Mobile Cloud Computing 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%.

A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

Cloud computing (CC) provides fast and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is a new technology that brings CC and mobile devices together. MCC allows mobile devices access to cloud services. Describe the basic architectures for various MCC applications, as well as a brief comparison of cloud and MCC. As CC becomes more common, researchers in this field may need to conduct real-world experiments in their studies. Configuring and running these experiments in real-world cloud environments is costly. As a consequence, modeling and simulation approaches are suitable solutions that can be used to simulate cloud computing environments. Several simulation tools tailored to CC have been developed. The most powerful simulation methods in this field of study are described in this article. Among them are CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports.

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

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

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%.

Providing impersonation resistance for biometric-based authentication scheme in mobile cloud computing service

Mobile cloud computing (MCC) is an emerging computing model with the growing demands of mobile devices by services diversity using mobile cloud technology. Authentication plays a fundamental role between the smart portable devices located in different places and the mobile cloud computing service providers in MCC. Several authors presented many authentication schemes using different cryptographic approaches for MCC in the past years. However, those schemes have not solved the impersonation attack well. To conquer this suffering and support more security functionalities, we propose an impersonation resistance for a biometric-based authentication scheme using hashing and symmetric parameter function in an MCC environment. The proposed scheme can provide mutual authentication and anonymity using the software AVISPA. The security of the proposed scheme is formally proved in the random oracle model. Regarding functionality and performance analysis, the comparative studies demonstrate that the design has a trade-off between resource consumption and security strengthens for MCC services. Therefore, the proposed scheme is more practical in MCC application.

Mobile Cloud Computing and Wireless Sensor Networks: A review, integration architecture, and future directions

In today's fast advancing world, sensors are used in various applications to provide complete data about different objects present around us. The sensor data when integrated with Mobile Cloud Computing (MCC) is used for further computation to provide useful results advanced warning, forecasting, planning, and plethora of other applications in this uncertain world. Few works have been proposed where sensor nodes are integrated with Wireless Sensor Networks (WSNs) which give a new direction in modern research for developing new technologies which help the users for fast access of sensory data over mobile devices. Herein, a systematic literature review of both MCC and WSNs are conducted and subsequently assimilated them via an architecture which is efficient and achievable within short time-span for a variety of applications. The MCC applications are reviewed from two main perspectives that are energy and information management. The communication aspect in MCC and WSNs has been discussed and the need for integration between the two has been justified in this work. The work done on mobility management in MCC and WSNs is reviewed. Also, the challenges in MCC-WSNs integration along with the comprehensive analysis and findings of the review are identified. Finally, the conclusion and specific future direction of research in this area are provided.

Emergency management systems using mobile cloud computing: A survey

SummaryAmong today's rapidly evolving technologies, artificial intelligence plays a significant role in making decisions by a system without any human intervention. An Emergency Management System (EMS) is a decision support system where emergencies such as tsunami, landslide, fire, cyclone, and electrical short circuits can be prevented with prior detection and can be addressed immediately in an efficient way after the emergency occurred. Automation of EMS can avoid or manage multiple emergencies, which alternatively can save lives, economy, and environment. Quality demand response with fast data transfer, error minimized computation, and effective resource utilization is very much essential while developing the EMSs. This will provide a bridge between the technology and emergency responders. Resource‐limited smart devices can be made rich in computational behavior by outsourcing their requirements such as storage, virtual servers, and web services using Mobile Cloud Computing (MCC). In this work, we have done a detailed survey on both MCC applications and EMS applications proposed in the literature, and we have also identified the design challenges handled in both MCC and EMS applications. We have presented the design challenges and possible solutions for development of EMS using MCC. We propose an architecture for building an automated EMS using MCC. Finally, we conclude the paper with specific future directions.

An Efficient Privacy-Aware Authentication Scheme With Hierarchical Access Control for Mobile Cloud Computing Services

In the last few years, mobile cloud computing (MCC) gains a huge development because of the popularity of mobile applications and cloud computing. User authentication and access control are two indispensable security components in the MCC environment. To the best of our knowledge, they are generally designed in different procedures. Access control can be executed after the authentication completes successfully. In order to improve efficiency, this article constructs an integrated scheme of authentication and hierarchical access control using self-certified public key cryptography (SCPKC) and the Chinese remainder theorem (CRT) for MCC environment. The proposed scheme can achieve mutual authentication while determining the access rights of mobile users without storing any access control list in the MCC service provider side. Besides, we also give a dynamic adding or deletion of MCC service provider to efficiently address potential changes in the hierarchy. The security of our proposed scheme is proved by the random oracle model. Compared with recently related multi-server authentication schemes for the MCC environment, the proposed scheme not only adds a new function of hierarchical access control but also has better computation and communication efficiencies. Therefore, the proposed scheme is more suitable for real-life MCC applications.

Mobile cloud computing applications penetration testing model design

Mobile cloud computing (MCC) is a promising technology due to its features that mitigate mobile computing limitations and enhances cloud services. However, penetration testing is more challenging when conducted on MCC applications. These applications use offloading, and thus another layer of complexity in generating, selecting and executing test cases, which implies and requires an MCC applications penetration testing offloading-awareness model. To overcome these challenges, a penetration testing model for mobile cloud computing applications is designed. This model defines the process of penetration testing over MCC applications including penetration test preparation, test case generation, selection and execution processes. Key components of this offloading-awareness model are state management and mobile agent while other components are adapted from previous penetration testing models for the web, cloud or mobile applications. This model will enable penetration testers to tackle the mobile cloud computing complexity and uniqueness. Currently, we are preparing the evaluation of the model against these MCC applications.

EERA: An Energy-Efficient Resource Allocation Strategy for Mobile Cloud Workflows

Mobile cloud computing (MCC) which can invoke cloud services and offload tasks from the mobile device to the cloud has become an appropriate computing paradigm to provide many useful and complex workflow applications. However, offloading tasks needs extra communication time and energy, which leads to a conflict between saving the energy and improving the QoS. Thus, how to allocate these different resources (mobile or cloud) efficiently during the workflow execution process to optimize the system energy consumption under the deadline constraint is still a huge challenge in MCC. To address such an issue, this article proposes an energy-efficient resource allocation strategy for workflow applications in MCC. Firstly, we formulate the resource allocation problem into an optimization problem with the goal of minimizing the UtilityCost which represents the trade-off between energy consumption and the total execution time. Then, we use an energy-efficient resource allocation algorithm shortened as EERA to solve the problem. The algorithm firstly finds all the Partial critical paths of the workflow graph to segment the whole optimization problem into several local problems, and then uses the discrete particle swarm optimization algorithm to find the local optimal solution for every local problem, finally the local optimal solution is integrated to obtain the global one. In addition, we have adapted several representative algorithms for evaluation and comparison purpose. Comprehensive experimental results show that our EERA based strategy achieves the best overall performance on three key measurements including the energy consumption of the device, the execution time of the workflow and the UtilityCost.

Comprehensive Review: Privacy Protection of User in Location-Aware Services of Mobile Cloud Computing

One of the recent trends of networking and mobile technology is mobile cloud computing (MCC) that provides rich computational, storage resources and services in clouds to mobile users. MCC applications provide a variety of services to users and one of them is the location-based services (LBS) applications that are widely spread. By using mobile applications and LBS, mobile devices act as a thin client where the abundant data locations are collected and stored at the mobile cloud to provide corresponding services. Privacy of the user’s location has been a renewed research interest and extensively studied in recent years. However, privacy is one of the most important challenges in MCC because the user’s location on mobile devices is offloaded from mobile devices to cloud providers which can be utilized by third parties. Since protecting the privacy of the user is the key to maintain the trust on the mobile environment. LBS faces issues in protecting privacy such as, the privacy of user’s current location, which may contain private information. In case, if the user’s current location is compromised through unauthorized access, it possibly results in severe consequences. Therefore, protecting location privacy of the user while achieving precise location is still a challenge in MCC. This comprehensive research review will provide the challenge of protecting the privacy of user’s location in MCC; analyze several related works regarding the issue. In addition, it suggests possible solutions related to the issue, in lighted few shortcomings which still needs attention with few related case studies.

