A Mobile Cloud Middleware to Support Mobility and Cloud Interoperability

2016 ◽  
Vol 7 (1) ◽  
pp. 41-58 ◽  
Author(s):  
Khadija Akherfi ◽  
Hamid Harroud ◽  
Michael Gerndt
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Cloud Computing ◽  
Relevant Information ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Computing Power ◽  
Cloud Interoperability

With the recent advances in cloud computing and the improvement in the capabilities of mobile devices in terms of speed, storage, and computing power, Mobile Cloud Computing (MCC) is emerging as one of important branches of cloud computing. MCC is an extension of cloud computing with the support of mobility. In this paper, the authors first present the specific concerns and key challenges in mobile cloud computing. They then discuss the different approaches to tackle the main issues in MCC that have been introduced so far, and finally focus on describing the proposed overall architecture of a middleware that will contribute to providing mobile users data storage and processing services based on their mobile devices capabilities, availability, and usage. A prototype of the middleware is developed and three scenarios are described to demonstrate how the middleware performs in adapting the provision of cloud web services by transforming SOAP messages to REST and XML format to JSON, in optimizing the results by extracting relevant information, and in improving the availability by caching. Initial analysis shows that the mobile cloud middleware improves the quality of service for mobiles, and provides lightweight responses for mobile cloud services.

Mobile Cloud Computing

2018 ◽  
pp. 105-123
Author(s):  
Parkavi R ◽  
Priyanka C ◽  
Sujitha S. ◽  
Sheik Abdullah A
Keyword(s):  
Cloud Computing ◽  
Mobile Computing ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Mobile Service ◽  
Service Users ◽  
Security Issues ◽  
Mobile Computing Environment

Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of the industry ring words and a major conversation thread in the IT world with an explosive development of the mobile applications and emerging of cloud computing idea, the MCC has become a possible technology for the mobile service users. The concepts of Cloud computing are naturally meshed with mobile devices to allow on-the-go functionalities and benefits. The mobile cloud computing is emerging as one of the most important branches of cloud computing and it is expected to expand the mobile ecosystems. As more mobile devices enter the market and evolve, certainly security issues will grow as well. Also, enormous growth in the variety of devices connected to the Internet will further drive security needs. MCC provides a platform where mobile users make use of cloud services on mobile devices. The use of MCC minimizes the performance, compatibility, and lack of resources issues in mobile computing environment.

Mobile Cloud Computing

2015 ◽  
pp. 1-23
Author(s):  
Jyoti Grover ◽  
Gaurav Kheterpal
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Applications ◽  
Large Scale ◽  
Mobile Cloud Computing ◽  
Research Area ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Mobile Environment

Mobile Cloud Computing (MCC) has become an important research area due to rapid growth of mobile applications and emergence of cloud computing. MCC refers to integration of cloud computing into a mobile environment. Cloud providers (e.g. Google, Amazon, and Salesforce) support mobile users by providing the required infrastructure (e.g. servers, networks, and storage), platforms, and software. Mobile devices are rapidly becoming a fundamental part of human lives and these enable users to access various mobile applications through remote servers using wireless networks. Traditional mobile device-based computing, data storage, and large-scale information processing is transferred to “cloud,” and therefore, requirement of mobile devices with high computing capability and resources are reduced. This chapter provides a survey of MCC including its definition, architecture, and applications. The authors discuss the issues in MCC, existing solutions, and approaches. They also touch upon the computation offloading mechanism for MCC.

Data Storage Security in Mobile Cloud Computing (MCC) using Improved Blowfish Algorithm

2021 ◽  
pp. 100-111
Author(s):  
Seada Abdu Wakene ◽  
Sisay Muleta Hababa ◽  
Gutema Seboka Daba ◽  
K S Ananda Kumar
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Device ◽  
Data Security ◽  
Mobile Cloud Computing ◽  
Data Access ◽  
Security And Privacy ◽  
Mobile Cloud ◽  
Technical Requirements

