Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Wireless Personal Communications
Published in: Wireless Personal Communications 1/2020

29-07-2020

Using Markov Learning Utilization Model for Resource Allocation in Cloud of Thing Network

Authors: Seyedeh Maedeh Mirmohseni, Chunming Tang, Amir Javadpour

Published in: Wireless Personal Communications | Issue 1/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The integration of the Internet of Things (IoT) and cloud environment has led to the creation of Cloud of Things, which has given rise to new challenges in IoT area. In this paper, using the Markov model learning method and calculating the need probability of each object to resources shortly to reduce latency and maximize network utilization, allocating resources in the fog layer has been possible and processed. By using simulations in the CloudSim platform, it is examined the processor productivity for the number of tasks, the workflow overhead for the number of tasks, physical machine’s energy consumption for the number of tasks, the data locality for the number of tasks, resource utilization for the number of tasks, and completion of task for the number of tasks and compared with the SMDP (SemiMarkov decision processes) and MDP methods, results show that the proposed research is effective and promising.
previous article Tomato Leaf Disease Classification using Multiple Feature Extraction Techniques
next article Full Duplex DD for In-Band D2D Communication

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
2.
Javadpour, A., Abharian, S. K., Wang, G. (2017). Feature selection and intrusion detection in cloud environment based on machine learning algorithms. In 2017 IEEE international symposium on parallel and distributed processing with applications and 2017 IEEE international conference on ubiquitous computing and communications (ISPA/IUCC) (pp. 1417–1421).
3.
Liyanage, M., Braeken, A., Kumar, P., & Ylianttila, M. (2019). IoT security: Advances in authentication. Hoboken: Wiley.
4.
Park, J., Salim, M. M., Jo, J. H., Sicato, J. C. S., Rathore, S., & Park, J. H. (2019). CIoT-Net: A scalable cognitive IoT based smart city network architecture. Human-centric Comput. Inf. Sci., 9(1), 29.CrossRef
5.
Javadpour, A., Wang, G., Rezaei, S., Chend, S. (2018). Power curtailment in cloud environment utilising load balancing machine allocation. In 2018 IEEE SmartWorld, ubiquitous intelligence computing, advanced trusted computing, scalable computing communications, cloud big data computing, internet of people and smart city innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 1364–1370).
6.
Javadpour, A., Saedifar, K., Wang, G., & Li, K.-C. (2020). Optimal execution strategy for large orders in big data: Order type using Q-learning considerations. Wireless Personal Communications, 112, 123–148.CrossRef
7.
Javadpour, A. (2019). An optimize-aware target tracking method combining MAC layer and active nodes in wireless sensor networks. Wireless Personal Communications, 108, 711–728.CrossRef
8.
Chowdary, E. D., Yakobu, D. (2016). Cloud of things (CoT) integration challenges. In 2016 IEEE international conference on computational intelligence and computing research (ICCIC) (pp. 1–5).
9.
Atlam, H., Walters, R., & Wills, G. (2018). Fog computing and the internet of things: A review. Big Data and Cognitive Computing, 2(2), 10.CrossRef
10.
Yousefpour, A., et al. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, 98, 289–330.CrossRef
11.
Tekinerdogan, B., & Oral, A. (2017). Chapter 8—Performance isolation in cloud-based big data architectures. In I. Mistrik, R. Bahsoon, N. Ali, M. Heisel, & B. Maxim (Eds.), Software architecture for big data and the cloud (pp. 127–145). Boston: Morgan Kaufmann.CrossRef
12.
Javadpour, A., Wang, G., Xing, X. (2018). Managing heterogeneous substrate resources by mapping and visualization based on software-defined network. In 2018 IEEE international conference on parallel distributed processing with applications, ubiquitous computing communications, big data cloud computing, social computing networking, sustainable computing communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom) (pp. 316–321).
13.
Ashrafi, T. H., Hossain, M. A., Arefin, S. E., Das, K. D. J., & Chakrabarty, A. (2017). Service based FOG computing MODEL for IoT. In 2017 IEEE 3rd international conference on collaboration and internet computing (CIC) (pp. 163–172).
14.
Javadpour, A., Adelpour, N., Wang, G., & Peng, T. (2018). Combing fuzzy clustering and PSO algorithms to optimize energy consumption in WSN networks. In 2018 IEEE SmartWorld, ubiquitous intelligence & computing, advanced & trusted computing, scalable computing & communications, cloud & big data computing, internet of people and smart city innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 1371–1377).
15.
16.
Javadpour, A., & Memarzadeh-Tehran, H. (2015). A wearable medical sensor for provisional healthcare. In: ISPTS 2015 - 2nd international symposium on physics and technology of sensors dive deep into sensors (pp. 293–296).
17.
Javadpour, A., Wang, G., & Li, K.-C. (2019). A high throughput MAC protocol for wireless body area networks in intensive care. In Smart city and informatization (pp. 23–34).
18.
Javadpour, A., & Memarzadeh-Tehran, H. (2015). Implementing a smart method to eliminate artifacts of life signals. In 2015 international conference on smart sensors and application (ICSSA) (pp. 155–160).
19.
Javadpour, A., & Mohammadi, A. R. (2016). Improving brain magnetic resonance image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Journal of Biomedical Physics and Engineering, 6(2), 95–108.
