survey

Performance Interference of Virtual Machines: A Survey

Authors:

Chennian Xiong,

Minxian XuAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 12

Article No.: 254, Pages 1 - 37

https://doi.org/10.1145/3573009

Published: 02 March 2023 Publication History

Abstract

The rapid development of cloud computing with virtualization technology has benefited both academia and industry. For any cloud data center at scale, one of the primary challenges is how to effectively orchestrate a large number of virtual machines (VMs) in a performance-aware and cost-effective manner. A key problem here is that the performance interference between VMs can significantly undermine the efficiency of cloud data centers, leading to performance degradation and additional operation cost. To address this issue, extensive studies have been conducted to investigate the problem from different aspects. In this survey, we make a comprehensive investigation into the causes of VM interference and provide an in-depth review of existing research and solutions in the literature. We first categorize existing studies on interference models according to their modeling objectives, metrics used, and modeling methods. Then we revisit interference-aware strategies for scheduling optimization as well as co-optimization-based approaches. Finally, the survey identifies open challenges with respect to VM interference in data centers and discusses possible research directions to provide insights for future research in the area.

References

[1]

Laurence Goasduff. 2021. Gartner Says Cloud Will Be the Centerpiece of New Digital Experiences. Retrieved December 9, 2022 from https://www.gartner.com/en/newsroom/press-releases/2021-11-10-gartner-says-cloud-will-be-the-centerpiece-of-new-digital-experiences.

[2]

Susan Moore. 2022. Gartner Says More Than Half of Enterprise IT Spending in Key Market Segments Will Shift to the Cloud by 2025. Retrieved December 9, 2022 from https://www.gartner.com/en/newsroom/press-releases/2022-02-09-gartner-says-more-than-half-of-enterprise-it-spending.

[3]

Paul Barham, Boris Dragovic, Keir Fraser, Steven Hand, Tim Harris, Alex Ho, Rolf Neugebauer, Ian Pratt, and Andrew Warfield. 2003. Xen and the art of virtualization. ACM SIGOPS Operating Systems Review 37, 5 (2003), 164–177.

Digital Library

[4]

Christopher Clark, Keir Fraser, Steven Hand, Jacob Gorm Hansen, Eric Jul, Christian Limpach, Ian Pratt, and Andrew Warfield. 2005. Live migration of virtual machines. In Proceedings of the 2nd Conference on Networked Systems Design and Implementation. 273–286.

Digital Library

[5]

Younggyun Koh, Rob Knauerhase, Paul Brett, Mic Bowman, Zhihua Wen, and Calton Pu. 2007. An analysis of performance interference effects in virtual environments. In Proceedings of the 2007 IEEE International Symposium on Performance Analysis of Systems and Software. IEEE, Los Alamitos, CA, 200–209.

[6]

Sean Kenneth Barker and Prashant Shenoy. 2010. Empirical evaluation of latency-sensitive application performance in the cloud. In Proceedings of the 1st Annual ACM SIGMM Conference on Multimedia Systems. 35–46.

Digital Library

[7]

Xing Pu, Ling Liu, Yiduo Mei, Sankaran Sivathanu, Younggyun Koh, Calton Pu, and Yuanda Cao. 2012. Who is your neighbor: Net I/O performance interference in virtualized clouds. IEEE Transactions on Services Computing 6, 3 (2012), 314–329.

Digital Library

[8]

Yi Yuan, Haiyang Wang, Dan Wang, and Jiangchuan Liu. 2013. On interference-aware provisioning for cloud-based big data processing. In Proceedings of the 2013 IEEE/ACM 21st International Symposium on Quality of Service (IWQoS’13). IEEE, Los Alamitos, CA, 1–6.

[9]

Renyu Yang, Ismael Solis Moreno, Jie Xu, and Tianyu Wo. 2013. An analysis of performance interference effects on energy-efficiency of virtualized cloud environments. In Proceedings of the 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, Vol. 1. IEEE, Los Alamitos, CA, 112–119.

Digital Library

[10]

S. Wang, W. Zhang, H. Heng, Y. Song, J. Wei, H. Zhong, and T. Huang. 2015. Approach of quantifying virtual machine performance interference based on hardware performance counter (in Chinese). Journal of Software 26, 8 (2015), 2074–2090.

[11]

Ram Srivatsa Kannan, Animesh Jain, Michael A. Laurenzano, Lingjia Tang, and Jason Mars. 2018. Proctor: Detecting and investigating interference in shared datacenters. In Proceedings of the 2018 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS’18). IEEE, Los Alamitos, CA, 76–86.

