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review-article

Resource provisioning in edge/fog computing: : A Comprehensive and Systematic Review

Authors:

Hamid Shakarami,

Mostafa Ghobaei-Arani,

Elaheh Nikougoftar,

Mohammad Faraji-MehmandarAuthors Info & Claims

Volume 122, Issue C

https://doi.org/10.1016/j.sysarc.2021.102362

Published: 01 January 2022 Publication History

Abstract

Close computing paradigms such as fog and edge have become promising technologies for mobile applications running on pervasive mobile equipment utilized by a wide range of users to remove such types of equipment’ inherent limitations. In such environments, competition is a severe challenge to gain computation and communication resources’ capabilities. Therefore, resource allocation in the mentioned environments are becoming a requirement, which is an essential challenging issue addressed by different approaches, including resource provisioning. However, to the best of the authors’ knowledge, any systematic, comprehensive, and detailed survey related to resource provisioning has not been applied in computation environments despite its importance. This paper provides a review of the resource provisioning approaches in computation paradigms in the form of a standard classification to identify the existing approaches on this critical topic and offer open issues. The proposed classification can be organized into five main fields: framework-based, heuristic/meta-heuristic-based, model-based, machine learning-based, and game theoretic-based mechanisms. Next, these classes are compared based on some essential features such as performance metrics, case studies, utilized techniques, and evaluation tools. Finally, open issues and uncovered or insufficiently covered future research challenges, including resource performance, resource location, uncertainties, resource elasticity, and resource migration are discussed, and the survey is concluded.

References

[1]

J.C. Guevara, R.D.S. Torres, N.L. da Fonseca, On the classification of fog computing applications: a machine learning perspective, J. Netw. Comput. Appl. (2020),.

[2]

S. Vogel, Universal confidence sets for solutions of stochastic optimization problems—A contribution to quantification of uncertainty, Workshop On Stochastic Models, Statistics and Their Application, Springer, Cham, 2019, pp. 207–218,.

[3]

K. Li, How to Stabilize a Competitive Mobile Edge Computing Environment: A Game Theoretic Approach, IEEE Access, 2019,.

[4]

Masoumeh Etemadi, et al., A cost-efficient auto-scaling mechanism for IoT applications in fog computing environment: a deep learning-based approach, Cluster Computing 24 (2021) 3277–3292,.

Digital Library

[5]

A.K. Dwivedi, N. Kumar, M. Pathela, Distributed and lazy auditing of outsourced data, in: International Conference on Distributed Computing and Internet Technology, Cham, Springer, 2020, pp. 364–379,.

Digital Library

[6]

European Telecommunications Standards Institute (ETSI), accessed 24 July 2021,https://www.etsi.org/technologies/multi-access-edge-computing.

[7]

OpenFog Consortium, accessed 24 July 2021, https://www.iiconsortium.org/pdf/OpenFog_Reference_Architecture_2_09_17.pdf.

[8]

Z. Xu, Y. Zhang, H. Li, W. Yang, Q. Qi, Dynamic resource provisioning for cyber-physical systems in cloud-fog-edge computing, J. Cloud Comput. 9 (1) (2020) 1–16,.

[9]

T.L. Duc, R.G. Leiva, P. Casari, P.O. Östberg, Machine learning methods for reliable resource provisioning in edge-cloud computing: a survey, ACM Comput. Surv. (CSUR) 52 (5) (2019) 1–39,.

Digital Library

[10]

S. Taherizadeh, V. Stankovski, Auto-scaling applications in edge computing: taxonomy and challenges, in: Proceedings of the International Conference on Big Data and Internet of Thing, 2017, pp. 158–163,.

Digital Library

[11]

S. Taherizadeh, A.C. Jones, I. Taylor, Z. Zhao, V. Stankovski, Monitoring self-adaptive applications within edge computing frameworks: a state-of-the-art review, J. Syst. Softw. 136 (2018) 19–38,.

Digital Library

[12]

M. Songhorabadi, M. Rahimi, A.M.M. Farid, M.H. Kashani, Fog Computing Approaches in Smart Cities: a State-of-the-Art Review, arXiv preprint arXiv:2011.14732. (2020).

[13]

F. Spinelli, V. Mancuso, Towards enabled industrial verticals in 5G: a survey on MEC-based approaches to provisioning and flexibility, IEEE Commun. Surv. Tutor. (2020),.

