mobile cloud computing thesis

Mobile Cloud Computing: A Survey, State of Art and Future Directions

• Published: 01 November 2013
• Volume 19 , pages 133–143, ( 2014 )

Cite this article

• M. Reza Rahimi 1 ,
• Jian Ren 2 ,
• Chi Harold Liu 2 ,
• Athanasios V. Vasilakos 3 &
• Nalini Venkatasubramanian 1  

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In the recent years, cloud computing frameworks such as Amazon Web Services, Google AppEngine and Windows Azure have become increasingly popular among IT organizations and developers. Simultaneously, we have seen a phenomenal increase in the usage and deployment of smartphone platforms and applications worldwide. This paper discusses the current state of the art in the merger of these two popular technologies, that we refer to as Mobile Cloud Computing (MCC). We illustrate the applicability of MCC in various domains including mobile learning, commerce, health/wellness and social medias. We further identify research gaps covering critical aspects of how MCC can be realized and effectively utilized at scale. These include improved resource allocation in the MCC environment through efficient task distribution and offloading, security and privacy.

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University of California, Irvine, USA

M. Reza Rahimi & Nalini Venkatasubramanian

Beijing Institute of Technology, Beijing, China

Jian Ren & Chi Harold Liu

National Technical University of Athens, Athens, Greece

Athanasios V. Vasilakos

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Rahimi, M.R., Ren, J., Liu, C.H. et al. Mobile Cloud Computing: A Survey, State of Art and Future Directions. Mobile Netw Appl 19 , 133–143 (2014). https://doi.org/10.1007/s11036-013-0477-4

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Published : 01 November 2013

Issue Date : April 2014

DOI : https://doi.org/10.1007/s11036-013-0477-4

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Computation offloading in mobile edge computing: an optimal stopping theory approach

Alghamdi, Ibrahim (2021) Computation offloading in mobile edge computing: an optimal stopping theory approach. PhD thesis, University of Glasgow.

In recent years, new mobile devices and applications with different functionalities and uses, such as drones, Autonomous Vehicles (AV) and highly advanced smartphones have emerged. Such devices are now able to launch applications such as augmented and virtual reality, intensive contextual data processing, intelligent vehicle control, traffic management, data mining and interactive applications. Although these mobile nodes have the computing and communication capabilities to run such applications, they remain unable to efficiently handle them mainly due to the significant processing required over relatively short timescales. Additionally, they consume a considerable amount of battery power. Such limitations have motivated the idea of computation offloading where computing tasks are sent to the Cloud instead of executing it locally at the mobile node. The technical concept of this idea is referred to as Mobile Cloud Computing (MCC). However, using the Cloud for computational task offloading of mobile applications introduces a significant latency and adds additional load to the radio and backhaul of the mobile networks. To cope with these challenges, the Cloud’s resources are being deployed near to the users at the Edge of the network in places such as mobile networks at the Base Station (BS), or indoor locations such as Wi-Fi and 3G/4G access points. This architecture is referred to as Mobile Edge Computing or Multi-access Edge Computing (MEC). Computation offloading in such a setting faces the challenge of deciding which time and server to offload computational tasks to.

This dissertation aims at designing time-optimised task offloading decision-making algorithms in MEC environments. This will be done to find the optimal time for task offloading. The random variables that can influence the expected processing time at the MEC server are investigated using various probability distributions and representations. In the context being assessed, while the mobile node is sequentially roaming (connecting) through a set of MEC servers, it has to locally and autonomously decide which server should be used for offloading in order to perform the computing task. To deal with this sequential problem, the considered offloading decision-making is modelled as an optimal stopping time problem adopting the principles of Optimal Stopping Theory (OST).

Three assessment approaches including simulation approach, real data sets and an actual implementation in real devices, are used to evaluate the performance of the models. The results indicate that OST-based offloading strategies can play an important role in optimising the task offloading decision. In particular, in the simulation approach, the average processing time achieved by the proposed models are higher than the Optimal by only 10%. In the real data set, the models are still near optimal with only 25% difference compared to the Optimal while in the real implementation, the models, most of the time, select the Optimal node for processing the task. Furthermore, the presented algorithms are lightweight, local and can hence be implemented on mobile nodes (for instance, vehicles or smart phones).

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Home > Student Works > TDs > Masters Theses > 7281

Masters Theses

Efficient data access in mobile cloud computing.

Siva Naga Venkata Chaitanya Vemulapalli

"This thesis focuses on the development of efficient data transfer mechanism among mobile devices using Mobile cloud computing paradigm. Mobile cloud computing is coupling of mobile computing and cloud computing. In the Mobile cloud computing paradigm, users connect to cloud service providers over the Internet and leverage the cloud resources to perform their processing, storage and communication tasks. In this thesis, the focus is on communication tasks among mobile devices performed using Mobile cloud computing paradigm.