[12]

Melanie Kambadur, Tipp Moseley, Rick Hank, and Martha A. Kim. 2012. Measuring interference between live datacenter applications. In Proceedings of the International Conference on High Performance Computing, Networking, Storage, and Analysis (SP’12). IEEE, Los Alamitos, CA, 1–12.

Digital Library

[13]

Ilia Pietri and Rizos Sakellariou. 2016. Mapping virtual machines onto physical machines in cloud computing: A survey. ACM Computing Surveys 49, 3 (2016), Article 49, 30 pages.

Digital Library

[14]

Mcs Filho, C. C. Monteiro, Pedro R. M. Inacio, and Mario M. Freire. 2017. Approaches for optimizing virtual machine placement and migration in cloud environments: A survey. Journal of Parallel and Distributed Computing 111 (Jan. 2017), 222–250.

[15]

Minxian Xu and Rajkumar Buyya. 2019. Brownout approach for adaptive management of resources and applications in cloud computing systems: A taxonomy and future directions. ACM Computing Surveys 52, 1 (2019), 1–27.

Digital Library

[16]

F. Zhang, G. Liu, X. Fu, and R. Yahyapour. 2018. A survey on virtual machine migration: Challenges, techniques, and open issues. IEEE Communications Surveys Tutorials 20, 2 (2018), 1206–1243. DOI:

[17]

T. Bloch, R. Sridaran, and C. S. R. Prashanth. 2014. Analysis and survey of issues in live virtual machine migration interferences. International Journal of Advanced Networking & Applications 2014 (2014), 151–157.

[18]

Tarannum Bloch, R. Sridaran, and C. S. R. Prashanth. 2018. Understanding live migration techniques intended for resource interference minimization in virtualized cloud environment. In Big Data Analytics, V. B. Aggarwal, Vasudha Bhatnagar, and Durgesh Kumar Mishra (Eds.). Springer Singapore, Singapore, 487–497.

[19]

S. Amri, H. Hamdi, and Z. Brahmi. 2017. Inter-VM interference in cloud environments: A survey. In Proceedings of the 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA’17). 154–159. DOI:

[20]

F. Xu, F. Liu, H. Jin, and A. V. Vasilakos. 2014. Managing performance overhead of virtual machines in cloud computing: A survey, state of the art, and future directions. Proceedings of the IEEE 102, 1 (2014), 11–31. DOI:

[21]

Armando Fox, Rean Griffith, Anthony Joseph, Randy Katz, Andrew Konwinski, Gunho Lee, David Patterson, et al. 2009. Above the Clouds: A Berkeley View of Cloud Computing. Report UCB/EECS-2009-28. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley.

[22]

Dejan Novaković, Nedeljko Vasić, Stanko Novaković, Dejan Kostić, and Ricardo Bianchini. 2013. DeepDive: Transparently identifying and managing performance interference in virtualized environments. In Proceedings of the 2013 USENIX Annual Technical Conference (USENIX ATC’13). 219–230.

[23]

Kartik Joshi, Arun Raj, and Dharanipragada Janakiram. 2017. Sherlock: Lightweight detection of performance interference in containerized cloud services. In Proceedings of the 2017 IEEE 19th International Conference on High Performance Computing and Communications, the IEEE 15th International Conference on Smart City, and the IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS’17). IEEE, Los Alamitos, CA, 522–530.

[24]

Wikepedia. n.d. Virtualization. Retrieved December 9, 2022 from http://en.wikipedia.org/wiki/Virtualization.

[25]

Xiaoxing Wang and Xiangying Kong. 2013. Analysis and research on performance isolation of virtualization. Electronic Measurement Technology 8 (2013), 6–14.

[26]

Quan Chen, Shuai Xue, Shang Zhao, Shanpei Chen, Zhuo Song, Yihao Wu, Yu Xu, Tao Ma, Yong Yang, and Minyi Guo. 2020. Alita: Comprehensive performance isolation through bias resource management for public clouds. In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’20). IEEE, Los Alamitos, CA, 442–454.

[27]

Keyvan RahimiZadeh and Abbas Dehghani. 2021. Design and evaluation of a joint profit and interference-aware VMs consolidation in IaaS cloud datacenter. Cluster Computing 24, 4 (2021), 3249–3275.

Digital Library

[28]

Willis Lang, Karthik Ramachandra, David J. DeWitt, Shize Xu, Qun Guo, Ajay Kalhan, and Peter Carlin. 2016. Not for the timid: On the impact of aggressive over-booking in the cloud. Proceedings of the VLDB Endowment 9, 13 (2016), 1245–1256.

