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2014年12月12-14日 北京 · 新云南皇冠假日酒店

2014中国大数据技术大会

暨第二届CCF大数据学术会议

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Ion Stoica

Ion Stoica

UC Berkeley计算机教授,AMPLab共同创始人,Spark、Mesos核心设计者

Ion Stoica is a Professor in the EECS Department at University of California at Berkeley. He received his PhD from Carnegie Mellon University in 2000. He does research on cloud computing and networked computer systems. Past work includes the Dynamic Packet State (DPS), Chord DHT, Internet Indirection Infrastructure (i3), declarative networks, replay-debugging, and multi-layer tracing in distributed systems. His current research focuses on resource management and scheduling for data centers, cluster computing frameworks, and network architectures. He is an ACM Fellow and has received numerous awards, including the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001). In 2006, he co-founded Conviva, a startup to commercialize technologies for large scale video distribution, and in 2013, he co-founded Databricks a startup to commercialize technologies for Big Data processing.

UC Berkeley计算机教授,AMPLab共同创始人,Spark、Mesos核心设计者

Taming Big Data with Berkeley Data Analytics Stack (BDAS) One of the most interesting developments over the past decade is the rapid increase in data; we are now deluged by data from online services (PBs per day), scientific instruments (PBs per minute), gene sequencing (250GB per person) and many other sources. Researchers and practitioners collect this massive data with one goal in mind: extract “value” through sophisticated exploratory analysis, and use it as the basis to make decisions as varied as personalized treatment and ad targeting. Unfortunately, today’s data analytics tools are slow in answering even simple queries, as they typically require to sift through huge amounts of data stored on disk, and are even less suitable for complex computations, such as machine learning algorithms. These limitations leave the potential of extracting value of big data unfulfilled. To address this challenge, we are developing BDAS, an open source data analytics stack that provides interactive response times for complex computations on massive data. To achieve this goal, BDAS supports efficient, large-scale in-memory data processing, and allows users and applications to trade between query accuracy, time, and cost. In this talk, I’ll present the architecture, challenges, early results, and our experience with developing BDAS. Some BDAS components have already been released: Mesos, a platform for cluster resource management has been deployed by Twitter on 6,000+ servers, while Spark, an in-memory cluster computing frameworks, is already being used by tens of companies and research institutions.

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中国计算机学会

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CCF大数据专家委员会

南京大学、复旦大学(学术会议)

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