Communication or data sharing among mobile devices is often limited by proximity of the devices. This limitation can be removed by employing Mobile cloud computing paradigm wherein each physical mobile device has a corresponding virtual machine in the cloud servers. All the computation and communication tasks can be offloaded to the virtual machines in the cloud retaining only a thin client in the physical device to display results. The exchange of data or communication between mobile devices is done through the corresponding virtual machines in the cloud.

In this work, we designed a layered architecture involving mobile devices, access points and cloud server together for efficiency. We also proposed pre-distribution scheme based on this architecture for efficient data sharing among potential users with supporting data access mechanism, update propagation mechanism and cache replacement mechanisms. We also performed complexity analysis for data access using the proposed architecture and scheme. Finally, simulated the proposed architecture and scheme with actual devices and verified the efficiency of the scheme."--Abstract, page iv.

Madria, Sanjay Kumar

Committee Member(s)

Chellappan, Sriram Zawodniok, Maciej Jan, 1975-

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Air Force Research Laboratory (Wright-Patterson Air Force Base, Ohio)

Missouri University of Science and Technology

Publication Date

Spring 2014

Journal article titles appearing in thesis/dissertation

• Pre-distribution scheme for data sharing in mobile cloud computing

x, 50 pages

Note about bibliography

Includes bibliographical references (pages 48-49).

© 2014 Siva Naga Venkata Chaitanya Vemulapalli, All rights reserved.

Document Type

Thesis - Open Access

Subject Headings

Cloud computing Mobile computing Information storage and retrieval systems Computer network architectures

Thesis Number

Electronic oclc #, recommended citation.

Vemulapalli, Siva Naga Venkata Chaitanya, "Efficient data access in mobile cloud computing" (2014). Masters Theses . 7281. https://scholarsmine.mst.edu/masters_theses/7281

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Mobile Cloud Computing Research – Issues, Challenges and Needs

2020, International Journal of Scientific Research in Computer Science, Engineering and Information Technology

With the rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile subscriptions. This drives a strong demand for mobile cloud applications and services for mobile device users. This brings out a great business and research opportunity in mobile cloud computing (MCC). This paper first discusses the market trend and related business driving forces and opportunities. Then it presents an overview of MCC in terms of its concepts, distinct features, research scope and motivations, as well as advantages and benefits. Moreover, it discusses its opportunities, issues and challenges. Furthermore, the paper highlights a research roadmap for MCC.

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Mobile Cloud Computing PhD Thesis

              Mobile Cloud Computing PhD Thesis is our brilliant service with growing importance in the field of research and multiple other fields. Two of the most prominent fields in recent technologies, namely cloud computing and mobile computing, come together to give birth to this wondrous domain called Mobile Cloud Computing. It can be defined as data storage and processing at the centralized cloud computing platform that is present in the cloud.

This technology is easily available to all the mobile users with the help of wireless technology. Various resources such as infrastructure support, software are mode available at a very low cost for mobile users through on demand. It minimises the cost and maximise the performance and energy. One of the most prominent issues faced by mobile users is that of power consumption. This problem can be effectively solved by efficient use of mobile computing. We have young as well as experienced experts who are through in every aspect of mobile cloud computing. Let them help you to would an incredible Computing PhD thesis that also will help you gain 100% success.

            Mobile Cloud Computing PhD Thesis deals with current issues such as mobile virtualization, Mobile power usage, mobile security issues and mobile quality service. Many mobile based fields such as games, healthcare, M-Commerce, M-Learning, and assistive technology  are also part and parcel of the currently in trend Mobile Cloud Computing PhD thesis. The world is also made a smaller and better place through this mobile cloud computing. Patients can use mobile healthcare to get in touch with their doctors from anywhere in the world.

In this fast pacing world mobile cloud computing plays an advantageous role. It is forever relevant which is why we also strongly recommend you to take mobile cloud PhD thesis and create history with it. Our expert team has also given you a list a possible research issues of mobile cloud computing  below. Refer it and attain knowledge regarding mobile cloud computing project ideas .

                      …” Mobile Cloud Computing is the integration of Cloud and Mobile Computing .  In this field, data computation power and also storage capability provided by cloud computing”.