Digital Library

[29]

Xing Pu, Ling Liu, Yiduo Mei, Sankaran Sivathanu, Younggyun Koh, and Calton Pu. 2010. Understanding performance interference of I/O workload in virtualized cloud environments. In Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing. IEEE, Los Alamitos, CA, 51–58.

Digital Library

[30]

Dan Huang. Managing IO Resource for Co-Running Data Intensive Applications in Virtual Clusters. Ph.D. dissertation. College of Engineering and Computer Science, University of Central Florida.

[31]

Homa Aghilinasab, Waqar Ali, Heechul Yun, and Rodolfo Pellizzoni. 2020. Dynamic memory bandwidth allocation for real-time GPU-based SoC platforms. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, 11 (2020), 3348–3360.

[32]

Luis A. Garrido and Paul Carpenter. 2017. vMCA: Memory capacity aggregation and management in cloud environments. In Proceedings of the 2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS’17). IEEE, Los Alamitos, CA, 674–683.

[33]

Shin-Gyu Kim, Hyeonsang Eom, and Heon Y. Yeom. 2013. Virtual machine consolidation based on interference modeling. Journal of Supercomputing 66, 3 (2013), 1489–1506.

Digital Library

[34]

Liuhua Chen, Haiying Shen, and Stephen Platt. 2016. Cache contention aware virtual machine placement and migration in cloud datacenters. In Proceedings of the 2016 IEEE 24th International Conference on Network Protocols (ICNP’16). 1–10. DOI:

[35]

Xiangping Bu, Jia Rao, and Cheng-Zhong Xu. 2013. Interference and locality-aware task scheduling for MapReduce applications in virtual clusters. In Proceedings of the 22nd International Symposium on High-Performance Parallel and Distributed Computing. 227–238.

Digital Library

[36]

Xuesong Peng, Barbara Pernici, and Monica Vitali. 2018. Virtual machine profiling for analyzing resource usage of applications. In Proceedings of the International Conference on Services Computing. 103–118.

[37]

Fei Xu, Fangming Liu, Linghui Liu, Hai Jin, Bo Li, and Baochun Li. 2013. iAware: Making live migration of virtual machines interference-aware in the cloud. IEEE Transactions on Computers 63, 12 (2013), 3012–3025.

Digital Library

[38]

Rachael Shaw, Enda Howley, and Enda Barrett. 2019. An energy efficient and interference aware virtual machine consolidation algorithm using workload classification. In Proceedings of the International Conference on Service-Oriented Computing. 251–266.

Digital Library

[39]

Yuxia Cheng, Wenzhi Chen, Zonghui Wang, and Yang Xiang. 2017. Precise contention-aware performance prediction on virtualized multicore system. Journal of Systems Architecture 72 (2017), 42–50.

Digital Library

[40]

Jiacheng Zhao, Huimin Cui, Jingling Xue, and Xiaobing Feng. 2015. Predicting cross-core performance interference on multicore processors with regression analysis. IEEE Transactions on Parallel and Distributed Systems 27, 5 (2015), 1443–1456.

Digital Library

[41]

Quan Chen, Hailong Yang, Minyi Guo, Ram Srivatsa Kannan, Jason Mars, and Lingjia Tang. 2017. Prophet: Precise QoS prediction on non-preemptive accelerators to improve utilization in warehouse-scale computers. In Proceedings of the 22nd International Conference on Architectural Support for Programming Languages and Operating Systems. 17–32.

Digital Library

[42]

Ron C. Chiang and H. Howie Huang. 2011. TRACON: Interference-aware scheduling for data-intensive applications in virtualized environments. In Proceedings of the 2011 International Conference for High Performance Computing, Networking, Storage, and Analysis. 1–12.

Digital Library

[43]

Seyyed Ahmad Javadi, Sagar Mehra, Bharath Kumar Reddy Vangoor, and Anshul Gandhi. 2016. UIE: User-centric interference estimation for cloud applications. In Proceedings of the 2016 IEEE International Conference on Cloud Engineering (IC2E’16). IEEE, Los Alamitos, CA, 119–122.

[44]

Hailong Yang, Alex Breslow, Jason Mars, and Lingjia Tang. 2013. Bubble-Flux: Precise online QoS management for increased utilization in warehouse scale computers. ACM SIGARCH Computer Architecture News 41, 3 (2013), 607–618.

Digital Library

[45]

Xiao Zhang, Eric Tune, Robert Hagmann, Rohit Jnagal, Vrigo Gokhale, and John Wilkes. 2013. CPI2: CPU performance isolation for shared compute clusters. In Proceedings of the 8th ACM European Conference on Computer Systems. 379–391.

