Proactive planning of bandwidth resource using simulation-based what-if predictions forWeb services in the cloud

doi:10.1007/s11704-019-9117-x

Front. Comput. Sci.

2021, Vol. 15

Issue (1) : 151201 https://doi.org/10.1007/s11704-019-9117-x

RESEARCH ARTICLE

Proactive planning of bandwidth resource using simulation-based what-if predictions forWeb services in the cloud

Jianpeng HU^1,², Linpeng HUANG¹(

), Tianqi SUN², Ying FAN², Wenqiang HU², Hao ZHONG¹

¹. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
². School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Download: PDF(1695 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Resource planning is becoming an increasingly important and timely problem for cloud users. As more Web services are moved to the cloud, minimizing network usage is often a key driver of cost control. Most existing approaches focus on resources such as CPU, memory, and disk I/O. In particular, CPU receives the most attention from researchers, but the bandwidth is somehow neglected. It is challenging to predict the network throughput of modern Web services, due to the factors of diverse and complex response, evolvingWeb services, and complex network transportation. In this paper, we propose a methodology of what-if analysis, named Log2Sim, to plan the bandwidth resource of Web services. Log2Sim uses a lightweight workload model to describe user behavior, an automated mining approach to obtain characteristics of workloads and responses from massive Web logs, and traffic-aware simulations to predict the impact on the bandwidth consumption and the response time in changing contexts. We use a real-life Web system and a classic benchmark to evaluate Log2Sim in multiple scenarios. The evaluation result shows that Log2Sim has good performance in the prediction of bandwidth consumption. The average relative error is 2% for the benchmark and 8% for the real-life system. As for the response time, Log2Sim cannot produce accurate predictions for every single service request, but the simulation results always show similar trends on average response time with the increase of workloads in different changing contexts. It can provide sufficient information for the system administrator in proactive bandwidth planning.

Keywords what-if analysis bandwidth management network simulation Web service log mining resource planning evolution OPNET

Corresponding Author(s): Linpeng HUANG

Just Accepted Date: 15 October 2019 Issue Date: 24 September 2020

Cite this article:

Jianpeng HU,Linpeng HUANG,Tianqi SUN, et al. Proactive planning of bandwidth resource using simulation-based what-if predictions forWeb services in the cloud[J]. Front. Comput. Sci., 2021, 15(1): 151201.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-019-9117-x
https://academic.hep.com.cn/fcs/EN/Y2021/V15/I1/151201

