iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s10922-019-09504-0
Resource Management in a Containerized Cloud: Status and Challenges | Journal of Network and Systems Management Skip to main content
Springer Nature Link
Log in

Resource Management in a Containerized Cloud: Status and Challenges

  • Published:
Journal of Network and Systems Management Aims and scope Submit manuscript

Abstract

Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Swift—openstack. https://wiki.openstack.org/wiki/Swift. Accessed 9 Sept 2019

  2. Openstack—build the future of open infrastructure. http://openstack.org. Accessed 9 Sept 2019

  3. Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R.H., Morrow, M.J., Polakos, P.A.: A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun. Surv. Tutor. 20(1), 416–464 (2018). https://doi.org/10.1109/COMST.2017.2771153

    Article  Google Scholar 

  4. Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, Mobidata ’15, pp. 37–42. ACM, New York (2015). https://doi.org/10.1145/2757384.2757397

  5. Pahl, C., Brogi, A., Soldani, J., Jamshidi, P.: Cloud container technologies: a state-of-the-art review. In: IEEE Transactions on Cloud Computing, pp. 1–1 (2018). https://doi.org/10.1109/TCC.2017.2702586

  6. Rodriguez, M.A., Buyya, R.: Container-based cluster orchestration systems: a taxonomy and future directions. Softw. Pract. Exp. (2018). https://doi.org/10.1002/spe.2660

    Article  Google Scholar 

  7. Bittencourt, L.F., Goldman, A., Madeira, E.R., da Fonseca, N.L., Sakellariou, R.: Scheduling in distributed systems: a cloud computing perspective. Comput. Sci. Rev. 30, 31–54 (2018). https://doi.org/10.1016/j.cosrev.2018.08.002

    Article  Google Scholar 

  8. Herrera, J.G., Botero, J.F.: Resource allocation in NFV: a comprehensive survey. IEEE Trans. Netw. Serv. Manag. 13(3), 518–532 (2016). https://doi.org/10.1109/TNSM.2016.2598420

    Article  Google Scholar 

  9. Jennings, B., Stadler, R.: Resource management in clouds: survey and research challenges. IEEE Trans. Netw. Serv. Manag. 23(3), 567–619 (2015). https://doi.org/10.1007/s10922-014-9307-7

    Article  Google Scholar 

  10. Kumar, D., Baranwal, G., Raza, Z., Vidyarthi, D.P.: A survey on spot pricing in cloud computing. J. Netw. Syst. Manag. 26(4), 809–856 (2018). https://doi.org/10.1007/s10922-017-9444-x

    Article  Google Scholar 

  11. Mann, Z.A.: Allocation of virtual machines in cloud data centers—a survey of problem models and optimization algorithms. ACM Comput. Surv. 48(1), 11:1–11:34 (2015). https://doi.org/10.1145/2797211

    Article  Google Scholar 

  12. Masdari, M., Salehi, F., Jalali, M., Bidaki, M.: A survey of PSO-based scheduling algorithms in cloud computing. J. Netw. Syst. Manag. 25(1), 122–158 (2017). https://doi.org/10.1007/s10922-016-9385-9

    Article  Google Scholar 

  13. Poullie, P., Bocek, T., Stiller, B.: A survey of the state-of-the-art in fair multi-resource allocations for data centers. IEEE Trans. Netw. Serv. Manag. 15(1), 169–183 (2018). https://doi.org/10.1109/TNSM.2017.2743066

    Article  Google Scholar 

  14. Yousafzai, A., Gani, A., Noor, R.M., Sookhak, M., Talebian, H., Shiraz, M., Khan, M.K.: Cloud resource allocation schemes: review, taxonomy, and opportunities. Knowl. Inf. Syst. 50(2), 347–381 (2017). https://doi.org/10.1007/s10115-016-0951-y

    Article  Google Scholar 

  15. Zhan, Z.H., Liu, X.F., Gong, Y.J., Zhang, J., Chung, H.S.H., Li, Y.: Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput. Surv. 47(4), 63:1–63:33 (2015). https://doi.org/10.1145/2788397

    Article  Google Scholar 

  16. Mell, P., Grance, T.: Sp 800-145. The NIST definition of cloud computing. Technical report (2011)

  17. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010). https://doi.org/10.1145/1721654.1721672

    Article  Google Scholar 

  18. Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., Barcellos, M., Felber, P., Riviere, E.: Edge-centric computing: vision and challenges. SIGCOMM Comput. Commun. Rev. 45(5), 37–42 (2015). https://doi.org/10.1145/2831347.2831354

    Article  Google Scholar 

  19. Hong, H.: From cloud computing to FOG computing: unleash the power of edge and end devices. In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 331–334 (2017). https://doi.org/10.1109/CloudCom.2017.53

  20. Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, MCC ’12, pp. 13–16. ACM, New York (2012). https://doi.org/10.1145/2342509.2342513

  21. Iorga, M., Feldman, L.B., Barton, R., Martin, M., Goren, N.S., Mahmoudi, C.: Sp 500-325. Fog computing conceptual model. Technical report (2018)

  22. Dolui, K., Datta, S.K.: Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing. In: 2017 Global Internet of Things Summit (GIoTS), pp. 1–6 (2017). https://doi.org/10.1109/GIOTS.2017.8016213

  23. Haouari, F., Faraj, R., AlJa’am, J.M.: Fog computing potentials, applications, and challenges. In: 2018 International Conference on Computer and Applications (ICCA), pp. 399–406 (2018). https://doi.org/10.1109/COMAPP.2018.8460182

  24. Santos, J., Wauters, T., Volckaert, B., De Turck, F.: Fog computing: enabling the management and orchestration of smart city applications in 5G networks. Entropy (2018). https://doi.org/10.3390/e20010004

    Article  Google Scholar 

  25. Sarkar, S., Chatterjee, S., Misra, S.: Assessment of the suitability of fog computing in the context of internet of things. IEEE Trans. Cloud Comput. 6(1), 46–59 (2018). https://doi.org/10.1109/TCC.2015.2485206

