Purpose Supply chain is the area that requires effective and integrated means of communication, shared risk, collaboration and orchestration in order to work successfully and the cloud computing has lot to offer to this domain. Cloud computing has appeared as a modern paradigm in supply chain networks for creating intelligent industries of future. The purpose of this paper is to propose a framework that can transform supply chain stakeholders toward Industry 4.0. Design/methodology/approach Cloud computing is attributed with increasing competitiveness by focusing on cost reduction, greater elasticity, flexibility and maximum utilization of resources which results in successfully achieving business goals. This paper proposes a Hybrid Supply Chain Cloud model, which integrates the infrastructure, the resources and the configurations of platforms. Findings This research paper is aimed at proposing a hybrid cloud that essentially helps in integrating the supply chain network with the flexibility and efficiency. It is important that a supply chain network adds value to ensure customer satisfaction and this can be best achieved by collaborating it with hybrid cloud. Research limitations/implications This research provides a consistent central management and comprehensive view of all computing resources, which gives organizations the knowledge they need to optimize workload placement. Practical implications The findings derived from this research aim to facilitate policy makers and practitioners to develop effective courses of action in current and future supply chain management. Therefore, upon implementation, this model can provide long-term benefits for the organizations by improving the overall efficiency of its supply chain network. Originality/value The proposed hybrid cloud will provide deep level of integration in Industry 4.0 situation and thereby brought up portable comprehensive infrastructure based on resources and required configuration in real-time environment.
The use of cloud computing data centers is growing rapidly to meet the tremendous increase in demand for high-performance computing (HPC), storage and networking resources for business and scientific applications. Virtual machine (VM) consolidation involves the live migration of VMs to run on fewer physical servers, and thus allowing more servers to be switched off or run on low-power mode, as to improve the energy consumption efficiency, operating cost and CO 2 emission. A crucial step in VM consolidation is host overload detection, which attempts to predict whether or not a physical server will be oversubscribed with VMs. In contrast to the majority of previous work which use CPU utilization as the sole indicator for host overload, a recent study has proposed a multiple regression host overload detection algorithm, which takes multiple factors into consideration: CPU, memory and network BW utilization. This paper provides further improvement along two directions. First, we provide Multi-Dimensional Regression Host Utilization (MDRHU) algorithms that combine CPU, memory and network BW utilization via Euclidean Distance (MDRHU-ED) and absolute summation (MDRHU-AS), respectively. This leads to improved results in terms of energy consumption and service level agreement violation. Second, the study explicitly takes real-world HPC workloads into consideration. Our extensive simulation study further illustrates the superiority of our proposed algorithms over existing methods. In particular, as compared to the most recently proposed multiple regression algorithm that is based on Geometric Relation (GR), our proposed algorithms provide an improvement of at least 12% in energy consumption, and an improvement of at least 80% in a metric that combines energy consumption, service-level-violation, and number of VM migrations.
Purpose Blockchain technology offers a lot of potential benefits in supply chain management. However, there is a need of a reference model which addresses the gaps in existing frameworks. This paper aims to propose a blockchain technology-based reference model which can be applied to global logistics operations. Design/methodology/approach The researchers have integrated the fit-for-purpose theoretical framework and prototyping methodology to design the reference model, a blockchain-based logistics, tracking and traceability system (BLTTS). The researchers demonstrated the application of the reference model through a health-care supply chain case study. The proposed BLTTS can be implemented across global logistics operations for business performance improvement. Findings The research provides a framework and recommendations for global companies to consider when adopting the blockchain technology for implementation. The researchers found that the Ethereum blockchain technology improves security of the data shared within the block through the secure hashing algorithm 1. The hash algorithm ensures anonymity of the involved parties. The model integrates blockchain with supply chain thus creating transparent process, efficiency and real-time communication. Research limitations/implications The reference model will offer a better solution to global logistics operations challenges. It provides recommendations to key stakeholders involved in logistics operations segment of the logistics industry while adopting blockchain technology. Apart from the methodological limitation of the study, the system compatibility and the layer configuration aspects might be posing potential challenges while upscaling the implementation. Originality/value The proposed reference model overcomes the drawbacks of existing models as it integrates Ethereum technology. In addition, the researchers have applied the model to demonstrate its functioning in real-time environment, which could guide for future research.
The adoption of High-Performance Computing (HPC) applications has gained an extensive interest in the Cloud computing. Current cloud vendors utilize separate management tools for HPC and non-HPC applications, missing out on the consolidation benefits of virtualization. Non-HPC applications executed in the cloud may interfere with resource-hungry HPC applications, which is a key performance challenge. Furthermore, correlations between application major performance indicators, such as response time and throughput, with resource capacities reveal that conventional placement strategies are impacting virtual machine efficiency, resulting in poor resource optimization, increased operating expenses, and longer wait times. Since applications often underutilized the hardware, smart execution of HPC and Non-HPC applications on the same node can boost system and energy efficiency. This research incorporates proactive dynamic VM consolidation to enhance the resource usage and performance while maintaining energy efficiency. The proposed algorithm generates a workload-aware fine-grained classification by employing machine learning techniques to generate complimentary profiles that alleviate cross-application interference by intelligently co-locating non-HPC and HPC applications. The research used CloudSim to simulate real HPC workloads. The results verified that the proposed algorithm outperforms all heuristic methods with respect to the metrics in key areas.
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