Predicting relative topological stability of mobile users in a P2P mobile cloud

The use of Cloud Computing in the mobile networks offer more advantages and possibilities to the mobile users such as storing, downloading and making calculation on data on demand and its offer more resources to these users such as the storage resources and calculation power. So, Mobile Cloud Computing allows users to fully utilize mobile technologies to store, to download, share and retrieve their personal data anywhere and anytime. As many recent researches show, the main problem of fully expansion and use of mobile cloud computing is security, and it's because the increasing flows and data circulation through internet that many security problems emerged and sparked the interest of the attackers. To face all this security problems, we propose in this paper an authentication and confidentiality scheme based on homomorphic encryption, and also a recovery mechanism to secure access for mobile users to the remote multi cloud servers. We also provide an implementation of our framework to demonstrate its robustness and efficiently, and a security analysis.

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Cloud resource optimization based on poisson linear deep gradient learning for mobile cloud computing

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-200799 ◽

2021 ◽

Vol 40 (1) ◽

pp. 787-797

Author(s):

G. Saravanan ◽

N. Yuvaraj

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Mobile Applications ◽

Mobile Cloud Computing ◽

Service Providers ◽

Mobile User ◽

Mobile Users ◽

Mobile Cloud ◽

Queuing Model ◽

Gradient Learning

Mobile Cloud Computing (MCC) addresses the drawbacks of Mobile Users (MU) where the in-depth evaluation of mobile applications is transferred to a centralized cloud via a wireless medium to reduce load, therefore optimizing resources. In this paper, we consider the resource (i.e., bandwidth and memory) allocation problem to support mobile applications in a MCC environment. In such an environment, Mobile Cloud Service Providers (MCSPs) form a coalition to create a resource pool to share their resources with the Mobile Cloud Users. To enhance the welfare of the MCSPs, a method for optimal resource allocation to the mobile users called, Poisson Linear Deep Resource Allocation (PL-DRA) is designed. For resource allocation between mobile users, we formulate and solve optimization models to acquire an optimal number of application instances while meeting the requirements of mobile users. For optimal application instances, the Poisson Distributed Queuing model is designed. The distributed resource management is designed as a multithreaded model where parallel computation is provided. Next, a Linear Gradient Deep Resource Allocation (LG-DRA) model is designed based on the constraints, bandwidth, and memory to allocate mobile user instances. This model combines the advantage of both decision making (i.e. Linear Programming) and perception ability (i.e. Deep Resource Allocation). Besides, a Stochastic Gradient Learning is utilized to address mobile user scalability. The simulation results show that the Poisson queuing strategy based on the improved Deep Learning algorithm has better performance in response time, response overhead, and energy consumption than other algorithms.

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ERMO2 algorithm: an energy efficient mobility management in mobile cloud computing system for 5G heterogeneous networks

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i3.pp1957-1967 ◽

2019 ◽

Vol 9 (3) ◽

pp. 1957

Author(s):

L. Pallavi ◽

A. Jagan ◽

B. Thirumala Rao

Keyword(s):

Cloud Computing ◽

Heterogeneous Networks ◽

Mobility Management ◽

Packet Loss ◽

Energy Efficient ◽

Mobile Cloud Computing ◽

Computing System ◽

Mobile Users ◽

Mobile Cloud ◽

Cloud Computing System

Recently, mobile devices are becoming the primary platforms for every user who always roam around and access the cloud computing applications. Mobile cloud computing (MCC) combines the both mobile and cloud computing, which provides optimal services to the mobile users. In next-generation mobile environments, mainly due to the huge number of mobile users in conjunction with the small cell size and their portable information‟s, the influence of mobility on the network performance is strengthened. In this paper, we propose an energy efficient mobility management in mobile cloud computing (E2M2MC2) system for 5G heterogeneous networks. The proposed E2M2MC2 system use elective repeat multi-objective optimization (ERMO2) algorithm to determine the best clouds based on the selection metrics are delay, jitter, bit error rate (BER), packet loss, communication cost, response time, and network load. ERMO2 algorithm provides energy efficient management of user mobility as well as network resources. The simulation results shows that the proposed E2M2MC2 system helps in minimizing delay, packet loss rate and energy consumption in a heterogeneous network.

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A Novel Multi-Tier Architecture Based Mobile Cloud Computing For Enhanced Energy Utilization

Journal of ISMAC - June 2019 ◽

10.36548/jismac.2020.1.006 ◽

2020 ◽

Vol 2 (1) ◽

pp. 62-72

Author(s):

Prof. Sathish ◽

Dr. Smys S.

