Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

Distributed Computing Framework Based on Software Containers for Heterogeneous Embedded Devices

The Internet of Things (IoT) is represented by millions of everyday objects enhanced with sensing and actuation capabilities that are connected to the Internet. Traditional approaches for IoT applications involve sending data to cloud servers for processing and storage, and then relaying commands back to devices. However, this approach is no longer feasible due to the rapid growth of IoT in the network: the vast amount of devices causes congestion; latency and security requirements demand that data is processed close to the devices that produce and consume it; and the processing and storage resources of devices remain underutilized. Fog Computing has emerged as a new paradigm where multiple end-devices form a shared pool of resources where distributed applications are deployed, taking advantage of local capabilities. These devices are highly heterogeneous, with varying hardware and software platforms. They are also resource-constrained, with limited availability of processing and storage resources. Realizing the Fog requires a software framework that simplifies the deployment of distributed applications, while at the same time overcoming these constraints. In Cloud-based deployments, software containers provide a lightweight solution to simplify the deployment of distributed applications. However, Cloud hardware is mostly homogeneous and abundant in resources. This work establishes the feasibility of using Docker Swarm -- an existing container-based software framework -- for the deployment of distributed applications on IoT devices. This is realized with the use of custom tools to enable minimal-size applications compatible with heterogeneous devices; automatic configuration and formation of device Fog; remote management and provisioning of devices. The proposed framework has significant advantages over the state of the art, namely, it supports Fog-based distributed applications, it overcomes device heterogeneity and it simplifies device initialization

Addressing the Challenges in Federating Edge Resources

This book chapter considers how Edge deployments can be brought to bear in a global context by federating them across multiple geographic regions to create a global Edge-based fabric that decentralizes data center computation. This is currently impractical, not only because of technical challenges, but is also shrouded by social, legal and geopolitical issues. In this chapter, we discuss two key challenges - networking and management in federating Edge deployments. Additionally, we consider resource and modeling challenges that will need to be addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and Paradigms; Editors Buyya, Sriram

VIoLET: A Large-scale Virtual Environment for Internet of Things

IoT deployments have been growing manifold, encompassing sensors, networks, edge, fog and cloud resources. Despite the intense interest from researchers and practitioners, most do not have access to large-scale IoT testbeds for validation. Simulation environments that allow analytical modeling are a poor substitute for evaluating software platforms or application workloads in realistic computing environments. Here, we propose VIoLET, a virtual environment for defining and launching large-scale IoT deployments within cloud VMs. It offers a declarative model to specify container-based compute resources that match the performance of the native edge, fog and cloud devices using Docker. These can be inter-connected by complex topologies on which private/public networks, and bandwidth and latency rules are enforced. Users can configure synthetic sensors for data generation on these devices as well. We validate VIoLET for deployments with > 400 devices and > 1500 device-cores, and show that the virtual IoT environment closely matches the expected compute and network performance at modest costs. This fills an important gap between IoT simulators and real deployments.Comment: To appear in the Proceedings of the 24TH International European Conference On Parallel and Distributed Computing (EURO-PAR), August 27-31, 2018, Turin, Italy, europar2018.org. Selected as a Distinguished Paper for presentation at the Plenary Session of the conferenc

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201