Digital Library

[46]

Seyyed Ahmad Javadi and Anshul Gandhi. 2017. Dial: Reducing tail latencies for cloud applications via dynamic interference-aware load balancing. In Proceedings of the 2017 IEEE International Conference on Autonomic Computing (ICAC’17). IEEE, Los Alamitos, CA, 135–144.

[47]

Yogesh D. Barve, Shashank Shekhar, Ajay Chhokra, Shweta Khare, Anirban Bhattacharjee, Zhuangwei Kang, Hongyang Sun, and Aniruddha Gokhale. 2019. FECBench: A holistic interference-aware approach for application performance modeling. In Proceedings of the 2019 IEEE International Conference on Cloud Engineering (IC2E’19). IEEE, Los Alamitos, CA, 211–221.

[48]

Xi Chen, Lukas Rupprecht, Rasha Osman, Peter Pietzuch, Felipe Franciosi, and William Knottenbelt. 2015. CloudScope: Diagnosing and managing performance interference in multi-tenant clouds. In Proceedings of the 2015 IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems. IEEE, Los Alamitos, CA, 164–173.

Digital Library

[49]

Fan-Hsun Tseng, Xiaofei Wang, Li-Der Chou, Han-Chieh Chao, and Victor C. M. Leung. 2017. Dynamic resource prediction and allocation for cloud data center using the multiobjective genetic algorithm. IEEE Systems Journal 12, 2 (2017), 1688–1699.

[50]

Tajwar Mehmood, Seemab Latif, and Sheheryaar Malik. 2018. Prediction of cloud computing resource utilization. In Proceedings of the 2018 15th International Conference on Smart Cities: Improving Quality of Life Using ICT and IoT (HONET-ICT’18). IEEE, Los Alamitos, CA, 38–42.

[51]

Xiaoli Sun, Qingbo Wu, Yusong Tan, and Fuhui Wu. 2014. MVEI: An interference prediction model for CPU-intensive application in cloud environment. In Proceedings of the 2014 13th International Symposium on Distributed Computing and Applications to Business, Engineering, and Science. IEEE, Los Alamitos, CA, 83–87.

Digital Library

[52]

Giuliano Casale, Stephan Kraft, and Diwakar Krishnamurthy. 2011. A model of storage I/O performance interference in virtualized systems. In Proceedings of the 2011 31st International Conference on Distributed Computing Systems Workshops. IEEE, Los Alamitos, CA, 34–39.

Digital Library

[53]

Achilleas Tzenetopoulos. 2020. Interference-aware container orchestration in Kubernetes clusters. In Proceedings of the 2020 Workshops on High Performance Computing. 321–330.

[54]

Christina Delimitrou and Christos Kozyrakis. 2013. iBench: Quantifying interference for datacenter applications. In Proceedings of the 2013 IEEE International Symposium on Workload Characterization (IISWC’13). IEEE, Los Alamitos, CA, 23–33.

[55]

Jason Mars, Lingjia Tang, and Mary Lou Soffa. 2011. Directly characterizing cross core interference through contention synthesis. In Proceedings of the 6th International Conference on High Performance and Embedded Architectures and Compilers (HiPEAC’11).167–176.

[56]

Sriram Govindan, Jie Liu, Aman Kansal, and Anand Sivasubramaniam. 2011. Cuanta: Quantifying effects of shared on-chip resource interference for consolidated virtual machines. In Proceedings of the 2nd ACM Symposium on Cloud Computing. Article 22, 14 pages.

[57]

Hamidreza Moradi, Wei Wang, Amanda Fernandez, and Dakai Zhu. 2020. uPredict: A user-level profiler-based predictive framework in multi-tenant clouds. In Proceedings of the 2020 IEEE International Conference on Cloud Engineering (IC2E’20). IEEE, Los Alamitos, CA, 73–82.

[58]

Javid Taheri, Albert Y. Zomaya, and Andreas Kassler. 2017. vmBBProfiler: A black-box profiling approach to quantify sensitivity of virtual machines to shared cloud resources. Computing 99, 12 (2017), 1149–1177.

Digital Library

[59]

Dimosthenis Masouros, Sotirios Xydis, and Dimitrios Soudris. 2020. Rusty: Runtime interference-aware predictive monitoring for modern multi-tenant systems. IEEE Transactions on Parallel and Distributed Systems 32, 1 (2020), 184–198.

[60]

Thomas Willhalm, Roman Dementiev, and Patrick Fay. 2012. Intel Performance Counter Monitor—A Better Way to Measure CPU Utilization. Intel.

[61]

David Buchaca, Joan Marcual, Josep LLuis Berral, and David Carrera. 2020. Sequence-to-sequence models for workload interference prediction on batch processing datacenters. Future Generation Computer Systems 110 (2020), 155–166.

