PhD Defense: Building Efficient and Cost-Effective Cloud-Based Big Data Management Systems

In today's big data world data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the cloud due to the economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go cloud environment the cost of analytics can grow linearly with time and the resources required. Reducing the cost of data analytics in the cloud thus remains a primary challenge. In my dissertation research I have focussed on building efficient and cost-effective cloud-baseddata management systems for different application domains that are predominant in cloud computing environments.
In the first part of my dissertation I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions and incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime.
In the second part of my dissertation, I focus on progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides them with user-control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred on such analytics.
Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph structured data in the cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph.; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that an analysis task is interested in and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at thelevel of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over large-scale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of data analytics in the cloud.
The results of our extensive experimental evaluation of these prototypes with several real world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the cloud.
Examining committee:
Chair: Dr. Amol Deshpande
Dean’s rep: Dr. Richard Marciano
Members: Dr. Alan Sussman
Dr. Jimmy Lin
Dr. Pete Keleher