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The industrial world is undergoing a transformation known as Industry 4.0, or the Industrial Internet of Things. This transformation is leading to smart manufacturing systems with more compute capability at the edge. However, adding more compute capability increases OPEX, due to complexity of computer integration, as well as cabling, power and maintenance costs. The solution: “workload consolidation.”In this session, you’ll learn how advances in processing performance, real-time operating systems, and virtualization technology are enabling workload consolidation in the factory to drive global industries from “automatic to autonomous” for Industry 4.0.
MindSphere* is the open, cloud-based IoT operating system from Siemens designed for the industrial Internet of Things. It offers an intuitive, straightforward environment that lets users build, scale and deploy new ideas quickly.
In this session, Siemens MindSphere technical experts share what MindSphere developer offerings are all about, as they discuss key features and functionality, and answer technical questions. The session is designed to help application developers, solution architects and other professionals make use of powerful technology resources.
The use of IoT and Artificial intelligence in plants, warehouses and machines is drastically changing the way businesses make informed decisions that are critical to performance.
This session demonstrates how the IoT, Artificial Intelligence, ERP cloud, maintenance, Finance, WMS, robots and drones are all integrated at the business use case level. These technologies are enabling systemic, predictive and proactive decisions, for new best practices to improve performance, human safety and quality. IoT and AI are not standalone practices and have to be considered in a holistic way with business applications to create value.
Many of today´s applications across a variety of industry sectors require compatibility with more than one operating system. Often, at least one workload requires deterministic real-time behavior at minimum jitter and lowest possible interrupt latencies. Consolidating such high-performance, real-time workloads running, for example, in Microsoft Windows* or another general-purpose operating system (GPOS) in addition to an IoT gateway – all on a single multicore platform – can be challenging.
Guest operating systems executing various workloads in parallel must not influence each other. And, virtualization overhead for real-time operating systems must be kept to an absolute minimum.
This session details the impacts on real-time behavior, jitter and latencies due to simultaneous parallel access of shared resources. We’ll also cover the use of cache or advanced power management features. And, we’ll share best practices, including the use of Intel Cache Allocation Ttechnology (CAT) and other remedies to optimize real-time system performance.
Intelligent machines are here, rapidly changing the way our society works, including the way we design, build, manage and inhabit the built environment. Huge advances in robotic equipment, construction materials and manufacturing techniques are completely changing the way construction work is carried out, putting the construction industry at the forefront of the Fourth Industrial Revolution.This session explores the technological advances in some core construction activities: concrete 3D printing, robotic bricklaying, robotic welding, and UAV site surveying. In addition, we’ll show how applications of machine learning, augmented and virtual reality and the integration of advanced sensors are disrupting the industry.
The growth of high velocity, real-time digital, audio and video streaming data present new challenges to industrial analytics. Traditional procedural programming isn’t well suited to rapidly changing conditions, multi-stream correlations and inferences. Foghorn’s complex event processor uses a “reactive expression” approach that links the flow of program execution to and within the analytic to the streaming data available to it.
Our language, Vel, provides a syntax that can describe reactions to events in streaming data in a simple and logical fashion. This simplicity reduces the amount of coding required for streaming edge analytics, which also improves its maintainability. The complex event processor, in turn, is tightly integrated with data consumption, publications, and machine learning modules that complete the Foghorn edge computing platform.
The reactive approach enables a host of new benefits for real-world applications such as predictive maintenance, condition monitoring, yield optimization, and anomaly detection.
This session provides insight into:
• Reactive expressions as applied to unbounded streaming data
• The advantages of reactive expressions over procedural programming in industrial edge analytics
• Real-world use cases detailing where the technology is being applied today
The IoT in 2018 is beyond the hype curve and we’re seeing a variety of market segments derive business value. As these systems scale in the next few years, a fundamental change in the core plumbing will be required that will dramatically change how IoT devices are designed, deployed and maintained through their lifecycle.
This session demonstrates that understanding and implementing concepts of workload consolidation is key to scaling IoT applications. Workload consolidation has been loosely associated with various cloud technologies and is sometimes bundled under the umbrella of fog computing.
We’ll cover key concepts of workload consolidation, as well as the business drivers and special challenges of implementing solutions at the edge. You’ll come away with a fair understanding of terms, components involved in workload consolidation, and how you should start planning your devices and applications to be "consolidation-friendly." This session is targeted at business executives, system architects and IoT software application engineers who wonder if “workload consolidation” is just another over-hyped phrase.