    Article  Google Scholar 

  26. Yao, J., Ansari, N.: Qos-aware fog resource provisioning and mobile device power control in IOT networks. IEEE Trans. Netw. Serv. Manag. 16(1), 1 (2018). https://doi.org/10.1109/TNSM.2018.2888481

    Article  Google Scholar 

  27. Adufu, T., Choi, J., Kim, Y.: Is container-based technology a winner for high performance scientific applications? In: 2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 507–510 (2015). https://doi.org/10.1109/APNOMS.2015.7275379

  28. Eberbach, E., Reuter, A.: Toward El Dorado for cloud computing: lightweight VMs, containers, meta-containers and oracles. In: Proceedings of the 2015 European Conference on Software Architecture Workshops, ECSAW ’15, pp. 13:1–13:7. ACM, New York (2015). https://doi.org/10.1145/2797433.2797446

  29. Sharma, P., Chaufournier, L., Shenoy, P., Tay, Y.C.: Containers and virtual machines at scale: A comparative study. In: Proceedings of the 17th International Middleware Conference, Middleware ’16, pp. 1:1–1:13. ACM, New York (2016). https://doi.org/10.1145/2988336.2988337

  30. Tesfatsion, S.K., Klein, C., Tordsson, J.: Virtualization techniques compared: performance, resource, and power usage overheads in clouds. In: Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering, ICPE ’18, pp. 145–156. ACM, New York (2018). https://doi.org/10.1145/3184407.3184414

  31. Linux containers. https://linuxcontainers.org. Accessed 9 Sept 2019

  32. Docker—enterprise container platform. https://www.docker.com. Accessed 9 Sept 2019

  33. Docker–docker hub. https://www.docker.com/products/docker-hub. Accessed 9 Sept 2019

  34. Kubernetes—production-grade container orchestration. https://kubernetes.io. Accessed 9 Sept 2019

  35. Docker—swarm mode overview. https://docs.docker.com/engine/swarm/. Accessed 9 Sept 2019

  36. Docker blog—extending docker enterprise edition to support kubernetes. https://blog.docker.com/2017/10/docker-enterprise-edition-kubernetes/. Accessed 9 Sept 2019

  37. Reniers, V.: The prospects for multi-cloud deployment of SaaS applications with container orchestration platforms. In: Proceedings of the Doctoral Symposium of the 17th International Middleware Conference, Middleware Doctoral Symposium’16, pp. 5:1–5:2. ACM, New York (2016). https://doi.org/10.1145/3009925.3009930

  38. Zhang, F., Liu, G., Fu, X., Yahyapour, R.: A survey on virtual machine migration: challenges, techniques, and open issues. IEEE Commun. Surv. Tutor. 20(2), 1206–1243 (2018). https://doi.org/10.1109/COMST.2018.2794881

    Article  Google Scholar 

  39. Stoyanov, R., Kollingbaum, M.J.: Efficient live migration of Linux containers. In: Yokota, R., Weiland, M., Shalf, J., Alam, S. (eds.) High Performance Computing, pp. 184–193. Springer International Publishing, Cham (2018)

    Chapter  Google Scholar 

  40. CRIU—checkpoint/restore in userspace. https://criu.org. Accessed 9 Sept 2019

  41. Govindaraj, K., Artemenko, A.: Container live migration for latency critical industrial applications on edge computing. In: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), vol. 1, pp. 83–90. (2018). https://doi.org/10.1109/ETFA.2018.8502659

  42. Mattetti, M., Shulman-Peleg, A., Allouche, Y., Corradi, A., Dolev, S., Foschini, L.: Securing the infrastructure and the workloads of Linux containers. In: 2015 IEEE Conference on Communications and Network Security (CNS), pp. 559–567 (2015). https://doi.org/10.1109/CNS.2015.7346869

  43. Young, E.G., Zhu, P., Caraza-Harter, T., Arpaci-Dusseau, A.C., Arpaci-Dusseau, R.H.: The true cost of containing: a gvisor case study. In: 11th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 19). USENIX Association, Renton (2019). https://www.usenix.org/conference/hotcloud19/presentation/young

  44. Bui, T.: Analysis of Docker Security. arXiv e-prints (2015)

  45. Prakash, C., Prashanth, P., Bellur, U., Kulkarni, P.: Deterministic container resource management in derivative clouds. In: 2018 IEEE International Conference on Cloud Engineering (IC2E), pp. 79–89 (2018). https://doi.org/10.1109/IC2E.2018.00030

  46. Wolke, A., Bichler, M., Setzer, T.: Planning vs. dynamic control: resource allocation in corporate clouds. IEEE Trans. Cloud Comput. 4(3), 322–335 (2016). https://doi.org/10.1109/TCC.2014.2360399

    Article  Google Scholar 

  47. Chi, Y., Li, X., Wang, X., Leung, V.C.M., Shami, A.: A fairness-aware pricing methodology for revenue enhancement in service cloud infrastructure. IEEE Syst. J. 11(2), 1006–1017 (2017). https://doi.org/10.1109/JSYST.2015.2448719

    Article  Google Scholar 

  48. Mashayekhy, L., Nejad, M.M., Grosu, D.: Physical machine resource management in clouds: a mechanism design approach. IEEE Trans. Cloud Comput. 3(3), 247–260 (2015). https://doi.org/10.1109/TCC.2014.2369419

    Article  Google Scholar 

  49. Mashayekhy, L., Nejad, M.M., Grosu, D., Vasilakos, A.V.: An online mechanism for resource allocation and pricing in clouds. IEEE Trans. Comput. 65(4), 1172–1184 (2016). https://doi.org/10.1109/TC.2015.2444843

    Article  MathSciNet  MATH  Google Scholar 

  50. Mikavica, B., Kostić-Ljubisavljević, A.: Pricing and bidding strategies for cloud spot block instances. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0384–0389 (2018). https://doi.org/10.23919/MIPRO.2018.8400073