[62]

Vinícius Meyer, Dionatrã F. Kirchoff, Matheus L. da Silva, and A. F. De Rose César. 2020. An interference-aware application classifier based on machine learning to improve scheduling in clouds. In Proceedings of the 2020 28th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP’20). IEEE, Los Alamitos, CA, 80–87.

[63]

Uillian L. Ludwig, Miguel G. Xavier, Dionatrã F. Kirchoff, Ian B. Cezar, and César A. F. De Rose. 2019. Optimizing multi-tier application performance with interference and affinity-aware placement algorithms. Concurrency and Computation: Practice and Experience 31, 18 (2019), e5098.

[64]

Jingwei Li, Yong Qi, Wei Wei, Jinwei Lin, Marcin Wozniak, and Robertas Damasevicius. 2019. dCCPI-predictor: A state-aware approach for effectively predicting cross-core performance interference. Future Generation Computer Systems 105 (2019), 184–195.

[65]

V. R. Anu and Sherly Elizabeth. 2019. IALM: Interference aware live migration strategy for virtual machines in cloud data centres. In Data Management, Analytics and Innovation. Springer, 499–511.

[66]

Sa Wang, Wenbo Zhang, Tao Wang, Chunyang Ye, and Tao Huang. 2015. VMon: Monitoring and quantifying virtual machine interference via hardware performance counter. In Proceedings of the 2015 IEEE 39th Annual Computer Software and Applications Conference, Vol. 2. IEEE, Los Alamitos, CA, 399–408.

Digital Library

[67]

Luis Carlos Jersak and Tiago Ferreto. 2016. Performance-aware server consolidation with adjustable interference levels. In Proceedings of the 31st Annual ACM Symposium on Applied Computing. 420–425.

Digital Library

[68]

Yu Gan, Yanqi Zhang, Kelvin Hu, Dailun Cheng, Yuan He, Meghna Pancholi, and Christina Delimitrou. 2019. Seer: Leveraging big data to navigate the complexity of performance debugging in cloud microservices. In Proceedings of the 24th International Conference on Architectural Support for Programming Languages and Operating Systems. 19–33.

Digital Library

[69]

Scott Votke, Seyyed Ahmad Javadi, and Anshul Gandhi. 2017. Modeling and analysis of performance under interference in the cloud. In Proceedings of the 2017 IEEE 25th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS’17). IEEE, Los Alamitos, CA, 232–243.

[70]

Zoltán Ádám Mann. 2015. Allocation of virtual machines in cloud data centers—A survey of problem models and optimization algorithms. ACM Computing Surveys 48, 1 (2015), 1–34.

Digital Library

[71]

Wei Zhang, Sundaresan Rajasekaran, Shaohua Duan, Timothy Wood, and Mingfa Zhu. 2015. Minimizing interference and maximizing progress for Hadoop virtual machines. ACM SIGMETRICS Performance Evaluation Review 42, 4 (2015), 62–71.

Digital Library

[72]

Altino M. Sampaio, Jorge G. Barbosa, and Radu Prodan. 2015. PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers. Simulation Modelling Practice and Theory 57 (2015), 142–160.

[73]

Maicon Melo Alves, Luan Teylo, Yuri Frota, and Lúcia M. A. Drummond. 2018. An interference-aware virtual machine placement strategy for high performance computing applications in clouds. In Proceedings of the Symposium on High Performance Computing Systems.

[74]

Hedi Hamdi, Sabrine Amri, and Zaki Brahmi. 2019. Managing performance interference effects for intelligent and efficient virtual machines placement based on GWO approach in cloud. International Journal of Computing and Digital Systems 8, 4 (2019), 317.

[75]

Jenn Wei Lin and Chien Hung Chen. 2012. Interference-aware virtual machine placement in cloud computing systems. In Proceedings of the International Conference on Computer and Information Science.

[76]

M. Reza HoseinyFarahabady, Javid Taheri, Albert Y. Zomaya, and Zahir Tari. 2021. Data-intensive workload consolidation in serverless (Lambda/FaaS) platforms. In Proceedings of the 2021 IEEE 20th International Symposium on Network Computing and Applications (NCA’21). 1–8. DOI:

[77]

C. K. Swain and A. Sahu. 2021. Interference aware workload scheduling for latency sensitive tasks in cloud environment. Computing3 (2021), 1–26.

[78]

Francisco Romero and Christina Delimitrou. 2018. Mage: Online and interference-aware scheduling for multi-scale heterogeneous systems. In Proceedings of the 27th International Conferenceon Parallel Architectures and Compilation Techniques. Article 19, 13 pages.