[14]

Masoumeh Etemadi, et al., A learning-based resource provisioning approach in the fog computing environment, Journal of Experimental & Theoretical Artificial Intelligence 3 (6) (2021) 1033–1056,.

[15]

M. Faraji-Mehmandar, S. Jabbehdari, H. Haj SeyyedJavadi, A proactive fog service provisioning framework for Internet of Things applications: an autonomic approach, Trans. Emerg. Telecommun. Technol. (2021) e4342,.

Digital Library

[16]

M. FarajiMehmandar, S. Jabbehdari, H. Haj SeyyedJavadi, A dynamic fog service provisioning approach for IoT applications, Int. J. Commun. Syst. 33 (14) (2020) e4541,.

[17]

W. Wu, R. Li, G. Xie, J. An, Y. Bai, J. Zhou, K. Li, A survey of intrusion detection for in-vehicle networks, IEEE Trans. Intell. Transp. Syst. 21 (3) (2019) 919–933,.

[18]

W. Wei, R. Yang, H. Gu, W. Zhao, C. Chen, S. Wan, Multi-objective optimization for resource allocation in vehicular cloud computing networks, IEEE Trans. Intell. Transp. Syst. (2021),.

[19]

K. Toczé, S. Nadjm-Tehrani, A taxonomy for management and optimization of multiple resources in edge computing, Wirel. Commun. Mob. Comput. 2018 (2018),.

Digital Library

[20]

B.H. Bhavani, H.S. Guruprasad, Resource provisioning techniques in cloud computing environment: a survey, Int. J. Res. Comput. Commun. Technol. 3 (3) (2014) 395–401.

[21]

C. Mechalikh, H. Taktak, F. Moussa, PureEdgeSim: a simulation framework for performance evaluation of cloud, edge and mist computing environments, Comput. Sci. Inf. Syst. (00) (2020) 42,. -42.

[22]

Z. Zhao, C. Rong, M.G. Jaatun, A trustworthy blockchain-based decentralised resource management system in the cloud, in: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), IEEE, 2020, pp. 617–624,.

[23]

L. Huang, S. Bi, Y.J.A. Zhang, Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks, IEEE Trans. Mob. Comput. 19 (11) (2019) 2581–2593,.

[24]

G. Fan, L. Chen, H. Yu, W. Qi, Multi-objective optimization of container-based microservice scheduling in edge computing, Comput. Sci. Inf. Syst. (00) (2020) 41,. -41.

[25]

R. Yu, G. Xue, X. Zhang, Application provisioning in fog computing-enabled internet-of-things: a network perspective, in: IEEE INFOCOM 2018-IEEE Conference on Computer Communications, IEEE, 2018, pp. 783–791,.

Digital Library

[26]

Zhang, H., Dong, Y. and Yang, Y., 2020. Mobility-aware personalized service recommendation in mobile edge computing, 10.21203/rs.3.rs-117144/v1.

[27]

X. Zhang, C. Wu, Z. Li, F.C.M. Lau, A truthful (1-ε)-optimal mechanism for on-demand cloud resource provisioning, in: 2015 IEEE Conference on Computer Communications (INFOCOM), IEEE, 2015, pp. 1053–1061,.

Digital Library

[28]

M. Etemadi, M. Ghobaei-Arani, A. Shahidinejad, Resource provisioning for IoT services in the fog computing environment: an autonomic approach, Comput. Commun. 161 (2020) 109–131,.

[29]

A.G. Tasiopoulos, O. Ascigil, I. Psaras, G. Pavlou, Edge-MAP: auction markets for edge resource provisioning, in: 2018 IEEE 19th International Symposium on" A World of Wireless, Mobile and Multimedia Networks"(WoWMoM), IEEE, 2018, pp. 14–22,.

[30]

Y. Ma, W. Liang, M. Huang, W. Xu, S. Guo, Virtual network function service provisioning in mec via trading off the usages between computing and communication resources, IEEE Ann. Hist. Comput. (01) (2020) 1,. -1.

[31]

N. Hoque, B. Ramamurthy, Dynamic wavelength and bandwidth allocation for supporting diverse customers and prioritized traffic in NG-PON2 networks, Photonic Netw. Commun. (2020) 1–15,.

Digital Library

[32]

T. Zhou, T. Pan, M.C. Meyer, Y. Dong, T. Watanabe, Multi-shape task placement algorithm based on low fragmentation resource management on 2D heterogeneous dynamic partial reconfigurable devices, IEEE Access 8 (2020) 186362–186375,.