1	M Goncalves, M Cunha, N C Mendonca, A Sampaio. Performance inference: a novel approach for planning the capacity of IaaS cloud applications. In: Proceedings of the 8th IEEE International Conference on Cloud Computing. 2015, 813–820 https://doi.org/10.1109/CLOUD.2015.112
2	A Wolke, M Bichler, T Setzer. Planning vs. dynamic control: resource allocation in corporate clouds. IEEE Transactions on Cloud Computing, 2016, 4(3): 322–335 https://doi.org/10.1109/TCC.2014.2360399
3	M Amiri, L Mohammad-Khanli. Survey on prediction models of applications for resources provisioning in cloud. Journal of Network and Computer Applications, 2017, 82: 93–113 https://doi.org/10.1016/j.jnca.2017.01.016
4	J Hu, L Huang, J Huang, T Sun, Y Ouyang. What-if model construction and validation of Web systems based on log mining. In: Proceedings of the 24th Asia-Pacific Software Engineering Conference. 2017, 505–512 https://doi.org/10.1109/APSEC.2017.57
5	M Guzek, P Bouvry, E G Talbi. A survey of evolutionary computation for resource management of processing in cloud computing. IEEE Computational Intelligence Magazine, 2015, 10(2): 53–67 https://doi.org/10.1109/MCI.2015.2405351
6	K I Kim, W Wang, M Humphrey. PICS: a public iaas cloud simulator. In: Proceedings of IEEE International Conference on Cloud Computing. 2015, 211–220 https://doi.org/10.1109/CLOUD.2015.37
7	J Hu, L Huang, T Sun, Y Xu, X Gong. Log2Sim: automating what-if modeling and prediction for bandwidth management of cloud hosted Web services. In: Proceedings of IEEE International Conference on Web Services. 2018, 99–106 https://doi.org/10.1109/ICWS.2018.00020
8	A Ciancone, A Filieri, M L Drago, R Mirandola, U Grassi. KlaperSuite: an integrated model-driven environment for reliability and performance analysis of component-based systems. In: Proceedings of the 49th International Conference on Objects, Models, Components, Patterns. 2011, 99–114 https://doi.org/10.1007/978-3-642-21952-8_9
9	C Rathfelder, S Kounev, D Evans. Capacity planning for event-based systems using automated performance predictions. In: Proceedings of the 26th IEEE/ACM International Conference on Automated Software Engineering. 2011, 352–361 https://doi.org/10.1109/ASE.2011.6100073
10	D F Garcia, J Garcia. TPC-W e-commerce benchmark evaluation. Computer, 2003, 36(2): 42–48 https://doi.org/10.1109/MC.2003.1178045
11	J Hu, L Huang, Y Fan, L Tong, W Hu. Bandwidth planning of Web services in changing contexts based on network simulation. In: Proceedings of IEEE International Conference on Web Services. 2019, 242–246 https://doi.org/10.1109/ICWS.2019.00049
12	A Bahga, V K Madisetti. Synthetic workload generation for cloud computing applications. Journal of Software Engineering and Applications, 2011, 4(7): 396 https://doi.org/10.4236/jsea.2011.47046
13	F Abbors, D Truscan, T Ahmad. Mining Web server logs for creating workload models. In: Proceedings of the 9th International Joint Conference on Software Technologies. 2015, 131–150 https://doi.org/10.1007/978-3-319-25579-8_8
14	C Vogele, A van Hoorn, E Schulz, W Hasselbring, H Krcmar. WESSBAS: extraction of probabilistic workload specifications for load testing and performance prediction-a model-driven approach for session-based application systems. Software and Systems Modeling, 2018, 17(2): 443–447 https://doi.org/10.1007/s10270-016-0566-5
15	C Amza, E Cecchet, A Chanda, A L Cox, S Elnikety, R Gil, et al. Specification and implementation of dynamic Web site benchmarks. In: Proceedings of IEEE International Workshop on Workload Characterization. 2002 https://doi.org/10.1109/WWC.2002.1226489
16	H Oi, S Niboshi. Workload analysis of SPECjEnterprise2010. In: Proceedings of IEEE International Symposium on Parallel and Distributed Processing with Applications. 2012 https://doi.org/10.1109/ISPA.2012.52
17	P Dan, A W Moore. X-means: extending k-means with efficient estimation of the number of clusters. In: Proceedings of the 17th International Conference on Machine Learning. 2000, 727–734
18	S Becker, H Koziolek, R Reussner. The Palladio component model for model-driven performance prediction. Journal of Systems and Software, 2009, 82(1): 3–22 https://doi.org/10.1016/j.jss.2008.03.066
19	A Varga. Using the OMNeT++ discrete event simulation system in education. IEEE Transactions on Education, 1999, 42(4): 372 https://doi.org/10.1109/13.804564
20	H Koziolek. Performance evaluation of component-based software systems: a survey. Performance Evaluation, 2010, 67(8): 634–658 https://doi.org/10.1016/j.peva.2009.07.007
21	P Desnoyers, T Wood, P Shenoy, R Singh, S Patil, H Vin. Modellus: automated modeling of complex internet data center applications. ACM Transactions on the Web, 2012, 6(2): 1–29 https://doi.org/10.1145/2180861.2180865