  51. Weinman, J.: Cloud pricing and markets. IEEE Cloud Comput. 2(1), 10–13 (2015). https://doi.org/10.1109/MCC.2015.3

    Article  Google Scholar 

  52. ACM Transactions on Internet Technology (TOIT). https://dl.acm.org/citation.cfm?id=J780. Accessed 9 Sept 2019

  53. IEEE transactions on cloud computing (tcc). https://www.computer.org/csdl/journal/cc. Accessed 9 Sept 2019

  54. IEEE transactions on parallel and distributed systems (TPDS). https://www.computer.org/csdl/journal/td. Accessed 9 Sept 2019

  55. IEEE transactions on network and service management (TNSM). https://www.comsoc.org/publications/journals/ieee-tnsm. Accessed 9 Sept 2019

  56. Springer journal of network and systems management (jnsm). https://www.springer.com/computer/communication+networks/journal/10922. Accessed 9 Sept 2019

  57. Wiley journal of software: practice and experience (spe). https://onlinelibrary.wiley.com/journal/1097024x. Accessed 9 Sept 2019

  58. Aazam, M., Huh, E.: Fog computing micro datacenter based dynamic resource estimation and pricing model for iot. In: 2015 IEEE 29th International Conference on Advanced Information Networking and Applications, pp. 687–694 (2015). https://doi.org/10.1109/AINA.2015.254

  59. Abdelbaky, M., Diaz-Montes, J., Parashar, M., Unuvar, M., Steinder, M.: Docker containers across multiple clouds and data centers. In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), pp. 368–371 (2015)

  60. Amannejad, Y., Krishnamurthy, D., Far, B.: Managing performance interference in cloud-based web services. IEEE Trans. Netw. Serv. Manag. 12(3), 320–333 (2015). https://doi.org/10.1109/TNSM.2015.2456172

    Article  Google Scholar 

  61. Chiang, Y., Ouyang, Y., Hsu, C.: An efficient green control algorithm in cloud computing for cost optimization. IEEE Trans. Cloud Comput. 3(2), 145–155 (2015). https://doi.org/10.1109/TCC.2014.2350492

    Article  Google Scholar 

  62. Dabbagh, M., Hamdaoui, B., Guizani, M., Rayes, A.: Energy-efficient resource allocation and provisioning framework for cloud data centers. IEEE Trans. Netw. Serv. Manag. 12(3), 377–391 (2015). https://doi.org/10.1109/TNSM.2015.2436408

    Article  Google Scholar 

  63. Dhakate, S., Godbole, A.: Distributed cloud monitoring using docker as next generation container virtualization technology. In: 2015 Annual IEEE India Conference (INDICON), pp. 1–5 (2015). https://doi.org/10.1109/INDICON.2015.7443771

  64. Huang, X., Yu, R., Kang, J., Ding, J., Maharjan, S., Gjessing, S., Zhang, Y.: Dynamic resource pricing and scalable cooperation for mobile cloud computing. In: 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), pp. 786–792 (2015). https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.155

  65. Jin, H., Wang, X., Wu, S., Di, S., Shi, X.: Towards optimized fine-grained pricing of IaaS cloud platform. IEEE Trans. Cloud Comput. 3(4), 436–448 (2015). https://doi.org/10.1109/TCC.2014.2344680

    Article  Google Scholar 

  66. Katsalis, K., Paschos, G.S., Viniotis, Y., Tassiulas, L.: Cpu provisioning algorithms for service differentiation in cloud-based environments. IEEE Trans. Netw. Serv. Manag. 12(1), 61–74 (2015). https://doi.org/10.1109/TNSM.2015.2397345

    Article  Google Scholar 

  67. Kumbhare, A.G., Simmhan, Y., Frincu, M., Prasanna, V.K.: Reactive resource provisioning heuristics for dynamic dataflows on cloud infrastructure. IEEE Trans. Cloud Comput. 3(2), 105–118 (2015). https://doi.org/10.1109/TCC.2015.2394316

    Article  Google Scholar 

  68. Lee, Y.C., Kim, Y., Han, H., Kang, S.: Fine-grained, adaptive resource sharing for real pay-per-use pricing in clouds. In: 2015 International Conference on Cloud and Autonomic Computing, pp. 236–243 (2015). https://doi.org/10.1109/ICCAC.2015.36

  69. Li, W., Kanso, A.: Comparing containers versus virtual machines for achieving high availability. In: 2015 IEEE International Conference on Cloud Engineering, pp. 353–358 (2015). https://doi.org/10.1109/IC2E.2015.79

  70. Liu, J., Zhang, Y., Zhou, Y., Zhang, D., Liu, H.: Aggressive resource provisioning for ensuring qos in virtualized environments. IEEE Trans. Cloud Comput. 3(2), 119–131 (2015). https://doi.org/10.1109/TCC.2014.2353045

    Article  Google Scholar 

  71. Moens, H., Dhoedt, B., Turck, F.D.: Allocating resources for customizable multi-tenant applications in clouds using dynamic feature placement. Future Gener. Comput. Syst. 53, 63–76 (2015). https://doi.org/10.1016/j.future.2015.05.017

    Article  Google Scholar 

  72. Mukherjee, J., Krishnamurthy, D., Rolia, J.: Resource contention detection in virtualized environments. IEEE Trans. Netw. Serv. Manag. 12(2), 217–231 (2015). https://doi.org/10.1109/TNSM.2015.2407273

    Article  Google Scholar 

  73. Petri, I., Diaz-Montes, J., Zou, M., Beach, T., Rana, O., Parashar, M.: Market models for federated clouds. IEEE Trans. Cloud Comput. 3(3), 398–410 (2015). https://doi.org/10.1109/TCC.2015.2415792

    Article  Google Scholar 

  74. Sharma, B., Thulasiram, R.K., Thulasiraman, P., Buyya, R.: Clabacus: a risk-adjusted cloud resources pricing model using financial option theory. IEEE Trans. Cloud Comput. 3(3), 332–344 (2015). https://doi.org/10.1109/TCC.2014.2382099

