This special issue solicits papers in the area of large-scale distributed systems for big data. Big data can be defined as datasets/data streams that become so large that they become awkward to work with using on-hand computer data and computation management tools.
Such datasets are often from various sources (Variety) in unstructured form, such as those from social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions and web logs. In general, these data streams are not only of a large size (Volume) that cannot be stored but also have fast data in/out (Velocity) and hide valuable knowledge.
Big data size is beyond the capacity of commonly used system storage or computing capabilities within a reasonable time frame, hence demanding new innovative solutions. Considering the complexity and scale of big data, using traditional techniques and models is not enough, and we need to propose new methodologies and frameworks for big data.
This opens several research questions and challenges, including investigating computing systems which can handle the storage, processing and networking requirements of big data. Using cloud resources for the storage and processing of big data applications is currently under investigation by many researchers.
In this special issue, our aim is to explore a broad range of distributed systems including cloud, grid, multi-cluster and volunteer computing systems to handle big data. We believe this issue will be an excellent venue to help the research community define the current state, determine future goals, and propose new frameworks for big data processing.
The issue will carry revised and substantially extended versions of selected papers presented at the 12th Australasian Symposium on Parallel and Distributed Computing (AusPDC 2014), but we also strongly encourage researchers unable to participate in the conference to submit articles for this call.
Suitable topics include, but are not limited to, the following:
- Performance analysis of big data on distributed systems (cloud, grid, multi-cluster)
- Computational models for big data on distributed systems
- Performance modelling and evaluation of big data applications
- Big data theoretical models, standards and theories
- Simulation and debugging of big data systems and tools
- Modelling and simulation frameworks for big data
- Modelling cloud services for big data
- Performance optimisation of big data applications on distributed systems
- Big data processing in e-research and e-science
- Workflow models for big data on distributed systems
- Distributed storage models for big data
Submission of manuscripts: 30 April, 2014