Application-Agnostic, Distributed-Aware Cloud Platforms
Timothy Wood, George Washington University, PI
This project is supported by the National Science Foundation’s CAREER Grant #1253575.
Cloud Computing has radically changed how businesses run their applications by allowing a huge number of computers to be economically shared by many different users. The applications running inside these cloud data centers are growing in size and complexity. Even a relatively straightforward web application is likely to be composed of multiple interacting service components such as a web server, a database, and a data cache. The result is a complicated distributed application that may exhibit performance bottlenecks or consistency requirements between components. Unfortunately, existing resource management and reliability tools consider these components individually, and are often unaware of the important relations between them.
This work is predicated on the belief that future data centers must be application-agnostic, yet distributed-computing-aware. Cloud data centers rely on virtualization to partition servers into isolated components, but there are benefits and drawbacks of sending information across the virtualization abstraction layer. This project has explored these trade-offs in the context of memory management (VEE 14), storage (SC 15, IC2E 16) and scheduling (CCGrid 14), with a focus on distributed cloud applications. We have also explored how network function virtualization offers a new opportunity to customize network management for cloud applications in a transparent way (NSDI 14, ICAC 2016 Best Paper). The work has resulted in several open source extensions to popular cloud software such as Xen and Hadoop, twenty publications, and a best paper award. A diverse body of students, including four PhD students, six undergraduate students, three MS students, and three high school students, were involved with the research projects.
- Jinho Hwang - Completed PhD in 2013, now at IBM Research
- Sundaresan Rajasekaran - Completed PhD in 2018, now at Barkly
- Wei Zhang - Completed PhD in 2018, now at Microsoft Azure
- Michael Trotter - Current PhD student
MS/Undergraduate Students: Neel Shah (BS ‘17, VMware), Phil Lopreiato (BS ‘17, Facebook), Harpreet Singh (MS ‘17, Stony Brook), Shaohua Duan (MS ‘16, Rutgers), Chenghu He (MS ‘16, EMC), Ben Carleton (BS ‘16), Abigail Shriver (BS ‘17), Lucas Chaufournier (BS ‘15, UMass), Rian Shambaugh (BS ‘15, UMass).
- J. Hwang and T. Wood, “Adaptive Performance-Aware Distributed Memory Caching,” in International Conference on Autonomic Computing (ICAC 2013), 2013. PDF
- J. Hwang, S. Zeng, F. Wu, and T. Wood, “A Component Based Performance Comparison of Four Hypervisors,” in IFIP/IEEE Integrated Network Management Symposium (IM 2013), 2013. PDF
- B. Sharma, T. Wood, and C. R. Das, “HybridMR: A Hierarchical MapReduce Scheduler for Hybrid Data Centers,” in IEEE International Conference on Distributed Computing Systems (ICDCS 2013), 2013. PDF
- W. Zhang, S. Rajasekaran, and T. Wood, “Big Data in the Background: Maximizing Productivity while Minimizing Virtual Machine Interference,” in Workshop on Architectures and Systems for Big Data (co-located with ISCA 2013), 2013. PDF
- R. C. Chiang, J. Hwang, H. Huang, and T. Wood, “Matrix: Achieving Predictable Virtual Machine Performance in the Clouds,” in USENIX International Conference on Autonomic Computing (ICAC), 2014. PDF
- J. Hwang, G. Liu, S. Zeng, F. Y. Wu, and T. Wood, “Topology Discovery and Service Classification for Distributed-Aware Clouds,” in IEEE International Workshop on Cloud Analytics (IWCA), 2014, pp. 385–390. PDF
- J. Hwang, K. K. Ramakrishnan, and T. Wood, “NetVM: High Performance and Flexible Networking using Virtualization on Commodity Platforms,” in Symposium on Networked System Design and Implementation, 2014. PDF
- J. Hwang, A. Uppal, T. Wood, and H. Huang, “Mortar: Filling the Gaps in Data Center Memory,” in International Conference on Virtual Execution Environments (VEE), 2014. PDF
- J. Hwang, W. Zhang, R. C. Chiang, T. Wood, and H. Huang, “UniCache: Hypervisor Managed Data Storage in RAM and Flash,” in IEEE International Conference on Cloud Computing (CLOUD), 2014. PDF
- W. Zhang, S. Rajasekaran, T. Wood, and M. Zhu, “MIMP: Deadline and Interference Aware Scheduling of Hadoop Virtual Machines,” in IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2014. PDF
- W. Zhang, T. Wood, H. H. Huang, J. Hwang, and K. K. Ramakrishnan, “Load Balancing of Heterogeneous Workloads in Memcached Clusters,” in Usenix International Workshop on Feedback Computing, 2014. PDF
- W. Zhang, T. Wood, K. K. Ramakrishnan, and J. Hwang, “SmartSwitch: Blurring the Line Between Network Infrastructure & Cloud Applications,” in 6th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 14), Philadelphia, PA, 2014. PDF
- R. C. Chiang, H. H. Huang, T. Wood, C. Liu, and O. Spatscheck, “IOrchestra: Supporting High-performance Data-intensive Applications in the Cloud via Collaborative Virtualization,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), New York, NY, USA, 2015, pp. 45:1–45:12. PDF
- J. Hwang, K. K. Ramakrishnan, and T. Wood, “NetVM: High Performance and Flexible Networking Using Virtualization on Commodity Platforms,” IEEE Transactions on Network and Service Management, vol. 12, no. 1, pp. 34–47, Mar. 2015. PDF
- G. Liu and T. Wood, “Cloud-Scale Application Performance Monitoring with SDN and NFV,” in IEEE International Workshop on Cloud Analytics (IWCA15), 2015. PDF
- W. Zhang, S. Rajasekaran, S. Duan, T. Wood, and M. Zhu, “Minimizing Interference and Maximizing Progress for Hadoop Virtual Machines,” ACM SIGMETRICS Performance Evaluation Review, Mar. 2015. PDF
- G. Liu, M. Trotter, Y. Ren, and T. Wood, “Netalytics: Cloud-Scale Application Performance Monitoring with SDN and NFV,” in ACM/IFIP/USENIX Middleware, 2016. PDF
- S. Rajasekaran, S. Duan, W. Zhang, and T. Wood, “Multi-Cache: Dynamic, Efficient Partitioning for Multi-Tier Caches in Consolidated VM Environments,” in IEEE International Conference on Cloud Engineering (IC2E), 2016. PDF
- W. Zhang, T. Wood, and J. Hwang, “NetKV: Scalable, Self-Managing, Load Balancing as a Network Function,” in 2016 IEEE International Conference on Autonomic Computing (ICAC), 2016, pp. 5–14. PDF Best Paper Award
- M. Trotter, G. Liu, and T. Wood, “Into the Storm: Descrying Optimal Configurations Using Genetic Algorithms and Bayesian Optimization,” in 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W), 2017, pp. 175–180.
- M. Trotter, T. Wood, and J. Hwang, “Forecasting a Storm: Divining Optimal Configurations using Genetic Algorithms and Supervised Learning,” in IEEE International Conference on Autonomic Computing (ICAC), 2019. PDF
Open Source Releases: