Hera Object Storage : a seamless, automated multi-tiering solution on top of OpenStack Swift

Over the last couple of decades, the demand for storage in the Cloud has grown exponentially. Distributed Cloud storage and object storage for the increasing share of unstructured data, are in high focus in both academic and industrial research activities. At the same time, efficient storage and the corresponding costs are often contrasting parameters raising a trade-off problem for any proposed solution. To this aim, classifying the data in terms of access probability became a hot topic. This paper introduces Hera Object Storage, a storage system built on top of OpenStack Swift that aims at selecting the most appropriate storage tier for any object to be stored. The goal of the multi-tiering storage we propose is to be automated and seamless, guaranteeing the required storage performance at the lowest possible cost. The paper discusses the design challenges, the proposed algorithmic solutions to the scope and, based on a prototype implementation it presents a basic proof-of-concept validation

Monitoring resilience in a rook-managed containerized cloud storage system

Distributed cloud storage solutions are currently gaining high momentum in industry and academia. The enterprise data volume growth and the recent tendency to move as much as possible data to the cloud is strongly stimulating the storage market growth. In this context, and as a main requirement for cloud native applications, it is of utmost importance to guarantee resilience of the deployed applications and the infrastructure. Indeed, with failures frequently occurring, a storage system should quickly recover to guarantee service availability. In this paper, we focus on containerized cloud storage, proposing a resilience monitoring solution for the recently developed Rook storage operator. While, Rook brings storage systems into a cloud-native container platform, in this paper we design an additional module to monitor and evaluate the resilience of the Rook-based system. Our proposed module is validated in a production environment, with software components generating a constant load and a controlled removal of system elements to evaluate the self-healing capability of the storage system. Failure recovery time revealed to be 41 and 142 seconds on average for a 32GB and a 215GB object storage device respectively

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

Recharging <i>vs</i>. Replacing Sensor Nodes Using Mobile Robots for Network Maintenance

International audienceWireless sensor networks (WSNs) have been of very high interest for the research community since years, but the quest for deploying a self-sustained network and effectively prolonging its lifetime has not found a satisfactory answer yet. Two main approaches can be identified that target this objective: either "recharging'' or "replacing'' the sensor nodes that are running out of energy. Of particular interest are solutions where mobile robots are used to execute the above mentioned tasks to automatically and autonomously maintain the WSN, thus reducing human intervention.Recently, the progress in wireless power transfer techniques has boosted research activities in the direction of battery recharging, with high expectations for its application to WSNs. Similarly, also sensor replacement techniques have been widely studied as a means to provide service continuity in the network. Objective of this paper is to investigate the limitations and the advantages of these two research directions. Key decision points must be identified for effectively supporting WSN self-maintenance: (i) which sensor nodes have to be recharged/replaced; (ii) in which order the mobile robot is serving (i.e., recharging/replacing) the nodes and by following which path; (iii) how much energy is delivered to a sensor when recharged. The influence that a set of parameters, relative to both the sensors and the mobile robot, on the decisions will be considered. Centralized and distributed solutions are compared in terms of effectiveness in prolonging the network lifetime and in allowing network self-sustainability. The performance evaluation in a variety of scenarios and network settings offers the opportunity to draw conclusions and to discuss the boundaries for one technique being preferable to the other

Cloud native robotic applications with GPU sharing on Kubernetes

Accepted submission at the Workshop "Cloud and Fog Robotics In The Age of Deep Learning".In this paper we discuss our experience in teaching the Robotic Applications Programming course at ZHAW combining the use of a Kubernetes (k8s) cluster and real, heterogeneous, robotic hardware. We discuss the main advantages of our solutions in terms of seamless "simulation to real'' experience for students and the main shortcomings we encountered with networking and sharing GPUs to support deep learning workloads. We describe the current and foreseen alternatives to avoid these drawbacks in future course editions and propose a more cloud-native approach to deploying multiple robotics applications on a k8s cluster

AI-powered Infrastructures for intelligence and automation in beyond-5G systems

In this paper, a vision for beyond-5G systems is proposed where automation, intelligence and data privacy in cloudnative infrastructures are in focus. Exploiting the convergence of cloud technologies at the edge and mobile communication networks, a set of architectural and technological solutions is discussed that will play a fundamental role on the path from 5G towards future sixth-generation systems. Currently, a strong need is felt in the telecommunication world for greater automation to meet the extreme requirements expected for future 6G applications. In this regard, Artificial Intelligence (AI) is gaining high momentum as one of the central enabling technologies for beyond-5G networks. Reinforcement Learning (RL) and Federated Learning (FL) are here proposed as technologies to enhance network automation and enable privacy-aware applications. Blockchain is proposed as a solution for non-repudiation and trustworthiness in the AI pipelines. These technologies are brought together in a comprehensive cloud-native architectural vision to fill the gap between current 5G systems and AI-powered secure systems of the future