A new Opportunity of Rapidly Changing World


In Last few decades booming mobile market led us into a new era where some new types of technology emerge Mobile Cloud Computing (MCC) is one of them. This technology brings us to a whole new set of opportunities and challenges. In this paper, I am going to present overview of that how this works, what is its architecture and characteristics. And we will also explore that what could be the new area where we can go for deep research. I will present the issues and concerns regarding Mobile cloud computing and try to find some approaches that may resolve these issues. And the way forward to do future work is discussed.

Keywords—Cloud Computing for Mobiles; MCC; Future technologies; MCC Architecture; Challenges in MCC


In past few years, popularity of cloud computing growing exponentially among developers’ communities and companies. Cloud computing is providing virtualized software, platform, and infrastructure on demand for customers [1].
Rise of the Internet and cloud services encouraged the development of the Mobile Terminals. But there are some issues which is faced by Mobile computing like limited battery capacity, unstable wireless connectivity, limited size of storage, limited RAM, bandwidth variability etc. The Service quality provided by network terminals are more prone to damage by change in environment like change in weather, terrain of earth or buildings. To solve these problems a probable solution emerges as cloud computing through these end user mobile devices which eventually led us to concept of Mobile cloud computing. In Mobile cloud computing computational resources and application software are provided by the service provider. MCC allocate the desired resources virtually to the user devices which do their computations in virtual space and provide services to the users.


The mobile cloud computing is described as an integration of cloud computing technology with mobile devices [2]. In this technology mobile devices obtain resources from cloud by generalization of the smartphones. Users can get the all needed information very easily with the help of MCC.


In the given figure users’ wireless mobile devices are connected with given network terminal (e.g., access point, mobile base station or satellites) through mobile network. This mobile network act as a bond between user device and internet service provider.
The mobile network services which act as interface for communication among cloud service and user device is connected through internet to cloud. The user can get cloud services via internet. It could be done through any type of internet service e.g. Wi-Fi, GPRS, 2G, 3G or 4G depends on the telecom service operator.
In Cloud computing architecture there are three type of services provide Software as a service, Platform as a service and Infrastructure as a service. In SaaS user get the software’s and database to use according to his/her needs. In PaaS gives user an online platform where he can develop, code, test his application. In IaaS user get whole infrastructure as a service like virtual machines.


As we earlier discussed that mobile cloud computing is the combination of two technologies so it has characteristics of both cloud computing as well as mobile computing.
If we discuss about mobile computing characteristics then it consists with services like communication, movability and entertainment. Mobile phones have basic fetcher of communication as it was made for communicating over cellular network from different geographical locations. As we know cellular network provides communication from different-different locations movability becomes the key characteristic of mobile computing. We can access internet from anywhere and anytime through mobile devices. In today’s world of smartphone entertainment can be availed by many different applications available on play store.
Cloud computing characters consist with services like Resource pooling, On-demand services, Rapid elasticity, Virtualization, Broad network access, Measured Service. Cloud computing gives us option to use the different devices as a single device by use pooling technique. In pooling we combine storage and computing capacity of different devices and treat them as one device. These services can be provided On-demand when user needs this. When demand increase suddenly then we can mount new devices rapidly into pool. With the help of Virtualization provider can integrate devices virtually and decrease the cost dynamically. User can access the cloud services by using any device at any time. And last but not least provider and user both can control and monitor the use of services.


Here, these are some issues of Mobile cloud computing and approaches related to them.\nA. Limited Bandwidth\nMobile cloud computing has one of the basic limitations of bandwidth. In past this problem tackled by increasing bandwidth like 2G to 3G, 3G to 4G but it comes with jump in overall cost of the bandwidth.
A framework for conversion of bandwidth was discussed in [3], which may allocate and adjust bandwidths between operator and users for service level agreement. And system can also utilize this bandwidth in future.
Reference [4] discussed that how end user devices can co-corporate and may share bandwidth among them when content is same.\nB. Limited power supply
We may reuse the RAM and internal storage of device for number of times but we can’t do the same with its battery. Once we consume its battery it can only be reused by providing power from an outer resource So Mobile cloud computing has an issue of limited power.
We have some traditional methods to solve this problem like having a monitor on battery resource. As in Reference [5] discussed to display accurate power left in battery. By monitoring the uses pattern and user behaviors we can optimize some of the fetchers to save energy e.g. auto brightness settings, frequency of CPU.
In some other approaches we may use the cloud to do background computations. Offloading method was proposed in reference [6]. In this approach application runs partially on cloud to reduce the energy using.


Mobile cloud computing has an asymmetry in hardware devices, architecture, cloud platforms, hardware and network Telco’s. software and hardware’s may cause asymmetry between mobile devices. Asymmetry cloud caused by the different cloud service providers. Asymmetry in network caused by different network system environment. For the asymmetrical environment reference [7] come with a heterogeneous mobile cloud model. In reference [8] author represents some novel mathematical framework and an architecture to resource sharing in a heterogenous system.

