A Modelling Language to Support Evolution of Multi-Tenant Cloud Data Architectures

Multi-tenant data architectures enable efficient resource utilization in cloud applications, but are currently being implemented in industry and research using manual coding techniques that tend to be time consuming and error prone. We propose a novel domain-specific modeling language, CadaML, to automatically manage the development and evolution of cloud data architectures that (a) adopt multi-tenancy and/or (b) comprise of a combination of different storage solutions such as relational and non-relational databases, and blob storage. CadaML provides concepts and notations to support abstract modelling of a multi-tenant data architecture, and also provides tools to validate the data architecture and automatically produce application code. We rigorously evaluate CadaML through a user experiment where developers of various capabilities are asked to re-architect the data layer of an industrial business process analysis application. We observe that CadaML users required 3.5x less development time than manual coders. In addition to improved productivity, CadaML users highlighted other benefits gained in terms of reliability of generated code and usability

Cloud service localisation

The essence of cloud computing is the provision of software and hardware services to a range of users in dierent locations. The aim of cloud service localisation is to facilitate the internationalisation and localisation of cloud services by allowing their adaption to dierent locales. We address the lingual localisation by providing service-level language translation techniques to adopt services to dierent languages and regulatory localisation by providing standards-based mappings to achieve regulatory compliance with regionally varying laws, standards and regulations. The aim is to support and enforce the explicit modelling of aspects particularly relevant to localisation and runtime support consisting of tools and middleware services to automating the deployment based on models of locales, driven by the two localisation dimensions. We focus here on an ontology-based conceptual information model that integrates locale specication in a coherent way

Systematic survey on evolution of cloud architectures

Cloud architectures are becoming an active area of research. The quality and durability of a software system are defined by its architecture. The architecture approaches that are used to build cloud-based systems are not available in a blended fashion to achieve an effective universal architecture solution. The paper aims to contribute to the systematic literature review (SLR) to assist researchers who are striving to contribute in this area. The main objective of this review is to systematically identify and analyse the recently published research topics related to software architecture for cloud with regard to research activity, used tools and techniques, proposed approaches, domains. The applied method is SLR based on four selected electronic databases proposed by (Kitchenham and Charters, 2007). Out of 400 classified publications, we regard 121 as relevant for our research domain. We outline taxonomy of their topics and domains, provide lists of used methods and proposed approaches. At present, there is little research coverage on software architectures for cloud, while other disciplines have become more active. The future work is to develop a secure architecture to achieve quality of service and service level agreements

Big SaaS: The Next Step Beyond Big Data

Software-as-a-Service (SaaS) is a model of cloud computing in which software functions are delivered to the users as services. The past few years have witnessed its global flourishing. In the foreseeable future, SaaS applications will integrate with the Internet of Things, Mobile Computing, Big Data, Wireless Sensor Networks, and many other computing and communication technologies to deliver customizable intelligent services to a vast population. This will give rise to an era of what we call Big SaaS systems of unprecedented complexity and scale. They will have huge numbers of tenants/users interrelated in complex ways. The code will be complex too and require Big Data but provide great value to the customer. With these benefits come great societal risks, however, and there are other drawbacks and challenges. For example, it is difficult to ensure the quality of data and metadata obtained from crowdsourcing and to maintain the integrity of conceptual model. Big SaaS applications will also need to evolve continuously. This paper will discuss how to address these challenges at all stages of the software lifecycle

Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Developing sustainability pathways for social simulation tools and services

The use of cloud technologies to teach agent-based modelling and simulation (ABMS) is an interesting application of a nascent technological paradigm that has received very little attention in the literature. This report fills that gap and aims to help instructors, teachers and demonstrators to understand why and how cloud services are appropriate solutions to common problems they face delivering their study programmes, as well as outlining the many cloud options available. The report first introduces social simulation and considers how social simulation is taught. Following this factors affecting the implementation of agent-based models are explored, with attention focused primarily on the modelling and execution platforms currently available, the challenges associated with implementing agent-based models, and the technical architectures that can be used to support the modelling, simulation and teaching process. This sets the context for an extended discussion on cloud computing including service and deployment models, accessing cloud resources, the financial implications of adopting the cloud, and an introduction to the evaluation of cloud services within the context of developing, executing and teaching agent-based models