Mobile Cloud Applications Research Articles

An efficient dynamic decision-based task optimization and scheduling approach for microservice-based cost management in mobile cloud computing applications

The use of smartphones and mobile devices has increased significantly, as have Mobile Cloud Applications based on cloud computing. These applications are used in various fields, including Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. While existing cloud-based frameworks provide such services on Virtual Machines, they incur problems such as overhead, lengthy boot time, and high costs. To address these issues, the paper proposes a Dynamic Decision-Based Task Scheduling Approach for Microservice-based Mobile Cloud Computing Applications (MSCMCC) that can run delay-sensitive applications and mobility with less cost than existing approaches. The study focuses on Task Offloading problems on heterogeneous Mobile Cloud servers. It proposes a Task Offloading and Microservices based Computational Offloading (TSMCO) framework to solve Task Scheduling in steps such as Resource Matching, Task Sequencing, and Task Offloading. Experimental results show that the proposed MSCMCC and TSMCO enhance Mobile Server Utilization while minimizing costs and improving boot time, resource utilization, and task arrival time for various applications. Specifically, the proposed system effectively reduces the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.

AI-based big data analytics model for medical applications

Today, people's daily lives depended on their mobile phones, facilitating many beneficial tasks. Mobile computing and cloud computing combine cloud-based mobile computing to improve their features and overcoming their disadvantages such as memory space, processor, and longer battery life. By combining the four "V's" of volume, variety, velocity and veracity, Big Data analytics tools allow the utility to be extracted through knowledge. This paper explores the development of interconnected healthcare systems and the contribution of mobile cloud applications and big data analysis. With the implementation of cloud computing in the healthcare system, inspiration and development of interconnected health - software and care services are given. Big Data services for the healthcare sector on a cloudlet-based mobile cloud computing platform. Big data analysis methods, equipment, and solutions are examined. We have concluded the development of interconnected medical systems using Big Data and mobile cloud computing.

Mobile Cloud Computing

Cloud computing is used to offer programmes to mobile devices via mobile cloud computing. These mobile apps can be delivered remotely utilizing development tools that provide speed and flexibility. Cloud services allow for speedy development and revision of mobile cloud applications. It combines mobile app development and cloud-based technologies to make cloud and mobile app distribution easier. In distant data centers, data is stored and apps are run.. Despite its increasing popularity, fully achieving the potential of mobile computing is difficult due to inherent constraints such as resource scarcity, frequent disconnections, and mobility. By executing mobile apps on resource providers outside of the mobile device, mobile cloud computing can address these issues. In this paper, we provide an in-depth analysis of mobile cloud computing research, with an emphasis on mobile cloud computing's unique problems. We propose a taxonomy based on the major issues in this field and the many approaches taken to overcome them. We finish the paper with a critical assessment of difficulties that have yet to be fully addressed, as well as recommendations for future research.

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks.

Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications’ execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.

A Cost and Energy Efficient Task Scheduling Technique to Offload Microservices Based Applications in Mobile Cloud Computing

The number of smartphone users and mobile devices has increased significantly. The Mobile Cloud Applications based on cloud computing have also been increased. The mobile apps can be used in Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. The modern cloud-based frameworks provide such services on Virtual Machines. The existing frameworks worked well, but these suffered the problems such as overhead, resource utilization, lengthy boot-time, and cost of running Mobile Applications. This study addresses these problems by proposing a Dynamic Decision-Based Task Scheduling Technique for Microservice-based Mobile Cloud Computing Applications (MSCMCC). The MSCMCC runs delay-sensitive applications and mobility with less cost than existing approaches. The study focused on Task Scheduling problems on heterogeneous Mobile Cloud servers. We further propose Task Scheduling and Microservices based Computational Offloading (TSMCO) framework to solve the Task Scheduling in steps, such as Resource Matching, Task Sequencing, and Task Scheduling. Furthermore, the experimental results elaborate that the proposed MSCMCC and TSMCO enhance the Mobile Server Utilization. The proposed system effectively minimizes the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.

