Towards a Model-Based Serverless Platform for the Cloud-Edge-IoT Continuum

One of the most prominent implementations of the serverless programming model is Function-as-a-Service (FaaS). Using FaaS, application developers provide source code of serverless functions, typically describing only parts of a larger application, and define triggers for executing these functions on infrastructure components managed by the FaaS provider. There are still challenges that hinder the wider adoption of the FaaS model across the whole Cloud-Edge-IoT continuum. These include the high heterogeneity of the Edge and IoT infrastructure, vendor lock-in, the need to deploy and adapt serverless functions as well as their supporting services and software stacks into their cyber-physical execution environment. As a first step towards addressing these challenges, we introduce the SERVERLEss4I0T platform for the design, deployment, and maintenance of applications over the Cloud-Edge-IoT continuum. In particular, our platform enables the specification and deployment of serverless functions on Cloud and Edge resources, as well as the deployment of their supporting services and software stacks over the whole Cloud-Edge-IoT continuum.acceptedVersio

Cross-tier application and data partitioning of web applications for hybrid cloud deployment

Hybrid cloud deployment offers flexibility in trade-offs between the cost-savings/scalability of the public cloud and control over data resources provided at a private premise. However, this flexibility comes at the expense of complexity in distributing a system over these two locations. For multi-tier web applications, this challenge manifests itself primarily in the partitioning of application- and database-tiers. While there is existing research that focuses on either application-tier or data-tier partitioning, we show that optimized partitioning of web applications benefits from both tiers being considered simultaneously. We present our research on a new cross-tier partitioning approach to help developers make effective trade-offs between performance and cost in a hybrid cloud deployment. In two case studies the approach results in up to 54% reduction in monetary costs compared to a premise only deployment and 56% improvement in execution time compared to a naïve partitioning where application-tier is deployed in the cloud and data-tier is on private infrastructure

Quarterly Report (QR4)

In the fourth quarter, two beta releases of the StratusLab cloud distribution were made and deployed on the project's reference cloud infrastructure. The v1.0 production release is expected just after the close of Q4 due to the scheduling of the project's development sprints. The project continues to operate a production grid service over the StratusLab cloud distribution, demonstrating its functionality and stability. The project has also demonstrated the deployment of a grid site with the Claudia service manager with some autoscaling features

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

QoS-aware Deployment of IoT Applications Through the Fog

Fog computing aims at extending the Cloud by bringing computational power, storage and communication capabilities to the edge of the network, in support of the IoT. Segmentation, distribution and adaptive deployment of functionalities over the continuum from Things to Cloud are challenging tasks, due to the intrinsic heterogeneity, hierarchical structure and very large scale infrastructure they will have to exploit. In this paper we propose a simple, yet general, model to support the QoS-aware deployment of multi-component IoT applications over Fog infrastructures. The model describes operational systemic qualities of the available infrastructure (latency and bandwidth), interactions among software components and Things, and business policies. Algorithms to determine eligible deployment plans for an application over a Fog infrastructure are presented. A Java tool, FogTorch, based on the proposed model has been prototyped

Support Service for Reciprocal Computational Resource Sharing in Wireless Community Networks

In community networks, individuals and local organizations from a geographic area team up to create and run a community-owned IP network to satisfy the community's demand for ICT, such as facilitating Internet access and providing services of local interest. Most current community networks use wireless links for the node interconnection, applying off-the-shelf wireless equipment. While IP connectivity over the shared network infrastructure is successfully achieved, the deployment of applications in community networks is surprisingly low. To address the solution of this problem, we propose in this paper a service to incentivize the contribution of computing and storage as cloud resources to community networks, in order to stimulate the deployment of services and applications. Our final goal is the vision that in the long term, the users of community networks will not need to consume applications from the Internet, but find them within the wireless community network

Security-as-a-Service in Multi-cloud and Federated Cloud Environments: 9th IFIP WG 11.11 International Conference, IFIPTM 2015, Hamburg, Germany, May 26-28, 2015, Proceedings

The economic benefits of cloud computing are encouraging customers to bring complex applications and data into the cloud. However security remains the biggest barrier in the adoption of cloud, and with the advent of multi-cloud and federated clouds in practice security concerns are for applications and data in the cloud. This paper proposes security as a value added service, provisioned dynamically during deployment and operation management of an application in multi-cloud and federated clouds. This paper specifically considers a data protection and a host & application protection solution that are offered as a SaaS appli- cation, to validate the security services in a multi-cloud and federated cloud environment. This paper shares our experiences of validating these security services over a geographically distributed, large scale, multi-cloud and federated cloud infrastructure