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Predictive service placement in cloud using deep learning and frequent subgraph mining

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Abstract

Over the last few years, service placement has become a strategic and fundamental management operation that allows cloud providers to deploy and arrange their services on the high-performance computation/storage servers, while taking various constraints (e.g., resource usage, security levels, data transfer time, SLA) into consideration. Despite the huge number of service placement schemes, most of them are static and do not take the cloud changes into account. To cope with this issue, predicting the cloud zones’ performance and availability should precede the placement task. For this purpose, we adopt gated recurrent neural network as a deep learning variant that allows forecasting the next short-term resource consumption on cloud servers and predicting the future service migration traffic between them. Also, to place cloud services’ application/data components on the optimum cloud zones, the frequently used high-performance servers are selected by mining the graph-like placement history, i.e. previous placement plans. To do so, we propose a Frequent Subgraph Mining algorithm that is reinforced with a tuning method to increase the probability of executing the past placement schemes. Experimental results have proved that our predictive approach outperforms state-of-the-art placement schemes in terms of performance and prediction quality.

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Notes

  1. https://github.com/deepakrana47/GRU_implementation.

  2. https://www.cs.ucsb.edu/~xyan/software/gSpan.htm.

References

  • Abdelhamid E, Canim M, Sadoghi M, Bhattacharjee B, Chang Y-C, Kalnis P (2017) Incremental frequent subgraph mining on large evolving graphs. IEEE Trans Knowl Data Eng 29(12):2710–2723

    Article  Google Scholar 

  • Altmann J, Kashef MM (2014) Cost model based service placement in federated hybrid clouds. Futur Gener Comput Syst 41:79–90

    Article  Google Scholar 

  • Bhardwaj S, Sahoo B (2015) A particle swarm optimization approach for cost effective saas placement on cloud. In: International conference on computing, communication & automation. IEEE, pp 686–690

  • Bowen Y, Shaochun W (2012) An adaptive simulated annealing genetic algorithm for the data placement problem in saas. In: Industrial control and electronics engineering (ICICEE), 2012 international conference on. IEEE, pp 1037–1043

  • Brik B, Frangoudis PA, Ksentini A (2020) Service-oriented mec applications placement in a federated edge cloud architecture. In: ICC 2020-2020 IEEE International conference on communications (ICC). IEEE, pp 1–6

  • Cappanera P, Paganelli F, Paradiso F (2019) Vnf placement for service chaining in a distributed cloud environment with multiple stakeholders. Comput Commun 133:24–40

    Article  Google Scholar 

  • Chen Z, Hu J, Min G, Zomaya A, El-Ghazawi T (2019) Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning. IEEE Trans Parall Distrib Syst

  • Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555

  • Espling D, Larsson L, Li W, Tordsson J, Elmroth E (2016) Modeling and placement of cloud services with internal structure. IEEE Trans Cloud Comput 4(4):429–439

    Article  Google Scholar 

  • Fang J, Ma A (2020) Iot application modules placement and dynamic task processing in edge-cloud computing. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3007751

    Article  Google Scholar 

  • Farhadi V, Mehmeti F, He T, Porta TL, Khamfroush H, Wang S, Chan KS (2019) Service placement and request scheduling for data-intensive applications in edge clouds. In: IEEE INFOCOM 2019-IEEE conference on computer communications. IEEE, pp 1279–1287

  • Foschini L, Tortonesi M (2013) Adaptive and business-driven service placement in federated cloud computing environments. In: Integrated network management (IM 2013), 2013 IFIP/IEEE international symposium On. IEEE, pp 1245–1251

  • Goettelmann E, Fdhila W, Godart C (2013) Partitioning and cloud deployment of composite web services under security constraints. In: Cloud Engineering (IC2E), 2013 IEEE international conference on. IEEE, pp 193–200

  • Gomes R, Lima J, Costa F, da Rocha R, Georgantas N (2015) A model-based approach for the pragmatic deployment of service choreographies. In: European conference on service-oriented and cloud computing. Springer, pp 153–165

  • Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press, Cambridge

    MATH  Google Scholar 

  • Gupta L, Samaka M, Jain R, Erbad A, Bhamare D, Metz C (2017) Colap: a predictive framework for service function chain placement in a multi-cloud environment. In: 2017 IEEE 7th Annual computing and communication workshop and conference (CCWC). IEEE, pp 1–9