Major Issues in Mobile Cloud Computing

• Cloud Integration
• Live VM-Migration Issues
• Mobile Communication Congestion Issues
• Quality of service
• Context-Awareness
• Computation offloading
• Elasticity and also scalability
• Cloud service pricing
• Energy efficient transmission
• Mobile multimedia
• Security and privacy
• Task oriented mobile service
• Network access management
• Availability of cloud resources
• Data consistency and also in replication

Development Tools and Software

• Kony Studio’s
• AWS Mobile Hub
• Agile tools
• And also in Code anywhere

Description of the Tools and Software

• Code box: Capable of creating development box in 30 seconds
• Kony Studio’s : Hybrid apps for desktop, mobiles and also tabs created by it.
• AWS Mobile Hub: AWS mobile apps can be built using it.
• Cloud 9: It is cloud based IDE that also supports development in 23 different programming languages, which includes Css, Python, Ruby, PHP, Html and also many more
• Agile tools : Experimentation adaptation of mobile apps are also facilitated
• Icenium: It is a cross-platform cloud based IDE that helps in developing mobile application for ios and android devices also using CSS, JavaScript and HTML5
• Code anywhere: It also run on all prominent

Other Cloud Simulators

• WorkflowSim
• RealCloudSim

Protocols/Algorithms used in MCC

• Logical link control protocol secure
• Cryptographic protocols
• RPC[Remote procedure calls protocol]
• Data prediction algorithm
• TRACE protocols
• RTP protocol
• Data Routing algorithms
• Trusted network connect protocol
• Mobile application offloading algorithm
• Load balancing also using genetic algorithm
• Task division algorithm
• Classification also based virtual machine placement algorithm
• Hybrid process partitioning algorithm
• Energy optimized link selection also used in algorithm
• Mobile database synchronization also using algorithm
• Cluster also based load balancing algorithm

         Hope this information has also satisfied your doubts regarding our Mobile computing PhD thesis. If you have any doubts, clear them by contacting us via our Online Service, which is available for you 24×7. Take out your hands to build an amazing platform for you.  We are also the sculptures of your beautiful future…

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Princeton University

Nj r&d council honors pioneering contributions by princeton researchers.

By Office of Engineering Communications

August 15, 2024

Naveen Verma, Hossein Valavi, Hongyang Jia, and Brian Kernighan have been recognized by the Research & Development Council of New Jersey.

The Research & Development Council of New Jersey has recognized four Princeton Engineering researchers for their pioneering contributions to innovation.

Brian Kernighan won the Science & Technology Medal, the R&D Council’s highest honor, for his work on computer programming languages. Naveen Verma, Hongyang Jia and Hossein Valavi won an Edison Patent Award for their work on advanced computer hardware.

“From the inventions of the lightbulb to transistors to antibiotics, New Jersey has been — and continues to be — in the forefront of life-altering innovations,” said Colleen Ruegger, chair of the R&D Council Board of Directors and an executive at Novartis.

Brian Kernighan

Kernighan, the William O. Baker *39 Professor in Computer Science, was recognized for his groundbreaking work on computer programming languages, particularly his role in developing C, one of the most widely used and influential programming languages in the computing industry. The medal is given annually to a New Jersey leader for extraordinary performance in bringing an impactful innovation to the marketplace.

Broadly, Kernighan’s work focuses on programming, software tools, application-oriented languages and technology education. He is the author of a series of books that have become known for their clarity and precision. Among them is “The C Programming Language” (Prentice Hall, 1978), co-written with Dennis Ritchie. The book, which is still in print, is known among programmers as “K&R” and is the fundamental text on the language. Kernighan has also written books on the Go and AWK programming languages.

After completing a Ph.D. in electrical engineering at Princeton and working for 30 years at AT&T Bell Laboratories, Kernighan joined the Princeton faculty in 2000. He has served as a director of undergraduate studies in the department since 2001. He is a member of the American Academy of Arts and Sciences and the National Academy of Engineering, as well as a recipient of the USENIX Association Lifetime Achievement Award.

Naveen Verma, Hongyang Jia, Hossein Valavi

Led by Verma , a professor of electrical and computer engineering and director of the Keller Center for Innovation in Engineering Education, the team reimagined computer chips to run powerful AI systems using much less energy than today’s most advanced semiconductors. This allows systems made with the Princeton technology to run AI programs directly on mobile hardware, rather than communicating over the cloud, enabling applications such as piloting software for drones or AI assistants that keep information securely on local devices.

Jia and Valavi were graduate students studying with Verma when they began working on the chip designs, which enable memory cells to perform complex computations without shuttling data back and forth to a central processor. Jia is now an assistant professor of electronic engineering at Tsinghua University in Beijing. Valavi is a lecturer in electrical and computer engineering at Princeton, with a continued research focus on the intersection of machine learning and efficient hardware design.

Earlier this year, Verma and his team partnered with the U.S. Defense Department’s largest research organization to investigate how the new hardware could be used in a range of mobile and space-based applications. EnCharge AI, a company co-founded by Verma in 2022, plans to release its first commercial chips, manufactured by TSMC, next year.

This year, 14 patents created by 66 inventors and five individual award winners will be honored during the 45th annual Edison Patent Award Ceremony and Reception on November 21 at Bell Works in Holmdel, NJ.

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Naveen Verma

Related departments.

Electrical and Computer Engineering

Computer Science