of preemptible instances in cloud is presented. Different techniques used in state-of-the-art research for efficient utilization of spot instances have been classified and categorized in the ...
Comments
Information & Contributors
Information
Published In
May 2024
4826 pages
ISBN:9798400701719
DOI:10.1145/3589334
- General Chairs:
- Tat-Seng Chua,
- Chong-Wah Ngo,
- Proceedings Chair:
- Roy Ka-Wei Lee,
- Program Chairs:
- Ravi Kumar,
- Hady W. Lauw
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
Check for updates
Badges
Author Tags
Qualifiers
- Research-article
Funding Sources
- NRF
- IITP
Conference
WWW '24
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 284Total Downloads
- Downloads (Last 12 months)284
- Downloads (Last 6 weeks)44
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in