Edge-as-a-Service: Towards Distributed Cloud Architectures

We present an Edge-as-a-Service (EaaS) platform for realising distributed cloud architectures and integrating the edge of the network in the computing ecosystem. The EaaS platform is underpinned by (i) a lightweight discovery protocol that identifies edge nodes and make them publicly accessible in a computing environment, and (ii) a scalable resource provisioning mechanism for offloading workloads from the cloud on to the edge for servicing multiple user requests. We validate the feasibility of EaaS on an online game use-case to highlight the improvement in the QoS of the application hosted on our cloud-edge platform. On this platform we demonstrate (i) low overheads of less than 6%, (ii) reduced data traffic to the cloud by up to 95% and (iii) minimised application latency between 40%-60%.Comment: 10 pages; presented at the EdgeComp Symposium 2017; will appear in Proceedings of the International Conference on Parallel Computing, 201

A Study to Optimize Heterogeneous Resources for Open IoT

Recently, IoT technologies have been progressed, and many sensors and actuators are connected to networks. Previously, IoT services were developed by vertical integration style. But now Open IoT concept has attracted attentions which achieves various IoT services by integrating horizontal separated devices and services. For Open IoT era, we have proposed the Tacit Computing technology to discover the devices with necessary data for users on demand and use them dynamically. We also implemented elemental technologies of Tacit Computing. In this paper, we propose three layers optimizations to reduce operation cost and improve performance of Tacit computing service, in order to make as a continuous service of discovered devices by Tacit Computing. In optimization process, appropriate function allocation or offloading specific functions are calculated on device, network and cloud layer before full-scale operation.Comment: 3 pages, 1 figure, 2017 Fifth International Symposium on Computing and Networking (CANDAR2017), Nov. 201

A Distributed Multilevel Force-directed Algorithm

The wide availability of powerful and inexpensive cloud computing services naturally motivates the study of distributed graph layout algorithms, able to scale to very large graphs. Nowadays, to process Big Data, companies are increasingly relying on PaaS infrastructures rather than buying and maintaining complex and expensive hardware. So far, only a few examples of basic force-directed algorithms that work in a distributed environment have been described. Instead, the design of a distributed multilevel force-directed algorithm is a much more challenging task, not yet addressed. We present the first multilevel force-directed algorithm based on a distributed vertex-centric paradigm, and its implementation on Giraph, a popular platform for distributed graph algorithms. Experiments show the effectiveness and the scalability of the approach. Using an inexpensive cloud computing service of Amazon, we draw graphs with ten million edges in about 60 minutes.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

Monitoring Large-Scale Cloud Systems with Layered Gossip Protocols

Monitoring is an essential aspect of maintaining and developing computer systems that increases in difficulty proportional to the size of the system. The need for robust monitoring tools has become more evident with the advent of cloud computing. Infrastructure as a Service (IaaS) clouds allow end users to deploy vast numbers of virtual machines as part of dynamic and transient architectures. Current monitoring solutions, including many of those in the open-source domain rely on outdated concepts including manual deployment and configuration, centralised data collection and adapt poorly to membership churn. In this paper we propose the development of a cloud monitoring suite to provide scalable and robust lookup, data collection and analysis services for large-scale cloud systems. In lieu of centrally managed monitoring we propose a multi-tier architecture using a layered gossip protocol to aggregate monitoring information and facilitate lookup, information collection and the identification of redundant capacity. This allows for a resource aware data collection and storage architecture that operates over the system being monitored. This in turn enables monitoring to be done in-situ without the need for significant additional infrastructure to facilitate monitoring services. We evaluate this approach against alternative monitoring paradigms and demonstrate how our solution is well adapted to usage in a cloud-computing context.Comment: Extended Abstract for the ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2013) Poster Trac

Towards a unified management of applications on heterogeneous clouds

J. Carrasco, F. Durán y E. Pimentel. "Towards a Unified Management of Applications on Heterogeneous Clouds". Proceedings of the PhD Symposium at the 5th European Conference on Service-Oriented and Cloud Computing. G. Zavattaro and W. Zimmermann (eds). University Halle-Wittenberg. Technical Report 2016/02, 40-47. 2016.The diversity in the way cloud providers o↵er their services, give their SLAs, present their QoS, or support di↵erent technologies, makes very difficult the portability and interoperability of cloud applications, and favours the well-known vendor lock-in problem. We propose a model to describe cloud applications and the required resources in an agnostic, and providers- and resources-independent way, in which individual application modules, and entire applications, may be re-deployed using different services without modification. To support this model, and after the proposal of a variety of cross-cloud application management tools by different authors, we propose going one step further in the unification of cloud services with a management approach in which IaaS and PaaS services are integrated into a unified interface. We provide support for deploying applications whose components are distributed on different cloud providers, indistinctly using IaaS and PaaS services.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Computing support for advanced medical data analysis and imaging

We discuss computing issues for data analysis and image reconstruction of PET-TOF medical scanner or other medical scanning devices producing large volumes of data. Service architecture based on the grid and cloud concepts for distributed processing is proposed and critically discussed.Comment: 9 p, 3 figs, based on talk given at Symposium on Positron Emission Tomography, Sept. 19-22, 2013, Jagiellonian University, Krak\'ow, P