The Cloud Services Innovation Platform-Enabling Service-Based Environmental Modelling Using Infrastructure-As-A-Service Cloud Computing

Service oriented architectures allow modelling engines to be hosted over the Internet abstracting physical hardware configuration and software deployments from model users. Many existing environmental models are deployed as desktop applications running on user\u27s personal computers (PCs). Migration to service - based modelling centralizes the modelling functions to service hosts on the Internet . Users no longer require high-end PCs to run models and model updates encapsulating science advances can be disseminated more rapidly by hosting the modelling functions centrally via an Internet host instead of requiring software updates to user\u27s PCs . In this paper we present the Cloud Services Innovation Platform (CSIP), an Infrastructure -as -a -Service cloud application architecture , used to prototype development of distributed and scalable environmental modelling services. CSIP aims to provide modelling as a service to support both interactive (synchronous) and batch (asynchronous) modelling. CSIP enables c loud-based computing resources to be harnessed for both new and existing environmental models supporting the disaggregation of work into subtasks which execute in parallel using a scalable number of virtual machines. This paper presents CSIP \u27s implementation using the RUSLE2 model as a prototype model. RUSLE2 model service benchmarks are presented to demonstrate performance gains from using cloud resources. We also provide benchmarks for virtualization overhead observed using popular virtual machine hypervisors and demonstrate how application profile characteristics significantly impact performance when virtualized

VoroCrust: Voronoi Meshing Without Clipping

Polyhedral meshes are increasingly becoming an attractive option with particular advantages over traditional meshes for certain applications. What has been missing is a robust polyhedral meshing algorithm that can handle broad classes of domains exhibiting arbitrarily curved boundaries and sharp features. In addition, the power of primal-dual mesh pairs, exemplified by Voronoi-Delaunay meshes, has been recognized as an important ingredient in numerous formulations. The VoroCrust algorithm is the first provably-correct algorithm for conforming polyhedral Voronoi meshing for non-convex and non-manifold domains with guarantees on the quality of both surface and volume elements. A robust refinement process estimates a suitable sizing field that enables the careful placement of Voronoi seeds across the surface circumventing the need for clipping and avoiding its many drawbacks. The algorithm has the flexibility of filling the interior by either structured or random samples, while preserving all sharp features in the output mesh. We demonstrate the capabilities of the algorithm on a variety of models and compare against state-of-the-art polyhedral meshing methods based on clipped Voronoi cells establishing the clear advantage of VoroCrust output.Comment: 18 pages (including appendix), 18 figures. Version without compressed images available on https://www.dropbox.com/s/qc6sot1gaujundy/VoroCrust.pdf. Supplemental materials available on https://www.dropbox.com/s/6p72h1e2ivw6kj3/VoroCrust_supplemental_materials.pd

Real-time Batched Distance Computation for Time-Optimal Safe Path Tracking

In human-robot collaboration, there has been a trade-off relationship between the speed of collaborative robots and the safety of human workers. In our previous paper, we introduced a time-optimal path tracking algorithm designed to maximize speed while ensuring safety for human workers. This algorithm runs in real-time and provides the safe and fastest control input for every cycle with respect to ISO standards. However, true optimality has not been achieved due to inaccurate distance computation resulting from conservative model simplification. To attain true optimality, we require a method that can compute distances 1. at many robot configurations to examine along a trajectory 2. in real-time for online robot control 3. as precisely as possible for optimal control. In this paper, we propose a batched, fast and precise distance checking method based on precomputed link-local SDFs. Our method can check distances for 500 waypoints along a trajectory within less than 1 millisecond using a GPU at runtime, making it suited for time-critical robotic control. Additionally, a neural approximation has been proposed to accelerate preprocessing by a factor of 2. Finally, we experimentally demonstrate that our method can navigate a 6-DoF robot earlier than a geometric-primitives-based distance checker in a dynamic and collaborative environment

New geometric algorithms and data structures for collision detection of dynamically deforming objects