22	D Caban, T Walkowiak. Prediction of the performance of Web based systems. In: Zamojski W, Sugier J, eds. Dependability Problems of Complex Information Systems. Springer International Publishing, 2015 https://doi.org/10.1007/978-3-319-08964-5_1
23	W Hao, Z Zhengxin, L Jiacheng, Y Kun, H Ching-Hsien. Multiple attributes QoS prediction via deep neural model with contexts. IEEE Transactions on Services Computing, 2018
24	M Tariq, A Zeitoun, V Ualancius, H Feamster, M Ammar. Answering what-if deployment and configuration questions with wise. IEEE/ACM Transactions on Networking, 2013, 21(1): 1–13 https://doi.org/10.1109/TNET.2012.2230448
25	L Zhang, B Zhang, C Pahl, L Xu, Z Zhu. Personalized quality prediction for dynamic service management based on invocation patterns. In: Proceedings of International Conference on Service-Oriented Computing. 2013, 84–98 https://doi.org/10.1007/978-3-642-45005-1_7
26	P Viswanath, R Pinkesh. I-DBSCAN: a fast hybrid density based clustering method. In: Proceedings of International Conference on Pattern Recognition. 2006, 912–915 https://doi.org/10.1109/ICPR.2006.741
27	Y Li, B Liu. A normalized Levenshtein distance metric. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6): 1091–1095 https://doi.org/10.1109/TPAMI.2007.1078
28	N Cardwell, S Savage, T Anderson. Modeling TCP latency. In: Proceedings of the 19th Joint Conference of the IEEE Computer and Communications Societies. 2002
29	H W Cain, R Rajwar, M Marden, MH Lipasti. An architectural evaluation of Java TPC-W. In: Proceedings of the 17th International Symposium on High-Performance Computer Architecture. 2001
30	G K Shyam, S S Manvi. Virtual resource prediction in cloud environment: a Bayesian approach. Journal of Network and Computer Applications, 2016, 65: 144–154 https://doi.org/10.1016/j.jnca.2016.03.002
31	H Wang, L Wang, Q Yu, Z Zheng, M Lyu, A Bouguettaya. Online reliability prediction via motifs-based dynamic bayesian networks for serviceoriented systems. IEEE Transactions on Software Engineering, 2016, 43(6): 556–579 https://doi.org/10.1109/TSE.2016.2615615
32	C Stewart, K Shen. Performance modeling and system management for multi-component online services. In: Proceedings of the International Symposium on Networked Systems Design and Implementation. 2005
33	F M Alam, S Mohan, J W Fowler, M Gopalakrishnan. A discrete event simulation tool for performance management of Web-based application systems. Journal of Simulation, 2012, 6(1): 21–32 https://doi.org/10.1057/jos.2011.8
34	H Koziolek, B Schlich, S Becker, M Hauck. Performance and reliability prediction for evolving service-oriented software systems. Empirical Software Engineering, 2013, 18(4): 746–790 https://doi.org/10.1007/s10664-012-9213-0
35	W Zheng, R Bianchini, G J Janakiraman, J R Santos, Y Turner. JustRunIt: experiment-based management of virtualized data centers. In: Proceedings of USENIX Annual Technical Conference. 2009
36	D Jayasinghe, G Swint, S Malkowski, J Li, Q Wang, et al. Expertus: a generator approach to automate performance testing in iaas clouds. In: Proceedings of the 5th IEEE International Conference on Cloud Computing. 2012, 73–80 https://doi.org/10.1109/CLOUD.2012.98
37	T Verdickt, B Dhoedt, F De Turck, P Demeester. Hybrid performance modeling approach for network intensive distributed software. In: Proceedings of the 6th International Workshop on Software and Performance. 2007, 189–200 https://doi.org/10.1145/1216993.1217026
38	G Jung, T Mukherjee, S Kunde, H Kim, N Sharma, F Goetz. CloudAdvisor: a recommendation-as-a-service platform for cloud configuration and pricing. In: Proceedings of the 9th IEEE World Congress on Services. 2013, 456–463 https://doi.org/10.1109/SERVICES.2013.55
39	A Li, X Yang, S Kandula, M Zhan. CloudCmp: comparing public cloud providers. In: Proceedings of the 2010 ACM SIGCOMM Conference on Internet Measurement. 2010 https://doi.org/10.1145/1879141.1879143
40	Z Zheng, H Ma, M R Lyu, I King. Qos-aware Web service recommendation by collaborative filtering. IEEE Transactions on Services Computing, 2011, 4(2): 140–152 https://doi.org/10.1109/TSC.2010.52
41	C Yu, L Huang. A Web service qos prediction approach based on timeand- location-aware collaborative filtering. Service Oriented Computing and Applications, 2016, 10(2): 135–149 https://doi.org/10.1007/s11761-014-0168-4
42	W Lo, J Yin, Y Li, Z Wu. EfficientWeb service QoS prediction using local neighborhood matrix factorization. Engineering Applications of Artificial Intelligence, 2015, 38: 14–23 https://doi.org/10.1016/j.engappai.2014.10.010
43	H Wu, K Yue, B Li, B Zhang, C H Hsu. Collaborative QoS prediction with context-sensitive matrix factorization. Future Generation Computer Systems, 2018, 82: 669–678 https://doi.org/10.1016/j.future.2017.06.020

