Abstract
Mobile cloud computing offers computing, storage, and memory resources to mobile users through its high computing cloud servers. Mobile services are facilitated with efficient resource provisioning in the cloud. However, due to its inherent nature such as disconnections and limited battery life mobile users experience poor quality of service (QoS) in their connectivity with the cloud. This paper proposes an efficient request state aware resource provisioning technique that provisions the resources considering the current context information viz., battery, and connection quality of the mobile client. Various state viz., Accepted, Submitted and Running, Submitted and Paused, Resumed, Fulfilled, Rejected and Exit has been used to monitor the Client’s state. Besides, an intelligent resource capacity prediction technique based on a random forest algorithm has been incorporated to predict the future resource capacity requirement and schedule the client’s job in the cloud. The proposed technique has been implemented using CloudSim and analyzed using a mobile cloud storage dataset. Performance analysis proves that the proposed technique outperforms the state of art techniques in terms of waiting time, deadline violation, and accuracy of the prediction technique.
Similar content being viewed by others
References
Abedinia O, Zareinejad M, Doranehgard MH, Fathi G, Ghadimi N (2019) Optimal offering and bidding strategies of renewable energy-based large consumer using a novel hybrid robust-stochastic approach. J Clean Prod 215:878–889
Akki P, Vijayarajan V (2019) An efficient system model to minimize signal interference and delay in mobile cloud environment. Evol Intell. https://doi.org/10.1007/s12065-019-00285-8
Alonso-Monsalve S, García-Carballeira F, Calderón A (2018) A heterogeneous mobile cloud computing model for hybrid clouds. Future Gener Comput Syst 87:651–666
Bagal HA, Soltanabad YN, Dadjuo M, Wakil K, Ghadimi N (2018) Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory. Sol Energy 169:343–352
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50
Chen J, Wang Y (2018) A resource demand prediction method based on EEMD in cloud computing. Proc Comput Sci 131:116–123
Chunlin L, LaYuan L (2015) Cost and energy aware service provisioning for mobile client in cloud computing environment. J Supercomput 71(4):1196–1223
D’Aniello G, Gaeta A, Gaeta M, Tomasiello S (2018) Self-regulated learning with approximate reasoning and situation awareness. J Ambient Intell Humaniz Comput 9(1):151–164
Daniel E, Vasanthi NA (2019) LDAP: a lightweight deduplication and auditing protocol for secure data storage in cloud environment. Clust Comput 22(1):1247–1258
Daniel E, Vasanthi NA (2020) ES-DAS: an enhanced and secure dynamic auditing scheme for data storage in cloud environment. J Internet Technol 21(1):173–182
Daniel E, Durga S, Seetha S (2019) Panoramic view of cloud storage security attacks: an insight and security approaches. In: 2019 3rd international conference on computing methodologies and communication (ICCMC) (pp. 1029–1034), IEEE
Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mob Comput 13(18):1587–1611
Durga S, Mohan S (2013) Mobile cloud media computing applications: a survey. In: Proceedings of the fourth international conference on signal and image processing 2012, ICSIP 2012, pp 619–628. Springer, India
Durga S, Mohan S, Peter JD et al (2019) Context-aware adaptive resource provisioning for mobile clients in intra-cloud environment. Cluster Comput 22:9915–9928. https://doi.org/10.1007/s10586-018-1945-1
Durga S, Mohan S, Dinesh Peter J (2020) Proximity-based cloud resource provisioning for deep learning applications in smart healthcare. Expert Syst. https://doi.org/10.1111/exsy.12524
Fernandes R, D’Souza GR, Rodrigues AP (2019) A new framework to locate, connect and share mobile web services through intelligence techniques. Evol Intell. https://doi.org/10.1007/s12065-019-00249-y
Gao W, Darvishan A, Toghani M, Mohammadi M, Abedinia O, Ghadimi N (2019) Different states of multi-block based forecast engine for price and load prediction. Int J Electr Power Energy Syst 104:423–435
Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63(1):3–42
Ghadimi N, Akbarimajd A, Shayeghi H, Abedinia O (2018) Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy 161:130–142
Ghaffar Z, Ahmed S, Mahmood K, Islam SH, Hassan MM, Fortino G (2020) An improved authentication scheme for remote data access and sharing over cloud storage in cyber-physical-social-systems. IEEE Access 8:47144–47160
Ghobaei-Arani M, Khorsand R, Ramezanpour M (2019) An autonomous resource provisioning framework for massively multiplayer online games in cloud environment. J Netw Comput Appl 142:76–97
Hosseinzadeh M, Sinopoli B, Garone E (2019) Feasibility and detection of replay attack in networked constrained cyber-physical systems. In: 2019 57th annual allerton conference on communication, control, and computing (Allerton), pp 712–717, IEEE
Khodaei H, Hajiali M, Darvishan A, Sepehr M, Ghadimi N (2018) Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Appl Therm Eng 137:395–405
La QD, Ngo MV, Dinh TQ et al. (2019) Enabling intelligence in fog computing to achieve energy and latency reduction. Digit Commun Netw
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
McCullagh P (2019) Generalized linear models. Routledge
Mobile Cloud storage dataset (2018) [Online] http://fi.ict.ac.cn/data/cloud.html
Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobotics 7:21
Rahimi MR, Venkatasubramanian N, Mehrotra S, Vasilakos AV (2012) MAPCloud: mobile applications on an elastice 2-tier cloud architecture. Submitted to IEEE GLOBECOM
Rani DS, Pounambal M (2019) Deep learning based dynamic task offloading in mobile cloudlet environments. Evol Intell. https://doi.org/10.1007/s12065-019-00284-9
Rashidi S, Sharifian S (2017) A hybrid heuristic queue based algorithm for task assignment in mobile cloud. Fut Gener Comput Syst 68:331–345
Saeedi M, Moradi M, Hosseini M, Emamifar A, Ghadimi N (2019) Robust optimization based optimal chiller loading under cooling demand uncertainty. Appl Therm Eng 148:1081–1091
Wang N, Varghese B, Matthaiou M, Nikolopoulos DS (2020) ENORM: A Framework For Edge NOde Resource Management. IEEE Trans Serv Comput 13(6):1086–1099. https://doi.org/10.1109/TSC.2017.2753775
Wang C, Zhang S, Zhang H, Qian Z, Lu S (2017) Edge cloud capacity allocation for low delay computing on mobile devices. In: 2017 IEEE international symposium on parallel and distributed processing with applications and 2017 IEEE international conference on ubiquitous computing and communications (ISPA/IUCC). IEEE, pp 290–297
Wang T, Liang Y, Jia W, Arif M, Liu A, Xie M (2019) Coupling resource management based on fog computing in smart city systems. J Netw Comput Appl 135:11–19
Yussupova N, Rizvanov D (2018) Decision-making support in resource management in manufacturing scheduling. IFAC Pap On Line 51(30):544–547
Zhao Y, Chen H, Zhao S, Wang Y (2017) The storage of virtual machine disk image in cloud computing: a survey. In: 2017 international conference on networking and network applications (NaNA), pp 263–267, IEEE
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Durga, S., Daniel, E. & Leelipushpam, P.G.J. A novel request state aware resource provisioning and intelligent resource capacity prediction in hybrid mobile cloud. J Ambient Intell Human Comput 13, 2637–2650 (2022). https://doi.org/10.1007/s12652-021-03093-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-021-03093-0