    Article  Google Scholar 

  75. Stankovski, V., Taherizadeh, S., Taylor, I., Jones, A., Mastroianni, C., Becker, B., Suhartanto, H.: Towards an environment supporting resilience, high-availability, reproducibility and reliability for cloud applications. In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), pp. 383–386 (2015). https://doi.org/10.1109/UCC.2015.61

  76. Wang, X., Wang, X., Che, H., Li, K., Huang, M., Gao, C.: An intelligent economic approach for dynamic resource allocation in cloud services. IEEE Trans. Cloud Comput. 3(3), 275–289 (2015). https://doi.org/10.1109/TCC.2015.2415776

    Article  Google Scholar 

  77. Wuhib, F., Yanggratoke, R., Stadler, R.: Allocating compute and network resources under management objectives in large-scale clouds. J. Netw. Syst. Manag. 23(1), 111–136 (2015). https://doi.org/10.1007/s10922-013-9280-6

    Article  Google Scholar 

  78. Zhang, Q., Li, S., Li, Z., Xing, Y., Yang, Z., Dai, Y.: Charm: a cost-efficient multi-cloud data hosting scheme with high availability. IEEE Trans. Cloud Comput. 3(3), 372–386 (2015). https://doi.org/10.1109/TCC.2015.2417534

    Article  Google Scholar 

  79. Aazam, M., Huh, E., St-Hilaire, M., Lung, C., Lambadaris, I.: Cloud customer’s historical record based resource pricing. IEEE Trans. Parallel Distrib. Syst. 27(7), 1929–1940 (2016). https://doi.org/10.1109/TPDS.2015.2473850

    Article  Google Scholar 

  80. Ayoubi, S., Zhang, Y., Assi, C.: A reliable embedding framework for elastic virtualized services in the cloud. IEEE Trans. Netw. Serv. Manag. 13(3), 489–503 (2016). https://doi.org/10.1109/TNSM.2016.2581484

    Article  Google Scholar 

  81. Choi, S., Myung, R., Choi, H., Chung, K., Gil, J., Yu, H.: GPSF: General-purpose scheduling framework for container based on cloud environment. In: 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 769–772 (2016). https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.162

  82. Da Cunha Rodrigues, G., Calheiros, R.N., Guimaraes, V.T., Santos, G.L.d., de Carvalho, M.B., Granville, L.Z., Tarouco, L.M.R., Buyya, R.: Monitoring of cloud computing environments: concepts, solutions, trends, and future directions. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, SAC ’16, pp. 378–383. ACM, New York (2016). https://doi.org/10.1145/2851613.2851619

  83. Dai, X., Wang, J.M., Bensaou, B.: Energy-efficient virtual machines scheduling in multi-tenant data centers. IEEE Trans. Cloud Comput. 4(2), 210–221 (2016). https://doi.org/10.1109/TCC.2015.2481401

    Article  Google Scholar 

  84. Elgazzar, K., Martin, P., Hassanein, H.S.: Cloud-assisted computation offloading to support mobile services. IEEE Trans. Cloud Comput. 4(3), 279–292 (2016). https://doi.org/10.1109/TCC.2014.2350471

    Article  Google Scholar 

  85. Espling, D., Larsson, L., Li, W., Tordsson, J., Elmroth, E.: Modeling and placement of cloud services with internal structure. IEEE Trans. Cloud Comput. 4(4), 429–439 (2016). https://doi.org/10.1109/TCC.2014.2362120

    Article  Google Scholar 

  86. Goudarzi, H., Pedram, M.: Hierarchical sla-driven resource management for peak power-aware and energy-efficient operation of a cloud datacenter. IEEE Trans. Cloud Comput. 4(2), 222–236 (2016). https://doi.org/10.1109/TCC.2015.2474369

    Article  Google Scholar 

  87. Huang, Z., Tsang, D.H.K.: M-convex VM consolidation: towards a better VM workload consolidation. IEEE Trans. Cloud Comput. 4(4), 415–428 (2016). https://doi.org/10.1109/TCC.2014.2369423

    Article  MathSciNet  Google Scholar 

  88. Kang, D., Choi, G., Kim, S., Hwang, I., Youn, C.: Workload-aware resource management for energy efficient heterogeneous docker containers. In: 2016 IEEE Region 10 Conference (TENCON), pp. 2428–2431 (2016). https://doi.org/10.1109/TENCON.2016.7848467

  89. Khatua, S., Sur, P.K., Das, R.K., Mukherjee, N.: Heuristic-based resource reservation strategies for public cloud. IEEE Trans. Cloud Comput. 4(4), 392–401 (2016). https://doi.org/10.1109/TCC.2014.2369434

    Article  Google Scholar 

  90. Mishra, M., Bellur, U.: Whither tightness of packing? The case for stable VM placement. IEEE Trans. Cloud Comput. 4(4), 481–494 (2016). https://doi.org/10.1109/TCC.2014.2378756

    Article  Google Scholar 

  91. Nakagawa, G., Oikawa, S.: Behavior-based memory resource management for container-based virtualization. In: 2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science Engineering (ACIT-CSII-BCD), pp. 213–217 (2016). https://doi.org/10.1109/ACIT-CSII-BCD.2016.049

  92. Pantazoglou, M., Tzortzakis, G., Delis, A.: Decentralized and energy-efficient workload management in enterprise clouds. IEEE Trans. Cloud Comput. 4(2), 196–209 (2016). https://doi.org/10.1109/TCC.2015.2464817

    Article  Google Scholar 

  93. d R Righi, R., Rodrigues, V.F., da Costa, C.A., Galante, G., de Bona, L.C.E., Ferreto, T.: Autoelastic: automatic resource elasticity for high performance applications in the cloud. IEEE Trans. Cloud Comput. 4(1), 6–19 (2016). https://doi.org/10.1109/TCC.2015.2424876

    Article  Google Scholar 

  94. Salah, K., Elbadawi, K., Boutaba, R.: An analytical model for estimating cloud resources of elastic services. J. Netw. Syst. Manag. 24(2), 285–308 (2016). https://doi.org/10.1007/s10922-015-9352-x

