NeTS: CSR: Medium: Collaborative Research: Enabling Flexible and High Performance Big Data Analytics Over Geo-Distributed Clouds

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

Large organizations and small enterprises alike leverage datacenters across the globe to offer Internet services to their users. These sites routinely gather data pertaining to end user activities to provide better services, and they collect server monitoring logs and performance counters to ensure uninterrupted service. Although fast, efficient, and cost-effective analyses of these large datasets can significantly improve users' quality of experience and enable novel applications, the wide area network (WAN) that connects the datacenters poses a considerable challenge: because WAN bandwidth is limited and expensive, and WAN latency is high and variable, both the performance and timeliness of analytics are affected by the WAN. This project aims to build a new WAN-aware big data stack customized for flexible geo-distributed data analytics. The project will not impose any constraints on the set of queries that can be issued, and it will support a variety of performance objectives including obtaining timely responses, minimizing batch completion times, or using minimal bandwidth. To account for unpredictable and fine-timescale changes to WAN conditions and to enable coordination among the actions taken by different layers of the analytics stack, this project will enable holistic, cross-layer visibility and optimizations. It will incorporate awareness of the geo-distributed setting in the stack's upper layers (e.g., query optimization) and of application-level objectives in the lower layers (e.g., networking). This will result in a radical re-factoring of the API and interfaces between query optimization, query execution, resource negotiation, wide-area storage, and network routing/scheduling. Software artifacts from this project will be incorporated into existing open source big data stacks, making the research outcomes broadly available for public reuse. The experimental harnesses will be made available to ensure repeatability and to foster follow up research. The research outcomes will guide industry evolution as the industry slowly shifts from single-datacenter to geo-distributed settings. The project has a substantial educational component involving the introduction of new courses on big data systems at both graduate and undergraduate levels that will involve hands-on exercises with state-of-the-art big data software, and it will reach out to high-school students, women, and underrepresented minorities through big data boot camps.