Digital Library

[79]

Evangelos Angelou, Konstantinos Kaffes, Athanasia Asiki, Georgios Goumas, and Nectarios Koziris. 2016. Improving virtual host efficiency through resource and interference aware scheduling. arXiv preprint arXiv:1601.07400 (2016).

[80]

Navaneeth Rameshan, Leandro Navarro, Enric Monte, and Vladimir Vlassov. 2014. Stay-Away, protecting sensitive applications from performance interference. In Proceedings of the 15th ACM/IFIP/USENIX International Middleware Conference. 301–312.

[81]

H. Lee, J. Lee, I. Yeom, and H. Woo. 2020. Panda: Reinforcement learning-based priority assignment for multi-processor real-time scheduling. IEEE Access 8 (2020), 185570–185583. DOI:

[82]

Y. Bao, Y. Peng, and C. Wu. 2019. Deep learning-based job placement in distributed machine learning clusters. In Proceedings of the IEEE Conference on Computer Communications(IEEE INFOCOM’19). 505–513. DOI:

Digital Library

[83]

Jeongseob Ahn, Changdae Kim, Jaeung Han, Young-Ri Choi, and Jaehyuk Huh. 2012. Dynamic virtual machine scheduling in clouds for architectural shared resources. In Proceedings of the 4th USENIX Conference on Hot Topics in Cloud Computing (HotCloud’12). 1–19.

[84]

Hadi Salimi and Mohsen Sharifi. 2013. Batch scheduling of consolidated virtual machines based on their workload interference model. Future Generation Computer Systems 29, 8 (2013), 2057–2066.

Digital Library

[85]

R. Nishtala, V. Petrucci, P. Carpenter, and M. Sjalander. 2020. Twig: Multi-agent task management for colocated latency-critical cloud services. In Proceedings of the 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA’20). 167–179. DOI:

[86]

Qian Zhu and Teresa Tung. 2014. Performance interference model for managing consolidated workloads in QOS-aware clouds. US Patent 8,732,291.

[87]

Yiling Qin, Lun Zhang, Fei Xu, and Daidong Luo. 2019. Interference and topology-aware VM live migrations in software-defined networks. In Proceedings of the 2019 IEEE 21st International Conference on High Performance Computing and Communications, the IEEE 17th International Conference on Smart City, and the IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS’19). IEEE, Los Alamitos, CA, 1068–1075.

[88]

Mohsen Tarighi, Seyed Ahmad Motamedi, and Ehsan Arianyan. 2010. Performance improvement of virtualized cluster computing system using TOPSIS algorithm. In Proceedings of the 40th International Conference on Computers and Industrial Engineering. IEEE, Los Alamitos, CA, 1–6.

[89]

Renuga Kanagavelu, Bu Sung Lee, Nguyen The Dat Le, Luke Ng Mingjie, and Khin Mi Mi Aung. 2014. Virtual machine placement with two-path traffic routing for reduced congestion in data center networks. Computer Communications 53, 1 (Nov. 2014), 1–12.

Digital Library

[90]

Subhadra Bose Shaw and Anil Kumar Singh. 2015. Use of proactive and reactive hotspot detection technique to reduce the number of virtual machine migration and energy consumption in cloud data center. Computers & Electrical Engineering 47 (2015), 241–254.

Digital Library

[91]

Linjiun Tsai and Wanjiun Liao. 2012. Cost-aware workload consolidation in green cloud datacenter. In Proceedings of the IEEE International Conference on Cloud Networking.

[92]

Chen Wei, Qiao Xiaoqiang, Wei Jun, and Huang Tao. 2012. A profit-aware virtual machine deployment optimization framework for cloud platform providers. In Proceedings of the IEEE 5th International Conference on Cloud Computing.

[93]

Aman Kansal, Feng Zhao, Jie Liu, Nupur Kothari, and Arka A. Bhattacharya. 2010. Virtual machine power metering and provisioning. In Proceedings of the 1st ACM Symposium on Cloud Computing. 39–50.

Digital Library

[94]

Ismael Solis Moreno, Renyu Yang, Jie Xu, and Tianyu Wo. 2013. Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement. In Proceedings of the 2013 IEEE 11th International Symposium on Autonomous Decentralized Systems (ISADS’13). IEEE, Los Alamitos, CA, 1–8.

[95]

Xibo Jin, Fa Zhang, Lin Wang, Songlin Hu, Biyu Zhou, and Zhiyong Liu. 2015. Joint optimization of operational cost and performance interference in cloud data centers. IEEE Transactions on Cloud Computing 5, 4 (2015), 697–711.

[96]

R. Nasim, J. Taheri, and A. J. Kassler. 2016. Optimizing virtual machine consolidation in virtualized datacenters using resource sensitivity. In Proceedings of the 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom’16).

