Software Sustainability: Research and Practice from a Software Architecture Viewpoint

Context: Modern societies are highly dependent on complex, large-scale, software-intensive systems that increasingly operate within an environment of continuous availability, which is challenging to maintain and evolve in response to the inevitable changes in stakeholder goals and requirements of the system. Software architectures are the foundation of any software system and provide a mechanism for reasoning about core software quality requirements. Their sustainability – the capacity to endure in changing environments – is a critical concern for software architecture research and practice. Problem: Accidental software complexity accrues both naturally and gradually over time as part of the overall software design and development process. From a software architecture perspective, this allows several issues to overlap including, but not limited to: the accumulation of technical debt design decisions of individual components and systems leading to coupling and cohesion issues; the application of tacit architectural knowledge resulting in unsystematic and undocumented design decisions; architectural knowledge vaporisation of design choices and the continued ability of the organization to understand the architecture of its systems; sustainability debt and the broader cumulative effects of flawed architectural design choices over time resulting in code smells, architectural brittleness, erosion, and drift, which ultimately lead to decay and software death. Sustainable software architectures are required to evolve over the entire lifecycle of the system from initial design inception to end-of-life to achieve efficient and effective maintenance and evolutionary change. Method: This article outlines general principles and perspectives on sustainability with regards to software systems to provide a context and terminology for framing the discourse on software architectures and sustainability. Focusing on the capacity of software architectures and architectural design choices to endure over time, it highlights some of the recent research trends and approaches with regards to explicitly addressing sustainability in the context of software architectures. Contribution: The principal aim of this article is to provide a foundation and roadmap of emerging research themes in the area of sustainable software architectures highlighting recent trends, and open issues and research challenges

Towards a Reference Architecture with Modular Design for Large-scale Genotyping and Phenotyping Data Analysis: A Case Study with Image Data

With the rapid advancement of computing technologies, various scientific research communities have been extensively using cloud-based software tools or applications. Cloud-based applications allow users to access software applications from web browsers while relieving them from the installation of any software applications in their desktop environment. For example, Galaxy, GenAP, and iPlant Colaborative are popular cloud-based systems for scientific workflow analysis in the domain of plant Genotyping and Phenotyping. These systems are being used for conducting research, devising new techniques, and sharing the computer assisted analysis results among collaborators. Researchers need to integrate their new workflows/pipelines, tools or techniques with the base system over time. Moreover, large scale data need to be processed within the time-line for more effective analysis. Recently, Big Data technologies are emerging for facilitating large scale data processing with commodity hardware. Among the above-mentioned systems, GenAp is utilizing the Big Data technologies for specific cases only. The structure of such a cloud-based system is highly variable and complex in nature. Software architects and developers need to consider totally different properties and challenges during the development and maintenance phases compared to the traditional business/service oriented systems. Recent studies report that software engineers and data engineers confront challenges to develop analytic tools for supporting large scale and heterogeneous data analysis. Unfortunately, less focus has been given by the software researchers to devise a well-defined methodology and frameworks for flexible design of a cloud system for the Genotyping and Phenotyping domain. To that end, more effective design methodologies and frameworks are an urgent need for cloud based Genotyping and Phenotyping analysis system development that also supports large scale data processing. In our thesis, we conduct a few studies in order to devise a stable reference architecture and modularity model for the software developers and data engineers in the domain of Genotyping and Phenotyping. In the first study, we analyze the architectural changes of existing candidate systems to find out the stability issues. Then, we extract architectural patterns of the candidate systems and propose a conceptual reference architectural model. Finally, we present a case study on the modularity of computation-intensive tasks as an extension of the data-centric development. We show that the data-centric modularity model is at the core of the flexible development of a Genotyping and Phenotyping analysis system. Our proposed model and case study with thousands of images provide a useful knowledge-base for software researchers, developers, and data engineers for cloud based Genotyping and Phenotyping analysis system development

Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

Advanced Techniques for Assets Maintenance Management

16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018 Bergamo, Italy, 11–13 June 2018. Edited by Marco Macchi, László Monostori, Roberto PintoThe aim of this paper is to remark the importance of new and advanced techniques supporting decision making in different business processes for maintenance and assets management, as well as the basic need of adopting a certain management framework with a clear processes map and the corresponding IT supporting systems. Framework processes and systems will be the key fundamental enablers for success and for continuous improvement. The suggested framework will help to define and improve business policies and work procedures for the assets operation and maintenance along their life cycle. The following sections present some achievements on this focus, proposing finally possible future lines for a research agenda within this field of assets management

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201