A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing

As mobile computing has been developed for decades, a new model for mobile computing, namely, mobile cloud computing, emerges resulting from the marriage of powerful yet affordable mobile devices and cloud computing. In this paper we survey existing mobile cloud computing applications, as well as speculate future generation mobile cloud computing applications. We provide insights for the enabling technologies and challenges that lie ahead for us to move forward from mobile computing to mobile cloud computing for building the next generation mobile cloud applications. For each of the challenges, we provide a survey of existing solutions, identify research gaps, and suggest future research areas.

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.

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.

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).

Process state synchronization-based application execution management for mobile edge/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%.

Purpose– Cloud Computing (CC) technology is getting implemented rapidly in the educational sector to improve learning, research and other administrative process. As evident from the literature review, most of these implementations are happening in the western countries such as USA, UK, while the level of implementation of CC in developing countries such as India is rare. Moreover, implementing CC technology in the educational sector require various decisions to be made by the managers of the Information Technology (IT) department such as selecting suitable deployment model, vendor providing cloud service, etc. in their respective university or institute. The purpose of this paper is to attempt to address one such decision. Since, different types of CC deployment are available; selecting a suitable one plays a key role, as it might have an impact on the requirements of various stakeholders such as students, teachers, administrative staff (especially the staff members in the IT department), etc. apart from affecting the overall performance of the facilities such as a laboratory. Naturally, a proper decision by analysing multiple perspectives has to be made while carrying out such strategic initiatives by any educational institute.Design/methodology/approach– A case study methodology has been chosen as the research methodology to discuss and demonstrate the above decision problem that was faced in real time by one of the educational institutes in India, offering high-quality management education. The IT managers of this institute were planning to switch over to CC technology for the computer laboratory and they have to make a decision of choosing suitable alternative CC deployment models such as private cloud (PRC), public cloud (PUC), community cloud (COC), hybrid cloud (HYC), etc. by analysing and comparing them based on various factors and perspectives such as elasticity, availability, scalability, etc. Since, multiple factors are involved in making such a strategic decision, the most commonly used Multi-Criteria Decision Making (MCDM) model – namely, the Analytic Hierarchy Process (AHP) is used as a decision support during the decision making process.Findings– The team of decision makers, who were planning to implement CC in the case institute, found that PRC is best as they believed that it would provide adequate cost savings, apart from providing necessary security to maintain confidential information such as student's detail, grades, etc.Research limitations/implications– The results obtained are based on a single case study. Hence, they cannot be generalized for institutions across educational sector. However, the decision making situation and understanding its impact on the stakeholders of the educational institute can be common across various educational institute.Practical implications– Using a real-life case study of an educational institute, this paper presented a strategic decision making situation, which needs to be considered by the IT managers of the educational institutes when they decide to switch over to CC technology. Various criteria to be considered during the decision making process was identified from the literature review were identified and enumerated. These factors would useful for the IT managers of the different educational institute and they can suitably add or delete these decision criteria as per their requirements and situation at hand. Moreover, the algorithm of AHP, which was used as a decision support, was presented in a step-by-step manner, which should be beneficial for the practitioners to apply the same for similar decision making situations.Originality/value– It is believed that this paper would be the first to report on a strategic decision of choosing the deployment model for CC technology especially in the educational sector. Similarly, this paper would also contribute to the field of CC, as it lists out the decision criteria that are to be considered for making the above decision, which has not got adequate importance. Lastly, this paper is also unique in the realm of AHP because application for a decision problem in the field of CC especially in the educational sector is least reported.

The convergence of mobile computing and cloud computing enables new mobile applications that are both resource-intensive and interactive. For these applications, end-to-end network bandwidth and latency matter greatly when cloud resources are used to augment the computational power and battery life of a mobile device. This dissertation designs and implements a new architectural element called a cloudlet, that arises from the convergence of mobile computing and cloud computing. Cloudlets represent the middle tier of a 3-tier hierarchy, mobile device — cloudlet—cloud, to achieve the right balance between cloud consolidation and network responsiveness. We first present quantitative evidence that shows cloud location can affect the performance of mobile applications and cloud consolidation. We then describe an architectural solution using cloudlets that are a seamless extension of todays cloud computing infrastructure. Finally, we define minimal functionalities that cloudlets must offer above/beyond standard cloud computing, and address corresponding technical challenges.

Mobile cloud computing (MCC) is a technique or model, in which mobile applications are built, powered and hosted using cloud computing technology. It refers to the availability of Cloud Computing services in a mobile environment and it is the combination of the heterogeneous fields like mobile phone device, cloud computing and wireless networks. Of all that has been written about cloud computing, precious little attention has been paid to authentication in the cloud. In this paper we have designed a new effective security model for mobile cloud Database as a Service (DBaaS). A user can change his/her password, whenever demanded. Furthermore, security analysis realizes the feasibility of the proposed model for DBaaS and achieves efficiency. We also proposed an efficient authentication scheme to solve the authentication problem in MCC. The proposed solution which we have provided is based mainly on improved Needham-Schroeder’s protocol to prove the users’ identity to determine if this user is authorized or not. The results showed that this scheme is very strong and difficult to break it.