Mobile cloud computing (MCC) combines cloud computing and mobile computing to deliver vast computational resources to mobile consumers, network operators, and cloud computing providers. You may access your data from anywhere in the globe using any mobile device that is linked to the Internet. Cloud computing provides access to data in real-time whenever and wherever want. Any conventional mobile device can benefit from MCC's infrastructure, computational capacity, software, and platform services. Network security, web application security, data access, authentication, authorization, data confidentiality, and data breach are all concerns of MCC's security. Because mobile devices lack sufficient storage and processing power, their data storage capacity is limited. Users of mobile devices may inadvertently provide sensitive information over the network or through the application. Therefore, data security is the main concern for mobile device users. The objective of this paper is to find a solution that can enhance technical requirements with relation to user’s data security and privacy in mobile cloud computing. To achieve this improved blowfish encryption algorithm is used to encrypt each user’s data security and where the shared secret key is hash down using message digest called secured hash function. Hashing can increase the integrity and privacy of user data. The proposed algorithm is evaluated with a normal blowfish algorithm and 3DES with different parameters. Improved blowfish algorithm shows better performance than normal blowfish algorithm and 3DES. In this work, we have developed web-based application where the Amazon MySQL RDS database is used for data storage.

scholarly journals A Survey on Communication Issues in Mobile Cloud Computing

2016 ◽  
Vol 15 (1) ◽  
pp. 1-17
Author(s):  
Dasari Naga RAJU ◽  
Vankadara SARITHA
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Extensive Survey ◽  
Battery Power ◽  
Research Challenges ◽  
Open Research ◽  
Processing Power

Despite the expanding utilization of mobile devices, exploring their full resources is an issue due to their limited battery power, processing power and data storage. The integration of cloud computing with mobile devices solves these issues by offloading major computation in to the cloud. This paper provides a survey on Mobile Cloud Computing (MCC), which helps to understand the MCC architecture, communication issues and applications. An extensive survey is made of communication issues and different approaches are discussed to overcome the communication issues. Finally open research challenges are also provided which will be helpful for active researchers in the field of MCC.

Fuzzy simplified swarm optimization for multisite computational offloading in mobile cloud computing

2020 ◽  
Vol 39 (6) ◽  
pp. 8285-8297
Author(s):  
V. Meena ◽  
Obulaporam Gireesha ◽  
Kannan Krithivasan ◽  
V.S. Shankar Sriram
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Swarm Optimization ◽  
Computational Offloading ◽  
Smart Mobile ◽  
Smart Mobile Devices

Mobile Cloud Computing (MCC)’s rapid technological advancements facilitate various computational-intensive applications on smart mobile devices. However, such applications are constrained by limited processing power, energy consumption, and storage capacity of smart mobile devices. To mitigate these issues, computational offloading is found to be the one of the promising techniques as it offloads the execution of computation-intensive applications to cloud resources. In addition, various kinds of cloud services and resourceful servers are available to offload computationally intensive tasks. However, their processing speeds, access delays, computation capability, residual memory and service charges are different which retards their usage, as it becomes time-consuming and ambiguous for making decisions. To address the aforementioned issues, this paper presents a Fuzzy Simplified Swarm Optimization based cloud Computational Offloading (FSSOCO) algorithm to achieve optimum multisite offloading. Fuzzy logic and simplified swarm optimization are employed for the identification of high powerful nodes and task decomposition respectively. The overall performance of FSSOCO is validated using the Specjvm benchmark suite and compared with the state-of-the-art offloading techniques in terms of the weighted total cost, energy consumption, and processing time.

scholarly journals PRESENCE: Monitoring and Modelling the Performance Metrics of Mobile Cloud SaaS Web Services

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Abdallah A. Z. A. Ibrahim ◽  
Muhammad Umer Wasim ◽  
Sebastien Varrette ◽  
Pascal Bouvry
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Mobile Cloud Computing ◽  
Performance Metrics ◽  
Service Level ◽  
Individual Performance ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Quantifying In

Service Level Agreements (SLAs) are defining the quality of the services delivered from the Cloud Services Providers (CSPs) to the cloud customers. The services are delivered on a pay-per-use model. The quality of the provided services is not guaranteed by the SLA because it is just a contract. The developments around mobile cloud computing and the advent of edge computing technologies are contributing to the diffusion of the cloud services and the multiplication of offers. Although the cloud services market is growing for the coming years, unfortunately, there is no standard mechanism which exists to verify and assure that delivered services satisfy the signed SLA agreement in an automatic way. The accurate monitoring and modelling of the provided Quality of Service (QoS) is also missing. In this context, we aim at offering an automatic framework named PRESENCE, to evaluate the QoS and SLA compliance of Web Services (WSs) offered across several CSPs. Yet unlike other approaches, PRESENCE aims at quantifying in a fair and by stealth way the performance and scalability of the delivered WS. This article focuses on the first experimental results obtained on the accurate modelisation of each individual performance metrics. Indeed, 19 generated models are provided, out of which 78.9% accurately represent the WS performance metrics for two representative SaaS web services used for the validation of the PRESENCE approach. This opens novel perspectives for assessing the SLA compliance of Cloud providers using the PRESENCE framework.