Keyword(s):

Cloud Computing ◽

Mobile Devices ◽

Mobile Cloud Computing ◽

Energy Utilization ◽

Mobile Users ◽

Mobile Cloud ◽

Network Simulator ◽

Energy Balancing ◽

Cluster Process ◽

Computationally Intensive

The recent advancements in the mobile devices and the growing needs of the mobile users led to the clubbing of mobile devices with the cloud computing developing a platform coined as mobile cloud computing. The main scope of conceiving this clubbed mobile and cloud paradigm is to direct the tasks that are resource and computationally intensive to the cloud for its execution. While the execution takes place in the cloud, the resources of the mobile users that are clubbed with the cloud remains wasted until the responses are received from the cloud. These results in the excess battery drain causing frequent recharges. To put an end to this, the paper puts forward an the multi-tier architectures in-built with the various level of cluster process for processing to properly handle the mobile device participating in the cloud , minimizing the number of idle mobile users and enhances the energy efficiency. The proposed method is validated using the network simulator-II to evince the energy balancing achieved through the proposed multi-tier architecture based MCC-(mobile cloud computing).

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Secure Mobile Multi Cloud Architecture for Authentication and Data Storage

International Journal of Cloud Applications and Computing ◽

10.4018/ijcac.2017040105 ◽

2017 ◽

Vol 7 (2) ◽

pp. 62-76 ◽

Cited By ~ 15

Author(s):

Karim Zkik ◽

Ghizlane Orhanou ◽

Said El Hajji

Keyword(s):

Cloud Computing ◽

Data Storage ◽

Mobile Networks ◽

Mobile Cloud Computing ◽

Homomorphic Encryption ◽

Personal Data ◽

Mobile Technologies ◽

Mobile Users ◽

Mobile Cloud ◽

Multi Cloud

The use of Cloud Computing in the mobile networks offer more advantages and possibilities to the mobile users such as storing, downloading and making calculation on data on demand and its offer more resources to these users such as the storage resources and calculation power. So, Mobile Cloud Computing allows users to fully utilize mobile technologies to store, to download, share and retrieve their personal data anywhere and anytime. As many recent researches show, the main problem of fully expansion and use of mobile cloud computing is security, and it's because the increasing flows and data circulation through internet that many security problems emerged and sparked the interest of the attackers. To face all this security problems, we propose in this paper an authentication and confidentiality scheme based on homomorphic encryption, and also a recovery mechanism to secure access for mobile users to the remote multi cloud servers. We also provide an implementation of our framework to demonstrate its robustness and efficiently, and a security analysis.

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Exploration of Location based Services using AI for Mobile Cloud Services

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e2573.039520 ◽

2020 ◽

Vol 9 (6) ◽

pp. 564-567

Keyword(s):

Mobile Devices ◽

Mobile Device ◽

Mobile User ◽

Cloud Services ◽

Location Based Services ◽

Cloud System ◽

Mobile Users ◽

Mobile Cloud ◽

Major Work

Mobile devices have several sensors, including GPS that can capture information about the location of a mobile user. The use of certain devices will, therefore, simplify services and make it simpler for operators to respond to the demands of mobile users. The main aim of this analysis is to incorporate middleware to pick suitable cloud services that leverage from mobile device position and cost preferences. If the number of small activities within a meta feature exceeds the number of major work, the Max min algorithm device operations are conducted in addition to big tasks, where the design of the process is dependent on how many functions it does. The model is wide since tasks cannot be conducted simultaneously. A new amendment to the computation system is used to overcome the drawbacks of the Max-Min algorithm. It encompasses the positives of Max-Min and eliminates drawbacks. This study focuses specifically on the number of resources and incidents. The work can be further expanded with the algorithm suggested for the cloud system and several other parameters such as scalability, performance, reliability, and others can be taken into account.

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A Comparison of Execution Mechanisms: Fog and Edge Cloud Computing

10.31224/osf.io/tp2eb ◽

2020 ◽

Author(s):

Francis Tina

Keyword(s):

Cloud Computing ◽

Mobile Devices ◽

Mobile Cloud Computing ◽

Fog Computing ◽

Mobile Technologies ◽

Smart Devices ◽

Mobile Users ◽

Mobile Cloud ◽

Attractive Feature ◽

Cloud Technologies

Cloud computing is a technology that was developed a decade ago to provide uninterrupted, scalable services to users and organizations. Cloud computing also became an attractive feature for mobile users due to the limited features of mobile devices. The combination of cloud technologies with mobile technologies gave a new area of computing called mobile cloud computing. This combined technology is used to augment the resources existing in smart devices. In recent times Fog computing, Edge computing and Clone Cloud computing techniques have become the latest trends after mobile cloud computing, which have all been developed to address the limitations in cloud computing. This paper reviews these recent technologies in detail. This paper also addresses the differences in these technologies and how each of them are effective to organizations and developers.

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