Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling Cloud Resources for Big Data and Cyber-Physical Integration <s> Cloud computing has recently emerged as a new computing paradigm in many application areas comprising office and enterprise systems. It offers various solutions to provide a dynamic and flexible infrastructure to host computing resources and deliver them as a service on-demand. Since industrial automation systems of the future have to be adaptable and agile, cloud computing can be considered as a promising solution for this area. However, the requirements of industrial automation systems differ significantly from the office and enterprise world. This paper provides an overview of the latest concepts of cloud computing technology for industrial automation. Existing works are categorized based on the levels of automation systems and research gaps are discussed based on these findings. A general cloud model for automation is derived from existing proposals by refining them further. This cloud model will offer automation functions as services from a dynamic infrastructure. <s> BIB001 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling Cloud Resources for Big Data and Cyber-Physical Integration <s> Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and high-performance computing on large-scale data. This work briefly introduces the data intensive computing system and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical research to make the vast amount of diversification data meaningful and usable. <s> BIB002 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling Cloud Resources for Big Data and Cyber-Physical Integration <s> To improve the research productivity in bioinformatics study by using effective means of large scale data analysis, there are many obstacles that need to be overcome They are standardization of data collection and analysis, management of computing and storage resources, easiness of parallel programming, and efficiency of data analysis job execution, to name a few. Among these, easiness of parallel programming is a crucial factor that contributes to usability and efficiency of large scale data analysis. This paper describes a biologic data analysis platform based on cloud computing infrastructure. The platform provides an easy-to-use parallel data analysis environment, and ultimately enhances the productivity of bioinformatics research. <s> BIB003 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling Cloud Resources for Big Data and Cyber-Physical Integration <s> Scheduling of dynamic and multitasking workloads for big-data analytics is a challenging issue, as it requires a significant amount of parameter sweeping and iterations. Therefore, real-time scheduling becomes essential to increase the throughput of many-task computing. The difficulty lies in obtaining a series of optimal yet responsive schedules. In dynamic scenarios, such as virtual clusters in cloud, scheduling must be processed fast enough to keep pace with the unpredictable fluctuations in the workloads to optimize the overall system performance. In this paper, ordinal optimization using rough models and fast simulation is introduced to obtain suboptimal solutions in a much shorter timeframe. While the scheduling solution for each period may not be the best, ordinal optimization can be processed fast in an iterative and evolutionary way to capture the details of big-data workload dynamism. Experimental results show that our evolutionary approach compared with existing methods, such as Monte Carlo and Blind Pick, can achieve higher overall average scheduling performance, such as throughput, in real-world applications with dynamic workloads. Furthermore, performance improvement is seen by implementing an optimal computing budget allocating method that smartly allocates computing cycles to the most promising schedules. <s> BIB004 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling Cloud Resources for Big Data and Cyber-Physical Integration <s> Abstract Today, in an Industry 4.0 factory, machines are connected as a collaborative community. Such evolution requires the utilization of advance- prediction tools, so that data can be systematically processed into information to explain uncertainties, and thereby make more “informed” decisions. Cyber-Physical System-based manufacturing and service innovations are two inevitable trends and challenges for manufacturing industries. This paper addresses the trends of manufacturing service transformation in big data environment, as well as the readiness of smart predictive informatics tools to manage big data, thereby achieving transparency and productivity. <s> BIB005
The techniques discussed in the previous five subsections should prepare cloud computing in the era of big data, which is characterized by unprecedented volume, velocity, and variety  ]. It would be interesting and rewarding to investigate the role of EC as a whole in the growth of cloud computing and its resource scheduling in the presence of big data through the Internet. An industrial example is that automation systems via the Internet have been developed fast with potentially massive market demands in cloud computing and big data for "Industry 4.0," where cloud models can offer automation functions as services from a dynamic infrastructure BIB001 . Intelligently discovering knowledge, relationships, and trends inherent in big data could help foresee emerging customer needs and wants, and hence help scheduling cloud resources for cyber-physical and smart design-manufacture integrations more objectively and more profitably. With research for more optimal and powerful cloud computing machinery gaining momentum, there emerges a trend that cloud computing creates big data, while big data demand cloud computing in the quest for a sustainable e-infrastructure BIB004 BIB002 BIB003 . More importantly, "data-driven discovery" or synthesis of science, dubbed the "e-Science," is gradually recognized as the "fourth paradigm of science"  , where in history the experimental science based empirical discovery and description of natural phenomena is regarded as the first paradigm, the theoretical science (e.g., Maxwell's equations) the second, and the computer simulation based "virtual" systems or phenomena the third  ]. Ultimately, nature-inspired EC will assist cloud computing to play a significant role in finding apparently unconnected pieces of data a relationship between them, leading to data-driven scientific discovery and smart manufacturing, although optimal realization and resource scheduling could require the use of a supercomputer at the present time. To achieve agile and smart manufacturing, services, or management, cloud resource scheduling inevitably faces new challenges and uncertainties. Most of the challenges discussed so far have to be taken into consideration. The use of metaheuristics such as EC helps holistically identify what data may be classified as inputs and outputs from the cloud and identify how inputs and feedback of outputs may in turn influence the cloud scheduling requirements. Deep learning in EC algorithms for predictive data analytics for cloud scheduling would be one significant topic in the Industry 4.0 era BIB005 .