    Article  Google Scholar 

  95. Wajid, U., Cappiello, C., Plebani, P., Pernici, B., Mehandjiev, N., Vitali, M., Gienger, M., Kavoussanakis, K., Margery, D., Perez, D.G., Sampaio, P.: On achieving energy efficiency and reducing \({\rm CO}_2\) footprint in cloud computing. IEEE Trans. Cloud Comput. 4(2), 138–151 (2016). https://doi.org/10.1109/TCC.2015.2453988

    Article  Google Scholar 

  96. Wan, J., Zhang, R., Gui, X., Xu, B.: Reactive pricing: an adaptive pricing policy for cloud providers to maximize profit. IEEE Trans. Netw. Serv. Manag. 13(4), 941–953 (2016). https://doi.org/10.1109/TNSM.2016.2618394

    Article  Google Scholar 

  97. Wanis, B., Samaan, N., Karmouch, A.: Efficient modeling and demand allocation for differentiated cloud virtual-network as-a service offerings. IEEE Trans. Cloud Comput. 4(4), 376–391 (2016). https://doi.org/10.1109/TCC.2015.2389814

    Article  Google Scholar 

  98. Wu, H., Ren, S., Garzoglio, G., Timm, S., Bernabeu, G., Chadwick, K., Noh, S.: A reference model for virtual machine launching overhead. IEEE Trans. Cloud Comput. 4(3), 250–264 (2016). https://doi.org/10.1109/TCC.2014.2369439

    Article  Google Scholar 

  99. Xu, X., Dou, W., Zhang, X., Chen, J.: Enreal: An energy-aware resource allocation method for scientific workflow executions in cloud environment. IEEE Trans. Cloud Comput. 4(2), 166–179 (2016). https://doi.org/10.1109/TCC.2015.2453966

    Article  Google Scholar 

  100. Zhou, A., Wang, S., Zheng, Z., Hsu, C., Lyu, M.R., Yang, F.: On cloud service reliability enhancement with optimal resource usage. IEEE Trans. Cloud Comput. 4(4), 452–466 (2016). https://doi.org/10.1109/TCC.2014.2369421

    Article  Google Scholar 

  101. Awada, U., Barker, A.: Improving resource efficiency of container-instance clusters on clouds. In: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 929–934 (2017). https://doi.org/10.1109/CCGRID.2017.113

  102. Awada, U., Barker, A.: Resource efficiency in container-instance clusters. In: Proceedings of the Second International Conference on Internet of Things, Data and Cloud Computing, ICC ’17, pp. 181:1–181:5. ACM, New York (2017). https://doi.org/10.1145/3018896.3056798

  103. Babaioff, M., Mansour, Y., Nisan, N., Noti, G., Curino, C., Ganapathy, N., Menache, I., Reingold, O., Tennenholtz, M., Timnat, E.: Era: A framework for economic resource allocation for the cloud. In: Proceedings of the 26th International Conference on World Wide Web Companion, WWW ’17 Companion, pp. 635–642. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2017). https://doi.org/10.1145/3041021.3054186

  104. Chard, R., Chard, K., Wolski, R., Madduri, R., Ng, B., Bubendorfer, K., Foster, I.: Cost-aware cloud profiling, prediction, and provisioning as a service. IEEE Cloud Comput. 4(4), 48–59 (2017). https://doi.org/10.1109/MCC.2017.3791025

    Article  Google Scholar 

  105. Dalmazo, B.L., Vilela, J.P., Curado, M.: Performance analysis of network traffic predictors in the cloud. J. Netw. Syst. Manag. 25(2), 290–320 (2017). https://doi.org/10.1007/s10922-016-9392-x

    Article  Google Scholar 

  106. Hai, T.H., Nguyen, P.: A pricing model for sharing cloudlets in mobile cloud computing. In: 2017 International Conference on Advanced Computing and Applications (ACOMP), pp. 149–153 (2017). https://doi.org/10.1109/ACOMP.2017.13

  107. Hoque, S., d. Brito, M.S., Willner, A., Keil, O., Magedanz, T.: Towards container orchestration in fog computing infrastructures. In: 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 294–299 (2017). https://doi.org/10.1109/COMPSAC.2017.248

  108. Jin, X., Zhang, F., Wang, L., Hu, S., Zhou, B., Liu, Z.: Joint optimization of operational cost and performance interference in cloud data centers. IEEE Trans. Cloud Comput. 5(4), 697–711 (2017). https://doi.org/10.1109/TCC.2015.2449839

    Article  Google Scholar 

  109. Khasnabish, J.N., Mithani, M.F., Rao, S.: Tier-centric resource allocation in multi-tier cloud systems. IEEE Trans. Cloud Comput. 5(3), 576–589 (2017). https://doi.org/10.1109/TCC.2015.2424888

    Article  Google Scholar 

  110. Li, J., Ma, R., Guan, H., Wei, D.S.L.: Accurate cpu proportional share and predictable i/o responsiveness for virtual machine monitor: a case study in xen. IEEE Trans. Cloud Comput. 5(4), 604–616 (2017). https://doi.org/10.1109/TCC.2015.2441705

    Article  Google Scholar 

  111. Li, J.Z., Woodside, M., Chinneck, J., Litiou, M.: Adaptive cloud deployment using persistence strategies and application awareness. IEEE Trans. Cloud Comput. 5(2), 277–290 (2017). https://doi.org/10.1109/TCC.2015.2409873

    Article  Google Scholar 

  112. Lloyd, W.J., Pallickara, S., David, O., Arabi, M., Wible, T., Ditty, J., Rojas, K.: Demystifying the clouds: harnessing resource utilization models for cost effective infrastructure alternatives. IEEE Trans. Cloud Comput. 5(4), 667–680 (2017). https://doi.org/10.1109/TCC.2015.2430339

    Article  Google Scholar 

  113. Maenhaut, P.J., Moens, H., Volckaert, B., Ongenae, V., Turck, F.D.: A dynamic tenant-defined storage system for efficient resource management in cloud applications. J. Netw. Comput. Appl. 93, 182–196 (2017). https://doi.org/10.1016/j.jnca.2017.05.014