  • Hajji MA, Mezni H (2018) A composite particle swarm optimization approach for the composite saas placement in cloud environment. Soft Comput 22(12):4025–4045

    Article  Google Scholar 

  • Han P, Liu Y, Guo L (2021) Interference-aware online multi-component service placement in edge cloud networks and its ai application. IEEE Internet Things J 8(13):10557–10572

    Article  Google Scholar 

  • Hassan HO, Azizi S, Shojafar M (2020) Priority, network and energy-aware placement of iot-based application services in fog-cloud environments. IET Commun 14(13):2117–2129

    Article  Google Scholar 

  • Hedhli A, Mezni H (2020) A dfa-based approach for the deployment of bpaas fragments in the cloud. Concurr Comput Pract Exp 32(14):e5075

    Article  Google Scholar 

  • Hedhli A, Mezni H (2021) A survey of service placement in cloud environments. Grid Comput:1–29

  • Hou S-L, Zhao S, Cheng B, Cheng Y-Y, Chen J-L (2016) Fragmentation and optimal deployment for iot-aware business process. In: Services computing (SCC), 2016 IEEE international conference on. IEEE, pp 657–664

  • Huang K-C, Shen B-J (2015) Service deployment strategies for efficient execution of composite saas applications on cloud platform. J Syst Softw 107:127–141

    Article  Google Scholar 

  • Huang K-C, Lu Y-C, Tsai M-H, Wu Y-J, Chang H-Y (2016) Performance-efficient service deployment and scheduling methods for composite cloud services. In: Proceedings of the 9th international conference on utility and cloud computing. ACM, pp 240–244

  • Jiang C, Coenen F, Zito M (2013) A survey of frequent subgraph mining algorithms. Knowl Eng Rev 28(1):75–105

    Article  Google Scholar 

  • Jozefowicz R, Zaremba W, Sutskever I (2015) An empirical exploration of recurrent network architectures. In: International conference on machine learning, pp 2342–2350

  • Kang Y, Zheng Z, Lyu MR (2012) A latency-aware co-deployment mechanism for cloud-based services. In: Cloud computing (CLOUD), 2012 IEEE 5th international conference on. IEEE, pp 630–637

  • Kwok T, Mohindra A (2008) Resource calculations with constraints, and placement of tenants and instances for multi-tenant saas applications. In: International conference on service-oriented computing. Springer, pp 633–648

  • Li B, Li J, Huai J, Wo T, Li Q, Zhong L (2009) Enacloud: an energy-saving application live placement approach for cloud computing environments. In: Cloud Computing, 2009. CLOUD’09. IEEE International Conference on. IEEE, pp 17–24

  • Lucas-Simarro JL, Moreno-Vozmediano R, Montero RS, Llorente IM (2013) Scheduling strategies for optimal service deployment across multiple clouds. Futur Gener Comput Syst 29(6):1431–1441

    Article  Google Scholar 

  • Ma H, Zhou Z, Chen X (2019) Predictive service placement in mobile edge computing. In: 2019 IEEE/CIC international conference on communications in China (ICCC). IEEE, pp 792–797

  • Mahdhi T, Mezni H (2018) A prediction-based vm consolidation approach in iaas cloud data centers. J Syst Softw 146:263–285

    Article  Google Scholar 

  • Mahmud R, Srirama SN, Ramamohanarao K, Buyya R (2020) Profit-aware application placement for integrated fog-cloud computing environments. J Parallel Distrib Comput 135:177–190

    Article  Google Scholar 

  • Mezni H, Kouki J (2017) A multi-swarm based approach with cooperative learning strategy for composite saas placement. In: Proceedings of the symposium on applied computing. ACM, pp 399–404

  • Miikkulainen R, Liang J, Meyerson E, Rawal A, Fink D, Francon O, Raju B, Shahrzad H, Navruzyan A, Duffy N et al (2019) Evolving deep neural networks. In: Artificial intelligence in the age of neural networks and brain computing. Elsevier, pp 293–312