[97]

K. Wang, M. Khan, N. Nguyen, and S. Gokhale. 2017. Design and implementation of an analytical framework for interference aware job scheduling on Apache Spark platform. Cluster Computing 22 (2017), 2223–2237. DOI:

Digital Library

[98]

Yasaman Amannejad, Diwakar Krishnamurthy, and Behrouz Far. 2015. Detecting performance interference in cloud-based web services. In Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM’15). IEEE, Los Alamitos, CA, 423–431.

[99]

Yusen Li, Chuxu Shan, Ruobing Chen, Xueyan Tang, Wentong Cai, Shanjiang Tang, Xiaoguang Liu, Gang Wang, Xiaoli Gong, and Ying Zhang. 2019. GAugur: Quantifying performance interference of colocated games for improving resource utilization in cloud gaming. In Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing. 231–242.

Digital Library

[100]

Faruk Caglar, Shashank Shekhar, and Aniruddha Gokhale. 2011. Towards a Performance Interference-Aware Virtual Machine Placement Strategy for Supporting Soft Real-Time Applications in the Cloud. Universidad Carlos III De Madrid.

[101]

Marisol García-Valls, Tommaso Cucinotta, and Chenyang Lu. 2014. Challenges in real-time virtualization and predictable cloud computing. Journal of Systems Architecture 60, 9 (2014), 726–740.

Digital Library

[102]

Zhiheng Zhong, Minxian Xu, Maria Alejandra Rodriguez, Chengzhong Xu, and Rajkumar Buyya. 2022. Machine learning-based orchestration of containers: A taxonomy and future directions. ACM Computing Surveys 54, 10s (2022), Article 217, 35 pages.

Digital Library

[103]

Wen-Yan Chen, Ke-Jiang Ye, Cheng-Zhi Lu, Dong-Dai Zhou, and Cheng-Zhong Xu. 2020. Interference analysis of co-located container workloads: A perspective from hardware performance counters. Journal of Computer Science and Technology 35 (2020), 412–417.

Digital Library

[104]

Z. Ou, H. Zhuang, J. K. Nurminen, A. Yl-Jski, and P. Hui. 2012. Exploiting hardware heterogeneity within the same instance type of Amazon EC2. In Proceedings of the 4th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud’12).

[105]

Benjamin Farley, Ari Juels, Venkatanathan Varadarajan, Thomas Ristenpart, and Michael M. Swift. 2012. More for your money: Exploiting performance heterogeneity in public clouds. In Proceedings of the ACM Symposium on Cloud Computing.

Digital Library

[106]

Fei Xu, Fangming Liu, and Hai Jin. 2016. Heterogeneity and interference-aware virtual machine provisioning for predictable performance in the cloud. IEEE Transactions on Computers 65, 8 (2016), 2470–2483.

Digital Library

[107]

Kui Su, Lei Xu, Cong Chen, Wenzhi Chen, and Zonghui Wang. 2015. Affinity and conflict-aware placement of virtual machines in heterogeneous data centers. In Proceedings of the 2015 IEEE 12th International Symposium on Autonomous Decentralized Systems. IEEE, Los Alamitos, CA, 289–294.

Digital Library

[108]

Kejiang Ye, Haiying Shen, Yang Wang, and Chengzhong Xu. 2022. Multi-tier workload consolidations in the cloud: Profiling, modeling and optimization. IEEE Transactions on Cloud Computing 10, 2 (2022), 899–912.

Cited By

Weiß MStümpfle JDettinger FJazdi NWeyrich MWeiß MStümpfle JDettinger FJazdi NWeyrich M(2024)Simulating Cloud Environments of Connected Vehicles for Anomaly DetectionSAE Technical Paper Series10.4271/2024-01-2996Online publication date: 2-Jul-2024
https://doi.org/10.4271/2024-01-2996
Nougnanke BLoye JBaffier JFerlin SBruyere MLabit Y(2024)gPerfIsol: GNN-Based Rate-Limits Allocation for Performance Isolation in Multi-Tenant Cloud2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN)10.1109/ICIN60470.2024.10494419(194-201)Online publication date: 11-Mar-2024
https://doi.org/10.1109/ICIN60470.2024.10494419
Muslim ARecker S(2024)Revisiting Controller-Based Admission Control for Virtual Machines2024 IEEE Cloud Summit10.1109/Cloud-Summit61220.2024.00022(95-100)Online publication date: 27-Jun-2024
https://doi.org/10.1109/Cloud-Summit61220.2024.00022
Show More Cited By