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.

Mobile cloud computing is a new paradigm to improve the quality of mobile services, which has drawn considerable attentions in both industrial and academic fields. In this paper, we consider the resource management and sharing problems for radio and computing resources to support mobile applications in mobile cloud computing. In such an environment, service providers can cooperate to form coalition to share their idle resources with each other. We propose a coalition game model based on two-sided matching theory. The coalition game model efficiently reflects the scalable cooperation among the service providers for sharing their idle resources. As a result, the resources can be better utilized and the quality of service for users can be improved. The simulation results indicate that our scheme can optimize the resource utilization and significantly improve the quality of service of the users.

Mobile cloud computing offers computing, storage, and memory resources to mobile users through its high computing cloud servers. Mobile services are facilitated with efficient resource provisioning in the cloud. However, due to its inherent nature such as disconnections and limited battery life mobile users experience poor quality of service (QoS) in their connectivity with the cloud. This paper proposes an efficient request state aware resource provisioning technique that provisions the resources considering the current context information viz., battery, and connection quality of the mobile client. Various state viz., Accepted, Submitted and Running, Submitted and Paused, Resumed, Fulfilled, Rejected and Exit has been used to monitor the Client’s state. Besides, an intelligent resource capacity prediction technique based on a random forest algorithm has been incorporated to predict the future resource capacity requirement and schedule the client’s job in the cloud. The proposed technique has been implemented using CloudSim and analyzed using a mobile cloud storage dataset. Performance analysis proves that the proposed technique outperforms the state of art techniques in terms of waiting time, deadline violation, and accuracy of the prediction technique.

Nowadays, much richer functionality of mobile applications encourages mobile devices to leverage the powerful cloud service for fast application execution by using the technology of Mobile Cloud Computing (MCC). To better utilize the computing resource of the cloud server, a novel resource allocation algorithm is proposed in this paper with the consideration of application partition offloading sequence while maintaining the high quality of service (QoS) of mobile users. The resource allocation problem is modeled as a semi-Markov decision process. Through maximizing the long-term discounted system reward, an optimal resource allocation policy is calculated for partitioned mobile applications using policy iteration approach, which makes a latter partition of the application more easily to obtain resource to speed up the application execution. Both theoretical and simulation results show that the system can adaptively adjust the allocation policy of whether to utilize the cloud and how much computing resource to allocate, according to the request traffic of mobile applications, the partition's position in the application, and the availability of system resources. The proposed algorithm outperforms Greedy admission control in terms of system throughput and QoS over a broad range of environments.

Mobile Cloud Computing is a combination of Mobile Computing, Cloud Computing and wireless networks to convey rich computational resources to mobile users, network operators, as well as cloud computing providers. Nowadays, the market of mobile devices and its applications are growing at an alarming speed. In general, single application for a specific purpose is not compatible to different operating systems. So the developer has to develop various versions of application which is compatible to different platforms. In order to overcome the drawback of compatibility and interoperability issues, HTML5-based mobile applications are built by using standard web technologies. HTML5-based mobile applications support same version of application in different platforms, but these applications are vulnerable to attacks because of the data and code being fused together. A new form of Code Injection Attack found in this type of mobile applications inherits the property of XSS attack and also uses many channels to inject malicious code such as Contact, SMS, WIFI, NFC, Barcode etc. This allows the attacker to inject malicious code to exhaust all the resources of the victim. Therefore, security is the major issue which impedes the development of Mobile Cloud Computing. This paper surveys the malicious code injection attacks in Mobile Cloud Computing environment and the possible solutions.

Cloud Computing is one of the distributed computing paradigms and it is mainly focusing on providing everything as service to the consumers. In Cloud Computing Infrastructure as a Service (IaaS) is one of the service delivery model and it provides computational or storage resources, database and network resources to the users or customers. Eucalyptus is one of the open source cloud middleware, it is capable of creating private cloud inside an organization. The main drawback of the open source toolkit is it does not offer an integral monitoring and discovery service. The scalable monitoring and discovery service is important for managing the cloud resources. The proposed and implemented Cloud Monitoring and Discovery Service (CMDS) integrated with external information provider's discovers the cloud resources in a scalable way. The proposed model is integrated with CARE Resource Broker (CRB) and it is helpful for CRB for making scheduling decisions in cloud resources for virtual resource creation and application execution. The proposed system enhances the throughput of jobs by scaling of resources using CMDS in addition to that it increases the job success ratios of CRB.