"The important thing in life is to have a great aim, and the determination to attain it" - Gothe.

DISCO: Distributed, Sustainable, and Cloud Computing Systems Lab

The DISCO Lab aims to explore in-depth understanding of Distributed, Sustainable and BigData Cloud computing and augmented services, and develop innovative technologies to enhance the system performance, dependability, scalability and sustainability. The research was supported in part by funding from the National Science Foundation and Air Force Research Lab.

The DISCO Lab is located in the new science and engineering building. The server room is furnished with cutting-edge HP data center blade facility that has three racks of HP ProLiant BL460C G6 blade server modules and a 40 TB HP EVA storage area network with 10 Gbps Ethernet and 8 Gbps Fibre/iSCSI dual channels. It has three APC InRow RP Air-Cooled and UPS equipments for maximum 40 kWs in the n+1 redundancy design.




Research Associates/Assistants

Graduated PhD Students

Sponsoring Projects

Research in Support of USAFA HPRC and ARC (Sponsor: Air Force Research Lab, PI: Andrew Ketsdever, CoPIs: Xiaobo Zhou, 08/2015-12/2017)

CSR: Moving MapReduce into the Cloud: Flexibility, Efficiency, and Elasticity (Sponsor: NSF CNS-1422119, PI: Xiaobo Zhou, CoPI: Jia Rao. 10/2014 - 09/2018)

MapReduce, a parallel and distributed programming model on clusters of commodity hardware, has emerged as the de facto standard for processing large data sets. Although MapReduce provides a simple and generic interface for parallel programming, it incurs several problems including low cluster resource utilization, suboptimal scalability and poor multi-tenancy support. This project explores and designs new techniques that let MapReduce fully exploit the benefits of flexible and elastic resource allocations in the cloud while addressing the overhead and issues caused?by server virtualization. It broadens impact by allowing a flexible and cost-effective way to perform big data analytics. This project also involves industry collaboration, curriculum development, and provides more avenues to bring women, minority, and underrepresented students into research and graduate programs.

SFS: A Security-Integrated Computer Science Curriculum for Intensive Capacity Building (Sponsor: NSF DGE-1438935, PI: Chuan Yue, CoPIs: Xiaobo Zhou, Edward Chow, and Terry Boult. 09/2014 - 08/2017)

This project will advance the state of art and practice in cybersecurity education by systematically exploring a novel security-integrated computer science curriculum approach.

CSR: System and Middleware Approaches to Predictable Services in Multi-Tenant Clouds (Sponsor: NSF CNS-1320122, PI: Jia Rao, CoPI: Xiaobo Zhou. 09/2013 - 12/2017)

Datacenter-based cloud services exhibit unpredictable performance variations due to multi-tenant interferences and the heterogeneity in datacenter hardware. The investigators attribute the causes of such performance unpredictability to the missing of two important service guarantees from existing cloud providers: resource capacity and application agility. To provide guaranteed resource capacity and enhanced application agility, this project develops independent but complementary approaches at system and middleware levels to reduce performance variations of in-cloud applications without compromising other objectives such as high datacenter utilization and good average performance. The deliverables are new system support in cloud resource management to account for interferences and hardware heterogeneity in shared infrastructures and middleware approaches to perform agile, non-invasive and application-centric resource provisioning. The research methodology combines architectural knowledge on the complex interplay between simultaneous multi-threading, multicore, and non-uniform memory access architectures with statistical learning algorithms to quantify interference and heterogeneity, and integrates the strength of self-optimizing learning and control techniques to automate resource provisioning under dynamic workloads. This project broadens impact by exploring inter-disciplinary techniques in computer system design and enhancing cloud services with predictability guarantees. The success will guide resource management and metering in future cloud systems.

STTR Phase-II: Autonomic Performance Assurance for Multi-Processor Supervisory Control (Sponsor: Air Force Research Lab/CEI Inc., Sole PI: Xiaobo Zhou. 07/2013 - 08/2015)

This project is to develop an autonomic performance assurance framework and associated techniques that support automated job scheduling and guarantee performance for multi-processor supervisory control.

CSR: Autonomous Performance and Power Control on Virtualized Servers (Sponsor: NSF CNS-1217979, Sole PI: Xiaobo Zhou. 09/2012 - 08/2017)

Modern data centers hosting popular Internet services face significant and multi-facet challenges in performance and power control. The challenges are mainly due to complex interaction of highly dynamic and heterogeneous workloads in complex virtualized computing systems. In this research project, the investigators take an organized approach to autonomic performance and power control on virtualized servers. The project designs and develops automated, agile and scalable techniques for server parameter tuning, virtual machine capacity planning, non-invasive energy-efficient performance isolation, and elastic power-aware resource provisioning. The deliverables are innovative and practical approaches and mechanisms that provide performance assurance of applications, maximize effective system throughput of data centers with resources and power budget, mitigate performance interference among heterogeneous applications, and achieve performance and power targets with flexible tradeoffs while assuring control accuracy and system stability. The research methodology integrates strengths of reinforcement learning, fast online learning neural networks, fuzzy logic control, model predictive controls and distributed and coordinated control. The project broadens impact by developing a testbed in a university prototype data center to demonstrate the orchestration of developed approaches and mechanisms for autonomous management of virtualized computing systems, middleware, and services. The success will guide autonomous resource management for sustainable computing in next-generation data centers.

Solving Dynamic N-Body Proximity Problems in 3-Dimensions (Sponsor: Air Force Research Lab/ISS Inc., Sole PI: Xiaobo Zhou. 12/2012 - 08/2014)

This project is to provide novel research, techniques and algorithms in the areas of study arising from the efficient query of dynamic spatial and temporal data sets.