    Article  Google Scholar 

  114. Mebrek, A., Merghem-Boulahia, L., Esseghir, M.: Efficient green solution for a balanced energy consumption and delay in the IOT-fog-cloud computing. In: 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), pp. 1–4 (2017). https://doi.org/10.1109/NCA.2017.8171359

  115. Mechtri, M., Hadji, M., Zeghlache, D.: Exact and heuristic resource mapping algorithms for distributed and hybrid clouds. IEEE Trans. Cloud Comput. 5(4), 681–696 (2017). https://doi.org/10.1109/TCC.2015.2427192

    Article  Google Scholar 

  116. Merzoug, S., Kazar, O., Derdour, M.: Intelligent strategy of allocation resource for cloud datacenter based on MAS CP approach. In: Proceedings of the International Conference on Computing for Engineering and Sciences, ICCES ’17, pp. 50–55. ACM, New York (2017). https://doi.org/10.1145/3129186.3129197

  117. Mireslami, S., Rakai, L., Far, B.H., Wang, M.: Simultaneous cost and qos optimization for cloud resource allocation. IEEE Trans. Netw. Serv. Manag. 14(3), 676–689 (2017). https://doi.org/10.1109/TNSM.2017.2738026

    Article  Google Scholar 

  118. Nardelli, M., Hochreiner, C., Schulte, S.: Elastic provisioning of virtual machines for container deployment. In: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion, ICPE ’17 Companion, pp. 5–10. ACM, New York (2017). https://doi.org/10.1145/3053600.3053602

  119. Nitu, V., Teabe, B., Fopa, L., Tchana, A., Hagimont, D.: Stopgap: Elastic VMS to enhance server consolidation. In: Proceedings of the Symposium on Applied Computing, SAC ’17, pp. 358–363. ACM, New York (2017). https://doi.org/10.1145/3019612.3019626

  120. Paya, A., Marinescu, D.C.: Energy-aware load balancing and application scaling for the cloud ecosystem. IEEE Trans. Cloud Comput. 5(1), 15–27 (2017). https://doi.org/10.1109/TCC.2015.2396059

    Article  Google Scholar 

  121. Rankothge, W., Le, F., Russo, A., Lobo, J.: Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms. IEEE Trans. Netw. Serv. Manag. 14(2), 343–356 (2017). https://doi.org/10.1109/TNSM.2017.2686979

    Article  Google Scholar 

  122. Tang, L., Chen, H.: Joint pricing and capacity planning in the IaaS cloud market. IEEE Trans. Cloud Comput. 5(1), 57–70 (2017). https://doi.org/10.1109/TCC.2014.2372811

    Article  MathSciNet  Google Scholar 

  123. Xu, D., Liu, X., Niu, Z.: Joint resource provisioning for internet datacenters with diverse and dynamic traffic. IEEE Trans. Cloud Comput. 5(1), 71–84 (2017). https://doi.org/10.1109/TCC.2014.2382118

    Article  Google Scholar 

  124. Yang, Y., Chang, X., Liu, J., Li, L.: Towards robust green virtual cloud data center provisioning. IEEE Trans. Cloud Comput. 5(2), 168–181 (2017). https://doi.org/10.1109/TCC.2015.2459704

    Article  Google Scholar 

  125. Yi, X., Liu, F., Niu, D., Jin, H., Lui, J.C.S.: Cocoa: dynamic container-based group buying strategies for cloud computing. ACM Trans. Model. Perform. Eval. Comput. Syst. 2(2), 81–831 (2017). https://doi.org/10.1145/3022876

    Article  Google Scholar 

  126. Yu, B., Pan, J.: Optimize the server provisioning and request dispatching in distributed memory cache services. IEEE Trans. Cloud Comput. 5(2), 193–207 (2017). https://doi.org/10.1109/TCC.2015.2469663

    Article  Google Scholar 

  127. Zhang, W., Xie, H., Hsu, C.: Automatic memory control of multiple virtual machines on a consolidated server. IEEE Trans. Cloud Comput. 5(1), 2–14 (2017). https://doi.org/10.1109/TCC.2014.2378794

    Article  Google Scholar 

  128. Alam, M., Rufino, J., Ferreira, J., Ahmed, S.H., Shah, N., Chen, Y.: Orchestration of microservices for IOT using docker and edge computing. IEEE Commun. Mag. 56(9), 118–123 (2018). https://doi.org/10.1109/MCOM.2018.1701233

    Article  Google Scholar 

  129. Aral, A., Ovatman, T.: A decentralized replica placement algorithm for edge computing. IEEE Trans. Netw. Serv. Manag. 15(2), 516–529 (2018). https://doi.org/10.1109/TNSM.2017.2788945

    Article  Google Scholar 

  130. Atrey, A., Seghbroeck, G.V., Volckaert, B., Turck, F.D.: Brahma+: a framework for resource scaling of streaming and asap time-varying workflows. IEEE Trans. Netw. Serv. Manag. 15(3), 894–908 (2018). https://doi.org/10.1109/TNSM.2018.2830311

    Article  Google Scholar 

  131. Barkat, A., Kechadi, M.T., Verticale, G., Filippini, I., Capone, A.: Green approach for joint management of geo-distributed data centers and interconnection networks. IEEE Trans. Netw. Serv. Manag. 26(3), 723–754 (2018). https://doi.org/10.1007/s10922-017-9441-0

    Article  Google Scholar 

  132. Balos, C., Vega, D.D.L., Abuelhaj, Z., Kari, C., Mueller, D., Pallipuram, V.K.: A2cloud: An analytical model for application-to-cloud matching to empower scientific computing. In: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), pp. 548–555 (2018). https://doi.org/10.1109/CLOUD.2018.00076

  133. Barrameda, J., Samaan, N.: A novel statistical cost model and an algorithm for efficient application offloading to clouds. IEEE Trans. Cloud Comput. 6(3), 598–611 (2018). https://doi.org/10.1109/TCC.2015.2513404

