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

Abstract

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.