STTR Phase-I: Autonomic Performance Assurance for Multi-Processor Supervisory Control (Sponsor: Air Force Research Lab/CEI Inc., Sole PI: Xiaobo Zhou, 2012)

This project is to develop an autonomic performance assurance framework and associated techniques that support automated job scheduling and guarantee performance for multi-processor supervisory control.

CAREER: Building Resilient Internet Services with Learning and Control (Sponsor: NSF CNS-0844983, Sole PI: Xiaobo Zhou. 09/2009 - 08/2015)

Due to the dynamic nature and unprecedented scale of the Internet, Internet services pose challenges including scalability, reliability, and availability to underlying networked systems. This CAREER project concentrates on building Internet services that are resilient to those challenges with machine learning and control techniques. Internet services build upon cluster-based computer systems that keep growing in scale and complexity. Such systems become so complicated that it is even a big challenge to get a good understanding of the entire system dynamic behaviors. The investigators take an analytical and organized approach to design an autonomous software infrastructure on networked systems for building resilient Internet services. The project builds empirical models using statistical learning to help overcome the challenges of scale and complexity in networked systems. It designs coordinated admission control and capacity planning algorithms with end-to-end quality-of-service on multi-tier clusters. Model-independent control techniques are used with empirical models to allocate resources and to dynamically reconfigure the system for performance optimization needs. It develops performance differentiation, isolation, and self-adaptive reconfiguration capabilities for enhancing system reliability and availability. It broadens the research impact by developing a testbed in a data center lab to demonstrate the orchestration of designed techniques for automated arrangement, coordination, and management of complex computer systems, middleware, and services.

CSR: Resource Allocation Optimization for Quantitative Service Differentiation on Multi-Tier Server Clusters (Sponsor: NSF CNS-0720524, Sole PI: Xiaobo Zhou. 08/2007 - 07/2011)

Internet services have become an important class of driving applications for scalable and quality aware distributed computing technologies. Service differentiation is to provide different quality levels to satisfy requirements of Internet services while maintaining resource availability. It is demanded due to the diversity of access devices and networks of users, but also because it can enhance the system scalability and dependability of the computing technologies. In this research project, the investigators take an analytical and organized approach to examine resource management techniques for quantitative service differentiation in popular multi-tier server clusters. The broad impact of the research will be on quality control for system scalability and dependability enhancement. This project will help society develop quality aware applications and salable computing technologies for popular Internet services.

STTR Phase-II: GPS-based Tracking Systems for Trauma Patients (Sponsor: Army Medical Research/NavSys, PIs: Terry Boult and Xiaobo Zhou, 01/2006-04/2008)

This project designed and developed a GPS-based tracking system for tracking the location and coordinating time of events for trauma patients.

Improving Measurable Performance with QoS-Adaptive Cyber-defense Technologies (Sponsor: Air Force Research Lab/NISSC, PIs: Xiaobo Zhou, Edward Chow, and Marijke Augusteijn, 06/2004-06/2006)

This project designed a high confidence software framework that supports the development and dynamic configuration of adaptive intrusion detection and response (IDR) systems. One of the main research thrusts is to investigate how to enhance the IDS to take advantages of the new capabilities created by our innovative adaptive intrusion response capabilities, which is based on advanced QoS techniques in routing and server resource allocation. The other is to tackle the huge alert traffic volume and false positives in an enterprise cyber defense system by using information fusion and artificial neural network techniques

Dynamic Data Fusion Network and QoS-aware Video Streaming for Video Surveillance (Sponsor: Air Force Research Lab/NISSC, PIs: Terry Boult and Xiaobo Zhou, 06/2004 - 08/2005)

This project designed a cross-layer bandwidth control framework with enhanced QoS techniques for effective networking bandwidth control.

Evaluating a Cluster-based Server Platform in Support of Intelligence/Information Fusion (Sponsor: Air Force Research Lab/NISSC, PIs: Xiaobo Zhou, Edward Chow, and Marijke Augusteijn, 02/2004-05/2005)

This project investigated various resource management mechanisms in providing performance isolations and improvements for different applications in a cluster-based server platform, and hence evaluate the impact of the mechanisms in support of information/data fusion applications. It investigated those challenging issues on how to exchange, verify, and correlate intelligence information for decision support, and how to allocate and coordinate sensors in different agencies for a set of tasks with different priorities.

Admission Control with Adaptive Resource Management for Mitigating Degrading DDoS Attacks (Sponsor: Air Force Research Lab/NISSC, PIs: Xiaobo Zhou and Edward Chow, 09/2003-12/2003)

This project designed an effective admission control mechanism with an adaptive resource management mechanism at the server side to defend emerging degrading DDoS attacks. It controls the access of clients to server resources based on their behaviors. The mechanisms will guarantee fair QoS to legitimate well-behaving clients. Thus, this defense strategy is based on QoS isolation and regulation by admission control and resource management at the server side. We have proposed a processing rate allocation scheme for provisioning of proportional response time differentiation to different kinds of clients. We also designed and implemented an adaptive process allocation strategy at application level on an Apache Web server.

Content Management in High-Performance I/O Systems(Sponsor: UCCS CRCW award, Sole PI: Xiaobo Zhou)

Due to the unprecedented scale of the Internet, popular Internet services must be scalable to support up to millions of concurrent client requests reliably, responsively, and economically. These scalability and availability requirements pose great challenges on both processing power and networking communication capacity, and their resource management and capacity planning. The architecture deploys a cluster of networked server nodes that work collectively to keep up with ever-increasing request load and provide scalable Internet services. This project is on resource management, high-performance I/O, and load balancing for data-intensive applications on clustered Internet servers.

Represenative Journal Articles