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Performance analysis of heterogeneous cloud-edge services: A modeling approach

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Abstract

With the growing demand of data processing/storage from Internet of Things (IoT) users and the increasing maturity of Cloud-Edge technologies, it becomes more and more critical to develop an effective and efficient performance evaluation approach in order to enhance the performance of Cloud-Edge services. Analytic modeling is an effective evaluation approach. The existing modeling researches on edge and/or cloud computing either ignored workload heterogeneity or ignored delay constraint of IoT tasks. This paper develops a hierarchical model for capturing the behaviors of Cloud-Edge datacenters, which provides service to tasks with different service priorities and requesting different number of service resources. Formulas for calculating performance measures of interest are also developed. The approximate accuracy of the proposed analytic model is verified through comparing numerical results and discrete-event simulation results.

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References

  1. Chen W, Dong W, Li K (2018) Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Trans Serv Comput 12:726–738. https://doi.org/10.1109/TSC.2018.2826544

    Article  Google Scholar 

  2. Abbas N, Zhang Y, Taherkordi A (2018) Tor Skeie: Mobile edge computing: a survey. IEEE Internet Things J 5(1):450–465

    Article  Google Scholar 

  3. Mouradian C, Naboulsi D, Yangui S, Glitho RH, Morrow MJ, Paul A (2018) Polakos: a comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun Surv Tutor 20(1):416–464

    Article  Google Scholar 

  4. Morabito R, Cozzolino V, Ding AY, Beijar N, Ott J (2018) Consolidate IoT edge computing with lightweight virtualization. IEEE Netw 32(1):102–111

    Article  Google Scholar 

  5. Ghosh R, Longo F, Naik VK, Trivedi KS (2013) Modeling and performance analysis of large scale IaaS Clouds. Future Gen Comp Syst 29(5):1216–1234

    Article  Google Scholar 

  6. Chang X, Xia R, Muppala J, Trivedi K, Liu J (2018) Effective modeling approach for IaaS data center performance analysis under heterogeneous workload. IEEE Transactions on Cloud Computing: 1–1

  7. Wang B, Chang X, Liu J (2015) Modeling heterogeneous virtual machines on IaaS data centers. IEEE Commun Lett 19(4):537–540

    Article  Google Scholar 

  8. Raei H, Yazdani N, Shojaee R (2017) Modeling and performance analysis of cloudlet in Mobile cloud computing. Perform Eval 107:34–53

    Article  Google Scholar 

  9. Raei H, Yazdani N (2017) Analytical performance models for resource allocation schemes of cloudlet in mobile cloud computing. J Supercomput 73(3):1274–1305

    Article  Google Scholar 

  10. Bilal K, Khalid O, Erbad A, Khan SU (2018) Potentials, trends, and prospects in edge technologies: fog, cloudlet, mobile edge, and micro data centers. Comput Netw 130:94–120

    Article  Google Scholar 

  11. Yu W, Liang F, He X, Hatcher WG, Lu C, Lin J, Yang X (2018) A survey on the edge computing for the internet of things. IEEE Access 6:6900–6919

    Article  Google Scholar 

  12. Routaib H, Elmachkour M, Sabir E, Badidi E, EIKoutbi M (2014) Modeling and evaluating a cloudlet-based architecture for Mobile Cloud Computing. International Conference on Intelligent Systems: Theories and Applications IEEE :1–7

  13. Brandwajn A, Begin T (2017) Multi-server preemptive priority queue with general arrivals and service times. Perform Eval 115(150–164):150–164

    Article  Google Scholar 

  14. Liu B, Chang X, Liu B, Chen Z (2017) Performance Analysis Model for Fog Services under Multiple Resource Types. International Conference on Dependable Systems and Their Applications:110–117

  15. El Kafhali S, Salah K (2017) Efficient and dynamic scaling of fog nodes for IoT devices. J Supercomput 73(12):5261–5284