[33]

F.H. Tseng, M.S. Tsai, C.W. Tseng, Y.T. Yang, C.C. Liu, L.D. Chou, A lightweight autoscaling mechanism for fog computing in industrial applications, IEEE Trans. Ind. Inf. 14 (10) (2018) 4529–4537,.

[34]

P. Borylo, G. Davoli, M. Rzepka, A. Lason, W. Cerroni, Unified and standalone monitoring module for NFV/SDN infrastructures, J. Netw. Comput. Appl. (2020),.

[35]

T. Bahreini, H. Badri, D. Grosu, Energy-aware capacity provisioning and resource allocation in edge computing systems, in: International Conference on Edge Computing, Cham, Springer, 2019, pp. 31–45,.

[36]

J. Wang, Z. Feng, S. George, R. Iyengar, P. Pillai, M. Satyanarayanan, Towards scalable edge-native applications, in: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, 2019, pp. 152–165,.

Digital Library

[37]

A. Yousefpour, A. Patil, G. Ishigaki, I. Kim, X. Wang, H.C. Cankaya, Q. Zhang, W. Xie, J.P. Jue, FogPlan: a lightweight QoS-aware dynamic fog service provisioning framework, IEEE Internet Things J. 6 (3) (2019) 5080–5096,.

[38]

Madan, N., Malik, A.W., Rahman, A.U. and Ravana, S.D., 2020. On-demand resource provisioning for vehicular networks using flying fog. Vehicular Communications, p.100252, 10.1016/j.vehcom.2020.100252.

[39]

F. Tonini, B.M. Khorsandi, E. Amato, C. Raffaelli, Scalable edge computing deployment for reliable service provisioning in vehicular networks, J. Sens. Actuator Netw. 8 (4) (2019) 51,.

[40]

S. Dehnavi, H.R. Faragardi, M. Kargahi, T. Fahringer, A reliability-aware resource provisioning scheme for real-time industrial applications in a Fog-integrated smart factory, Microprocess. Microsyst. 70 (2019) 1–14,.

Digital Library

[41]

J. Santos, T. Wauters, B. Volckaert, F. De Turck, Towards dynamic fog resource provisioning for smart city applications, in: 2018 14th International Conference on Network and Service Management (CNSM), IEEE, 2018, pp. 290–294.

[42]

T. Rahman, X. Yao, G. Tao, H. Ning, Z. Zhou, Efficient edge nodes reconfiguration and selection for the internet of things, IEEE Sens. J. 19 (12) (2019) 4672–4679,.

[43]

P.G.V. Naranjo, E. Baccarelli, M. Scarpiniti, Design and energy-efficient resource management of virtualized networked Fog architectures for the real-time support of IoT applications, J. Supercomput. 74 (6) (2018) 2470–2507,.

Digital Library

[44]

V. Porkodi, A.R. Singh, A.R.W. Sait, K. Shankar, E. Yang, C. Seo, G.P. Joshi, Resource provisioning for cyber–physical–social system in cloud-fog-edge computing using optimal flower pollination algorithm, IEEE Access 8 (2020) 105311–105319,.

[45]

J. Santos, T. Wauters, B. Volckaert, F. De Turck, Resource provisioning in Fog computing: from theory to practice, Sensors 19 (10) (2019) 2238,.

[46]

H. Santos, D. Alencar, R. Meneguette, D. Rosário, J. Nobre, C. Both, E. Cerqueira, T. Braun, A multi-tier fog content orchestrator mechanism with quality of experience support, Comput. Net. (2020),.

[47]

J. Choi, S. Ahn, Optimal service provisioning for the scalable fog/edge computing environment, Sensors 21 (4) (2021) 1506,.

[48]

S. Mishra, M.N. Sahoo, S. Bakshi, J.J. Rodrigues, Dynamic resource allocation in fog-cloud hybrid systems using multicriteria ahp techniques, IEEE Internet Things J. 7 (9) (2020) 8993–9000,.

[49]

U. Tadakamalla, D.A. Menascé, Autonomic resource management using analytic models for fog/cloud computing, in: 2019 IEEE International Conference on Fog Computing (ICFC), IEEE, 2019, pp. 69–79,.

[50]

N. Siasi, M. Jasim, A. Aldalbahi, N. Ghani, Delay-aware SFC provisioning in hybrid fog-cloud computing architectures, IEEE Access 8 (2020) 167383–167396,.

