Mobile edge computing is developing as an innovative computing paradigm that gives improved practice to mobile users through low latency connections and enlarged computation limits. As the amount of user requests is time- different, while the computation limit of the edge has is constrained, the Cloud Assisted Mobile Edge computing system is acquainted with improving the adaptability of the edge platform. To give ensured administrations at negligible framework latency, the edge resource provisioning and cloud redistributing of the cloud-assisted mobile edge computing structure ought to be wisely planned effectively. This work proposed a latency aware resource provisioning strategy for distributed cloud-assisted mobile edge computing structure. At first, the framework gets SFC requests for Virtual network functions (VNFs) to use both edge and cloud assets. Here, the efficient parameters, for example, execution time and workload of VNFs are evaluated and Fuzzy logic-based auto-scaling is executed for the overloaded VNFs that need more assets because of the progressively expanded measure of the system packets. Subsequently, the SFC requests are scheduled to the cloud-assisted edge network adequately utilizing the Adaptive Grey Wolf Optimization (AGWO) based asset provisioning algorithm. The exploratory outcomes show the superiority of the presented methodology comparing with the existing techniques as far as system cost, arrival rate, and average response time.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Futur Gener Comput Syst 29(7):1645–1660
Yadav R, Zhang W, Kaiwartya O, Singh PR, Elgendy IA, Tian Y-C (2018) Adaptive energy-aware algorithms for minimizing energy consumption and SLA violation in cloud computing. IEEE Access 6:55923–55936
Savaglio C, Fortino G, Zhou M (2016) Towards interoperable, cognitive and autonomic IoT systems: An agent-based approach. In 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT):58–63, IEEE
Fernando N, Loke SW, Rahayu W (2013) Mobile cloud computing: a survey. Futur Gener Comput Syst 29(1):84–106
Elgendy IA, El-kawkagy M, Keshk A (2015) An efficient framework to improve the performance of mobile applications. International Journal of Digital Content Technology and its Applications (JDCTA) 9(5):43–54
Elgendy MA, Shawish A, Moussa MI (2014) MCACC: New approach for augmenting the computing capabilities of mobile devices with Cloud Computing. In 2014 Science and Information Conference:79–86. IEEE
Hu YC, Patel M, Sabella D, Sprecher N, Young V (2015) Mobile edge computing—A key technology towards 5G. ETSI white paper 11(11):1–16
Zhang S, He P, Suto K, Yang P, Zhao L, Shen X (2017) Cooperative edge caching in user-centric clustered mobile networks. IEEE Transactions on Mobile Computing 17(8):1791–1805
Sarkar S, Chatterjee S, Misra S (2015) Assessment of the suitability of fog computing in the context of internet of things. IEEE Transactions on Cloud Computing 6(1):46–59
Aazam M, Huh E-N (2015) 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:687–694, IEEE
Jia M, Cao J, Liang W (2015) Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Transactions on Cloud Computing 5(4):725–737
Yang P, Zhang N, Zhang S, Yu L, Zhang J, Shen XS (2018) Content popularity prediction towards location-aware mobile edge caching. IEEE Transactions on Multimedia 21(4):915–929
Chen TY-H, Ravindranath L, Deng S, Bahl P, Balakrishnan H (2015) Glimpse: continuous, real-time object recognition on mobile devices. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems:155–168
Ha K, Pillai P, Richter W, Abe Y, Satyanarayanan M (2013) Just-in-time provisioning for cyber foraging. In Proceeding of the 11th annual international conference on Mobile systems, applications, and services:153–166
Liang B (2017) Mobile edge computing. In: Wong VWS, Schober R, Ng DWK, Wang L-C (eds) Key technologies for 5G wireless systems. University Press, Cambridge
Ma X, Zhang S, Li W, Zhang P, Lin C, Shen X (2017) Cost-efficient workload scheduling in cloud assisted mobile edge computing. In 2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS):1–10, IEEE
Ma X, Zhang S, Yang P, Zhang N, Lin C, Shen X (2017) Cost-efficient resource provisioning in cloud assisted mobile edge computing. In GLOBECOM 2017–2017 IEEE Global Communications Conference:1–6, IEEE
Avasalcai C, Dustdar S (2019) Latency-aware distributed resource provisioning for deploying iot applications at the edge of the network. In Future of Information and Communication Conference:377–391, Springer, Cham
Guo J, Li C, Yi C, Luo Y (2019) On-demand resource provision based on load estimation and service expenditure in edge cloud environment. J Netw Comput Appl 102506
Elgendy IA, Zhang W, Tian Y-C, Li K (2019) Resource allocation and computation offloading with data security for mobile edge computing. Futur Gener Comput Syst 100:531–541
Son J, Buyya R (2019) Latency-aware virtualized network function provisioning for distributed edge clouds. J Syst Softw 152:24–31
Li C, Sun H, Tang H, Luo Y (2019) Adaptive resource allocation based on the billing granularity in edge-cloud architecture. Comput Commun 145:29–42
Chen X, Li W, Lu S, Zhou Z, Xiaoming F (2018) Efficient resource allocation for on-demand mobile-edge cloud computing. IEEE Trans Veh Technol 67(9):8769–8780
Saremi S, Mirjalili SZ, Mirjalili SM (2015) Evolutionary population dynamics and grey wolf optimizer. Neural Comput & Applic 26(5):1257–1263
Fan Q, Ansari N (2019) On cost aware cloudlet placement for mobile edge computing. IEEE/CAA Journal of Automatica Sinica 6(4):926–937
Zhang PY, Shu S, Zhou MC (2018) An online fault detection model and strategies based on SVM-grid in clouds. IEEE/CAA Journal of Automatica Sinica 5(2):445–456
Huang J, Li S, Duan Q (2017) Constructing multicast routing tree for inter-cloud data transmission: an approximation algorithmic perspective. IEEE/CAA Journal of Automatica Sinica 5(2):514–522
Zhang Y, Zhou P, Cui G (2018) Multi-model based PSO method for burden distribution matrix optimization with expected burden distribution output behaviors. IEEE/CAA Journal of Automatica Sinica 6(6):1506–1512
Gao S, Zhou MC, Wang Y, Cheng J, Yachi H, Wang J (2018) Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction. IEEE transactions on neural networks and learning systems 30(2):601–614
Van Do T, Do NH, Kispal I, Galambosi N, Rotter C, Nemeth L (2018) A big switch abstraction to support service function chaining in cloud infrastructure. In 2018 21st Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN):1–5. IEEE
Carpio F, Dhahri S, Jukan A (2017) VNF placement with replication for Loac balancing in NFV networks. In 2017 IEEE International Conference on Communications (ICC):1–6. IEEE
Qi D, Shen S, Wang G (2019) Virtualized network function consolidation based on multiple status characteristics. IEEE Access 7:59665–59679
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Mulinti, R.B., Nagendra, M. An efficient latency aware resource provisioning in cloud assisted mobile edge framework. Peer-to-Peer Netw. Appl. (2021). https://doi.org/10.1007/s12083-020-01070-6
- Resource provisioning
- Execution time
- Workload measure