    Article  Google Scholar 

  16. Silva FA, Kosta S, Rodrigues M, Oliveira D, Maciel T, Mei A, Maciel PRM (2018) Mobile Cloud Performance Evaluation Using Stochastic Models. IEEE Trans Mob Comput 17(5):1134–1147

    Article  Google Scholar 

  17. Guo S, Wu D, Zhang H, Yuan D (2018) Resource modeling and scheduling for Mobile edge computing: a service Provider's perspective. IEEE Access 6:35611–35623

    Article  Google Scholar 

  18. Gupta H, Dastjerdi AV, Ghosh SK, Buyya R (2017) iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments. SoftwPract Exper 47(9):1275–1296

    Google Scholar 

  19. Nguyen T-D, Huh E-N (2018) ECSim++: an INET-based simulation tool for modeling and control in edge cloud computing. EDGE:80–86

  20. Svorobej S, Endo PT, Bendechache M, Filelis-Papadopoulos C, Giannoutakis KM, Gravvanis GA, Tzovaras D, Byrne J, Lynn T (2019) Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges. Future Internet 11(3):55. https://doi.org/10.3390/fi11030055

    Article  Google Scholar 

  21. Rimal BP, Maier M, Satyana-rayanan M (2018) Experimental Testbed for Edge Computing in Fiber-Wireless Broadband Access Networks. IEEE Commun Mag 56(8):160–167

    Article  Google Scholar 

  22. Neto JLD, Yu S-Y, Macedo DF, Nogueira JMS, Langar R, Secci S (2018) ULOOF: a User Level Online Offloading Framework for Mobile Edge Computing. IEEE Transact Mob Comput: PP(99):1–1

  23. Santos GL, Endo PT, da Silva Lisboa Tigre MFF, Ferreira D, Sadok D, Kelner J, Lynn T (2018) Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructures. J Cloud Comput 7:16

    Article  Google Scholar 

  24. Pereira J, Ricardo L, Luís M, Senna CR, Sargento S (2019) Assessing the reliability of fog computing for smart mobility applications in VANETs. Future Gen Comp Syst 94:317–332

    Article  Google Scholar 

  25. Tseng C-W, Tseng F-H, Yang Y-T, Liu C-C, Chou L-D (2018) Task Scheduling for Edge Computing with Agile VNFs On-Demand Service Model toward 5G and Beyond. Wireless Commun Mob Comput 7802797:1–7802797:13

  26. Yang Y, Chang X, Han Z (2018) Delay-Aware Secure Computation Offloading Mechanism in a Fog-Cloud Framework. 16th IEEE Intl Conf on Parallel and Distributed Processing with Applications: 346–353

  27. Nayyer MZ, Raza I, Hussain SA (2019) A Survey of Cloudlet-Based Mobile Augmentation Approaches for Resource Optimization. ACM Comput Surv 51(5): 107:1–107:28

  28. Di S, Kondo D, Cappello F (2014) Characterizing and modeling cloud applications/jobs on a Google data center. J Supercomput 69(1):139–160

    Article  Google Scholar 

  29. Cheng Y, Anwar A, Duan X (2018) Analyzing Alibaba's co-located datacenter workloads. BigData:292–297

  30. Maplesoft, Inc., Maple 18, http://www.maplesoft.com/products/maple

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Authors and Affiliations

  1. Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China

    Lili Jiang, Xiaolin Chang & Runkai Yang

  2. Department of Computer Science, Ryerson University, Toronto, Canada

    Jelena Mišić & Vojislav B. Mišić

Authors
  1. Lili Jiang
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  2. Xiaolin Chang
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  3. Jelena Mišić
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  4. Vojislav B. Mišić
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  5. Runkai Yang
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Correspondence to Lili Jiang.

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Jiang, L., Chang, X., Mišić, J. et al. Performance analysis of heterogeneous cloud-edge services: A modeling approach. Peer-to-Peer Netw. Appl. 14, 151–163 (2021). https://doi.org/10.1007/s12083-020-00968-5

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  • DOI: https://doi.org/10.1007/s12083-020-00968-5

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