[51]

H. Baghban, C.Y. Huang, C.H. Hsu, Resource provisioning towards OPEX optimization in horizontal edge federation, Comput. Commun. 158 (2020) 39–50,.

[52]

H. Xing, X. Zhou, X. Wang, S. Luo, P. Dai, K. Li, H. Yang, An integer encoding grey wolf optimizer for virtual network function placement, Appl. Soft Comput. 76 (2019) 575–594,.

Digital Library

[53]

P. Niu, S. Niu, L. Chang, The defect of the Grey Wolf optimization algorithm and its verification method, Knowl. Based Syst. 171 (2019) 37–43,.

Digital Library

[54]

J. Kangas, The analytic hierarchy process (AHP): standard version, forestry application and advances, Multiple Use of Forests and Other Natural Resources, Springer, Dordrecht, 1999, pp. 96–105,.

[55]

K.P. Kaliyamurthi, A Comparison of strength and weakness for analytical hierarchy process, Int. J. Pure Appl. Math 116 (8) (2017) 29–33.

[56]

O. Skarlat, S. Schulte, M. Borkowski, P. Leitner, Resource provisioning for IoT services in the fog, in: 2016 IEEE 9th international conference on service-oriented computing and applications (SOCA), IEEE, 2016, pp. 32–39,.

[57]

N. Wang, B. Varghese, M. Matthaiou, D.S. Nikolopoulos, ENORM: a framework for edge node resource management, IEEE Trans. Serv. Comput. (2017),.

[58]

F. Rossi, V. Cardellini, F.L. Presti, M. Nardelli, Geo-distributed efficient deployment of containers with Kubernetes, Comput. Commun. (2020),.

[59]

N.D. Nguyen, L.A. Phan, D.H. Park, S. Kim, T. Kim, ElasticFog: elastic resource provisioning in container-based fog computing, IEEE Access 8 (2020) 183879–183890,.

[60]

S. Taherizadeh, V. Stankovski, M. Grobelnik, A capillary computing architecture for dynamic internet of things: orchestration of microservices from edge devices to fog and cloud providers, Sensors 18 (9) (2018) 2938,.

[61]

A. Zanni, S. Forsstrom, U. Jennehag, P. Bellavista, Elastic provisioning of internet of things services using fog computing: an experience report, in: 2018 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), IEEE, 2018, pp. 17–22,.

[62]

Seo, D., Shahhosseini, S., Mehrabadi, M.A., Donyanavard, B., Lim, S.S., Rahmani, A.M. and Dutt, N., Dynamic iFogSim: a framework for full-stack simulation of dynamic resource management in IoT systems, 10.1109/COINS49042.2020.9191663.

[63]

P.O. Östberg, J. Byrne, P. Casari, P. Eardley, A.F. Anta, J. Forsman, J. Kennedy, T. Le Duc, M.N. Marino, R. Loomba, M.A.L. Pena, Reliable capacity provisioning for distributed cloud/edge/fog computing applications, in: 2017 European conference on networks and communications (EuCNC), IEEE, 2017, pp. 1–6,.

[64]

K.N. Vhatkar, G.P. Bhole, Optimal container resource allocation in cloud architecture: a new hybrid model, J. King Saud Univ. - Comput. Inf. Sci. (2019),.

Digital Library

[65]

P. Pereira, J. Araujo, M. Torquato, J. Dantas, C. Melo, P. Maciel, Stochastic performance model for web server capacity planning in fog computing, J. Supercomput. (2020) 1–25,.

Digital Library

[66]

W. Zhou, W. Fang, Y. Li, B. Yuan, Y. Li, T. Wang, Markov approximation for task offloading and computation scaling in mobile edge computing, Mob. Inf. Syst. 2019 (2019),.

[67]

N. Kherraf, H.A. Alameddine, S. Sharafeddine, C.M. Assi, A. Ghrayeb, Optimized provisioning of edge computing resources with heterogeneous workload in IoT networks, IEEE Trans. Netw. Serv. Manage. 16 (2) (2019) 459–474,.

[68]

T.Q. Dinh, B. Liang, T.Q. Quek, H. Shin, Online resource procurement and allocation in a hybrid edge-cloud computing system, IEEE Trans. Wireless Commun. 19 (3) (2020) 2137–2149,.

Digital Library

[69]

J. Yao, N. Ansari, QoS-aware fog resource provisioning and mobile device power control in IoT networks, IEEE Trans. Netw. Serv. Manage. 16 (1) (2018) 167–175,.