Any virtual environment that supports interactions between virtual objects and/or a user and objects, needs a collision detection system to handle all interactions in a physically correct or plausible way. A collision detection system is needed to determine if objects are in contact or interpenetrates. These interpenetrations are resolved by a collision handling system. Because of the fact, that in nearly all simulations objects can interact with each other, collision detection is a fundamental technology, that is needed in all these simulations, like physically based simulation, robotic path and motion planning, virtual prototyping, and many more. Most virtual environments aim to represent the real-world as realistic as possible and therefore, virtual environments getting more and more complex. Furthermore, all models in a virtual environment should interact like real objects do, if forces are applied to the objects. Nearly all real-world objects will deform or break down in its individual parts if forces are acted upon the objects. Thus deformable objects are becoming more and more common in virtual environments, which want to be as realistic as possible and thus, will present new challenges to the collision detection system. The necessary collision detection computations can be very complex and this has the effect, that the collision detection process is the performance bottleneck in most simulations. Most rigid body collision detection approaches use a BVH as acceleration data structure. This technique is perfectly suitable if the object does not change its shape. For a soft body an update step is necessary to ensure that the underlying acceleration data structure is still valid after performing a simulation step. This update step can be very time consuming, is often hard to implement and in most cases will produce a degenerated BVH after some simulation steps, if the objects generally deform. Therefore, the here presented collision detection approach works entirely without an acceleration data structure and supports rigid and soft bodies. Furthermore, we can compute inter-object and intraobject collisions of rigid and deformable objects consisting of many tens of thousands of triangles in a few milliseconds. To realize this, a subdivision of the scene into parts using a fuzzy clustering approach is applied. Based on that all further steps for each cluster can be performed in parallel and if desired, distributed to different GPUs. Tests have been performed to judge the performance of our approach against other state-of-the-art collision detection algorithms. Additionally, we integrated our approach into Bullet, a commonly used physics engine, to evaluate our algorithm. In order to make a fair comparison of different rigid body collision detection algorithms, we propose a new collision detection Benchmarking Suite. Our Benchmarking Suite can evaluate both the performance as well as the quality of the collision response. Therefore, the Benchmarking Suite is subdivided into a Performance Benchmark and a Quality Benchmark. This approach needs to be extended to support soft body collision detection algorithms in the future.Jede virtuelle Umgebung, welche eine Interaktion zwischen den virtuellen Objekten in der Szene zulässt und/oder zwischen einem Benutzer und den Objekten, benötigt für eine korrekte Behandlung der Interaktionen eine Kollisionsdetektion. Nur dank der Kollisionsdetektion können Berührungen zwischen Objekten erkannt und mittels der Kollisionsbehandlung aufgelöst werden. Dies ist der Grund für die weite Verbreitung der Kollisionsdetektion in die verschiedensten Fachbereiche, wie der physikalisch basierten Simulation, der Pfadplanung in der Robotik, dem virtuellen Prototyping und vielen weiteren. Auf Grund des Bestrebens, die reale Umgebung in der virtuellen Welt so realistisch wie möglich nachzubilden, steigt die Komplexität der Szenen stetig. Fortwährend steigen die Anforderungen an die Objekte, sich realistisch zu verhalten, sollten Kräfte auf die einzelnen Objekte ausgeübt werden. Die meisten Objekte, die uns in unserer realen Welt umgeben, ändern ihre Form oder zerbrechen in ihre Einzelteile, wenn Kräfte auf sie einwirken. Daher kommen in realitätsnahen, virtuellen Umgebungen immer häufiger deformierbare Objekte zum Einsatz, was neue Herausforderungen an die Kollisionsdetektion stellt. Die hierfür Notwendigen, teils komplexen Berechnungen, führen dazu, dass die Kollisionsdetektion häufig der Performance-Bottleneck in der jeweiligen Simulation darstellt. Die meisten Kollisionsdetektionen für starre Körper benutzen eine Hüllkörperhierarchie als Beschleunigungsdatenstruktur. Diese Technik ist hervorragend geeignet, solange sich die Form des Objektes nicht verändert. Im Fall von deformierbaren Objekten ist eine Aktualisierung der Datenstruktur nach jedem Schritt der Simulation notwendig, damit diese weiterhin gültig ist. Dieser Aktualisierungsschritt kann, je nach Hierarchie, sehr zeitaufwendig sein, ist in den meisten Fällen schwer zu implementieren und generiert nach vielen Schritten der Simulation häufig eine entartete Hüllkörperhierarchie, sollte sich das Objekt sehr stark verformen. Um dies zu vermeiden, verzichtet unsere Kollisionsdetektion vollständig auf eine Beschleunigungsdatenstruktur und unterstützt sowohl rigide, wie auch deformierbare Körper. Zugleich können wir Selbstkollisionen und Kollisionen zwischen starren und/oder deformierbaren Objekten, bestehend aus vielen Zehntausenden Dreiecken, innerhalb von wenigen Millisekunden berechnen. Um dies zu realisieren, unterteilen wir die gesamte Szene in einzelne Bereiche mittels eines Fuzzy Clustering-Verfahrens. Dies ermöglicht es, dass alle Cluster unabhängig bearbeitet werden und falls gewünscht, die Berechnungen für die einzelnen Cluster auf verschiedene Grafikkarten verteilt werden können. Um die Leistungsfähigkeit unseres Ansatzes vergleichen zu können, haben wir diesen gegen aktuelle Verfahren für die Kollisionsdetektion antreten lassen. Weiterhin haben wir unser Verfahren in die Physik-Engine Bullet integriert, um das Verhalten in dynamischen Situationen zu evaluieren. Um unterschiedliche Kollisionsdetektionsalgorithmen für starre Körper korrekt und objektiv miteinander vergleichen zu können, haben wir eine Benchmarking-Suite entwickelt. Unsere Benchmarking- Suite kann sowohl die Geschwindigkeit, für die Bestimmung, ob zwei Objekte sich durchdringen, wie auch die Qualität der berechneten Kräfte miteinander vergleichen. Hierfür ist die Benchmarking-Suite in den Performance Benchmark und den Quality Benchmark unterteilt worden. In der Zukunft wird diese Benchmarking-Suite dahingehend erweitert, dass auch Kollisionsdetektionsalgorithmen für deformierbare Objekte unterstützt werden

