Mobile Cloud Service Providers Research Articles

Compromise-resilient anonymous mutual authentication scheme for n by m-times ubiquitous mobile cloud computing services

Mobile cloud computing (MCC) combines mobile devices with cloud computing for efficient extension of infrastructures and cloud services to mobile devices using the existing mobile network. However, this usually attracts security and privacy-related attacks on both mobile cloud service providers and mobile users. Aside from this, an effective approach for limiting users to a specific number of services for a specific period on the cloud server is also a major issue in the MCC environment. To securely solve these, we propose a novel compromise-resilient anonymous mutual authentication scheme that allows mobile users to subscribe for n number of services at m-period on the mobile cloud server without compromising their privacy. The security of the proposed scheme is formally verified, and its functionalities and performances are compared with other state-of-the-art schemes. The results show that the scheme has insignificant computational and communication overheads compared to other schemes, and with more functionalities such as mutual authentication, resilience to forgery, insider attacks, masquerade attacks, user anonymity, and selective subscriptions feature for the prepaid subscription.

Cloud resource optimization based on poisson linear deep gradient learning for mobile cloud computing

Mobile Cloud Computing (MCC) addresses the drawbacks of Mobile Users (MU) where the in-depth evaluation of mobile applications is transferred to a centralized cloud via a wireless medium to reduce load, therefore optimizing resources. In this paper, we consider the resource (i.e., bandwidth and memory) allocation problem to support mobile applications in a MCC environment. In such an environment, Mobile Cloud Service Providers (MCSPs) form a coalition to create a resource pool to share their resources with the Mobile Cloud Users. To enhance the welfare of the MCSPs, a method for optimal resource allocation to the mobile users called, Poisson Linear Deep Resource Allocation (PL-DRA) is designed. For resource allocation between mobile users, we formulate and solve optimization models to acquire an optimal number of application instances while meeting the requirements of mobile users. For optimal application instances, the Poisson Distributed Queuing model is designed. The distributed resource management is designed as a multithreaded model where parallel computation is provided. Next, a Linear Gradient Deep Resource Allocation (LG-DRA) model is designed based on the constraints, bandwidth, and memory to allocate mobile user instances. This model combines the advantage of both decision making (i.e. Linear Programming) and perception ability (i.e. Deep Resource Allocation). Besides, a Stochastic Gradient Learning is utilized to address mobile user scalability. The simulation results show that the Poisson queuing strategy based on the improved Deep Learning algorithm has better performance in response time, response overhead, and energy consumption than other algorithms.

Robust Anonymous Mutual Authentication Scheme for <italic>n</italic>-Times Ubiquitous Mobile Cloud Computing Services

In recent years, mobile computing has gained a huge popularity among mobile users (MUs). It basically combines the mobile devices with the cloud computing. By the means of on-demand self-service and extendibility, it can offer the infrastructures, platform, entertainments, and software services in a cloud to MUs through the mobile network. However, offering secure access to these services by preserving the privacy of the MU is indeed a challenge for any mobile cloud service provider. In this paper, we aim to propose a new robust anonymous mutual authentication scheme for mobile cloud environment. Through this scheme, both the MU and the service cloud need to prove their legitimacy, and it eventually helps the legitimate mobile cloud user to enjoy n times all the ubiquitous services in a secure and efficient way, where the value of n may differ based on the principal he/she has paid for. The security of the proposed scheme is thoroughly analyzed using both formal as well as informal security analysis. Furthermore, functionality and performance comparisons using the testbed simulation among the proposed scheme and other existing relevant schemes reveal that the proposed scheme outperforms other existing schemes.

A Framework for Cooperative Resource Management in Mobile Cloud Computing

Mobile cloud computing is an emerging technology to improve the quality of mobile services. In this paper, we consider the resource (i.e., radio and computing resources) sharing problem to support mobile applications in a mobile cloud computing environment. In such an environment, mobile cloud service providers can cooperate (i.e., form a coalition) to create a resource pool to share their own resources with each other. As a result, the resources can be better utilized and the revenue of the mobile cloud service providers can be increased. To maximize the benefit of the mobile cloud service providers, we propose a framework for resource allocation to the mobile applications, and revenue management and cooperation formation among service providers. For resource allocation to the mobile applications, we formulate and solve optimization models to obtain the optimal number of application instances that can be supported to maximize the revenue of the service providers while meeting the resource requirements of the mobile applications. For sharing the revenue generated from the resource pool (i.e., revenue management) among the cooperative mobile cloud service providers in a coalition, we apply the concepts of core and Shapley value from cooperative game theory as a solution. Based on the revenue shares, the mobile cloud service providers can decide whether to cooperate and share the resources in the resource pool or not. Also, the provider can optimize the decision on the amount of resources to contribute to the resource pool.