Research on Cloud Enterprise Resource Integration and Scheduling Technology Based on Mixed Set Programming

With the development of Industry 4.0 and intelligent manufacturing, aiming at the incompatibility of heterogeneous manufacturing resource interfaces and the low efficiency of collaborative scheduling of manufacturing resources among enterprises，we proposed the resource integration and scheduling strategy among enterprises based on Mixed Set Programming [1]. By using the metadata and ontology modeling methods, we were able to realize a standardized model description of manufacturing resources. At last, an enterprise application case was discussed to verify the resources integration and scheduling strategy based on Mixed Set Programming is effective to optimize and improve the efficiency of the collaborative scheduling of resources among enterprises. The resources integration and scheduling strategy based on Mixed Set Programming could be applied to promote the optimal allocation of manufacturing resources

Energy-efficient resource allocation scheme based on enhanced flower pollination algorithm for cloud computing data center

Cloud Computing (CC) has rapidly emerged as a successful paradigm for providing ICT infrastructure. Efficient and environmental-friendly resource allocation mechanisms, responsible for allocatinpg Cloud data center resources to execute user applications in the form of requests are undoubtedly required. One of the promising Nature-Inspired techniques for addressing virtualization, consolidation and energyaware problems is the Flower Pollination Algorithm (FPA). However, FPA suffers from entrapment and its static control parameters cannot maintain a balance between local and global search which could also lead to high energy consumption and inadequate resource utilization. This research developed an enhanced FPA-based energy efficient resource allocation scheme for Cloud data center which provides efficient resource utilization and energy efficiency with less probable Service Level Agreement (SLA) violations. Firstly, an Enhanced Flower Pollination Algorithm for Energy-Efficient Virtual Machine Placement (EFPA-EEVMP) was developed. In this algorithm, a Dynamic Switching Probability (DSP) strategy was adopted to balance the local and global search space in FPA used to minimize the energy consumption and maximize resource utilization. Secondly, Multi-Objective Hybrid Flower Pollination Resource Consolidation (MOH-FPRC) algorithm was developed. In this algorithm, Local Neighborhood Search (LNS) and Pareto optimisation strategies were combined with Clustering algorithm to avoid local trapping and address Cloud service providers conflicting objectives such as energy consumption and SLA violation. Lastly, Energy-Aware Multi-Cloud Flower Pollination Optimization (EAM-FPO) scheme was developed for distributed Multi-Cloud data center environment. In this scheme, Power Usage Effectiveness (PUE) and migration controller were utilised to obtain the optimal solution in a larger search space of the CC environment. The scheme was tested on MultiRecCloudSim simulator. Results of the simulation were compared with OEMACS, ACS-VMC, and EA-DP. The scheme produced outstanding performance improvement rate on the data center energy consumption by 20.5%, resource utilization by 23.9%, and SLA violation by 13.5%. The combined algorithms have reduced entrapment and maintaned balance between local and global search. Therefore, based on the findings the developed scheme has proven to be efficient in minimizing energy consumption while at the same time improving the data center resource allocation with minimum SLA violation

The Functioning of Ecosystems

The ecosystems present a great diversity worldwide and use various functionalities according to ecologic regions. In this new context of variability and climatic changes, these ecosystems undergo notable modifications amplified by domestic uses of which it was subjected to. Indeed the ecosystems render diverse services to humanity from their composition and structure but the tolerable levels are unknown. The preservation of these ecosystemic services needs a clear understanding of their complexity. The role of the research is not only to characterise the ecosystems but also to clearly define the tolerable usage levels. Their characterisation proves to be important not only for the local populations that use it but also for the conservation of biodiversity. Hence, the measurement, management and protection of ecosystems need innovative and diverse methods. For all these reasons, the aim of this book is to bring out a general view on the biogeochemical cycles, the ecological imprints, the mathematical models and theories applicable to many situations

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science