Fuzzy-Engineered Multi-Cloud Resource Brokering for Data-intensive Applications

Multi-cloud resource brokering is becoming a critical requirement for applications that require high scale, diversity, and resilience. Applications demand timely selection of distributed data storage and computation platforms that span local private cloud resources as well as resources from multiple cloud service providers (CSPs). The distinct capabilities and policies, as well as performance/cost of the cloud services, are amongst the prime factors for CSP selection. However, application owners who need suitable cyber resources in community/public clouds, often have preliminary knowledge and preferences of certain CSPs. They also lack expert guidance to handle the problem of overwhelming resource choice from CSPs, and optimization to compensate for service dynamics. In this paper, we address this challenge of optimal resource selection while also leveraging limited user's expertise and preferences towards CSPs through multi-level fuzzy logic modeling based on convoluted factors of performance, agility, cost, and security. We evaluate the efficiency of our fuzzy-engineered resource brokering in improving allocation of resources as well as user satisfiability by using case studies and independent validations of CSPs evaluation.