Systematic Literature Review on Penetration Testing for Mobile Cloud Computing Applications

Mobile cloud computing (MCC) enables mobile devices to exploit seamless cloud services via offloading, and has numerous advantages and increased security and complexity. Penetration testing of mobile applications has become more complex and expensive due to several parameters, such as the platform, device heterogeneity, context event types, and offloading. Numerous studies have been published in the MCC domain, whereas few studies have addressed the common issues and challenges of MCC testing. However, current studies do not address MCC and penetration testing. Therefore, revisiting MCC and penetration testing domains is essential to overcoming the inherent complexity and reducing costs. Motivated by the importance of revisiting these domains, this paper pursues two objectives: to provide a comprehensive systematic literature review (SLR) of the MCC, security and penetration testing domains and to establish the requirements for penetration testing of MCC applications. This paper has systematically reviewed previous penetration testing models and techniques based on the requirements in Kitchenham's SLR guidelines. The SLR outcome has indicated the following deficiencies: the offloading parameter is disregarded; studies that address mobile, cloud, and web vulnerabilities are lacking; and a MCC application penetration testing model has not been addressed by current studies. In particular, offloading and mobile state management are two new and vital requirements that have not been addressed to reveal hidden security vulnerabilities, facilitate mutual trust, and enable developers to build more secure MCC applications. Beneficial review results that can contribute to future research are presented.

Performance prediction for supporting mobile applications’ offloading

Mobile cloud computing (MCC) is a technique for increasing the performance of mobile apps and reducing their energy consumption through code and data offloading. Developers may build MCC applications on a public cloud. The public cloud may offer economies of scale, but there are some considerations to take into account. Cloud providers charge their customers by data traffic, and wrong offloading decisions may lead to financial losses. This paper proposes an approach for estimating applications’ performance, data traffic generated by tasks offloading and its related costs on a public cloud. This work proposes both a stochastic Petri net (SPN)-based formal framework to represent MCC applications in a method-call level and a cost model to predict data traffic volume. Our approach enables designers to plan and tune MCC architectures based on three performance metrics: mean time to execute, cumulative distribution function, and throughput. Our SPN-based framework represents the use and sharing of the bandwidth available for offloading operations as well as the effect of bandwidth variation on the evaluated metrics. It allows a more accurate evaluation by developers about the performance of their applications taking into account specific network requirements, users, and offloading strategies. Two case studies were performed. Our approach has proven to be feasible, and it highlights the most appropriate strategies, supporting developers at design time by providing statistical information about applications’ behavior and costs estimations.

Survey on security threats in mobile cloud computing

Cell phones are turning into a basic piece of our day by day life because of most proficient and powerful specialized devices without time and space boundation. Everybody has a portable, tablet, tablet with calling office i.e. Fablet. Distributed computing (DC) has been broadly perceived as the cutting edge's registering foundation with the fast development of portable applications and the support of Cloud Computing for an assortment of administrations, the Mobile Cloud Computing (MCC) is presented as an incorporation of Cloud Computing into the Mobile Environment. Portable Cloud Computing is picking up stream. MCC is alluded to as the framework where both the information stockpiling and the information handling occur outside the cell phone. In MCC condition, Cloud Computing, Mobile Computing and Application confront a few difficulties like Mobile Computation Offloading, Seamless Connectivity, Vendor/Data Lock-in, Long WAN Latency, Live VM (Virtual Machine) relocation issues, Low Bandwidth, Energy-Efficient Transmissions and Trust-Security and Privacy Issues. In this paper, I have talked about around a few difficulties and issues identified with the Mobile Cloud Computing. This paper gives data about Mobile Cloud Computing Application, Security and Issues.