  • Moubayed A, Shami A, Heidari P, Larabi A, Brunner R (2020) Cost-optimal v2x service placement in distributed cloud/edge environment. In: 2020 16th International conference on wireless and mobile computing, networking and communications (WiMob)(50308). IEEE, pp 1–6

  • Mudam R, Bhartia S, Chattopadhyay S, Bhattacharya A (2020) Mobility-aware service placement for vehicular users in edge-cloud environment. In: International conference on service-oriented computing. Springer, pp 248–265

  • Na T, Park P, Ryu H, Kim T, Kim J, Park J (2020) Optimal service placement using pseudo service chaining mechanism for cloud-based multimedia services. Multimedia Tools Appl:1–19

  • Nagarajan R, Thirunavukarasu R (2018) A review on intelligent cloud broker for effective service provisioning in cloud. In: 2018 Second international conference on intelligent computing and control systems (ICICCS). IEEE, pp 519–524

  • Nagarajan N, Thirunavukarasu R (2020) Service-oriented broker for effective provisioning of cloud services—a survey. Int J Comput Digit Syst 9(5):863–879

    Article  Google Scholar 

  • Nagarajan R, Thirunavukarasu R (2019) A fuzzy-based decision-making broker for effective identification and selection of cloud infrastructure services. Soft Comput 23(19):9669–9683

    Article  Google Scholar 

  • Ni ZW, Pan XF, Wu ZJ (2012) An ant colony optimization for the composite saas placement problem in the cloud. In: Applied mechanics and materials, vol 130. Trans Tech Publ, pp 3062–3067

  • Ochei LC, Petrovski A, Bass JM (2019) Optimal deployment of components of cloud-hosted application for guaranteeing multitenancy isolation. J Cloud Comput 8(1):1

    Article  Google Scholar 

  • Ouyang T, Zhou Z, Chen X (2018) Follow me at the edge: mobility-aware dynamic service placement for mobile edge computing. IEEE J Sel Areas Commun 36(10):2333–2345

    Article  Google Scholar 

  • Park K, Pai VS (2006) Comon: a mostly-scalable monitoring system for planetlab. ACM SIGOPS Oper Syst Rev 40(1):65–74

    Article  Google Scholar 

  • Perez DAL, Rothenberg CE, Santos M, Gomes PH (2020) Ani: Abstracted network inventory for streamlined service placement in distributed clouds. In: 2020 6th IEEE conference on network softwarization (NetSoft). IEEE, pp 319–325

  • Qian Q, Jin R, Yi J, Zhang L, Zhu S (2015) Efficient distance metric learning by adaptive sampling and mini-batch stochastic gradient descent (sgd). Mach Learn 99(3):353–372

    Article  MathSciNet  MATH  Google Scholar 

  • Ramadoss R, Elango NM, Satheesh A, Hsu C-H (2018) Pspo: a framework for cost-effective service placement optimisation during enterprise modernisation on hybrid clouds. Int J Web Grid Serv 14(2):170–199

    Article  Google Scholar 

  • Rekik M, Boukadi K, Assy N, Gaaloul W, Ben-Abdallah H (2018) Optimal deployment of configurable business processes in cloud federations. IEEE Trans Netw Serv Manage 15(4):1692–1705

    Article  Google Scholar 

  • Sailer A, Head MR, Kochut A, Shaikh H (2010) Graph-based cloud service placement. In: Services computing (SCC), 2010 IEEE international conference on. IEEE, pp 89–96

  • Selimi M, Cerdà-Alabern L, Freitag F, Veiga L, Sathiaseelan A, Crowcroft J (2018) A lightweight service placement approach for community network micro-clouds. J Grid Comput:1–21

  • Souza VB, Masip-Bruin X, Marín-Tordera E, Sànchez-López S, Garcia J, Ren G-J, Jukan A, Ferrer AJ (2018) Towards a proper service placement in combined fog-to-cloud (f2c) architectures. Futur Gener Comput Syst 87:1–15

    Article  Google Scholar 

  • Tantawi AN (2015) Quantitative placement of services in hierarchical clouds. In: International conference on quantitative evaluation of systems. Springer, pp 195–210

  • Tortonesi M, Foschini L (2016) Business-driven service placement for highly dynamic and distributed cloud systems. IEEE Trans Cloud Comput 6(4):977–990

