Efficient management of virtualized information-centric networks

The Internet has rapidly evolved from a network, connecting a couple of dozens of computers, to a network containing billions of devices. Furthermore, the current Internet is mostly used to deliver complex services with increasingly stringent Quality of Service (QoS) requirements. However, the underlying network model has remained the same, making the Internet not well suited to optimally support the current user trends and services. Currently, a lot of effort is being made in the area of network virtualization and Information-Centric Networking (ICN) to support the evolution towards the QoS constraint distribution of large amounts of information. Even though both directions offer a lot of opportunities, multiple important challenges have to be faced when managing the placement of content inside the network and guaranteeing delivery efficiency. These challenges are further increased when a combination of both trends is considered. This paper gives an overview of these challenges and how this PhD will deal with the mutual influences of network virtualization and ICN in an efficient way

Lessons Learned from KYPO – Cyber Exercise & Research Platform Project

Cyber attacks became significant threat for a critical information infrastructure of a state. In order to face them it is necessary to study them, understand them, and train personnel to recognize them. For this purpose we have developed a KYPO - Cyber Exercise & Research Platform for simulation of numerous cyber attacks. In this paper we present the KYPO platform and first experience gained from Capture the Flag game training.Cyber attacks became significant threat for a critical information infrastructure of a state. In order to face them it is necessary to study them, understand them, and train personnel to recognize them. For this purpose we have developed a KYPO - Cyber Exercise & Research Platform for simulation of numerous cyber attacks. In this paper we present the KYPO platform and first experience gained from Capture the Flag game training

Virtualization of set-top-box devices in next generation SDN-NFV networks: the INPUT project perspective

Due to the emergence of Software Defined Networking (SDN) and Network Functions Virtualization (NFV) paradigms, coupled with a hyper-connectivity communication paradigm, the \u201csoftwarisation\u201d of the Internet infrastructure and of its network management framework is gaining increasing popularity. This is the target of the INPUT platform, a novel infrastructure and paradigm supporting Future Internet personal cloud services in a more scalable and sustainable way, and with innovative addedvalue capabilities. The INPUT technologies enable next-generation cloud applications to go beyond classical service models, and even replace physical Smart Devices, usually placed in users\u2019 homes (e.g., set-top boxes), with their virtual images, providing them to users \u201cas a Service\u201d. In this paper we present the Virtual set-top box from both architectural and functional points of view, demonstrating the feasibility of the softwarized SDN/NFV paradigm joined with the fog-computing approach to support personal cloud services

Challenges and Experiences in Designing Interpretable KPI-diagnostics for Cloud Applications

Automated root cause analysis of performance problems in modern cloud computing infrastructures is of a high technology value in the self-driving context. Those systems are evolved into large scale and complex solutions which are core for running most of today’s business applications. Hence, cloud management providers realize their mission through a “total” monitoring of data center flows thus enabling a full visibility into the cloud. Appropriate machine learning methods and software products rely on such observation data for real-time identification and remediation of potential sources of performance degradations in cloud operations to minimize their impacts. We describe the existing technology challenges and our experiences while working on designing problem root cause analysis mechanisms which are automatic, application agnostic, and, at the same time, interpretable for human operators to gain their trust. The paper focuses on diagnosis of cloud ecosystems through their Key Performance Indicators (KPI). Those indicators are utilized to build automatically labeled data sets and train explainable AI models for identifying conditions and processes “responsible” for misbehaviors. Our experiments on a large time series data set from a cloud application demonstrate that those approaches are effective in obtaining models that explain unacceptable KPI behaviors and localize sources of issues