This proposal seeks funding for the Center for Autonomic Computing (CAC) sites at the University of Florida (lead), Rutgers University, the University of Arizona, and Mississippi State University. Funding Requests for Fundamental Research are authorized by an NSF approved solicitation, NSF 10-601. The solicitation invites I/UCRCs to submit proposals for support of industry-defined fundamental research.
A unified cloud computing environment enabling anytime, anyplace access to limitless computing resources still represents an ideal given the current environment of heterogeneous resources distributed geographically and offered by vastly different service providers. The proposed effort spans the expertise and capabilities of the four center sites. The work aims to apply autonomic computing principles to address elements such as security, inter-cloud networking, and resource provisioning via thermal sensing and model-based adaptive performance control in order to create a framework for realization and management of trustworthy unified cloud computing environments. Results will be derived from the implementation of the proposed approaches on a planned distributed cloud testbed.
Advancement of the cloud computing paradigm has the potential to enable transformative change to user access to information technology enabling major advances in economic productivity and access to a broad range of new services. The proposed center effort works with a small member company and large systems integrator member. Industry will further benefit via dissemination of the results through the center membership and the extension of the center?s project portfolio into this area. Efficiency gains potentially realized at cloud computer centers from the proposed efforts has the potential to reduce energy costs system wide. The work plans to bring together a distributed cloud environment deployed across CAC sites to establish an open testbed for research and development of inter-cloud interoperability that has the potential to serve as a resource for the work of the broader community in this important area.
This project has made contributions in autonomic approaches for computing systems that are able to self-protect against intrusion, to self-organize in overlay networks to achieve high-throughput data transfers, and to detect and classify thermal anomalies within data centers. In one thrust of the project, a testbed to study and simulate the various available techniques for securing and protecting Supervisory Control and Data Acquisition (SCADA) systems against a wide range of cyber-attacks has been developed. The critical infrastructures of our society are in the process of being modernized. Most significantly impacted are the industrial control systems through replacement of old electromechanical systems with advanced computing and communication technologies. This modernization has introduced new vulnerabilities to those infrastructures. Securing critical infrastructures is a challenging research problem, as these control systems were not designed with security in mind. SCADA systems consist of human-machine interfaces (HMI), historical database management systems (HDBMS) and sophisticated monitoring and control applications. The SCADA system manages a collection of distributed industrial control components including remote terminal units (RTUs) for field sensor and actuator connectivity, programmable logic controllers (PLCs) that perform simple logic processes, and a wide variety of intelligent electronic devices (IEDs) for process data collection and control. Unexpected changes in local heat-generation and -extraction rates due to cooling equipment failures, misconfigurations, and attacks may over time cause large heat imbalances, and unexpected thermal hotspots. Thermal hotspots may also result in a thermal fugue, which is characterized by a continuous increase in the rate of temperature rise. Thermal anomalies, such as unexpected hotspots and fugues, lead to system operation in unsafe temperature regions, increase the server failure rate and the Total Cost of Ownership (TCO) of datacenters. In another thrust of the project, an online autonomic thermal-anomaly detection method that leverages the novel notion of the thermal signature of a datacenter has been developed. The heat-imbalance model is used to estimate approximate intensities and distribution of expected hotspots for a specific workload distribution (the datacenter's unique thermal signature). Autonomic middleware services researched in this project also address data access needs that are increasingly important in enterprise applications that are outsourced for execution in infrastructure-as-a-service, cloud computing platforms. In particular, the project focused on on-demand, piece-wise, cooperative transfer of large, read-only or read-mostly datasets, such as virtual machine images for large-scale cloud provisioning. Effective handling of data transfer and storage can lead to cost reductions in the use of provisioned cloud infrastructures, which charge customers on the basis of bytes stored and transferred over time. In this project, a novel architecture for a self-configuring, autonomic file system service has been researched, and a prototype based on a peer-to-peer (P2P) overlay, BitTorrent data transfer and cooperative caching, and file system in user space (FUSE) bindings has been implemented. Experiments have shown the ability to achieve higher data transfer throughput compared to traditional client/server file systems.