Energy saving offloading scheme for mobile cloud computing using CloudSim

In this paper, we accentuate on offloading for sparing energy in mobile cloud computing (MCC). MCC is emerging as a noticeable research area that is looking to bring the massive advantages of the cloud to the constrained mobile devices. The impediments of poor processing capacity and constrained battery life make it troublesome for mobile devices to process complex calculation undertakings. The energy system concentrates on mobile devices battery energy that utilises computational offloading for computationally serious mobile applications on the mobile devices. Each time an application in the physical mobile devices is initialised, the power utilisation is ascertained by the structure and it decides whether to make on offloading choice or not. This paper proposes a novel energy saving computational offloading framework for the preparing of serious mobile applications in MCC. It is found that energy utilisation of the selected application reduces up to 80.94% and execution time reduces up to 97.16% by computational offloading utilising CloudSim when contrasted with the conventional methods. It enhances the quality of services for mobiles and helps in keeping up constant reactions for mobile applications.

Provable dynamic auditing in mobile cloud computing for secure storage of ehealth data

The recent developments in mobile applications have met every need of the user. In spite of increasing usage, the smart mobile system face restrictions like resource scarcity, mobility and frequent disconnections. The emerging Mobile Cloud Computing (MCC) infrastructure exploits the services of Cloud Service Providers (CSP) by gathering the local data storage and computation to remote servers and thereby relieving the overhead incurred by mobile clients. There is a wide range of applications in MCC like m-Learning, m-Healthcare, etc. Though the restrictions are relaxed, the clients no longer have control over the privacy of outsourced data. A safe and secure remote access to highly sensitive private data like e-Health records has received the focus of research. It needs to ensure the integrity of outsourced health data along with its privacy preserved. The resource constrained mobile devices act as a thin client for the cloud server access and hence the computation for ensuring the proof of correctness for secure data storage proposed by existing solutions becomes an overhead. The proposed Provable Dynamic Data Auditing Protocol (PD-DAP) involves a trusted Third Party Auditor (TPA) who is in charge of blockless verification of data without retrieving the private health data. The usage of bilinear pairing and Merkle Hash Trees guarantees blockless verification by TPA. Also the proposed PD-DAP supports secure dynamic operations by the mobile user with the help of TPA. Hence, it saves the mobile user’s computational resource, thereby achieving cost-effectiveness to gain trust in the Cloud Storage Server (CSS).

Energy saving offloading scheme for mobile cloud computing using CloudSim

In this paper, we accentuate on offloading for sparing energy in mobile cloud computing (MCC). MCC is emerging as a noticeable research area that is looking to bring the massive advantages of the cloud to the constrained mobile devices. The impediments of poor processing capacity and constrained battery life make it troublesome for mobile devices to process complex calculation undertakings. The energy system concentrates on mobile devices battery energy that utilises computational offloading for computationally serious mobile applications on the mobile devices. Each time an application in the physical mobile devices is initialised, the power utilisation is ascertained by the structure and it decides whether to make on offloading choice or not. This paper proposes a novel energy saving computational offloading framework for the preparing of serious mobile applications in MCC. It is found that energy utilisation of the selected application reduces up to 80.94% and execution time reduces up to 97.16% by computational offloading utilising CloudSim when contrasted with the conventional methods. It enhances the quality of services for mobiles and helps in keeping up constant reactions for mobile applications.

