Background and Scope
Networking is one of the most crucial components in the overall system architecture of a data centric environment. Many of the current solutions both in industry and scientific domains depend on the underlying network infrastructure and its performance. As current technology enables faster storage devices and larger interconnect bandwidth, there is a substantial need for novel system design and middleware architecture to address increasing latency and scalability requirements. In addition to scalability and reliability in highly distributed systems, end-system limitations and system software performance also play an important role in today’s multicore environment. Traditional network and data management techniques are unlikely to scale to meet the needs of future data-intensive systems. We require new collaborations between data management and networking communities to develop intelligent networking middleware and efficient data management infrastructure.
This special issue seeks papers on seminal work done to evaluate emerging trends and current technological developments and to discuss future design principles of network-aware data management. We seek contributions focusing on intelligent data-flow and resource coordination, high-performance networking problems, novel abstraction techniques for data representation, network virtualization and practical experiences.
Topics of interest for the special issue include but are not limited to the following list:
System software and middleware design for emerging storage technologies
Performance analysis and optimizations for end-to-end application latency
Network function virtualization for large-scale data management
Workload characterization for latency/throughput sensitive distributed applications
Applications of software defined networking to large data flows
Performance evaluation and network support for data-intensive computing
Dynamic resource provisioning and network virtualization
Operating system and virtualization support for networking
Network-aware data management tools and systems
Practical experiences and prototypes for network-aware data management
Requirements and issues for network quality of service (QoS)
Application pipelines and network-aware toolkits for data distribution
Optimization and development of data transfer protocols
Data center networking and network management for Cloud environments
Heterogeneous and distributed resource coordination and management
The submitted papers must be written in English and describe original research which is not published nor currently under review by other journals or conferences. Author guidelines for preparation of manuscript can be found at http://www.elsevier.com/journals/journal-of-parallel-and-distributed-computing/0743-7315/guide-for-authors
For more information, please contact Mehmet Balman (firstname.lastname@example.org), Suren Byna (email@example.com), Brian L. Tierney (firstname.lastname@example.org)
All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EES). The authors must select “SI: Network-Aware Data Mgmt.” when they reach the \”Article Type\” step in the submission process. The EES website is located at: http://ees.elsevier.com/jpdc/
Guide for Authors
This site will guide you stepwise through the creation and uploading of your article. The guide for Authors can be found on the journal homepage (http://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing).
First submission expected: October 15, 2014
Submission deadline: Dec 15, 2014
Acceptance deadline:April 15, 2015
Guest Editors Bios
Mehmet Balman is a senior performance engineer at VMware Inc. and a guest scientist at Lawrence Berkeley National Laboratory. Previously, he has worked as a researcher engineer in the Computational Research Division at Lawrence Berkeley National Laboratory (Berkeley Lab). Before coming to Berkeley Lab, he was in the Center for Computation & Technology (CCT) at Louisiana State University (LSU). He received his doctoral degree in computer science in 2010 from LSU. His research interests include distributed systems, data-intensive computing, scheduling and resource management, performance problems, high-performance network protocols, bandwidth reservations, virtual circuits, and data transfer scheduling for large-scale applications.
Surendra Byna is a research scientist in the Scientific Data Management Group at Lawrence Berkeley National Lab. His research interests are in computer architecture, parallel computing, and optimizing data access performance for parallel computing. Before joining LBNL, he was a researcher at NEC Labs America, where he was a part of Computer Systems Architecture Department and was involved in Heterogeneous Cluster Computing project. He was a Research Assistant Professor in the Department of Computer Science at Illinois Institute of Technology (IIT). He was also a Guest Researcher at the Math. and Computer Science division of the Argonne National Laboratory and a Faculty Member of the Scalable Computing Software Laboratory at IIT.
Brian L. Tierney is a Staff Scientist and group leader of the ESnet Advanced Network Technologies Group at Lawrence Berkeley National Laboratory, and is PI of ESnet\’s 100G Network Testbed Project. His research interests include high-performance networking and network protocols; distributed system performance monitoring and analysis; network tuning issues; and the application of distributed computing to problems in science and engineering. He has been the PI for several DOE research projects in network and Grid monitoring systems for data intensive distributed computing. He was the principal designer of the Distributed Parallel Storage System (DPSS), where many of the ideas for GridFTP originated. He also designed the first version of the NetLogger Toolkit, and worked on the Bro Intrusion Detection System.
VMware Inc & Lawrence Berkeley National Laboratory
Email address: email@example.com
Lawrence Berkeley National Laboratory
Email address: firstname.lastname@example.org
Energy Sciences Network & Lawrence Berkeley National Laboratory
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