    Article  Google Scholar 

  134. Borjigin, W., Ota, K., Dong, M.: In broker we trust: a double-auction approach for resource allocation in NFV markets. IEEE Trans. Netw. Serv. Manag. 15(4), 1322–1333 (2018). https://doi.org/10.1109/TNSM.2018.2882535

    Article  Google Scholar 

  135. Bouet, M., Conan, V.: Mobile edge computing resources optimization: a geo-clustering approach. IEEE Trans. Netw. Serv. Manag. 15(2), 787–796 (2018). https://doi.org/10.1109/TNSM.2018.2816263

    Article  Google Scholar 

  136. Cheng, M., Li, J., Nazarian, S.: Drl-cloud: Deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers. In: 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 129–134 (2018). https://doi.org/10.1109/ASPDAC.2018.8297294

  137. Diaz-Montes, J., Diaz-Granados, M., Zou, M., Tao, S., Parashar, M.: Supporting data-intensive workflows in software-defined federated multi-clouds. IEEE Trans. Cloud Comput. 6(1), 250–263 (2018). https://doi.org/10.1109/TCC.2015.2481410

    Article  Google Scholar 

  138. Gill, S.S., Buyya, R., Chana, I., Singh, M., Abraham, A.: Bullet: particle swarm optimization based scheduling technique for provisioned cloud resources. J. Netw. Syst. Manag. 26(2), 361–400 (2018). https://doi.org/10.1007/s10922-017-9419-y

    Article  Google Scholar 

  139. Guo, T., Shenoy, P.: Providing geo-elasticity in geographically distributed clouds. ACM Trans. Internet Technol. 18(3), 38:1–38:27 (2018). https://doi.org/10.1145/3169794

    Article  Google Scholar 

  140. Guo, W., Lin, B., Chen, G., Chen, Y., Liang, F.: Cost-driven scheduling for deadline-based workflow across multiple clouds. IEEE Trans. Netw. Serv. Manag. 15(4), 1571–1585 (2018). https://doi.org/10.1109/TNSM.2018.2872066

    Article  Google Scholar 

  141. Guo, Y., Stolyar, A.L., Walid, A.: Shadow-routing based dynamic algorithms for virtual machine placement in a network cloud. IEEE Trans. Cloud Comput. 6(1), 209–220 (2018). https://doi.org/10.1109/TCC.2015.2464795

    Article  Google Scholar 

  142. Hauser, C.B., Wesner, S.: Reviewing cloud monitoring: towards cloud resource profiling. In: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), pp. 678–685 (2018). https://doi.org/10.1109/CLOUD.2018.00093

  143. Heidari, S., Buyya, R.: Cost-efficient and network-aware dynamic repartitioning-based algorithms for scheduling large-scale graphs in cloud computing environments. Softw. Pract. Exp. 48(12), 2174–2192 (2018). https://doi.org/10.1002/spe.2623

    Article  Google Scholar 

  144. Jia, B., Hu, H., Zeng, Y., Xu, T., Yang, Y.: Double-matching resource allocation strategy in fog computing networks based on cost efficiency. J. Commun. Netw. 20(3), 237–246 (2018). https://doi.org/10.1109/JCN.2018.000036

    Article  Google Scholar 

  145. Jia, G., Han, G., Jiang, J., Chan, S., Liu, Y.: Dynamic cloud resource management for efficient media applications in mobile computing environments. Pers. Ubiquitous Comput. 22(3), 561–573 (2018). https://doi.org/10.1007/s00779-018-1118-5

    Article  Google Scholar 

  146. Khabbaz, M., Assi, C.M.: Modelling and analysis of a novel deadline-aware scheduling scheme for cloud computing data centers. IEEE Trans. Cloud Comput. 6(1), 141–155 (2018). https://doi.org/10.1109/TCC.2015.2481429

    Article  Google Scholar 

  147. Lahmann, G., McCann, T., Lloyd, W.: Container memory allocation discrepancies: an investigation on memory utilization gaps for container-based application deployments. In: 2018 IEEE International Conference on Cloud Engineering (IC2E), pp. 404–405 (2018). https://doi.org/10.1109/IC2E.2018.00076

  148. Lin, Y., Lai, Y., Huang, J., Chien, H.: Three-tier capacity and traffic allocation for core, edges, and devices for mobile edge computing. IEEE Trans. Netw. Serv. Manag. 15(3), 923–933 (2018). https://doi.org/10.1109/TNSM.2018.2852643

    Article  Google Scholar 

  149. Nawrocki, P., Sniezynski, B.: Adaptive service management in mobile cloud computing by means of supervised and reinforcement learning. J. Netw. Syst. Manag. 26(1), 1–22 (2018). https://doi.org/10.1007/s10922-017-9405-4

    Article  Google Scholar 

  150. Prats, D.B., Berral, J.L., Carrera, D.: Automatic generation of workload profiles using unsupervised learning pipelines. IEEE Trans. Netw. Serv. Manag. 15(1), 142–155 (2018). https://doi.org/10.1109/TNSM.2017.2786047

    Article  Google Scholar 

  151. Rahimi, M.R., Venkatasubramanian, N., Mehrotra, S., Vasilakos, A.V.: On optimal and fair service allocation in mobile cloud computing. IEEE Trans. Cloud Comput. 6(3), 815–828 (2018). https://doi.org/10.1109/TCC.2015.2511729

    Article  Google Scholar 

  152. Sahni, J., Vidyarthi, D.P.: A cost-effective deadline-constrained dynamic scheduling algorithm for scientific workflows in a cloud environment. IEEE Trans. Cloud Comput. 6(1), 2–18 (2018). https://doi.org/10.1109/TCC.2015.2451649

    Article  Google Scholar 

  153. Scheuner, J., Leitner, P.: Estimating cloud application performance based on micro-benchmark profiling. In: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), pp. 90–97 (2018). https://doi.org/10.1109/CLOUD.2018.00019

