Designing Traceability into Big Data Systems

Providing an appropriate level of accessibility and traceability to data or process elements (so-called Items) in large volumes of data, often Cloud-resident, is an essential requirement in the Big Data era. Enterprise-wide data systems need to be designed from the outset to support usage of such Items across the spectrum of business use rather than from any specific application view. The design philosophy advocated in this paper is to drive the design process using a so-called description-driven approach which enriches models with meta-data and description and focuses the design process on Item re-use, thereby promoting traceability. Details are given of the description-driven design of big data systems at CERN, in health informatics and in business process management. Evidence is presented that the approach leads to design simplicity and consequent ease of management thanks to loose typing and the adoption of a unified approach to Item management and usage.Comment: 10 pages; 6 figures in Proceedings of the 5th Annual International Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2015), Singapore July 2015. arXiv admin note: text overlap with arXiv:1402.5764, arXiv:1402.575

Designing Reusable Systems that Can Handle Change - Description-Driven Systems : Revisiting Object-Oriented Principles

In the age of the Cloud and so-called Big Data systems must be increasingly flexible, reconfigurable and adaptable to change in addition to being developed rapidly. As a consequence, designing systems to cater for evolution is becoming critical to their success. To be able to cope with change, systems must have the capability of reuse and the ability to adapt as and when necessary to changes in requirements. Allowing systems to be self-describing is one way to facilitate this. To address the issues of reuse in designing evolvable systems, this paper proposes a so-called description-driven approach to systems design. This approach enables new versions of data structures and processes to be created alongside the old, thereby providing a history of changes to the underlying data models and enabling the capture of provenance data. The efficacy of the description-driven approach is exemplified by the CRISTAL project. CRISTAL is based on description-driven design principles; it uses versions of stored descriptions to define various versions of data which can be stored in diverse forms. This paper discusses the need for capturing holistic system description when modelling large-scale distributed systems.Comment: 8 pages, 1 figure and 1 table. Accepted by the 9th Int Conf on the Evaluation of Novel Approaches to Software Engineering (ENASE'14). Lisbon, Portugal. April 201

Service Oriented Toolkit for Research Data Management Final Report

The Service Oriented Toolkit for Research Data Management project was co-funded by the JISC Managing Research Data Programme 2011-2013 and The University of Hertfordshire. The project focused on the realisation of practical benefits for operationalising an institutional approach to good practice in RDM. The objectives of the project were to audit current best practice, develop technology demonstrators with the assistance of leading UH research groups, and then reflect these developments back into the wider internal and external research community via a toolkit of services and guidance. The overall aim was to contribute to the efficacy and quality of research data plans, and establish and cement good data management practice in line with local and national policy

Beyond generic lifecycles : reusable modeling of custom-fit management workflows for cloud applications

Automated management and orchestration of cloud applications have become increasingly important, partly due to the large skills shortage in IT operations and the increasing complexity of cloud applications. Cloud modeling languages play an important role in this, both for describing the structure of a cloud application and specifying the management actions around it. The TOSCA cloud model standard recently defined declarative workflows as the preferred way to specify these management actions but, as noted in the standard itself, this is far from ideal. This paper draws lessons from six years of using declarative workflows in Juju for deploying and managing complex platforms such as OpenStack and Kubernetes in production. This confirms the limitations: declarative workflows are inflexible, hard to reuse, and allow for related components to become silently incompatible. This paper proposes the reactive pattern to solve these issues by enabling the creation of emergent workflows using declarative flags and handlers, which can be easily grouped into reusable layers. After more than two years of using this pattern in production as part of our charms. reactive framework, it is clear that it enables reusability and ensures compatibility: 67% of reactive charms share parts of the management workflow and 73% of reactive charms share a relationship workflow

High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape