CNS Core: Small: RUI: Optimal and Efficient Resource Allocation in Policy-Driven Data Centers: A Network Flow Approach

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Emerging data- and communication-intensive applications such as online video streaming, social media, health and medical applications, scientific applications including high-energy physics and bioinformatics, and educational applications (such as the rapidly-growing Massive Open Online Courses) are all enabled by large cloud data centers. However, this underpinning infrastructure is increasingly stressed by the growing complexities of managing data center resources. This is evidenced by the frequent outages of cloud services from leading tech companies, including Amazon Cloud Storage and Microsoft Skype, and popular mobile apps such as Gmail and Whatsapp. To address this challenge, this project will create an optimal and efficient resource allocation framework for policy driven data centers (PDDCs), to manage cloud user applications and cloud resources (i.e., servers, networks, and power) in an integrated fashion. The goal of this project is to integrate compute, data, and middleboxes (MBs), three building blocks of PDDCs, into one framework to achieve optimal cloud resource management. A variety of important problems in PDDCs, including virtual machine (VM) migration and placement, load balancing, flow priority and fault tolerance can all be solved using network flow techniques that provide optimal and efficient resource allocation solutions. In particular, the project identifies a series of new policy-preserving problems that adaptively coordinate compute, data, and MBs, and invents a suite of policy-preserving algorithms that satisfy diverse cloud policies while consuming cloud resources efficiently. The proposed techniques include placing, migrating, replicating, and traffic engineering compute, data, and MBs in the PDDC. The project will compare results with integer linear programming (ILP)-based solutions and extend the approach to multi-objective optimization problems. Expected outcomes are fundamental theories, architectures, algorithms, and protocols for the PDDCs, and prototypes that provide long term policy-preserving cloud services.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.