Design, modeling, and analysis of online combinatorial double auction for mobile cloud computing markets

SummaryWith the burgeoning of cloud service companies, cloud computing is becoming an efficient means of providing computing resources. Amazon EC2, Rackspace, Google App, and Microsoft Azure are attracting more and more users over the Internet these years. However, in mobile cloud computing (MCC), traditional cloud pricing models can no longer support the above popular applications, because user behaviors are dynamic and time sensitive. As an MCC application is the combination of communication services (eg, wireless access services) and computation services (eg, cloud services), it lacks new auctions for capturing the feature of MCC markets based on the communication and computation cooperation (3C).In this paper, we design an efficient double‐sided combinatorial auction model in the context of 3C‐based MCC to mitigate this problem. We first propose the framework of online combinatorial double auctions to model mobile cloud computing market. On this base, we give four principles of design requirements, which can make the scheme more efficient and practical, and then we design a new winner determination algorithm that shows how the auction mechanism decides commodity allocation and transaction prices. At last, we conduct a series of experiments to deep analyze the property of our mechanism. The experiment results indicate that the proposed online auction mechanism obtains comparable allocation efficiency to the social optimal solution.

Towards Mobile Cloud Computing with Single Sign-on Access

The low computing power of mobile devices impedes the development of mobile applications with a heavy computing load. Mobile Cloud Computing (MCC) has emerged as the solution to this by connecting mobile devices with the “infinite” computing power of the Cloud. As mobile devices typically communicate over untrusted networks, it becomes necessary to secure the communications to avoid privacy-sensitive data breaches. This paper presents work on implementing MCC applications with secure communications. For that purpose, we built on COMPSs-Mobile, a redesigned implementation of the COMP Superscalar (COMPSs) framework aiming to MCC platorms. COMPSs-Mobile automatically exploits the parallelism inherent in an application and orchestrates its execution on loosely-coupled distributed environment. To avoid a vendor lock-in, this extension leverages on the Generic Security Services Application Program Interface (GSSAPI) (RFC2743) as a generic way to access security services to provide communications with authentication, secrecy and integrity. Besides, GSSAPI allows applications to take profit of more advanced features, such as Federated Identity or Single Sign-On, which the underlying security framework could provide. To validate the practicality of the proposal, we use Kerberos as the security services provider to implement SSO; however, applications do not authenticate themselves and require users to obtain and place the credentials beforehand. To evaluate the performance, we conducted some tests running an application on a smartphone offloading tasks to a private cloud. Our results show that the overhead of securing the communications is acceptable.

Minimizing contention collision probability and guaranteeing packet delay for cloud big data transmissions in 4G LTE-A packet random access

For transmitting Explosive Bursts Big Data of Mobile Cloud Computing applications, the 4G LTE/LTE-A standards are specified to provide extreme high data rate and low access delay for various real-time-demanded cloud services. In the uplink, data packet transmissions of different classes of traffic of various UEs need randomly contend for the limited number of preambles through the Uplink RACH channel time slots. Clearly, the extremely explosive data contentions certainly yield serious collisions, and then significantly increase access delay and packet dropping rate. That is, the quality of service (QoS) of the delay-sensitive-based real-time traffic and the loss-sensitive-based non-real-time traffic cannot be guaranteed satisfyingly. For overcoming the critical random access issue in cloud services over 4G LTE-A, 3GPP specifies the Uniform Distribution Backoff Procedure and Access Class Barring (ACB) as the random access mechanism. The Random Access CHannel (RACH) for random contentions in 3GPP LTE-A neglects some key factors: 1) different classes of traffic requiring different delay bounds, 2) how to reducing collision probability, 3) intensive congestion traffic and 4) differentiating the collision domains. This paper thus proposes an adaptive random contention approach (ARC) that consists of three phases: 1) Sigmoid-based Access Class Barring algorithm, 2) Dynamic Preamble Selection Range (DPSR) algorithm, and 3) Dynamic Initial Backoff (DIB) algorithm. The main contribution of ARC is based on the adaptive Sigmoid feature analysis of Cumulative Distribution Function of Normal Distribution according to the successful contention probability and the RACH congestion state. Numerical results demonstrate that the proposed approach outperforms the compared approaches in collision probability, goodput and access delay. Furthermore, the mathematical analytical model for the proposed approach is analyzed. The analysis result is very close the simulation result. It justifies the correctness and efficiency of the proposed approach.