Towards a Service-Oriented Enterprise: The Design of a Cloud Business Integration Platform in a Medium-Sized Manufacturing Enterprise

This case study research followed the two-year transition of a medium-sized manufacturing firm towards a service-oriented enterprise. A service-oriented enterprise is an emerging architecture of the firm that leverages the paradigm of services computing to integrate the capabilities of the firm with the complementary competencies of business partners to offer customers with value-added products and services. Design science research in information systems was employed to pursue the primary design of a cloud business integration platform to enable the secondary design of multi-enterprise business processes to enable the dynamic and effective integration of business partner capabilities with those of the enterprise. The results from the study received industry acclaim for the designed solutions innovativeness and business results in the case study environment. The research makes contributions to the IT practitioner and scholarly knowledge base by providing insight into key constructs associated with service-oriented design and deployment of a cloud enterprise architecture and cloud intermediation model to achieve business results. The study demonstrated how an outside-in service-oriented architecture adoption pattern and cloud computing model enabled a medium-sized manufacturing enterprise to focus on a comprehensive approach to business partner integration and collaboration. The cloud integration platform has enabled a range of secondary designs that leveraged business services to orchestrate inter-enterprise business processes for choreography into service systems and networks for the purposes of value creation. The study results demonstrated enhanced levels of business process agility enabled by the cloud platform leading to secondary designs of transactional, differentiated, innovative, and improvisational business processes. The study provides a foundation for future scholarly research on the role of cloud integration platforms in enterprise computing and the increased importance of service-oriented secondary designs to exploit cloud platforms for sustained business performance

Business process model customisation using domain-driven controlled variability management and rule generation

Business process models are abstract descriptions and as such should be applicable in different situations. In order for a single process model to be reused, we need support for configuration and customisation. Often, process objects and activities are domain-specific. We use this observation and allow domain models to drive the customisation. Process variability models, known from product line modelling and manufacturing, can control this customisation by taking into account the domain models. While activities and objects have already been studied, we investigate here the constraints that govern a process execution. In order to integrate these constraints into a process model, we use a rule-based constraints language for a workflow and process model. A modelling framework will be presented as a development approach for customised rules through a feature model. Our use case is content processing, represented by an abstract ontology-based domain model in the framework and implemented by a customisation engine. The key contribution is a conceptual definition of a domain-specific rule variability language

An agility-oriented and fuzziness-embedded semantic model for collaborative cloud service search, retrieval and recommendation

Cloud computing enables a revolutionary paradigm of consuming ICT services. However, due to the inadequately described service information, users often feel confused while trying to find the optimal services. Although some approaches are proposed to deal with cloud service semantic modelling and recommendation issues, they would only work for certain restricted scenarios in dealing with basic service specifications. Indeed, the missing extent is that most cloud services are "agile" whilst there are many vague service terms and descriptions. This paper proposes an agility-oriented and fuzziness-embedded ontology model, which adopts agility-centric design along with OWL2 (Web Ontology Language) fuzzy extensions. The captured cloud service specifications are maintained in an open and collaborative manner, as the fuzziness in the model accepts rating updates from users on the fly. The model enables comprehensive service specification by capturing cloud concept details and their interactions, even across multiple service categories and abstraction levels. Utilizing the model as a knowledge base, a service recommendation system prototype is developed. Case studies demonstrate that the approach can outperform existing practices by achieving effective service search, retrieval and recommendation outcomes

Complex Distributed Systems:The Need for Fresh Perspectives

Distributed systems are at a watershed due to their increasing complexity. The heart of the problem is the extreme level of heterogeneity exhibited by contemporary distributed systems coupled with the need to be dynamic and responsive to change. In effect, we have moved from distributed systems to systems of systems. Following on from this, middleware is also at a watershed. The traditional view of middleware is no longer valid (i.e. as a layer of abstraction, masking the complexity of the underlying distributed system and providing a high-level programming model). In practice, middleware is often by-passed with complex systems constructed in a rather ad hoc manner as a mash-up of a variety of technologies. The end result is that middleware is no longer sure of its form or purpose and this lack of a viable approach is a huge barrier to the emergence of areas such as smart cities and emergency response systems. This paper argues that there is a need to fundamentally rethink the middleware landscape related to complex distributed systems. The core contribution of the paper is a set of fresh perspectives, which lead us in turn to novel principles and patterns for middleware and subsequently to new styles of platform. These perspectives include a move to emergent middleware, seeking flexible meta-structures for distributed systems, and a step away from generic to domain-specific technologies. A number of case studies are also presented to demonstrate what this might mean for future distributed systems

