A Crowd Sourcing Service Model for Optimizing User-Desired Storage Resource Scheduling

Abstract

Cloud service, which has changed the business computing pattern, redefines the calculation scope of distributed systems by cloud computing approaches. The existing cloud service model provides different users an advanced on-demand computing model, and then achieves resource interaction between cloud service providers and users by cloud computing. However, this model, working as a centralized service strategy of resource allocation, also generates much scattered space debris resulting in a low efficiency of resource scheduling and utilization. To address this issue, we devise an improved model of crowd sourcing service and propose a technique of storage resource allocation and scheduling, where cloud service providers, as a resource agent, only provide leasing storage space for individual applicants instead of providing storage resources directly. This technique achieves allocation and scheduling of individual storage resources dynamically, according to user's expectations and individual storage providers constraints. The experimental results based on a large data set show that the improved model reduces the storage space of cloud service providers by 88.2% and improves storage space utilization rate of individuals by 65.2%, The improved model reduces the user fees by 64.8% and the cloud services development cost by 78.6% than that of existing models, In addition, the improved model and methods provide higher profits and efficiency for cloud service providers.