PriLok: Citizen-protecting distributed epidemic tracing

Contact tracing is an important instrument for national health services to fight epidemics. As part of the COVID-19 situation, many proposals have been made for scaling up contract tracing capacities with the help of smartphone applications, an important but highly critical endeavor due to the privacy risks involved in such solutions. Extending our previously expressed concern, we clearly articulate in this article, the functional and non-functional requirements that any solution has to meet, when striving to serve, not mere collections of individuals, but the whole of a nation, as required in face of such potentially dangerous epidemics. We present a critical information infrastructure, PriLock, a fully-open preliminary architecture proposal and design draft for privacy preserving contact tracing, which we believe can be constructed in a way to fulfill the former requirements. Our architecture leverages the existing regulated mobile communication infrastructure and builds upon the concept of "checks and balances", requiring a majority of independent players to agree to effect any operation on it, thus preventing abuse of the highly sensitive information that must be collected and processed for efficient contact tracing. This is enforced with a largely decentralised layout and highly resilient state-of-the-art technology, which we explain in the paper, finishing by giving a security, dependability and resilience analysis, showing how it meets the defined requirements, even while the infrastructure is under attack

Observing the clouds : a survey and taxonomy of cloud monitoring

This research was supported by a Royal Society Industry Fellowship and an Amazon Web Services (AWS) grant. Date of Acceptance: 10/12/2014Monitoring is an important aspect of designing and maintaining large-scale systems. Cloud computing presents a unique set of challenges to monitoring including: on-demand infrastructure, unprecedented scalability, rapid elasticity and performance uncertainty. There are a wide range of monitoring tools originating from cluster and high-performance computing, grid computing and enterprise computing, as well as a series of newer bespoke tools, which have been designed exclusively for cloud monitoring. These tools express a number of common elements and designs, which address the demands of cloud monitoring to various degrees. This paper performs an exhaustive survey of contemporary monitoring tools from which we derive a taxonomy, which examines how effectively existing tools and designs meet the challenges of cloud monitoring. We conclude by examining the socio-technical aspects of monitoring, and investigate the engineering challenges and practices behind implementing monitoring strategies for cloud computing.Publisher PDFPeer reviewe

Athlos: A Framework for Developing Scalable MMOG Backends on Commodity Clouds

The development of resource-intensive, distributed, real-time applications like Massively Multiplayer Online Game (MMOG) backends entails a variety of challenges, some of which have been extensively studied. Despite some advancements, the development and deployment of MMOG backends on commodity clouds and high-level computing layers continues to face several obstacles, including a non-standardized development methodology, lack of provisions for scalability, and the need for abstractions and tools to support the development process. In this paper, we describe a set of models, methods, and tools for developing scalable MMOG backends and hosting them on commodity cloud platforms. We present Athlos, a framework that allows game developers to leverage our methodology to rapidly prototype MMOG backends that can run on any type of cloud environment. We evaluate this framework by conducting simulations based on several case-study MMOGs to benchmark its performance and scalability, and compare the development effort needed, and quality of the code produced with other approaches. We find that MMOGs developed using this framework: (a) can support a very high number of simultaneous players under a given latency threshold, (b) elastically scale both in terms of runtime and state, and (c) significantly reduce the amount of effort required to develop them. Coupled with the advantages of high-level computing layers such as Platform, Backend, and Function-as-a-Service, we argue that our framework accelerates the development of high-performance, scalable MMOGs, that leverage the resources of commodity cloud platforms