Transfer Cost of Virtual Machine Live Migration in Cloud Systems

Virtualised frameworks typically form the foundations of Cloud systems, where Virtual Machine (VM) instances provide execution environments for a diverse range of applications and services. Modern VMs support Live Migration (LM) – a feature wherein a VM instance is transferred to an alternative node without stopping its execution. The focus of this research is to analyse and evaluate the LM transfer cost which we define as the total size of data to be transferred to another node for a particular migrated VM instance. Several different virtualisation approaches are categorised with a shortlist of candidate VMs for evaluation. The selection of VirtualBox as the best representative VM for our experiments and analysis is then discussed and justified. The paper highlights the major areas of the LM transfer process – CPU registers, memory, permanent storage, and network switching – and analyses their impact on the volume of information to be migrated which includes the VM instance with the required libraries, the application code and any data associated with it. Then, using several representative applications, we report experimental results for the transfer cost of LM for respective VirtualBox instances. We also introduce a novel Live Migration Data Transfer (LMDT) formula, which has been experimentally validated and confirms the exponential nature of the LMDT process. Our estimation model supports efficient design and development decisions in the process of analysing and building Cloud systems. The presented methodology is also applicable to the closely-related area of virtual containers which is part of our current and future work

Cloud Manufacturing Model to Optimise Manufacturing Performance

Being predicted as the future of modern manufacturing, cloud-based manufacturing has drawn the attention of researchers in academia and industry. Researches are being done towards transforming every service in to cloud based service-oriented manufacturing mode in the manufacturing industry. There are many challenges that would arise when travelling towards this paradigm shift which is being addressed by researchers, but there are very few researches that concentrate on the elastic capability of cloud. Elastic capability makes this paradigm unique from all the other approaches or technologies. If elasticity is not achievable then the necessity of migrating to cloud is unnecessary. So, it is imperative to identify if at all it is necessary to adopt cloud-based manufacturing mode and discuss the issues and challenges that would arise to achieve elasticity when shifting to this emerging manufacturing paradigm. This research explores the importance of adopting cloud-based manufacturing mode to improve manufacturing performance based on the competitive priorities such as cost, quality, delivery and flexibility and proposes an elasticity assessment tool to be included in the cloud-based manufacturing model for the users to assess the challenges and issues on the realisation of elasticity on the context of manufacturing, which is the novelty of this research. The contribution to knowledge is a clear understanding of the necessity of cloud based elastic manufacturing model in the manufacturing environment for the manufacturing SMEs to gain a competitive advantage by achieving the competitive priorities such as low-cost, high-quality, and on-time delivery. Finally, the research suggests the best combination of manufacturing parameters that has to be emphasised to improve the manufacturing performance and gain a competitive advantage

Cost-Based Automatic Recovery Policy in Data Centers

Today's data centers either provide critical applications to organizations or host computing clouds used by huge Internet populations. Their size and complex structure make management difficult, causing high operational cost. The large number of servers with various different hardware and software components cause frequent failures and need continuous recovery work. Much of the operational cost is from this recovery work. While there is significant research related to automatic recovery, from automatic error detection to different automatic recovery techniques, there is currently no automatic solution that can determine the exact fault, and hence the preferred recovery action. There is some study on how to automatically select a suitable recovery action without knowing the fault behind the error. In this thesis we propose an estimated-total-cost model based on analysis of the cost and the recovery-action-success probability. Our recovery-action selection is based on minimal estimated-total-cost; we implement three policies to use this model under different considerations of failed recovery attempts. The preferred policy is to reduce the recovery action-success probability when it failed to fix the error; we also study different reduction coefficients in this policy. To evaluate the various policies, we design and implement a simulation environment. Our simulation experiments demonstrate significant cost improvement over previous research based on simple heuristic models