Security mobile users security

Smartphones can be easily attacked during the operations related to connection [9]. In the era of growing application market probable chances of getting attacked is increasing exponentially by infected applications. we may defend our applications by proving them some sort of security software protection.
Knowledge-Based Temporal Abstraction was presented in Reference [10] which can detect any suspicious activities by measuring data continuously. In reference [11] they discussed about the Digital Right Manager which is used for protection in Motorola devices.

 Security of cloud data

The emerging mobile cloud computing provided us a leverage of storing our data on cloud but there are some major concerns about the security of the data. As we know that people upload their personal data on cloud so it causes a privacy concern. Recently some hackers revealed the private data and photos of celebrities on internet which raises a serious concern.
We have some methods of data protection in cloud storages. Just instance, allowing the transferring of only those data which are in encrypted form [12], or a multiparty authorization system cold work. Even we can use the digital signatures to establish a communication session between cloud storage and mobile devices. And for encryption we can use the different encryption algorithms like sha 512 or rail fence. We have number of encryption algorithms which is suitable for different types of problems.

Transeferring Computational tasks on cloud

Transferring the computational task of mobile application to the cloud could improve the efficiency and performance of the mobile device as well as applications. This is also known as computational offloading. It a leverage provided by mobile cloud computing to the mobile devices and applications.
One method could be that refer computational tasks to nearby devices and create a mobile device cloud network [13]. By this user can easily connect to nearby devices and use them by single point control.

Energy Efficient Computational Offloading Framework can work in offline as well as online mode [14]. When we have all required resources locally on device then we call it offline mode. And in online mode we have preconfigured the heavy services and computational work on demand basis.it provides us energy efficiency


Mobile Cloud Computing provide us cloud computing fetchers on our mobile computing devices. This gives users a leverage to do high computational task on their mobile devices. Here in this paper we discussed there some of the fetchers and architecture. This paper gives us an overview of the mobile cloud computing. And we also discussed some of the challenges and possible approaches to overcome them. We classified problems in this field and approaches to tackle them like bandwidth limitation, offloading of the heavy task over cloud, Asymmetry problem and many more.\nIf We talk about future areas of research in mobile cloud computing, there is a lot of work left to do e.g. issues related to management, efficiency in resource allocation, improvement in task delivery, improvement in service quality and cost reduction.


  1.  Foster, Ian, et al. \"Cloud computing and grid computing 360-degree compared.\" 2008 grid computing environments workshop. Ieee, 2008.
  2.  Othman, Mazliza, Sajjad Ahmad Madani, and Samee Ullah Khan. \"A survey of mobile cloud computing application models.\" IEEE communications surveys & tutorials 16.1 (2013): 393-413.
  3.  Olanrewaju, Rashidah Funke, Abdullah Egal, and Othman O. Khalifa. \"Bandwidth Conservation Framework for Mobile Cloud Computing: Challenges and Solutions.\" 2014 International Conference on Computer and Communication Engineering. IEEE, 2014.
  4.  Jin, Xin, and Yu-Kwong Kwok. \"Cloud assisted P2P media streaming for bandwidth constrained mobile subscribers.\" 2010 IEEE 16th international conference on parallel and distributed systems. IEEE, 2010.
  5.  Shye, Alex, Benjamin Scholbrock, and Gokhan Memik. \"Into the wild: studying real user activity patterns to guide power optimizations for mobile architectures.\" Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture. 2009.
  6.  Saab, Salwa Adriana, Ali Chehab, and Ayman Kayssi. \"Energy efficiency in mobile cloud computing: Total offloading selectively works. does selective offloading totally work?.\" 2013 4th Annual International Conference on Energy Aware Computing Systems and Applications (ICEAC). IEEE, 2013.
  7.  Si, Pengbo, et al. \"QoS-aware dynamic resource management in heterogeneous mobile cloud computing networks.\" China Communications 11.5 (2014): 144-159.
  8.  Nishio, Takayuki, et al. \"Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud.\" Proceedings of the first international workshop on Mobile cloud computing & networking. 2013.
  9.  La Polla, Mariantonietta, Fabio Martinelli, and Daniele Sgandurra. \"A survey on security for mobile devices.\" IEEE communications surveys & tutorials 15.1 (2012): 446-471.
  10.  Shabtai, Asaf, Uri Kanonov, and Yuval Elovici. \"Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method.\" Journal of Systems and Software 83.8 (2010): 1524-1537.
  11.  Bhatt, Siddharth, Radu Sion, and Bogdan Carbunar. \"A personal mobile DRM manager for smartphones.\" Computers & Security 28.6 (2009): 327-340.
  12.  Garg, Preeti, and Vineet Sharma. \"An efficient and secure data storage in Mobile Cloud Computing through RSA and Hash function.\" 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). IEEE, 2014.
  13.  Mtibaa, Abderrahmen, Khaled A. Harras, and Afnan Fahim. \"Towards computational offloading in mobile device clouds.\" 2013 IEEE 5th international conference on cloud computing technology and science. Vol. 1. IEEE, 2013.
  14.  Khan, Abdul Nasir, et al. \"A cloud-manager-based re-encryption scheme for mobile users in cloud environment: a hybrid approach.\" Journal of Grid Computing 13.4 (2015): 651-675.