On the Characterization and Risk Assessment of AI-Powered Mobile Cloud Applications

Ultra-reliable low-latency communication supports powerful mission-critical applications such as artificial intelligence-enabled mobile cloud applications designed to deliver the quality of service and quality of experience to their users. However, whether existing security mechanisms are ready to address the risks emerging from these applications operating over ultra-fast 5G and 6G infrastructures is an open question. The complexity of finding answers to this question is partly due to the lack of means to measure software applications’ intelligence levels and partly due to the limitations of existing risk assessment approaches. In this paper, first, we propose an ability-based scale to characterize intelligent software applications. After that, we propose a semi-quantitative approach for threat modeling and risk analysis of intelligent software applications. Focusing on Android, we define three intelligent mobile cloud applications’ scenarios and demonstrate the feasibility of the proposed scale and approach. We perform their risk analyses for assessing the readiness of Android security mechanisms to mitigate their risks and identify open problems. We propose to rethink intelligent mobile cloud computing applications’ characterization and warn security experts to redesign their security mechanisms to serve evolving privacy, security, and trust requirements.

Ready-time partitioning algorithm for computation offloading of workflow applications in mobile cloud computing

To augment the capabilities of mobile devices, application partitioning solutions in mobile cloud computing have emerged to decide the execution location of each application part between mobile device and cloud servers. To adapt to the network and server status changes during application execution, we propose a cloudlet-assisted ready-time partitioning technique, which partitions each task of the users’ workflow exactly when it is ready to run. It aims at minimizing the energy consumption of each mobile user’s device, while meeting user-defined deadlines. The proposed deadline distribution–ready-time partitioning algorithm has two phases: deadline distribution phase, which distributes each user deadline among its workflow tasks before application execution, and ready-time partitioning phase, which partitions each ready task by effectively considering the network and cloudlet status. Using real mobile applications, the experimental results prove its adaptability to environmental changes, its energy-efficiency, and its ability to satisfy the deadline over other methods.

Task offloading concept using cloud simulations in mobile computing

The seamless execution of application is important in mobile cloud applications. The limited processing power of mobile devices with insufficient resources to handle computational processing of complex applications is compared to the cloud infrastructure, which can handle multiple tasks simultaneously due to its multiple virtual machines and hosts. By simulating the concept of task offloading, application partitioning is handled to accommodate heavy computing requirements for mobile devices with insufficient resources. This research paper proposes the usage of CloudSim to simulate cloud architecture and the task-offloading concept using cloudlets. The authors present various application offloading architecture, cloudlet architecture, security challenges and issues in the realm of mobile cloud computing.

Task offloading concept using cloud simulations in mobile computing

The seamless execution of application is important in mobile cloud applications. The limited processing power of mobile devices with insufficient resources to handle computational processing of complex applications is compared to the cloud infrastructure, which can handle multiple tasks simultaneously due to its multiple virtual machines and hosts. By simulating the concept of task offloading, application partitioning is handled to accommodate heavy computing requirements for mobile devices with insufficient resources. This research paper proposes the usage of CloudSim to simulate cloud architecture and the task-offloading concept using cloudlets. The authors present various application offloading architecture, cloudlet architecture, security challenges and issues in the realm of mobile cloud computing.

Mobile Cloud Computing Research – Issues, Challenges and Needs

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.

To cloud or not to cloud: an on-line scheduler for dynamic privacy-protection of deep learning workload on edge devices

Recently deep learning applications are thriving on edge and mobile computing scenarios, due to the concerns of latency constraints, data security and privacy, and other considerations. However, because of the limitation of power delivery, battery lifetime and computation resource, offering real-time neural network inference ability has to resort to the specialized energy-efficient architecture, and sometimes the coordination between the edge devices and the powerful cloud or fog facilities. This work investigates a realistic scenario when an on-line scheduler is needed to meet the requirement of latency even when the edge computing resources and communication speed are dynamically fluctuating, while protecting the privacy of users as well. It also leverages the approximate computing feature of neural networks and actively trade-off excessive neural network propagation paths for latency guarantee even when local resource provision is unstable. Combining neural network approximation and dynamic scheduling, the real-time deep learning system could adapt to different requirements of latency/accuracy and the resource fluctuation of mobile-cloud applications. Experimental results also demonstrate that the proposed scheduler significantly improves the energy efficiency of real-time neural networks on edge devices.

