Practical high-throughput content-based routing using unicast state and probabilistic encodings

We address the problem that existing publish/subscribe messaging systems, including such commonly used ones as Apache’s ActiveMQ and IBM’s WebSphere MQ, exhibit degraded end-to-end throughput performance in a wide-area network setting. We contend that the cause of this problem is the lack of an appropriate routing protocol. Building on the idea of a content-based network, we introduce a protocol called B-DRP that can demonstrably improve the situation. A content-based network is a content-based publish/subscribe system architected as a datagram network: a message is forwarded hop-by-hop and delivered to any and all hosts that have expressed interest in the message content. This fits well with the character of a wide-area messaging system. B-DRP is based on two main techniques: a message delivery mechanism that utilizes and exploits unicast forwarding state, which can be easily maintained using standard protocols, and a probabilistic data structure to effciently represent and evaluate receiver interests. We present the design of B-DRP and the results of an experimental evaluation that demonstrates its support for improved throughput in a wide-area setting

Experimental evaluation of the cloud-native application design

Cloud-Native Applications (CNA) are designed to run on top of cloud computing infrastructure services with inherent support for self-management, scalability and resilience across clustered units of application logic. Their systematic design is promising especially for recent hybrid virtual machine and container environments for which no dominant application development model exists. In this paper, we present a case study on a business application running as CNA and demonstrate the advantages of the design experimentally. We also present Dynamite, an application auto-scaler designed for containerised CNA. Our experiments on a Vagrant host, on a private OpenStack installation and on a public Amazon EC2 testbed show that CNA require little additional engineering

Self-managing cloud-native applications : design, implementation and experience

Running applications in the cloud efficiently requires much more than deploying software in virtual machines. Cloud applications have to be continuously managed: (1) to adjust their resources to the incoming load and (2) to face transient failures replicating and restarting components to provide resiliency on unreliable infrastructure. Continuous management monitors application and infrastructural metrics to provide automated and responsive reactions to failures (health management) and changing environmental conditions (auto-scaling) minimizing human intervention. In the current practice, management functionalities are provided as infrastructural or third party services. In both cases they are external to the application deployment. We claim that this approach has intrinsic limits, namely that separating management functionalities from the application prevents them from naturally scaling with the application and requires additional management code and human intervention. Moreover, using infrastructure provider services for management functionalities results in vendor lock-in effectively preventing cloud applications to adapt and run on the most effective cloud for the job. In this paper we discuss the main characteristics of cloud native applications, propose a novel architecture that enables scalable and resilient self-managing applications in the cloud, and relate on our experience in porting a legacy application to the cloud applying cloud-native principles

Service Prototyping Lab Report - 2016 (Y1)

The annual activity report of the Service Prototyping Lab at Zurich University of Applied Sciences. Research trends and initiatives, research projects, transfer to education and local industry, academic community involvement, qualification and scientific development over the period of one year are among the covered topics

Combining reinforcement learning with supervised deep learning for neural active scene understanding

Awarded with the Dr. Waldemar Jucker award 2020 of the GSTWhile vision in living beings is an active process where image acquisition and classification are intertwined to gradually refine perception, much of today’s computer vision is build on the inferior paradigm of episodic classification of i.i.d. samples. We aim at improved scene understanding for robots by taking the sequential nature of seeing over time into account. We present a supervised multi-task approach to answer questions about different aspects of a scene such as the relationship between objects, their quantity or the their relative positions to the camera. For each question, we train a different output head which operates on input from one shared recurrent convolutional neural network that accumulates information over time steps. In parallel, we train an additional output head using reinforcement learning (RL) that uses the reduction in cumulative loss from the supervised heads as reward signal. It thereby learns to gradually improve the prediction confidence of e.g. partially occluded objects by moving the camera to a more favourable angle with respect to these objects. We present preliminary results on simulated RGB-D image sequences that show superior performance of our RL-based approach in answering questions quicker and more accurately than using static or random camera movement

Efficient delivery of robotics programming educational content using cloud robotics

In this paper, we report on our use of cloud-robotics solutions to teach a Robotics Applications Programming course at Zurich University of Applied Sciences (ZHAW). The usage of Kubernetes based cloud computing environment combined with real robots - turtlebots and Niryo arms - allowed us to: 1) minimize the set up times required to provide a Robotic Operating System (ROS) simulation and development environment to all students independently of their laptop architecture and OS; 2) provide a seamless “simulation to real” experience preserving the exciting experience of writing software interacting with the physical world; and 3) sharing GPUs across multiple student groups, thus using resources efficiently. We describe our requirements, solution design, experience working with the solution in the educational context and areas where it can be further improved. This may be of interest to other educators who may want to replicate our experience

The cloud-to-edge-to-IoT continuum as an enabler for search and rescue operations

When a natural or human disaster occurs, time is critical and often of vital importance. Data from the incident area containing the information to guide search and rescue (SAR) operations and improve intervention effectiveness should be collected as quickly as possible and with the highest accuracy possible. Nowadays, rescuers are assisted by different robots able to fly, climb or crawl, and with different sensors and wireless communication means. However, the heterogeneity of devices and data together with the strong low-delay requirements cause these technologies not yet to be used at their highest potential. Cloud and Edge technologies have shown the capability to offer support to the Internet of Things (IoT), complementing it with additional resources and functionalities. Nonetheless, building a continuum from the IoT to the edge and to the cloud is still an open challenge. SAR operations would benefit strongly from such a continuum. Distributed applications and advanced resource orchestration solutions over the continuum in combination with proper software stacks reaching out to the edge of the network may enhance the response time and effective intervention for SAR operation. The challenges for SAR operations, the technologies, and solutions for the cloud-to-edge-to-IoT continuum will be discussed in this paper

Modeling data-intensive Rich Internet Applications with server push support ⋆

Abstract. Rich Internet applications (RIAs) enable novel usage scenarios by overcoming the traditional paradigms of Web interaction. Conventional Web applications can be seen as reactive systems in which events are 1) produced by the user acting upon the browser HTML interface, and 2) processed by the server. In RIAs, distribution of data and computation across the client and the server broadens the classes and features of the produced events as they can originate, be detected, notified, and processed in a variety of ways. Server push technologies allow to get over the Web “pull ” paradigm, providing the base for a wide spectrum of new browser-accessible collaborative on-line applications. In this work, we investigate how events can be explicitly described and coupled to the other concepts of a Web modeling notation in order to specify server push-enabled Web applications.

A behavioral model for Rich Internet Applications

Abstract. Rich Internet Applications (RIAs) are reshaping the way in which the Web works. They change not only the appearance of the Web interfaces, but also the behavior of applications, permitting novel operations, like data distribution, partial page computation, and disconnected work. In this paper we try to understand the differences between the behavior that is considered natural for traditional HTML-based dynamic Web applications and the behavior of RIAs. The results of this work stem from our experience with the WebML modeling language and its actual implementation.