scholarly journals Research gaps based virtualization in mobile cloud computing

2020 ◽  
Vol 10 (51) ◽  
pp. 212-222
Author(s):  
Boubakeur Annane ◽  
Adel Alti ◽  
Osman Ghazali
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Virtual Machines ◽  
Cloud Services ◽  
Security And Privacy ◽  
Battery Life ◽  
Mobile Cloud ◽  
Sensitive Data ◽  
Cloud Servers

Recently, mobile computing is known as a fast-growing utilization of people's daily life. However, the main is the limited mobile devices’ resources such as processing capability, storage space and battery life. With the development of cloud computing, mobile devices’ resources are improved with the help of cloud services, which resulted an emerged technology named Mobile Cloud Computing (MCC). Although the MCC has several advantages for mobile users, it is also challenged by many critical issues like security and privacy of the mobile user's data that offloaded on the cloud’ servers and processed on the virtual machines (VMs). In virtualization, various investigations showed that malicious users are able to break down the cloud security methods by spreading their VMs in order to alter or violate the user sensitive data that executed on cloud’ VMs. This paper deeply analyzes the recent MCC based virtualization approaches and methods by criticizing them. We found out that no approach protects the data from being stolen while distributed VMs that deployed on different cloud servers exchanging data. Hence, the paper provides practical gaps related to virtualization in MCC and future perspectives.

scholarly journals Application Scheduling in Mobile Cloud Computing with Load Balancing

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xianglin Wei ◽  
Jianhua Fan ◽  
Ziyi Lu ◽  
Ke Ding
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Ant Colony Algorithm ◽  
Mobile Cloud Computing ◽  
Cloud Model ◽  
Mobile Cloud ◽  
Scheduling Problems ◽  
Simulation Experiments ◽  
Definition Of

Mobile cloud computing (MCC) enables the mobile devices to offload their applications to the cloud and thus greatly enriches the types of applications on mobile devices and enhances the quality of service of the applications. Under various circumstances, researchers have put forward several MCC architectures. However, how to reduce the response latency while efficiently utilizing the idle service capacities of the mobile devices still remains a challenge. In this paper, we firstly give a definition of MCC and divide the recently proposed architectures into four categories. Secondly, we present a Hybrid Local Mobile Cloud Model (HLMCM) by extending the Cloudlet architecture. Then, after formulating the application scheduling problems in HLMCM and bringing forward the Hybrid Ant Colony algorithm based Application Scheduling (HACAS) algorithm, we finally validate the efficiency of the HACAS algorithm by simulation experiments.

scholarly journals Joint optimization of computing ratio and access points’ density for mixed mobile edge/cloud computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Tianqi Jing ◽  
Shiwen He ◽  
Fei Yu ◽  
Yongming Huang ◽  
Luxi Yang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Coverage Probability ◽  
Optimization Problem ◽  
Mobile Cloud Computing ◽  
Low Complexity ◽  
Mixed Integer ◽  
Joint Optimization ◽  
Mobile Cloud ◽  
Convex Problem

AbstractCooperation between the mobile edge computing (MEC) and the mobile cloud computing (MCC) in offloading computing could improve quality of service (QoS) of user equipments (UEs) with computation-intensive tasks. In this paper, in order to minimize the expect charge, we focus on the problem of how to offload the computation-intensive task from the resource-scarce UE to access point’s (AP) and the cloud, and the density allocation of APs’ at mobile edge. We consider three offloading computing modes and focus on the coverage probability of each mode and corresponding ergodic rates. The resulting optimization problem is a mixed-integer and non-convex problem in the objective function and constraints. We propose a low-complexity suboptimal algorithm called Iteration of Convex Optimization and Nonlinear Programming (ICONP) to solve it. Numerical results verify the better performance of our proposed algorithm. Optimal computing ratios and APs’ density allocation contribute to the charge saving.

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