Digital Library

[70]

S. Lu, J. Wu, Y. Duan, N. Wang, J. Fang, Towards cost-efficient resource provisioning with multiple mobile users in fog computing, J. Parallel Distrib. Comput. 146 (2020) 96–106,.

[71]

H.R. Arkian, A. Diyanat, A. Pourkhalili, MIST: fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications, J. Netw. Comput. Appl. 82 (2017) 152–165,.

Digital Library

[72]

J. Santos, T. Wauters, B. Volckaert, F. De Turck, Towards end-to-end resource provisioning in Fog Computing over Low Power Wide Area Networks, J. Netw. Comput. Appl. (2020),.

[73]

J. Santos, T. Wauters, B. Volckaert, F. De Turck, Resource provisioning for IoT application services in smart cities, in: 2017 13th International Conference on Network and Service Management (CNSM), IEEE, 2017, pp. 1–9,.

[74]

M.A. Serhani, H.T. El-Kassabi, K. Shuaib, A.N. Navaz, B. Benatallah, A. Beheshti, Self-adapting cloud services orchestration for fulfilling intensive sensory data-driven IoT workflows, Future Gener. Comput. Syst. (2020),.

[75]

Q. Vo, D.A. Tran, Probabilistic partitioning for edge server assignment with time-varying workload, in: 2019 28th International Conference on Computer Communication and Networks (ICCCN), IEEE, 2019, pp. 1–8,.

[76]

M. Aazam, E.N. Huh, Dynamic resource provisioning through fog micro datacenter, in: 2015 IEEE international conference on pervasive computing and communication workshops (PerCom workshops), IEEE, 2015, pp. 105–110,.

[77]

L. Chen, P. Zhou, L. Gao, J. Xu, Adaptive fog configuration for the industrial Internet of Things, IEEE Trans. Ind. Inf. 14 (10) (2018) 4656–4664,.

[78]

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

Digital Library

[79]

S. El Kafhali, K. Salah, Performance modelling and analysis of internet of things enabled healthcare monitoring systems, IET Networks 8 (1) (2019) 48–58,.

[80]

A. Kiani, N. Ansari, A. Khreishah, Hierarchical capacity provisioning for fog computing, IEEE/ACM Trans. Networking 27 (3) (2019) 962–971,.

Digital Library

[81]

U. Tadakamalla, D. Menascé, FogQN: an analytic model for fog/cloud computing, in: 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), IEEE, 2018, pp. 307–313,.

[82]

Ma, X., Wang, S., Zhang, S., Yang, P., Lin, C. and Shen, X.S., 2019. Cost-efficient resource provisioning for dynamic requests in cloud assisted mobile edge computing. IEEE Transactions on Cloud Computing, 10.1109/TCC.2019.2903240.

[83]

G.L. Stavrinides, H.D. Karatza, Orchestration of real-time workflows with varying input data locality in a heterogeneous fog environment, in: 2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC), IEEE, 2020, pp. 202–209,.

[84]

S.K. Battula, S. Garg, J. Montgomery, B.H. Kang, An efficient resource monitoring service for fog computing environments, IEEE Trans. Serv. Comput. (2019),.

[85]

C. Li, J. Bai, Y. Luo, Efficient resource scaling based on load fluctuation in edge-cloud computing environment, J. Supercomput. (2020) 1–32,.

Digital Library

[86]

B. Liu, J. Guo, C. Li, Y. Luo, Workload forecasting based elastic resource management in edge cloud, Comput. Ind. Eng. 139 (2020),.

Digital Library

[87]

J. Guo, C. Li, Y. Chen, Y. Luo, On-demand resource provision based on load estimation and service expenditure in edge cloud environment, J. Netw. Comput. Appl. 151 (2020),.

Digital Library

[88]

C. Li, J. Bai, Y. Ge, Y. Luo, Heterogeneity-aware elastic provisioning in cloud-assisted edge computing systems, Future Gener. Comput. Syst. 112 (2020) 1106–1121,.

[89]

M. Ghobaei-Arani, A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud based systems, Soft comput. 25 (5) (2021) 3813–3830,.

Digital Library

[90]

X. Deng, J. Li, E. Liu, H. Zhang, Task Allocation algorithm and optimization model on edge collaboration, J. Syst. Archit. (2020),.