Index Terms

Performance Interference of Virtual Machines: A Survey
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing
2. General and reference
  1. Document types
    1. Surveys and overviews

Recommendations

Performance profiling of virtual machines
VEE '11

Profilers based on hardware performance counters are indispensable for performance debugging of complex software systems. All modern processors feature hardware performance counters, but current virtual machine monitors (VMMs) do not properly expose ...
Performance profiling of virtual machines
VEE '11: Proceedings of the 7th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments

Profilers based on hardware performance counters are indispensable for performance debugging of complex software systems. All modern processors feature hardware performance counters, but current virtual machine monitors (VMMs) do not properly expose ...
Batch scheduling of consolidated virtual machines based on their workload interference model

The use of virtualization technology (VT) has become widespread in modern datacenters and Clouds in recent years. In spite of their many advantages, such as provisioning of isolated execution environments and migration, current implementations of VT do ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 12

December 2023

825 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3582891

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

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 ACM 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 March 2023

Online AM: 30 November 2022

Accepted: 22 November 2022

Revised: 18 November 2022

Received: 22 October 2021

Published in CSUR Volume 55, Issue 12

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey

Funding Sources

National Natural Science Foundation of China
Guangdong Major Project of Basic and Applied Basic Research
Major Key Project of PCL
Guangzhou Development Zone Science and Technology Project

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
1,184
Total Downloads

Downloads (Last 12 months)505
Downloads (Last 6 weeks)28

Reflects downloads up to 11 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Weiß MStümpfle JDettinger FJazdi NWeyrich MWeiß MStümpfle JDettinger FJazdi NWeyrich M(2024)Simulating Cloud Environments of Connected Vehicles for Anomaly DetectionSAE Technical Paper Series10.4271/2024-01-2996Online publication date: 2-Jul-2024
https://doi.org/10.4271/2024-01-2996
Nougnanke BLoye JBaffier JFerlin SBruyere MLabit Y(2024)gPerfIsol: GNN-Based Rate-Limits Allocation for Performance Isolation in Multi-Tenant Cloud2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN)10.1109/ICIN60470.2024.10494419(194-201)Online publication date: 11-Mar-2024
https://doi.org/10.1109/ICIN60470.2024.10494419
Muslim ARecker S(2024)Revisiting Controller-Based Admission Control for Virtual Machines2024 IEEE Cloud Summit10.1109/Cloud-Summit61220.2024.00022(95-100)Online publication date: 27-Jun-2024
https://doi.org/10.1109/Cloud-Summit61220.2024.00022
Rajput KLi XLakhan AZhang JMahesar AKumar Sajnani D(2024)Task Scheduling in Multi-Cloud Environments for Spark Workflow under Performance Uncertainty2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD61410.2024.10580069(2752-2757)Online publication date: 8-May-2024
https://doi.org/10.1109/CSCWD61410.2024.10580069
Zhou GXie YLan HTian WBuyya RWu K(2024)Information interaction and partial growth-based multi-population growable genetic algorithm for multi-dimensional resources utilization optimization of cloud computingSwarm and Evolutionary Computation10.1016/j.swevo.2024.10157587(101575)Online publication date: Jun-2024
https://doi.org/10.1016/j.swevo.2024.101575
Banerjee SRoy SKhatua S(2024)Towards energy and QoS aware dynamic VM consolidation in a multi-resource cloudFuture Generation Computer Systems10.1016/j.future.2024.03.058157(376-391)Online publication date: Aug-2024
https://doi.org/10.1016/j.future.2024.03.058
Zhou GTian WBuyya RXue RSong L(2024)Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directionsArtificial Intelligence Review10.1007/s10462-024-10756-957:5Online publication date: 23-Apr-2024
https://doi.org/10.1007/s10462-024-10756-9
Vhaduri SPaik SHuber J(2024)Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People’s Cough Detection ModelsWireless Mobile Communication and Healthcare10.1007/978-3-031-60665-6_33(445-459)Online publication date: 28-Jun-2024
https://doi.org/10.1007/978-3-031-60665-6_33
Ruaro MBarral HBertolino MCataldo RMedina RKaraoui MBorde E(2023)The Last-Level-Cache Interference in Guest Performance: a Case-Study with Zephyr OS2023 26th Euromicro Conference on Digital System Design (DSD)10.1109/DSD60849.2023.00056(351-358)Online publication date: 6-Sep-2023
https://doi.org/10.1109/DSD60849.2023.00056
Tang SYu YWang HWang GChen WXu ZGuo SGao W(2023)A Survey on Scheduling Techniques in Computing and Network ConvergenceIEEE Communications Surveys & Tutorials10.1109/COMST.2023.332902726:1(160-195)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3329027

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents