Mobile Cloud Computing System Research Articles

Use of Context Aware Game Theoretic Approach to Minimize the Overall Energy Consumption of Mobile Cloud Computing

Mobile cloud computing (MCC) is an emerging technology which has been an important and a hot research topic, recently. It is a boon for resource constrained mobile phones as it allows them to offload the computation to the cloud servers, which helps them to execute the code remotely. There have been various models, designs, techniques and tools to support the MCC but as the user base and their demands are increasing day by day, we are required to come up with more efficient models which are well- defined and cost-effective. Building block of MCC or any such technology is user, no user no usage. Hence, the cost of this technology must be minimized, for the growth of MCC. In this paper, we talk about the role of energy consumption in reducing the cost of MCC for the mobile user. We propose a context-aware-game-theoretic approach to minimize the overall energy consumption of the MCC system; which, in turn, will minimize the cost of the service, for the user.

Design of an Interactive Geo-Location Mobile Application for Civil Societies in East Africa

This paper reports the developed mobile application through the use of advanced technologies such as mobile computing, cloud computing and Global Positioning System (GPS) in order to solve the challenges. The purpose of this study is find out how the recent advances and mass adoption of ICT by the public can best be leveraged to enhance the performance of Civil Society Organizations (CSOs) in East Africa. The data collection techniques used are through questionnaires, observations and conducting interviews with different stakeholders in the civil society arena i.e., the donors, the CSOs and the people in society that are given a voice by civil societies. In the system development phase, agile development methodology was used. Analysis of the data collected showed that there is a gap in the availability of a single or a centralized platform which can be easily accessed, user-friendly and reliable where different actors can readily get reliable and up-to-date information about the available CSOs they are interested in. To address this problem, an interactive online directory of CSOs has been developed. The platform is mobile based and enables CSOs to register and fill up their current details, thus ensuring that there is always correct and updated information. The platform is equipped with, among other features, a geo-mapping facility which enables users of the system to correctly geo-locate their civil societies of interest on a map view. The results of system evaluation showed that 88.125% of users were satisfied with the system basing on the evaluation criteria.

Bridging Internet of Things and Wireless Sensor Networks: Applications and Challenges

With the increasing demand of Wireless Sensor Networks (WSNs) innovations, it cuts numerous zones mobile communication, cloud computing and embedded system in modern living. Inter-net of Things (IoT) is widely used in environmental condition monitoring, logistic support and interfacing sensors and actuators wirelessly, which can be controlled from very long distance. This offers the capacity to control the world from a corner of a room. Wherein sensors and actuators operate reliably with the help of IoT. Wireless data transmission that uses Radio Frequency (RF) has major technical burdens and security vulnerability. The IoT replaced RF as it provides secure transmission capabilities. In this paper, we exhibit a technical overview of WSNs and IoT especially their drawbacks and challenges. Additionally, this paper discusses the progress of the WSNs and IoT innovation.

Mobility Management Challenges and Solutions in Mobile Cloud Computing System for Next Generation Networks

As of late, there is a dynamic improvement in the field of mobile computing, and mobile cloud computing (MCC) has been familiar with a potential development for portable administrations. Likewise, the mobile phones and their applications have high framework in the administration at any point and grow more rapidly. Again, MCC is depended upon to deliver on a very basic level increasingly inventive with multi applications. Moreover, Mobile handling incorporates versatile equipments, portable correspondence, cell programming, and right now there are various compact cloud applications. Versatility the board for supporting the area following and area based assistance is a significant issue of savvy city by giving the way to the smooth transportation of individuals and products. The mobility is valuable to contribute the development in both open and private transportation frameworks for keen urban communities. As the information is distributed computing and getting to it with cell phones all the exchanges experience the system so it is powerless against assault. For keeping the utilization of this fundamental apparatus of steady in this development world we are giving a portion of the answers for these difficulties to address in the field of MCC. In this paper, the main challenges faced by the enterprises and their corresponding solutions are discussed with the mobile cloud computing.

5G-destekli Mobil Uç Hesaplama Çerçevesi

Nowadays, mobile devices have increased rapidly due to the fact that devices connected to the Internet become more portable and can respond to user requests remotely. With this increase, the workload of mobile devices has also increased. However, it is sometimes impossible for mobile devices with limited resources to evaluate some critical and large transactions locally on their own and hence, it may adversely affect the user experience. The MEC (Mobile Edge Computing) system, which enables these processes to be evaluated and returned to the device faster and more efficiently, is now made possible with the 5G-network technology. The MEC concept differs from the previous MCC (Mobile Cloud Computing) system. In this study, the content of the basic MEC system such as the comparison of the MEC system with the MCC, the advantages it offers to mobile devices, usage cases, offloading scenarios, effective resource allocation and mobility management are discussed. In addition, in the scope of the study, high performance studies using MEC system effectively in the literature were examined.

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

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.

A Load and Distance Aware Cloudlet Selection Strategy in Multi-Cloudlet Environment

Day to day the usage of mobile devices (MD) is growing in people's lives. But still the MD is limited in terms of memory, battery life time, processing capacity. In order to overcome these issues, the new emerging technology named mobile cloud computing (MCC) has been introduced. The offloading mechanism execute the resource intensive application on the remote cloud to save both the battery utilization and execution time. But still the high latency challenges in MCC need to be addressed by executing resource intensive task at nearby resource cloud server. The key challenge is to find optimal cloudlet to execute task to save computation time. In this article, the authors propose a Round Robin algorithm based on cloudlet selection in heterogeneous MCC system. This article considers both load and distance of server to find optimal cloudlet and minimize waiting time of the user request at server queue. Additionally, the authors provide mathematical evaluation of the algorithm and compare with existing load balancing algorithms.

Multi-User Multi-Task Offloading and Resource Allocation in Mobile Cloud Systems

We consider a general multi-user Mobile Cloud Computing (MCC) system where each mobile user has multiple independent tasks. These mobile users share the computation and communication resources while offloading tasks to the cloud. We study both the conventional MCC where tasks are offloaded to the cloud through a wireless access point, and MCC with a computing access point (CAP), where the CAP serves both as the network access gateway and a computation service provider to the mobile users. We aim to jointly optimize the offloading decisions of all users as well as the allocation of computation and communication resources, to minimize the overall cost of energy, computation, and delay for all users. The optimization problem is formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. For the case without a CAP, an efficient approximate solution named MUMTO is proposed by using separable semidefinite relaxation (SDR), followed by recovery of the binary offloading decision and optimal allocation of the communication resource. To solve the more complicated problem with a CAP, we further propose an efficient three-step algorithm named MUMTO-C comprising of generalized MUMTO SDR with CAP, alternating optimization, and sequential tuning, which always computes a locally optimal solution. For performance benchmarking, we further present numerical lower bounds of the minimum system cost with and without the CAP. By comparison with this lower bound, our simulation results show that the proposed solutions for both scenarios give nearly optimal performance under various parameter settings, and the resultant efficient utilization of a CAP can bring substantial cost benefit.

A load-aware resource allocation and task scheduling for the emerging cloudlet system

Cloudlet-assisted mobile cloud computing (MCC) emerges as a vital paradigm to address the problems of mobile services such as application time-out, data caching and traffic congestion in wireless network. The cloudlet has adequate resources to process multiple mobile requests simultaneously, but it is not as sufficient as a remote cloud data center. Currently the performance of MCC system is a subject to the lengthy network transmission latency due to the long distance between cloudlet and remote cloud. In this article, we focus on the variable user’s QoS requirements and budget of cloudlet provider, design a load-aware resource allocation and task scheduling (LA-RATS) strategy which adaptively allocates resource in MCC system for delay-tolerant and delay-sensitive mobile applications according to cloudlet’s load profile. Subsequently, a tree generation based task backfilling algorithm is proposed to raise the utilization of the cloudlet. Particularly, when cloudlet is overloaded, the restrictions of delay-sensitive applications’ deadlines are satisfied through further offloading the allocated delay-tolerant tasks in the cloudlet to distant cloud. From several systematic evaluations, it is shown that our strategy can significantly reduce the cloudlet’s monetary cost and turnaround time for delay-tolerant applications, and increase the deadline satisfaction rate of delay-sensitive applications.

Task Offloading in Heterogeneous Mobile Cloud Computing: Modeling, Analysis, and Cloudlet Deployment

In this paper, we consider a heterogeneous mobile cloud computing (HMCC) system that consists of remote cloud servers, local cloudlets, task offloading mobile devices (TMDs), non-task offloading MDs (NTMDs), and radio access networks such as cellular networks and WLANs. TMDs have the capability of task offloading to remote cloud servers or cloudlets, whereas NTMDs are conventional cellular users that do not have such capability. By using stochastic geometry, we analyze the outage probability of task offloading in the MCC system with only remote cloud servers and that in the HMCC with both remote cloud servers and cloudlets. The analysis provides useful information, i.e., how the varying system parameters affect the outage probability. From the analysis, we show that there is an intrinsic limitation in reducing the outage probability in the MCC system due to the outage when accessing remote cloud servers. In addition, we show that the use of cloudlets is a promising solution to overcome this limitation. However, a tradeoff exists in using cloudlets due to their deployment and operation costs. Thus, to address this tradeoff, we also study the optimal cloudlet deployment to maximize the cloud service provider’s profit while guaranteeing maximum outage probability requirements.

Agile Development of Various Computational Power Adaptive Web-Based Mobile-Learning Software Using Mobile Cloud Computing

Mobile Cloud Computing (MCC) aims to improve all mobile applications such as m-learning systems. This study presents an innovative method to use web technology and software engineering's best practices to provide m-learning functionalities hosted in a MCC-learning system as service. Components hosted by MCC are used to empower developers to create m-learning systems in agile manner. The developed m-learning systems -as tenants of MCC-learning- provide cloud-quality services to their clients. The solution is a service-oriented and three-layered MCC system encompassing innovative m-learning components. To adapt e-contents sent to clients, a Device-Adaptive-Rendering component is used which takes platform, capability, physical and other contextual diversities of mobile devices into account. Using MCC increases the development speed and decreases the Lines Of Code (LOC) to build m-learning systems. Experiment outcomes demonstrate improvement in time taken by learners to answer the learning task questions and accuracy rates.

Analyzing the service availability of mobile cloud computing systems by fluid-flow approximation

Mobile cloud computing (MCC) has become a promising technique to deal with computation- or data-intensive tasks. It overcomes the limited processing power, poor storage capacity, and short battery life of mobile devices. Providing continuous and on-demand services, MCC argues that the service must be available for users at anytime and anywhere. However, at present, the service availability of MCC is usually measured by some certain metrics of a real-world system, and the results do not have broad representation since different systems have different load levels, different deployments, and many other random factors. Meanwhile, for large-scale and complex types of services in MCC systems, simulation-based methods (such as Monte-Carlo simulation) may be costly and the traditional state-based methods always suffer from the problem of state-space explosion. In this paper, to overcome these shortcomings, fluid-flow approximation, a breakthrough to avoid state-space explosion, is adopted to analyze the service availability of MCC. Four critical metrics, including response time of service, minimum sensing time of devices, minimum number of nodes chosen, and action throughput, are defined to estimate the availability by solving a group of ordinary differential equations even before the MCC system is fully deployed. Experimental results show that our method costs less time in analyzing the service availability of MCC than the Markov- or simulation-based methods.