[91]

M. Abdullah, W. Iqbal, A. Mahmood, F. Bukhari, A. Erradi, Predictive autoscaling of microservices hosted in fog microdata center, IEEE Syst. J. (2020),.

[92]

F. Gand, I. Fronza, N. El Ioini, H.R. Barzegar, S. Azimi, C. Pahl, A fuzzy controller for self-adaptive lightweight edge container orchestration, CLOSER (2020) 79–90,.

[93]

T. Dlamini, S. Vilakati, arXiv preprint, 2020,.

Digital Library

[94]

G. Russo Russo, M. Nardelli, V. Cardellini, F. Lo Presti, Multi-level elasticity for wide-area data streaming systems: a reinforcement learning approach, Algorithms 11 (9) (2018) 134,.

[95]

J. Na, H. Zhang, X. Deng, B. Zhang, Z. Ye, Accelerate personalized IoT service provision by cloud-aided edge reinforcement learning: a case study on smart lighting, in: International Conference on Service-Oriented Computing, Cham, Springer, 2020, pp. 69–84,.

Digital Library

[96]

Y.G. Kim, C.J. Wu, AutoScale: energy efficiency optimization for stochastic edge inference using reinforcement learning, in: 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), IEEE, 2020, pp. 1082–1096,.

[97]

S. Rahman, T. Ahmed, M. Huynh, M. Tornatore, B. Mukherjee, Auto-scaling VNFs using machine learning to improve QoS and reduce cost, in: 2018 IEEE International Conference on Communications (ICC), IEEE, 2018, pp. 1–6,.

[98]

Al-Makhadmeh, Z. and Tolba, A., 2020. SRAF: scalable resource allocation framework using machine learning in user-centric internet of things. Peer-to-Peer networking and applications, pp.1–11, 10.1007/s12083-020-00924-3.

[99]

X. Huang, W. Zhang, J. Yang, L. Yang, C.K. Yeo, Market-based dynamic resource allocation in Mobile Edge Computing systems with multi-server and multi-user, Comput. Commun. (2020),.

[100]

A. Kiani, N. Ansari, Toward hierarchical mobile edge computing: an auction-based profit maximization approach, IEEE Internet Things J. 4 (6) (2017) 2082–2091,.

[101]

A. Tasiopoulos, O. Ascigil, I. Psaras, S. Toumpis, G. Pavlou, Fogspot: spot pricing for application provisioning in edge/fog computing, IEEE Trans. Serv. Comput. (2019),.

[102]

F. Shahnaz, M.W. Berry, V.P. Pauca, R.J. Plemmons, Document clustering using nonnegative matrix factorization, Inf. Process. Manag. 42 (2006) 373–386,.

[103]

W. Xu, X. Liu, Y. Gong, Document clustering based on non-negative matrix factorization, in: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 2003, pp. 267–273,.

Digital Library

[104]

J.F. Burnham, Scopus database: a review, Biomed. Digit. Libr. 3 (1) (2006),.

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Resource provisioning in edge/fog computing: A Comprehensive and Systematic Review

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cover image Journal of Systems Architecture: the EUROMICRO Journal

Journal of Systems Architecture: the EUROMICRO Journal Volume 122, Issue C

Jan 2022

165 pages

ISSN:1383-7621

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Elsevier B.V.

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Elsevier North-Holland, Inc.

United States

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Published: 01 January 2022

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Cited By

Li SLiu HLi WSun W(2025)Edge bank: a novel resource pricing and management system for edge service providerThe Journal of Supercomputing10.1007/s11227-024-06578-981:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06578-9
Ismail AKhalifa NEl-Khoribi R(2025)A survey on resource scheduling approaches in multi-access edge computing environment: a deep reinforcement learning studyCluster Computing10.1007/s10586-024-04893-728:3Online publication date: 1-Jun-2025
https://dl.acm.org/doi/10.1007/s10586-024-04893-7
Faraji-Mehmandar MGhobaei-Arani MShakarami A(2025)A cost-aware IoT application deployment approach in fog computingCluster Computing10.1007/s10586-024-04873-x28:3Online publication date: 1-Jun-2025
https://dl.acm.org/doi/10.1007/s10586-024-04873-x
Dogani JKhunjush F(2024)Proactive auto-scaling technique for web applications in container-based edge computing using federated learning modelJournal of Parallel and Distributed Computing10.1016/j.jpdc.2024.104837187:COnline publication date: 1-May-2024
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