Mobile Cloud Forensic Readiness Process Model for Cloud-Based Mobile Applications

Nowadays, mobile cloud applications have attracted millions of smartphone users due to the proliferation of cyber technologies and a wide range of applications. Mobile cloud forensic investigation methodologies need tremendous growth due to the increasing crime incidents. The forensic readiness model plays a significant role in the forensic investigation framework by ensuring the ease of investigation to the forensic investigator. The existing forensic readiness supports either the mobile device or cloud environment, which lacks to prepare the information for the investigation. This article presents a mobile cloud forensic readiness process model to identify the factors and prepare the information that effectively supports forensic investigations. The proposed model involves requirements for the mobile cloud forensics from multiple perspectives with the aim of developing the forensic-ready system. As a result, the proposed forensic readiness model enables the mobile cloud forensic to improve the accuracy of investigation as well as reduces the investigation time significantly.

Improving the impact of power efficiency in mobile cloud applications using cloudlet model

SummaryThe applications and services of Information and Communication Technologies are becoming a very essential part of our daily life. In addition, the spread of advanced technologies including the cloud and mobile cloud computing (MCC), wireless communication, and smart devices made it easy to access the internet and utilize unlimited number of services. For example, we use mobile applications to carry out critical tasks in hospitals, education, finance, and many others. This wide useful usage makes the smart devices an essential component of our daily life. The limited processing capacity and battery lifetime of mobile devices are considered main challenges. This challenge is increased when executing intensive applications. The MCC is believed to overcome these limitations. There are many models in MCC and one efficient model is the cloudlet‐based computing. In this model, the mobile devices users communicate with the cloudlets using cheaper efficient technologies, and offload the job requests to be executed on the cloudlet rather than on the enterprise cloud or on the device itself. In this article, we investigated the cloudlet‐based MCC architecture, and more specifically, the cooperative cloudlets model. In this model, the applications that require intensive computations such as image processing are offloaded from the mobile device to the nearest cloudlet. If the task cannot be accomplished at this cloudlet, the cloudlets cooperate with each other to accomplish the user request and send the results back to the user. To demonstrate the efficiency of this cooperative cloudlet‐based MCC model, we conducted real experiments that execute selected applications such as: object code recognition, and array sorting to measure the delay and power consumption of the cloudlet‐based system. Moreover, suitable cloud/mobile cloud simulators such as CloudSim and MCCSim will be used to perform simulation experiments and obtain time and power results.

Designing a trivial information relaying scheme for assuring safety in mobile cloud computing environment

Due to increased attraction in cloud computing, mobile devices could store or acquire private and confidential information from everywhere at any point in time. In parallel, the information safety issues over mobile computing become rigorous and retard increased advancements in the mobile cloud. Crucial analysis were performed to enhance the safety in cloud computing. Most of them are not appropriate for mobile cloud computing due to limited energy resource, thus mobile devices are unable to perform assessments and complex tasks. The crucial requirement of mobile cloud application is to provide solution with minimum computational overhead. Thus the aim of the research is to design a trivial information relaying scheme (TIRS) for mobile cloud computing. The proposed scheme implements Ciphertext Policy Attribute-based Encryption (CP-ABE) to alter the general framework of access governance hierarchy to make it appropriate for mobile cloud environment. The TIRS displaces immense segments of the assessment concentrated access governance hierarchy modifications in CP-ABE from smart devices to the peripheral proxy servers. Furthermore, TIRS initiates element portrayal field to plan indolent cancellation which is a thriving dispute for CP-ABE system. The experimental analysis depicts that TIRS successfully minimize the overheads during user relaying information over the mobile cloud environment.

Packet Encryption for Securing Real-Time Mobile Cloud Applications

The evolution of smart devices has brought the most confidential data to mobile applications and cloud computing with its processing power is serving the huge processing requirements of these applications. However, these advancements has brought in serious data security concerns, as there is constant shuttling of data from devices to cloud and backforth, and encryption is the most commonly used technique for securing data in transmission. Every encryption technique is breakable, but its efficiency is calculated with the time it can withstand an attack. But in today’s world, as cloud computing comes with almost unlimited computing resources, the present day encryption techniques might become inadequate for securing confidential data in transmission and after transmission. Existing encryption techniques, encrypt the whole data packets in a session using the same session key. If a third party can decrypt a single packet during or after transmission, all the packets in that session can be compromised. This work proposes a packet encryption scheme, where a packet key once used will never be used throughout the session, with minimal latency and maximum security for real-time mobile cloud applications.