A modeling and simulation framework for mobile cloud computing

Mobile cloud computing (MCC) is an emerging paradigm for transparent elastic augmentation of mobile devices capabilities, exploiting ubiquitous wireless access to cloud storage and computing resources. MCC aims at increasing the range of resource-intensive tasks supported by mobile devices, while preserving and extending their resources. Its main concerns regard the augmentation of energy efficiency, storage capabilities, processing power and data safety, to improve the experience of mobile users. The design of MCC systems is a challenging task, because both the mobile device and the Cloud have to find energy-time tradeoffs and the choices on one side affect the performance of the other side. The analysis of the MCC literature points out that all existing models focus on mobile devices, considering the Cloud as a system with unlimited resources. Also, to the best of our knowledge, no MCC-specific simulation tool exists. To fill this gap, in this paper, we propose a modeling and simulation framework for the design and analysis of MCC systems, encompassing all their components. The main pillar of the proposed framework is the autonomic strategy consisting of adaptive loops between every mobile devices and the Cloud. The proposed model of the mobile device takes into account online estimations of the actual Cloud performance – not only the nominal values of the performance indicators. At the same time, the model of the Cloud takes into consideration the characteristics of the workload, to adapt its configuration in terms of active virtual machines and task management strategies. Moreover, the developed discrete event simulator is an effective tool for the evaluation of an MCC system as a whole, or single components, considering different classes of parallel jobs.

Large Scale Cloudlets Deployment for Efficient Mobile Cloud Computing

Interest in using Mobile Cloud Computing (MCC) for compute intensive mobile devices jobs such as multimedia applications has been growing. In fact, many new research efforts were invested to maximize the mobile intensive jobs offloading to the cloud within the cloud system coverage area. However, a large scale MCC deployment challenges and limitations are not considered. In this paper, a large scale Cloudlet-based MCC system deployment is introduced, aiming at reducing the power consumption and the network delay of multimedia applications while using MCC. The proposed deployment offers a large coverage area that can be used by mobile devices while they are moving from one location to another to reduce broadband communication needs. Practical experimental results and simulated results using show that using the proposed model reduces the power consumption of the mobile devices as well as reducing the communication latency when the mobile device requests a job to be performed remotely while satisfying the high quality of service requirements of mobile users.

Wireless-Uplinks-Based Energy-Efficient Scheduling in Mobile Cloud Computing

Mobile cloud computing (MCC) combines cloud computing and mobile internet to improve the computational capabilities of resource-constrained mobile devices (MDs). In MCC, mobile users could not only improve the computational capability of MDs but also save operation consumption by offloading the mobile applications to the cloud. However, MCC faces the problem of energy efficiency because of time-varying channels when the offloading is being executed. In this paper, we address the issue of energy-efficient scheduling for wireless uplink in MCC. By introducing Lyapunov optimization, we first propose a scheduling algorithm that can dynamically choose channel to transmit data based on queue backlog and channel statistics. Then, we show that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in a channel-aware MCC system. Simulation results show that the proposed scheduling algorithm can reduce the time average energy consumption for offloading compared to the existing algorithm.

Scalable Cloudlet-based Mobile Computing Model

Mobile Cloud Computing (MCC) has been introduced as a feasible solution to the inherited limitations of mobile computing. These limitations include battery lifetime, processing power and storage capacity. By using MCC, the processing and the storage of intensive mobile device jobs will take place in the cloud system and the results will be returned to the mobile device. This reduces the required power and time for completing such intensive jobs. However, connecting mobile devices with the cloud, suffers from the high network latency and the huge transmission power consumption especially when using 3G/LTE connections. On the other hand, multimedia applications are the most common applications in today's mobile devices; such applications require high computing resources. In this paper, a Cloudlet-based MCC system is introduced, aiming at reducing the power consumption and the network delay of multimedia applications while using MCC. The MCC concepts with the proposed Cloudlet framework are integrated and a new scalable framework for the MCC model is proposed. Our practical experimental results using multimedia applications show that using the proposed model reduces the power consumption of the mobile devices as well as reducing the communication latency when the mobile device requests a job to be performed remotely while satisfying the high quality of service requirements.