Mobile Cloud Robotics as a Service with OCCIware

Best Paper AwardInternational audienceWe have recently witnessed the emerging of cloud computing on one hand and robotics platforms on the other hand. Naturally, these two visions have been merging to give birth to the Cloud Robotics paradigm in order to offer even more remote services. But such a vision is still in its infancy. Architectures and platforms are still to be defined to efficiently program robots so they can provide different services, in a standardized way masking their heterogeneity. This paper introduces Open Mobile Cloud Robotics Interface (OMCRI), a Robot-as-a-Service vision based platform, which offers a unified easy access to remote heterogeneous mobile robots. OMCRI encompasses an extension of the Open Cloud Computing Interface (OCCI) standard and a gateway hosting mobile robot resources. We then provide an implementation of OMCRI based on the open source model-driven Eclipse-based OCCIware tool chain and illustrates its use for three off-the-shelf mobile robots: Lego Mindstorm NXT, Turtlebot, and Parrot AR.Drone

Model-Driven Configuration Management of Cloud Applications with OCCI

International audienceTo tackle the cloud-provider lock-in, the Open Grid Forum (OGF) is developing the Open Cloud Computing Interface (OCCI), a standardized interface for managing any kind of cloud resources. Besides the OCCI Core model, which defines the basic modeling elements for cloud resources, the OGF also defines extensions that reflect the requirements of different cloud service levels, such as IaaS and PaaS. However, so far the OCCI PaaS extension is very coarse grained and lacks of supporting use cases and implementations. Especially, it does not define how the components of the application itself can be managed. In this paper, we present a model-driven framework that extends the OCCI PaaS extension and is able to use different configuration management tools to manage the whole lifecycle of cloud applications. We demonstrate the feasibility of the approach by presenting four different use cases and prototypical implementations for three different configuration management tools

Specifying Semantic Interoperability between Heterogeneous Cloud Resources with the FCLOUDS Formal Language

International audienceWith the advent of cloud computing, different cloud providers with heterogeneous services and Application Programming Interfaces (APIs) have emerged. Hence, building an interop-erable multi-cloud system becomes a complex task. Our idea is to design FCLOUDS framework to achieve semantic interoperability in multi-clouds, i.e., to identify the common concepts between cloud APIs and to reason over them. In this paper, we propose to take advantage of the Open Cloud Computing Interface (OCCI) standard and the Alloy formal specification language to define the FCLOUDS language, which is a formal language for specifying heterogeneous cloud APIs. To do so, we formalize OCCI concepts and operational semantics, then we identify and validate five properties (consistency, sequentiality, reversibility, idempotence and safety) that denote their characteristics. To demonstrate the effectiveness of our cloud formal language, we present thirteen case studies where we formally specify infrastructure, platform, Internet of Things (IoT) and transverse cloud concerns. Thanks to the Alloy analyzer, we verify that these heterogeneous APIs uphold the properties of FCLOUDS and also validate their own specific properties. Then, thanks to formal transformation rules and equivalence properties, we draw a precise alignment between our case studies, which promotes semantic interoperability in a multi-cloud system

Softwarization of Large-Scale IoT-based Disasters Management Systems

The Internet of Things (IoT) enables objects to interact and cooperate with each other for reaching common objectives. It is very useful in large-scale disaster management systems where humans are likely to fail when they attempt to perform search and rescue operations in high-risk sites. IoT can indeed play a critical role in all phases of large-scale disasters (i.e. preparedness, relief, and recovery). Network softwarization aims at designing, architecting, deploying, and managing network components primarily based on software programmability properties. It relies on key technologies, such as cloud computing, Network Functions Virtualization (NFV), and Software Defined Networking (SDN). The key benefits are agility and cost efficiency. This thesis proposes softwarization approaches to tackle the key challenges related to large-scale IoT based disaster management systems. A first challenge faced by large-scale IoT disaster management systems is the dynamic formation of an optimal coalition of IoT devices for the tasks at hand. Meeting this challenge is critical for cost efficiency. A second challenge is an interoperability. IoT environments remain highly heterogeneous. However, the IoT devices need to interact. Yet another challenge is Quality of Service (QoS). Disaster management applications are known to be very QoS sensitive, especially when it comes to delay. To tackle the first challenge, we propose a cloud-based architecture that enables the formation of efficient coalitions of IoT devices for search and rescue tasks. The proposed architecture enables the publication and discovery of IoT devices belonging to different cloud providers. It also comes with a coalition formation algorithm. For the second challenge, we propose an NFV and SDN based - architecture for on-the-fly IoT gateway provisioning. The gateway functions are provisioned as Virtual Network Functions (VNFs) that are chained on-the-fly in the IoT domain using SDN. When it comes to the third challenge, we rely on fog computing to meet the QoS and propose algorithms that provision IoT applications components in hybrid NFV based - cloud/fogs. Both stationary and mobile fog nodes are considered. In the case of mobile fog nodes, a Tabu Search-based heuristic is proposed. It finds a near-optimal solution and we numerically show that it is faster than the Integer Linear Programming (ILP) solution by several orders of magnitude