The evolution of the Internet of Things (IoT) is driving an extraordinary growth of traffic and processing demands, persuading 5G players to change their infrastructures. In this context, Fog computing emerges as a potential solution, providing nearby resources to run IoT applications. However, the Fog raises several challenges which hinders its adoption. In this article, we consider thereconfiguration problem, i.e., how to dynamically adapt the placement of IoT applications running on the Fog, depending on application needs and evolution of resource usage. We propose and evaluate a series of reconfiguration algorithms, based on both online scheduling and online learning approaches. Through an extensive set of experiments in a realistic testbed, we demonstrate that the performance strongly depends on the quality and availability of information from both Fog infrastructure and IoT applications. This information mainly concerns the application’s resource usage (estimated by the user during the design of the application) and the availability of resources in the infrastructure (collected by commercial off-the-shelf monitoring tools). Finally, we show that a reactive and greedy strategy, which relies on this additional information, can overcome the performance of state-of-the-art online learning algorithms, even in a scenario with inaccurate information.

In recent years, Fog computing paradigm has received the attention of academic and industrial communities. By offering nearby computational, storage and network resources, this new architecture deals with the explosion of IoT (Internet of Things) traffic while responding to the stringent requirements of new applications. Unfortunately, as of today, there is a lack of practical solutions to enable the exploitation of this novel paradigm. To deal with this shortcoming, this demo gives an insight into FITOR, our proposed orchestration system for IoT applications in Fog. Our solution makes use of both Grid5000 [1] and FIT/IoT-LAB [2] to build a realistic fog environment. FITOR is responsible for the orchestration of micro-service based IoT applications while making use of a holistic monitoring of the fog infrastructure.

To this day, the Internet of Things (IoT) continues its explosive growth. Nevertheless, with the exceptional evolution of traffic demand, existing infrastructures are struggling to resist. In this context, Fog computing is shaping the future of IoT applications. It offers nearby computational, networking and storage resources to respond to the stringent requirements of these applications. However, despite its several advantages, Fog computing raises new challenges which slow its adoption down. Hence, there is a lack of practical solutions to enable the exploitation of this novel concept. To deal with this shortcoming, we propose FITOR, an orchestration system for IoT applications in the Fog environment. This solution builds a realistic Fog environment while offering efficient orchestration mechanisms. In order to optimize the provisioning of Fog-Enabled IoT applications, FITOR relies on O-FSP, an optimized fog service provisioning strategy which aims to minimize the provisioning cost of IoT applications, while meeting their requirements. Based on extensive experiments, the results obtained show that O-FSP optimizes the placement of IoT applications and outperforms the related strategies in terms of i) provisioning cost ii) resource usage and iii) acceptance rate.

To support the drastically increasing traffic generated by devices at the edge of the network, 5G players are urged to rethink their infrastructure design. Unfortunately, conventional Cloud infrastructures struggle to adapt to the huge volume of traffic. In this context, Fog computing has been developed to bridge Cloud data centers and edge devices servicing a multitude of heterogeneous devices. These nearby nodes offer analytics and data storage capabilities increasing considerably the capacity of the infrastructure. However, provisioning IoT applications on such a heterogeneous infrastructure, while meeting their stringent requirements is extremely challenging. In this paper, we study the Fog service provisioning issue in a practical manner. In this regard, we propose a novel strategy, which we call GO-FSP. GO-FSP optimizes the placement of IoT application components while coping with their strict performance requirements. To do so, we first propose an Integer Linear Programming (ILP) formulation for the IoT application provisioning problem. The latter targets to minimize the deployment cost while ensuring a load balancing between heterogeneous devices. Then, a GRASP-based approach is proposed to achieve the aforementioned objectives. Finally, we make use of the FITOR orchestration system to evaluate the performance of our solution under real conditions. Obtained results show that our scheme outperforms the related strategies.

Advances in inter-networking technology and computing components have enabled Volunteer Computing (VC) systems that allows volunteers to donate their computers' idle CPU cycles to a given project. BOINC is the most popular VC infrastructure today with over 580,000 hosts that deliver over 2,300 TeraFLOP per day. BOINC projects usually have hundreds of thousands of independent tasks and are interested in overall throughput. Each project has its own server which is responsible for distributing work units to clients, recovering results and validating them. The BOINC scheduling algorithms are complex and have been used for many years now. Their efficiency and fairness have been assessed in the context of throughput oriented projects. Yet, recently, burst projects, with fewer tasks and interested in response time, have emerged. Many works have proposed new scheduling algorithms to optimize individual response time but their use may be problematic in presence of other projects. In this article we show that the commonly used BOINC scheduling algorithms are unable to enforce fairness and project isolation. Burst projects may dramatically impact the performance of all other projects (burst or non-burst). To study such interactions, we perform a detailed, multi-player and multi-objective game theoretic study. Our analysis and experiments provide a good understanding on the impact of the different scheduling parameters and show that the non-cooperative optimization may result in inefficient and unfair share of the resources.

Advances in internetworking technology and the decreasing cost-performance ratio of commodity computing components have enabled Volunteer Computing (VC). VC platforms aggregate tens or hundreds of thousands of hosts. These hosts are typically volatile, which raises difficult research questions. Most research in this area relies on simulation. The main issue when developing VC simulators is scalability: How to perform simulations of large-scale VC platforms with reasonable amounts of memory and reasonably fast? To achieve scalability, state-of-the-art VC simulators employ simplistic simulation models and/or target on narrow platform and application scenarios. In this paper we enable VC simulations using the general-purpose SimGrid simulation framework, which provides significantly more realistic and flexible simulation capabilities than the aforementioned simulators. Our key contribution is a set of improvements to SimGrid so that it brings these benefits to VC simulations while achieving good scalability.