[1]

Article highlights

Download

[1]	Ibrahim ALSEADOON, Aakash AHMAD, Adel ALKHALIL, Khalid SULTAN. Migration of existing software systems to mobile computing platforms: a systematic mapping study[J]. Front. Comput. Sci., 2021, 15(2): 152204-.
[2]	Yihui LIANG, Han HUANG, Zhaoquan CAI. PSO-ACSC: a large-scale evolutionary algorithm for image matting[J]. Front. Comput. Sci., 2020, 14(6): 146321-.
[3]	Yaopeng LIU, Hao PENG, Jianxin LI, Yangqiu SONG, Xiong LI. Event detection and evolution in multi-lingual social streams[J]. Front. Comput. Sci., 2020, 14(5): 145612-.
[4]	Munish SAINI, Kuljit Kaur CHAHAL. Change profile analysis of open-source software systems to understand their evolutionary behavior[J]. Front. Comput. Sci., 2018, 12(6): 1105-1124.
[5]	Shuaiqiang WANG, Yilong YIN. Polygene-based evolutionary algorithms with frequent pattern mining[J]. Front. Comput. Sci., 2018, 12(5): 950-965.
[6]	Yong WANG, Zhi-Zhong LIU, Jianbin LI, Han-Xiong LI, Jiahai WANG. On the selection of solutions for mutation in differential evolution[J]. Front. Comput. Sci., 2018, 12(2): 297-315.
[7]	Bo SUN, Haiyan CHEN, Jiandong WANG, Hua XIE. Evolutionary under-sampling based bagging ensemble method for imbalanced data classification[J]. Front. Comput. Sci., 2018, 12(2): 331-350.
[8]	Jayanthi MANICASSAMY, Dinesh KARUNANIDHI, Sujatha POTHULA, Vengattaraman THIRUMAL, Dhavachelvan PONNURANGAM, Subramanian RAMALINGAM. GPS: a constraint-based gene position procurement in chromosome for solving large-scale multiobjective multiple knapsack problems[J]. Front. Comput. Sci., 2018, 12(1): 101-121.
[9]	Houkui ZHOU, Huimin YU, Roland HU. Topic evolution based on the probabilistic topic model: a review[J]. Front. Comput. Sci., 2017, 11(5): 786-802.
[10]	Maoguo GONG, Xiangming JIANG, Hao LI. Optimization methods for regularization-based ill-posed problems: a survey and a multi-objective framework[J]. Front. Comput. Sci., 2017, 11(3): 362-391.
[11]	Yu HAN,Guozhu JIA. Optimizing product manufacturability in 3D printing[J]. Front. Comput. Sci., 2017, 11(2): 347-357.
[12]	Yuan SU,Xi ZHANG,Lixin LIU,Shouyou SONG,Binxing FANG. Understanding information interactions in diffusion: an evolutionary game-theoretic perspective[J]. Front. Comput. Sci., 2016, 10(3): 518-531.
[13]	Yiqiao CAI,Yonghong CHEN,Tian WANG,Hui TIAN. Improving differential evolution with a new selection method of parents for mutation[J]. Front. Comput. Sci., 2016, 10(2): 246-269.
[14]	Xiaodong FU,Kun YUE,Li LIU,Ping ZOU,Yong FENG. Discovering admissibleWeb services with uncertain QoS[J]. Front. Comput. Sci., 2015, 9(2): 265-279.
[15]	Rong ZHANG, Koji ZETTSU, Yutaka KIDAWARA, Yasushi KIYOKI, Aoying ZHOU. Context-sensitive Web service discovery over the bipartite graph model[J]. Front Comput Sci, 2013, 7(6): 875-893.

Viewed

Full text

Abstract

Cited

Shared

Discussed