20.
Javadpour, A. (2019). Improving resources management in network virtualization by utilizing a software-based network. Wireless Personal Communications, 106(2), 505–519.CrossRef
21.
Javadpour, A., Rezaei, S., Li, K.-C., Wang, G. (2020) A scalable feature selection and opinion miner using whale optimization algorithm. In Advances in signal processing and intelligent recognition systems (pp. 237–247).
22.
Kling, A. A. (2014). Cloud computing. Lucent Books, A part of Gale, Cengage Learning.
23.
Zykov, S., & Shumsky, L. (2016). Application of information processes applicative modelling to virtual machines auto configuration. Procedia Computer Science, 96, 1041–1048.CrossRef
24.
Alohali, B., Merabti, M., & Kifayat, K. (2014). A cloud of things (CoT) based security for home area network (HAN) in the smart grid. In 2014 eighth international conference on next generation mobile apps, services and technologies (pp. 326–330).
25.
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on mobile cloud computing (pp. 13–16).
26.
El Kafhali, S., & Salah, K. (2017). Efficient and dynamic scaling of fog nodes for IoT devices. The Journal of Supercomputing, 73(12), 5261–5284.CrossRef
27.
Santos, J., Wauters, T., Volckaert, B., & de Turck, F. (2018). Fog computing: Enabling the management and orchestration of smart city applications in 5G networks. Entropy, 20(1), 4.CrossRef
28.
P. Marie, T. Desprats, S. Chabridon, M. Sibilla (2016) Enabling self-configuration of QoC-centric fog computing entities. In: 2016 international IEEE conferences on ubiquitous intelligence computing, advanced and trusted computing, scalable computing and communications, cloud and big data computing, internet of people, and smart world congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld) (pp. 526–533).
29.
Niknejad, N., Hussin, A. R. C., & Amiri, I. S. (2019). Introduction of service-oriented architecture (SOA) adoption. In The impact of service oriented architecture adoption on organizations. Cham: Springer International Publishing.
30.
Delicato, F. C., Pires, P. F., & Batista, T. (2017). Resource management for internet of things. Cham: Springer International Publishing.CrossRef
31.
Vasconcelos, D. R., Andrade, R. M. C., Severino, V., & de Souza, J. N. (2019). Cloud, fog, or mist in IoT? That is the question. ACM Transactions on Internet Technology, 19(2), 1–20.CrossRef
32.
Lin, C., Deng, D., & Yao, C. (2019). Resource allocation in vehicular cloud computing systems with heterogeneous vehicles and roadside units. IEEE Internet of Things Journal, 5(5), 3692–3700.CrossRef
33.
Madni, S. H. H., Latiff, M. S. A., Coulibaly, Y., & Abdulhamid, S. M. (2017). Resource scheduling for infrastructure as a service (IaaS) in cloud computing. Journal of Network and Computer Applications, 68(C), 173–200.
34.
Nassar, A., Yilmaz, Y. (2019). Resource allocation in fog RAN for heterogeneous IoT environments based on reinforcement learning. In ICC 2019–2019 IEEE international conference on communications (ICC) (pp. 1–6).
35.
Chen, J., & Menzies, T. (2018). {RIOT:} {A} Stochastic-based method for workflow scheduling in the cloud. In: 11th {IEEE} international conference on cloud computing, {CLOUD} 2018, San Francisco, CA, USA, July 2–7, 2018 (pp. 318–325).
36.
Deldari, A., Naghibzadeh, M., & Abrishami, S. (2017). CCA: A deadline-constrained workflow scheduling algorithm for multicore resources on the cloud. The Journal of Supercomputing, 73(2), 756–781.CrossRef
37.
Chrysikos, A., & Ward, R. (2014). Cloud computing within higher education: Applying knowledge as a service (KaaS). In Z. Mahmood (Ed.), Continued rise of the cloud: advances and trends in cloud computing (pp. 339–362). London: Springer.CrossRef
38.
Nasim, R., Zola, E., & Kassler, A. J. (2018). Robust optimization for energy-efficient virtual machine consolidation in modern datacenters. Cluster Computing, 21(3), 1681–1709.CrossRef
39.
Quang-Hung, N., Nien, P. D., Nam, N. H., Huynh Tuong, N., Thoai, N. (2013). A genetic algorithm for power-aware virtual machine allocation in private cloud. In: Proceedings of the 2013 international conference on information and communication technology (pp. 183–191).
40.
Ebrahimirad, V., Goudarzi, M., & Rajabi, A. (2015). Energy-aware scheduling for precedence-constrained parallel virtual machines in virtualized data centers. Journal of Grid Computing, 13(2), 233–253.CrossRef
41.
Ding, Y., Qin, X., Liu, L., & Wang, T. (2015). Energy efficient scheduling of virtual machines in cloud with deadline constraint. Future Generation Computer Systems, 50, 62–74.CrossRef
Metadata
Title
Using Markov Learning Utilization Model for Resource Allocation in Cloud of Thing Network
Authors
Seyedeh Maedeh Mirmohseni
Chunming Tang
Amir Javadpour
Publication date
29-07-2020
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 1/2020
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07591-w

Other articles of this Issue 1/2020

Wireless Personal Communications 1/2020 Go to the issue

OriginalPaper

RS Codes with Filtered-OFDM: A Waveform Contender for 5G Mobile Communication Systems

OriginalPaper

Cooperative Spectrum Sensing with Distributed/Centralized Relay Selection

OriginalPaper

Cloud Resource Scheduling Optimal Hypervisor (CRSOH) for Dynamic Cloud Computing Environment

OriginalPaper

Design of a Symmetric CPW-Fed Patch Antenna for WLAN/WIMAX Applications Using ANN

OriginalPaper

Modeling and Performance Evaluation of the IEEE 802.15.4K CSMA/CA with Priority Channel Access Mechanism Under Fading Channel

OriginalPaper

Performance Analysis of Modulation Recognition in Multipath Fading Channels using Pattern Recognition Classifiers