Dynamic Resource Pricing and Scalable Cooperation for 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.

Despite their progress and improvements, mobile devices are still regarded as restricted computer devices. Users are growing more discerning, expecting to be able to run computationally intensive applications on their smartphones or tablets. As a result, Mobile Cloud Computing (MCC) integrates mobile computing and Cloud Computing in order to use offloading techniques to increase the capabilities of mobile devices. Although offloading programmes to the cloud might improve mobile device performance, it can also increase processing delay. Inevitably, the quality of user service (QoS) suffers as a result, particularly for specific applications (especially workflow applications). To address the problem of network latency, a new paradigm known as mobile edge computing (MEC) has been developed, which may be thought of as a subset of MCC. We also see deployment of Cloudlets, which are a form of edge server, to minimize latency and energy usage by offloading applications to the cloud, resulting in an efficient and cost-effective architecture as well as various decision-making techniques for offloading. And so, a literature survey regarding this will be presented which focuses on various methods of offloading and its decision-making techniques.

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.

Secure and quality-of-service-supported service-oriented architecture for mobile cloud handoff process

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%.

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.

The purpose of this paper is to study quality of service (QoS) management and performance monitoring issues in B-ISDN. A QoS framework is proposed in order to satisfy the user's service quality requirements. The basic idea of this QoS framework is that the user's QoS requirement should be represented in terms of objectives set to QoS parameters which are in turn translated into asynchronous transfer mode (ATM) network performance (NP) parameters objectives; then, meeting NP parameters objectives ensures that the user's QoS requirements are fulfilled. Performance monitoring mechanisms based on operation and maintenance (OAM) cells flows in the ATM layer and estimation methods, which are both part of a QoS framework, are reviewed. We focus in particular on the definition and estimation of NP parameters in the case of non-conforming cells transmitted on an ATM connection. Finally, a QoS management scenario for the ATM layer is proposed. >

How to guarantee user's QoS (Quality of Service) demands becomes increasingly important in service-oriented grid environment. Current research on grid resource advance reservation, a well-known and effective mechanism to guarantee QoS, forces on proposing theoretic architecture to support advance reservation. Detailed research on advance reservation is scarce. For that, SNAP (Service Negotiation and Acquisition Protocol) is extended to support QoS description in fine granularity and new calculation method of QoS deviation distance is proposed. Then, advance reservation state switching is analyzed and QoS deviation distance based negotiation algorithm is addressed. Preliminary results show that proposed negotiation algorithm that considers QoS deviation distance can produce remarkable improvement in user satisfaction and resource utilization.

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).

With the rapid growth of mobile smart phone users, more and more mobile users are using mobile phones to access Internet services. Meanwhile, cloud computing is changing the landscape of Internet services, so as to affect the mobile services. Cloud computing is regarded as the future of mobile. However, cloud computing still faces a number of challenges, one of which is Quality of Services (QoS), that is, how a service provider can ensure QoS of its cloud services, especially for mobile users. In this paper, we present a QoS framework for mobile cloud computing and an adaptive QoS management process to manage QoS assurance in mobile cloud computing environment. Furthermore, we present a QoS management model based on Fuzzy Cognitive Map (FCM). With an example, we evaluate the proposed system and demonstrate its effectiveness and benefits.

The global optimal Web service selection based on quality of service (QoS) in cloud environment has become a research focus when there are lots of the same or similar services. In this environment, it is possible that many service users request the same or similar services at the same time, which will result in users' unsatisfied requirement and services' load imbalance. The existing service selection approaches usually suppose that service's load capacity is infinite and user always select the service with the best expected QoS despite the amount of user requests. Therefore, it is very important problem how to get a tradeoff between the users' satisfied requirement and service load balance. To solve this problem, this paper presents a global optimal service selection approach based on QoS and load-aware in cloud environment. In this approach, we first build a user QoS utility model which describes the relationship between user's request and services' QoS, and design a service's load capacity model to achieve the load capacity of a service. Then, we use 0-1 integer programming to build a global optimal model based on QoS utility of users and services' load capacity, and provide the optimal service selection algorithm for users. Finally, by conducting large-scale experiments based on a Web service dataset, we show that our approach can effectively help users to select high qualified services while keeping load balance of services in cloud environment.

Peer-to-peer (P2P) network is a high level logical network architecture build over end-user nodes interconnected by a physical network infrastructure. The main interest of their distributed structure is to avoid any centralized point. The characteristics of P2P networks make them a better choice for multimedia content sharing/streaming over IP networks. This kind of applications demands a QoS (quality of service) level. However, different customers are believed to have different QoS perception levels for the same application. Some customers may content to pay less in exchange for a lower QoS. On the other hand, in order to provide users the required QoS level, an incentive mechanism to motivate peers to contribute is required. In this paper we focus the problem of charging for real-time media streaming of IP packet over P2P networks. We consider a model of multiple senders to a single receiver. We define an incentive mechanism that allows peers generating revenues for their contributions. The incentive mechanism is based on competitive market model. Therefore, a charging model based on the QoS level is defined. Finally, the proposed model defines a sender peers selection mechanism based on the competitive market model and the user's QoS satisfaction level. (8 pages)

In Grid computing environment, quality of service (QoS) provisioning must be provided to the end users on the basis of their specific requirements. This paper proposes in a first step QoS attributes for Grid applications. In this matter, a mix of quantitative and qualitative parameters have to be considered. In the context, the analytical hierarchy process (AHP) technique [1] is a possible approach to formulate the QoS requirements of the users for Grid services. In order to apply QoS preference to actual application, we introduce a QoS function and a metric for user's satisfaction degrees. These both tools can be used as an evaluation criterion by the user. Subsequently, an algorithm of service selection considering the user's QoS preference is presented. Our empirical studies indicate that the application can reliably select the optimal service for users.

The resource reservation is one of the key techniques to ensure the quality of service (QoS) of a multimedia application. In mobile cloud computing (MCC), the resource reservation and allocation (RRA) in advance can significantly reduce the total provisioning cost of cloud service providers. However, the uncertain features of mobile users' demands for resources make RRA challengeable. In MCC, the QoS of a mobile application, such as voice IP or video, is determined by both of the radio resource (RR) and the cloud virtual machine resource (VMR) allocated to the mobile application, so we should jointly allocate these two types of resources. In this paper, RRA with uncertain demands of mobile users is formulated as a robust optimization model. Logarithmic utility functions are defined to capture the mobile users' satisfaction, which show how to match the allocations between RRs and VMRs according to the resource demands of the mobile applications. Then, a robust joint resource reservation and allocation algorithm in MCC (JRRA-MCC) is proposed to realize the optimal provisioning of RRs and VMRs. Simulation results show that the proposed JRRA-MCC can minimize the total resource provisioning cost of cloud service providers and enhance the resource utilization efficiently.