  154. Simonis, I.: Container-based architecture to optimize the integration of microservices into cloud-based data-intensive application scenarios. In: Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings, ECSA ’18, pp. 34:1–34:3. ACM, New York (2018). https://doi.org/10.1145/3241403.3241439

  155. Sathya Sofia, A., GaneshKumar, P.: Multi-objective task scheduling to minimize energy consumption and makespan of cloud computing using NSGA-II. J. Netw. Syst. Manag. 26(2), 463–485 (2018). https://doi.org/10.1007/s10922-017-9425-0

    Article  Google Scholar 

  156. Takahashi, K., Aida, K., Tanjo, T., Sun, J.: A portable load balancer for kubernetes cluster. In: Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2018, pp. 222–231. ACM, New York (2018). https://doi.org/10.1145/3149457.3149473

  157. Trihinas, D., Pallis, G., Dikaiakos, M.D.: Monitoring elastically adaptive multi-cloud services. IEEE Trans. Cloud Comput. 6(3), 800–814 (2018). https://doi.org/10.1109/TCC.2015.2511760

    Article  Google Scholar 

  158. Wang, L., Gelenbe, E.: Adaptive dispatching of tasks in the cloud. IEEE Trans. Cloud Comput. 6(1), 33–45 (2018). https://doi.org/10.1109/TCC.2015.2474406

    Article  Google Scholar 

  159. Wei, L., Foh, C.H., He, B., Cai, J.: Towards efficient resource allocation for heterogeneous workloads in iaas clouds. IEEE Trans. Cloud Comput. 6(1), 264–275 (2018). https://doi.org/10.1109/TCC.2015.2481400

    Article  Google Scholar 

  160. Xie, R., Jia, X.: Data transfer scheduling for maximizing throughput of big-data computing in cloud systems. IEEE Trans. Cloud Comput. 6(1), 87–98 (2018). https://doi.org/10.1109/TCC.2015.2464808

    Article  Google Scholar 

  161. Zhang, W., Wen, Y.: Energy-efficient task execution for application as a general topology in mobile cloud computing. IEEE Trans. Cloud Comput. 6(3), 708–719 (2018). https://doi.org/10.1109/TCC.2015.2511727

    Article  MathSciNet  Google Scholar 

  162. Zhang, Y., Ghosh, A., Aggarwal, V., Lan, T.: Tiered cloud storage via two-stage, latency-aware bidding. IEEE Trans. Netw. Serv. Manag. (2018). https://doi.org/10.1109/TNSM.2018.2875475

    Article  Google Scholar 

  163. Introducing Amazon EC2 spot instances for specific duration workloads. https://aws.amazon.com/about-aws/whats-new/2015/10/introducing-amazon-ec2-spot-instances-for-specific-duration-workloads/. Accessed 9 Sept 2019

  164. Juju solutions for container management. https://jaas.ai/containers. Accessed 9 Sept 2019

  165. Masip-Bruin, X., Marín-Tordera, E., Juan-Ferrer, A., Queralt, A., Jukan, A., Garcia, J., Lezzi, D., Jensen, J., Cordeiro, C., Leckey, A., Salis, A., Guilhot, D., Cankar, M.: mf2c: towards a coordinated management of the IOT-fog-cloud continuum. In: Proceedings of the 4th ACM MobiHoc Workshop on Experiences with the Design and Implementation of Smart Objects, SMARTOBJECTS ’18, pp. 8:1–8:8. ACM, New York (2018). https://doi.org/10.1145/3213299.3213307

  166. Almutairi, A., Sarfraz, M.I., Ghafoor, A.: Risk-aware management of virtual resources in access controlled service-oriented cloud datacenters. IEEE Trans. Cloud Comput. 6(1), 168–181 (2018). https://doi.org/10.1109/TCC.2015.2453981

    Article  Google Scholar 

  167. Zhai, Y., Yin, L., Chase, J., Ristenpart, T., Swift, M.: CQSTR: Securing cross-tenant applications with cloud containers. In: Proceedings of the Seventh ACM Symposium on Cloud Computing, SoCC ’16, pp. 223–236. ACM, New York (2016). https://doi.org/10.1145/2987550.2987558

  168. Lins, S., Schneider, S., Sunyaev, A.: Trust is good, control is better: creating secure clouds by continuous auditing. IEEE Trans. Cloud Comput. 6(3), 890–903 (2018). https://doi.org/10.1109/TCC.2016.2522411

    Article  Google Scholar 

  169. Maenhaut, P.J., Volckaert, B., Ongenae, V., De Turck, F.: Efficient resource management in the cloud: from simulation to experimental validation using a low-cost raspberry pi testbed. Softw. Pract. Exp. 49(3), 449–477 (2019). https://doi.org/10.1002/spe.2669

    Article  Google Scholar 

  170. Calheiros, R., Ranjan, R., Beloglazov, A., De Rose, C., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011). https://doi.org/10.1002/spe.995

    Article  Google Scholar 

  171. Fed4fire+—federation for fire plus. https://www.fed4fire.eu/. Accessed 9 Sept 2019

  172. FUTEBOL Brazil/UFRGS. http://futebol.inf.ufrgs.br/. Accessed 9 Sept 2019

  173. Eivy, A.: Be wary of the economics of “serverless” cloud computing. IEEE Cloud Comput. 4(2), 6–12 (2017). https://doi.org/10.1109/MCC.2017.32

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ghent University-imec, iGent, Technologiepark-Zwijnaarde 126, 9052, Gent, Belgium

    Pieter-Jan Maenhaut, Bruno Volckaert, Veerle Ongenae & Filip De Turck

Authors
  1. Pieter-Jan Maenhaut
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bruno Volckaert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Veerle Ongenae
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Filip De Turck
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pieter-Jan Maenhaut.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maenhaut, PJ., Volckaert, B., Ongenae, V. et al. Resource Management in a Containerized Cloud: Status and Challenges. J Netw Syst Manage 28, 197–246 (2020). https://doi.org/10.1007/s10922-019-09504-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10922-019-09504-0

Keywords

Search

Navigation