Introduction to the Special Issue on Mobile Cloud Applications and Challenges

We are happy to present this special issue of thee Scientific Journal Scalable Computing: Practice and Experience. In this special issue on Mobile Cloud Applications and Challenges (Volume 19 No 4 December 2018), we have selected five papers, which gone through a peer review according to the journal policy. All the papers represents novel results in the field of Mobile and Cloud Applications.
 
 The first paper presents overview of mobile cloud computing, cloudlet technology, security and privacy issues and limitations of mobile cloud commuting. The second paper presents a spatial partition, global index and map reduce operation were studied. The trail results that the proposed indexing cloud framework performs improved results. The third paper proposes an authentication model along with data security in a public cloud storage environment which successful detects the unauthenticated access or any anomaly in data. The fourth paper analyses the student success ratio which uses a cloud based technology to implement and design SaaS. Graph based complex network are used for analysing the course. The fifth paper presents Overlapping community detection in social networks. The proposed algorithm uses parallel processing engine to resolve the delay problem.

The importance of granularity in multiobjective optimization of mobile cloud hybrid applications

AbstractMobile devices can now support a wide range of applications, many of which demand high computational power. Backed by the virtually unbounded resources of cloud computing, today's mobile cloud (MC) computing can meet the demands of even the most computationally and resource‐intensive applications. However, many existing MC hybrid applications are inefficient in terms of achieving objectives like minimizing battery power consumption and network bandwidth usage, which form a trade‐off. To counter this problem, we propose a data‐driven technique that (1) does instrumentation by allowing class‐, method‐, and hybrid‐level configurations to be applied to the MC hybrid application and (2) measures, at runtime, how well the MC hybrid application meets these two objectives by generating data that are used to optimize the efficiency trade‐off. Our experimental evaluation considers two MC hybrid Android‐based applications. We modularized them first based on the granularity and the computationally intensive modules of the apps. They are then executed using a simple mobile cloud application framework while measuring the power and bandwidth consumption at runtime. Finally, the outcome is a set of configurations that consists of (1) statistically significant and nondominated configurations in collapsible sets and (2) noncollapsible configurations. The analysis of our results shows that from the measured data, Pareto‐efficient configurations, in terms of minimizing the two objectives, of different levels of granularity of the apps can be obtained. Furthermore, the reduction of battery power consumption with the cost of network bandwidth usage, by using this technique, in the two MC hybrid applications was (1) 63.71% less power consumption in joules with the cost of using 1.07 MB of network bandwidth and (2) 34.98% less power consumption in joules with the cost of using 3.73 kB of network bandwidth.

Mobile Cloud Computing Security Levels, Challenges and Applications

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

A Lightweight Secure Data Sharing Scheme for Mobile Cloud Computing

With the popularity of cloud computing, mobile devices can store/retrieve personal data from anywhere at any time. Consequently, the data security problem in mobile cloud becomes more and more severe and prevents further development of mobile cloud. There are substantial studies that have been conducted to improve the cloud security. However, most of them are not applicable for mobile cloud since mobile devices only have limited computing resources and power. Solutions with low computational overhead are in great need for mobile cloud applications. In this paper, we propose a lightweight data sharing scheme (LDSS) for mobile cloud computing. It adopts CP-ABE, an access control technology used in normal cloud environment, but changes the structure of access control tree to make it suitable for mobile cloud environments. LDSS moves a large portion of the computational intensive access control tree transformation in CP-ABE from mobile devices to external proxy servers. Furthermore, to reduce the user revocation cost, it introduces attribute description fields to implement lazy-revocation, which is a thorny issue in program based CP-ABE systems. The experimental results show that LDSS can effectively reduce the overhead on the mobile device side when users are sharing data in mobile cloud environments.

Development of Mobile Cloud Applications using UML

With the proliferation of cloud computing technologies, smartphone users are able to use a variety of cloud computing-based mobile services such as games, education, entertainment, and social networking. Despite the popularity of such a mobile cloud computing, the complicated multi-tier system configuration of the mobile application must be one of the major impediments to develop mobile cloud applications. This paper presents development processes and procedures for developing mobile cloud applications by effectively applying Unified Modeling Language (UML), a representative object-oriented modeling language. The paper is intended to enhance the development productivity of the mobile cloud application and to improve the effectiveness of communication between software developers. In addition, we used the Android mobile platform and Amazon Web Service for cloud computing in order to demonstrate the applicability of the proposed approach to systematically apply the UML profiles and diagrams for cloud-based mobile applications.