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 Chris Douglas

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Average citations per article25.89
Citation Count233
Publication count9
Publication years2012-2017
Available for download8
Average downloads per article1,393.63
Downloads (cumulative)11,149
Downloads (12 Months)4,841
Downloads (6 Weeks)356
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9 results found Export Results: bibtexendnoteacmrefcsv

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1 published by ACM
October 2017 ACM Transactions on Computer Systems (TOCS): Volume 35 Issue 2, October 2017
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 45,   Downloads (12 Months): 198,   Downloads (Overall): 198

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Resource Managers like YARN and Mesos have emerged as a critical layer in the cloud computing system stack, but the developer abstractions for leasing cluster resources and instantiating application logic are very low level. This flexibility comes at a high cost in terms of developer effort, as each application must ...
Keywords: resource management, Data processing

2 published by ACM
May 2017 SIGMOD '17: Proceedings of the 2017 ACM International Conference on Management of Data
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 214,   Downloads (12 Months): 3,220,   Downloads (Overall): 3,220

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Azure Data Lake Store (ADLS) is a fully-managed, elastic, scalable, and secure file system that supports Hadoop distributed file system (HDFS) and Cosmos semantics. It is specifically designed and optimized for a broad spectrum of Big Data analytics that depend on a very high degree of parallel reads and writes, ...
Keywords: big data, cloud service, hadoop, storage, azure, gce, map-reduce, tiered storage, aws, distributed file system, hdfs

3
July 2015 USENIX ATC '15: Proceedings of the 2015 USENIX Conference on Usenix Annual Technical Conference
Publisher: USENIX Association
Bibliometrics:
Citation Count: 6

Datacenter-scale computing for analytics workloads is increasingly common. High operational costs force heterogeneous applications to share cluster resources for achieving economy of scale. Scheduling such large and diverse workloads is inherently hard, and existing approaches tackle this in two alternative ways: 1) centralized solutions offer strict, secure enforcement of scheduling ...

4 published by ACM
May 2015 SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data
Publisher: ACM
Bibliometrics:
Citation Count: 6
Downloads (6 Weeks): 3,   Downloads (12 Months): 79,   Downloads (Overall): 532

Full text available: PDFPDF
Resource Managers like Apache YARN have emerged as a critical layer in the cloud computing system stack, but the developer abstractions for leasing cluster resources and instantiating application logic are very low-level. This flexibility comes at a high cost in terms of developer effort, as each application must repeatedly tackle ...
Keywords: distributed systems, databases, high performance computing, big data, hadoop, machine learning

5 published by ACM
November 2014 SOCC '14: Proceedings of the ACM Symposium on Cloud Computing
Publisher: ACM
Bibliometrics:
Citation Count: 20
Downloads (6 Weeks): 11,   Downloads (12 Months): 155,   Downloads (Overall): 944

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The continuous shift towards data-driven approaches to business, and a growing attention to improving return on investments (ROI) for cluster infrastructures is generating new challenges for big-data frameworks. Systems originally designed for big batch jobs now handle an increasingly complex mix of computations. Moreover, they are expected to guarantee stringent ...

6 published by ACM
October 2013 SOCC '13: Proceedings of the 4th annual Symposium on Cloud Computing
Publisher: ACM
Bibliometrics:
Citation Count: 155
Downloads (6 Weeks): 76,   Downloads (12 Months): 1,092,   Downloads (Overall): 4,722

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The initial design of Apache Hadoop [1] was tightly focused on running massive, MapReduce jobs to process a web crawl. For increasingly diverse companies, Hadoop has become the data and computational agorá ---the de facto place where data and computational resources are shared and accessed. This broad adoption and ubiquitous ...

7
August 2013 Proceedings of the VLDB Endowment: Volume 6 Issue 12, August 2013
Publisher: VLDB Endowment
Bibliometrics:
Citation Count: 7
Downloads (6 Weeks): 0,   Downloads (12 Months): 15,   Downloads (Overall): 219

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In this demo proposal, we describe REEF, a framework that makes it easy to implement scalable, fault-tolerant runtime environments for a range of computational models. We will demonstrate diverse workloads, including extract-transform-load MapReduce jobs, iterative machine learning algorithms, and ad-hoc declarative query processing. At its core, REEF builds atop YARN ...

8 published by ACM
October 2012 SoCC '12: Proceedings of the Third ACM Symposium on Cloud Computing
Publisher: ACM
Bibliometrics:
Citation Count: 19
Downloads (6 Weeks): 7,   Downloads (12 Months): 41,   Downloads (Overall): 409

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Data-intensive computing (DISC) frameworks scale by partitioning a job across a set of fault-tolerant tasks , then diffusing those tasks across large clusters. Multi-tenanted clusters must accommodate service-level objectives (SLO) in their resource model, often expressed as a maximum latency for allocating the desired set of resources to every job. ...
Keywords: checkpoint/restart, data-intensive computing, elasticity, multi-tenancy

9 published by ACM
May 2012 SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Publisher: ACM
Bibliometrics:
Citation Count: 12
Downloads (6 Weeks): 0,   Downloads (12 Months): 41,   Downloads (Overall): 905

Full text available: PDFPDF
Walnut is an object-store being developed at Yahoo! with the goal of serving as a common low-level storage layer for a variety of cloud data management systems including Hadoop (a MapReduce system), MObStor (a multimedia serving system), and PNUTS (an extended key-value serving system). Thus, a key performance challenge is ...
Keywords: cloud storage, hybrid object store, paxos-based replication



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