Strong Temporal Isolation among Containers in OpenStack for NFV Services

In this paper, the problem of temporal isolation among containerized software components running in shared cloud infrastructures is tackled, proposing an approach based on hierarchical real-time CPU scheduling. This allows for reserving a precise share of the available computing power for each container deployed in a multi-core server, so to provide it with a stable performance, independently from the load of other co-located containers. The proposed technique enables the use of reliable modeling techniques for end-to-end service chains that are effective in controlling the application-level performance. An implementation of the technique within the well-known OpenStack cloud orchestration software is presented, focusing on a use-case framed in the context of network function virtualization. The modified OpenStack is capable of leveraging the special real-time scheduling features made available in the underlying Linux operating system through a patch to the in-kernel process scheduler. The effectiveness of the technique is validated by gathering performance data from two applications running in a real test-bed with the mentioned modifications to OpenStack and the Linux kernel. A performance model is developed that tightly models the application behavior under a variety of conditions. Extensive experimentation shows that the proposed mechanism is successful in guaranteeing isolation of individual containerized activities on the platform

Performance Modeling in Predictable Cloud Computing

This paper deals with the problem of performance stability of software running in shared virtualized infrastructures. The focus is on the ability to build an abstract performance model of containerized application components, where real-time scheduling at the CPU level, along with traffic shaping at the networking level, are used to limit the temporal interferences among co-located workloads, so as to obtain a predictable distributed computing platform. A model for a simple client-server application running in containers is used as a case-study, where an extensive experimental validation of the model is conducted over a testbed running a modified OpenStack on top of a custom real-time CPU scheduler in the Linux kernel

Enabling Scalable and Sustainable Softwarized 5G Environments

The fifth generation of telecommunication systems (5G) is foreseen to play a fundamental role in our socio-economic growth by supporting various and radically new vertical applications (such as Industry 4.0, eHealth, Smart Cities/Electrical Grids, to name a few), as a one-fits-all technology that is enabled by emerging softwarization solutions \u2013 specifically, the Fog, Multi-access Edge Computing (MEC), Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) paradigms. Notwithstanding the notable potential of the aforementioned technologies, a number of open issues still need to be addressed to ensure their complete rollout. This thesis is particularly developed towards addressing the scalability and sustainability issues in softwarized 5G environments through contributions in three research axes: a) Infrastructure Modeling and Analytics, b) Network Slicing and Mobility Management, and c) Network/Services Management and Control. The main contributions include a model-based analytics approach for real-time workload profiling and estimation of network key performance indicators (KPIs) in NFV infrastructures (NFVIs), as well as a SDN-based multi-clustering approach to scale geo-distributed virtual tenant networks (VTNs) and to support seamless user/service mobility; building on these, solutions to the problems of resource consolidation, service migration, and load balancing are also developed in the context of 5G. All in all, this generally entails the adoption of Stochastic Models, Mathematical Programming, Queueing Theory, Graph Theory and Team Theory principles, in the context of Green Networking, NFV and SDN