Dealing with data and software interoperability issues in digital factories

The digital factory paradigm comprises a multi-layered integration of the information related to various activities along the factory and product lifecycle manufacturing related resources. A central aspect of a digital factory is that of enabling the product lifecycle stakeholders to collaborate through the use of software solutions. The digital factory thus expands outside the actual company boundaries and offers the opportunity for the business and its suppliers to collaborate on business processes that affect the whole supply chain. This paper discusses an interoperability architecture for digital factories. To this end, it delves into the issue by analysing the main challenges that must be addressed to support an integrated and scalable factory architecture characterized by access to services, aggregation of data, and orchestration of production processes. Then, it revises the state of the art in the light of these requirements and proposes a general architectural framework conjugating the most interesting features of serviceoriented architectures and data sharing architectures. The study is exemplified through a case study

Two-phase industrial manufacturing service management for energy efficiency of data centers

Data-driven industrial manufacturing services are proliferating. They use large amounts of data generated by Industrial-Internet-of-Things devices for intelligent services to end-service-users. However, cloud data centers hosting these services consumes huge amount of energy and contributing to high operational cost. To address this issue, this paper proposes an energy-efficient resources allocation framework for cloud services. It operates in two phases. Firstly, a multi-threshold based host CPU utilization classification scheme is developed to classify hosts into four groups. It is designed through analyzing the CPU utilization data using the least median squares regression model. Thereby, the scheme limits search space, thus reducing time complexity. Secondly, with a metaheuristic search, an energy and thermal-aware resource allocation method is developed to find an energy-efficient host for allocating resources to services. From real data center workload traces, extensive experiments show that our frame-work outperforms existing baseline approaches with 6.9%, 33.75%, and 34.1% on average in terms of temperature, energy consumption, and service-level-agreement violation respectively

Composition of Resource-Service Chain for Cloud Manufacturing

In distributed manufacturing systems, manufacturing resource composition is one of the most important problems. This is because efficiency of resource selection and resource utilization can all be improved if it is tackled well. However, most of the existing methods neglect temporal relationship between resources. This leads to an inefficient use of resources, because all resources have to be kept available before a business process is started. A temporal composition of resources is more suitable, as it expresses the scheduling and the flow of servicing to a business process. Therefore, resource services invoked in sequential order are called the resource-service chain (RSC), in view that distributed resources are encapsulated into cloud services in a cloud manufacturing (CMfg) environment. We propose an approach, called RSC composition algorithm (RSCCA) that can better cope with the temporal relationship between the resource services in a business process. Specifically, a two-stage composition method based on the degrees of dependency between resource services in workflow is proposed. To begin, in the build-time stage algorithm, RSCCA resolves initial compositions based on task relatedness and temporal dependencies between resource services, and then calculates the usage frequencies of ICs by mining workflow log at workflow runtime stage. Based on this, RSCCA can compose individual resource services as more than sets, especially as chains, allowing flow directions and dynamics to be considered. RSCCA has been tested with different data sets and the results show that it can be very promising.Department of Computin