    Article  Google Scholar 

  • Tran K-T, Agoulmine N (2011) Adaptive and cost-effective service placement. In: 2011 IEEE global telecommunications conference-GLOBECOM 2011. IEEE, pp 1–6

  • Tsipis A, Komianos V, Oikonomou K, Stavrakakis I (2020) Elastic distributed rendering service placement in capacitated cloud/fog gaming systems. In: 2020 11th international conference on information, intelligence, systems and applications IISA. IEEE, pp 1–8

  • Unuvar M, Tosi S, Doganata YN, Steinder MG, Tantawi AN (2015) Selecting optimum cloud availability zones by learning user satisfaction levels. IEEE Trans Serv Comput 8(2):199–211

    Article  Google Scholar 

  • Velampalli S, Jonnalagedda VRM (2018) Frequent subgraph mining algorithms: framework, classification, analysis, comparisons. In: Data engineering and intelligent computing. Springer, pp 327–336

  • Wang S, Urgaonkar R, He T, Chan K, Zafer M, Leung KK (2017) Dynamic service placement for mobile micro-clouds with predicted future costs. IEEE Trans Parallel Distrib Syst 28(4):1002–1016

    Article  Google Scholar 

  • Wang Y, Zhao C, Yang S, Ren X, Wang L, Zhao P, Yang X (2020) Mpcsm: microservice placement for edge-cloud collaborative smart manufacturing. IEEE Trans Ind Inf 17(9):5898–5908

    Article  Google Scholar 

  • Wen Z, Cala J, Watson P, Romanovsky A (2016) Cost effective, reliable and secure workflow deployment over federated clouds. IEEE Trans Serv Comput

  • Wen Z, Cała J, Watson P, Romanovsky A (2017) Cost effective, reliable and secure workflow deployment over federated clouds. IEEE Trans Serv Comput 10(6):929–941

    Article  Google Scholar 

  • Werbos PJ et al (1990) Backpropagation through time: what it does and how to do it. Proc IEEE 78(10):1550–1560

    Article  Google Scholar 

  • Williams RJ, Peng J (1990) An efficient gradient-based algorithm for on-line training of recurrent network trajectories. Neural Comput 2(4):490–501

    Article  Google Scholar 

  • Yan X, Han J (2002) gspan: Graph-based substructure pattern mining. In: 2002 IEEE international conference on data mining, 2002. Proceedings. IEEE, pp 721–724

  • Yuan X, Sun M, Lou W (2020) A dynamic deep-learning-based virtual edge node placement scheme for edge cloud systems in mobile environment. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2020.2974948

    Article  Google Scholar 

  • Yusoh ZIM, Tang M (2010) A penalty-based genetic algorithm for the composite saas placement problem in the cloud. In: IEEE congress on evolutionary computation. IEEE, pp 1–8

  • Zhang Q, Zhu Q, Zhani MF, Boutaba R, Hellerstein JL (2013) Dynamic service placement in geographically distributed clouds. IEEE J Sel Areas Commun 31(12):762–772

    Article  Google Scholar 

  • Zhang Q, Yang LT, Yan Z, Chen Z, Li P (2018) An efficient deep learning model to predict cloud workload for industry informatics. IEEE Trans Ind Inf 14(7):3170–3178

    Article  Google Scholar 

  • Zong B, Raghavendra R, Srivatsa M, Yan X, Singh AK, Lee K-W (2014) Cloud service placement via subgraph matching. In: 2014 IEEE 30th international conference on data engineering. IEEE, pp 832–843

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  1. These authors contributed equally to this work.

Authors and Affiliations

  1. SMART Lab, Jendouba University, Jendouba, Tunisia

    Haithem Mezni

  2. CIS Department, Taibah University, Al-Madinah, Saudi Arabia

    Haithem Mezni

  3. Department Computer Science, University of Tunis El Manar, Tunis, Tunisia

    Fatimetou Sidi Hamoud & Faouzi Ben Charrada

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  1. Haithem Mezni
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Mezni, H., Hamoud, F.S. & Charrada, F.B. Predictive service placement in cloud using deep learning and frequent subgraph mining. J Ambient Intell Human Comput 14, 11497–11516 (2023). https://doi.org/10.1007/s12652-022-03720-4

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