With the latest advances in digitalization and Industry 4.0, the manufacturing industry is collecting more and more production data. However, with the increasing interconnection of machines, not only the volume but also the variety of data is being expanded. The data life cycles of collection, processing, combining, analyzing and feeding new findings back into sources are becoming increasingly challenging for data scientists to complete. Reference architectures such as the RAMI 4.0 provide conceptual guidelines to address these problems. In this paper, we focus on the implementation of an agent-based architecture that is in line with RAMI 4.0. This architecture implements the guidelines provided by RAMI 4.0 by applying modern approaches from the areas of data lake based data acquisition, semantic description, look up and processing as well as information utilization.
Today Cellular Automata (CA) and Agent Based Model (ABM) based simulations are used to make predictions and discover properties of real world scenarios, e.g. predicting the spread of influenza contagions. In these situations it is important that we have maximum confidence in the simulations we employ. One factor that is known to affect the outcomes of a simulation is the choice of updating strategy used. Specific strategies can cause artifacts - i.e. outcomes of a simulation not due to any intrinsic properties of the model itself but caused directly by the updating strategy. To guard against artifacts multiple strategies should be used on a model and the outcomes compared. Currently, while both Synchronous and asynchronous updating are used in CA based simulations, AU is used almost exclusively in ABM. This is due to: (i) until recently, the lack of any well-known SU algorithm that can handle complex ABM interactions and (ii) belief that AU is a better model of how the real world works.
Amazon Elastic Compute Cloud (EC2) gives access to resources in the form of virtual servers, also known as instances. EC2 Spot Instances (SIs) offer spare compute capacity at steep discounts compared to reliable and fixed price on-demand instances. The drawback, however, is that waiting time until requested spots become fulfilled can be incredible high. In this paper, we propose a container migration-based solution to enhance the reliability of virtual cluster computing environments built on top of non-reserved EC2 pricing model instances. We compare the performance of our algorithm by executing different resource provisioning plans for running real-life workflow applications, constrained by user-defined deadline and budget Quality of Service (QoS) parameters. The results show that our solution is able to successfully conclude almost 98% of workflow applications and more than 99% of workflow tasks for on-demand- and spot block-based virtual compute environments. For SI-based virtual compute environments, our solution achieves similar results, completing more than 98% of workflow applications, and over 99% of workflow tasks, for a worse-case scenario.
Technical market indicators are used to measure the trends of financial markets. In practice they are conventionally expressed using a non-formal notation or a DSL specific to a certain development platform, which poorly correlates between individual trades and the high-level formulas operating on those trades and leaving very little room for optimization. In this paper we propose a formal, mathematically based notation for expressing technical market indicators, which represents trades as streams of data. We argue that this notation is more accurate and open to optimizations. We express three technical indicators from the ground up, demonstrate our optimization approach, and implement the indicators using Click router runtime. Finally, we benchmark various configurations and versions of the implemented indicators, running in kernel space as well as user space, and discuss the findings.
The world migration towards automatic and wire-less systems results an increased usage of Wireless Sensor Networks (WSNs). The noticeable popularity of WSNs has imposed enlarged computational in-node demands. Hence, the recourse to fully-integrated and sophisticated systems with low power is a challenging task. Since wireless sensor nodes have limited power resources, it is important to find a balance between energy consumption and computational performance. The traditional software optimizations are not usually suited or enough to find this tradeoff. Consequently, the use of codesign methodology and the careful implementation of hardware accelerator with low frequency processors could offer a good compromise between energy consumption and performance. In this paper, we present a SoC WSN node prototype based on Leon 3 processor for leak detection in water pipeline using Kalman Filter (KF). A hardware acceleration of the KF has been designed and implemented to reduce energy consumption. We have compared also the software implementation of the algorithm and its hardware acceleration in terms of the execution time, the energy consumption and the area requirements. The results show about 97% reduction in energy consumption and execution time without noticeable increased area.
