2016 Seventh International Workshop on Data-Intensive Computing in the Clouds (DataCloud)

14-14 Nov. 2016

Filter Results

Displaying Results 1 - 12 of 12
  • [Title page]

    Publication Year: 2016, Page(s): i
    Request permission for commercial reuse | PDF file iconPDF (162 KB)
    Freely Available from IEEE
  • [Copyright notice]

    Publication Year: 2016, Page(s): ii
    Request permission for commercial reuse | PDF file iconPDF (57 KB)
    Freely Available from IEEE
  • Table of contents

    Publication Year: 2016, Page(s): iii
    Request permission for commercial reuse | PDF file iconPDF (115 KB)
    Freely Available from IEEE
  • Asterism: Pegasus and Dispel4py Hybrid Workflows for Data-Intensive Science

    Publication Year: 2016, Page(s):1 - 8
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2294 KB) | HTML iconHTML

    We present Asterism, an open source data-intensive framework, which combines the strengths of traditional workflow management systems with new parallel stream-based dataflow systems to run data-intensive applications across multiple heterogeneous resources, without users having to: re-formulate their methods according to different enactment engines; manage the data distribution across systems; par... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An Ensemble-Based Recommendation Engine for Scientific Data Transfers

    Publication Year: 2016, Page(s):9 - 16
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1270 KB) | HTML iconHTML

    Big data scientists face the challenge of locating valuable datasets across a network of distributed storage locations. We explore methods for recommending storage locations (“endpoints”) for users based on a range of prediction models including collaborative filtering and heuristics that consider available information such as user, institution, access history, endpoint ownership, an... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Pecos: A Scalable Solution for Analyzing and Managing Qualitative Data

    Publication Year: 2016, Page(s):17 - 23
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2181 KB) | HTML iconHTML

    Large, heterogeneous, and complex data collections can be difficult to analyze and manage manually. There is a need for scalable and user-friendly approaches that can automate the analysis and management of such collections in a timely and efficient manner. To meet the aforementioned need, we are developing a system named Pecos which combines (1) an android application, (2) cloud computing middlew... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Data-Intensive Supercomputing in the Cloud: Global Analytics for Satellite Imagery

    Publication Year: 2016, Page(s):24 - 31
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1743 KB) | HTML iconHTML

    We present our experiences using cloud computing to support data-intensive analytics on satellite imagery for commercial applications. Drawing from our background in high-performance computing, we draw parallels between the early days of clustered computing systems and the current state of cloud computing and its potential to disrupt the HPC market. Using our own virtual file system layer on top o... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Model Driven Advanced Hybrid Cloud Services for Big Data: Paradigm and Practice

    Publication Year: 2016, Page(s):32 - 36
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (821 KB) | HTML iconHTML

    Advanced hybrid cloud services aim to serve big data applications by bridging multi-provider high performance cloud resources including direct connects, hypervisor bypassing VM interfaces, on premise clusters, parallel storage and high speed inter-cloud networks. We present a new “full-stack model driven orchestration” paradigm to integrate these diverse resources through semantic mo... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Improved Data-Aware Task Dispatching for Batch Queuing Systems

    Publication Year: 2016, Page(s):37 - 44
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (371 KB) | HTML iconHTML

    This paper describes a data-aware task dispatching strategy called Improved Data-Aware Task Dispatching (IDAD). This approach exploits the high-performance of local file access in non-uniform storage-access (NUSA) file systems and is based on our previous work, Data-Aware Dispatch (DAD). In IDAD, the method of calculating data placement is revised, and the CPU factor is removed, as it has no major... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Multi-tenant Fair Share Approach to Full-text Search Engine

    Publication Year: 2016, Page(s):45 - 50
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1567 KB) | HTML iconHTML

    Full text search engines underly the search of major content providers, Google, Bing and Yahoo. Open source search engines, such as Solr and ElasticSearch, are highly scalable and widely used in a Software-as-a-Service (SaaS) manner, in which multiple tenants share a single resource for improved resource utilization and lower management cost. Sharing of a full text search engine can exhibit unfair... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An Efficient Parallel Implementation of a Light-weight Data Privacy Method for Mobile Cloud Users

    Publication Year: 2016, Page(s):51 - 58
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2627 KB) | HTML iconHTML

    Cloud computing provides an opportunity to users to outsource their data and applications. However, data privacy is one of the key challenges for the users who are outsourcing data on some transparent cloud servers. Data encryption is the best option to protect users' data privacy on the cloud. However, computation overheads of encryption methods could be expensive to some small computing machines... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Author index

    Publication Year: 2016, Page(s): 59
    Request permission for commercial reuse | PDF file iconPDF (55 KB)
    Freely Available from IEEE