Big Data Analytics for QoS Prediction Through Probabilistic Model Checking

As competitiveness increases, being able to guaranting QoS of delivered services is key for business success. It is thus of paramount importance the ability to continuously monitor the workflow providing a service and to timely recognize breaches in the agreed QoS level. The ideal condition would be the possibility to anticipate, thus predict, a breach and operate to avoid it, or at least to mitigate its effects. In this paper we propose a model checking based approach to predict QoS of a formally described process. The continous model checking is enabled by the usage of a parametrized model of the monitored system, where the actual value of parameters is continuously evaluated and updated by means of big data tools. The paper also describes a prototype implementation of the approach and shows its usage in a case study.Comment: EDCC-2014, BIG4CIP-2014, Big Data Analytics, QoS Prediction, Model Checking, SLA compliance monitorin

Optimal and probabilistic resource and capability analysis for network slice as a service

Network Slice as a Service is one of the key concepts of the fifth generation of mobile networks (5G). 5G supports new use cases, like the Internet of Things (IoT), massive Machine Type Communication (mMTC) and Ultra-Reliable and Low Latency Communication (URLLC) as well as significant improvements of the conventional Mobile Broadband (MBB) use case. In addition, safety and security critical use cases move into focus. These use cases involve diverging requirements, e.g. network reliability, latency and throughput. Network virtualization and end-to-end mobile network slicing are seen as key enablers to handle those differing requirements and providing mobile network services for the various 5G use cases and between different tenants. Network slices are isolated, virtualized, end-to-end networks optimized for specific use cases. But still they share a common physical network infrastructure. Through logical separation of the network slices on a common end-to-end mobile network infrastructure, an efficient usage of the underlying physical network infrastructure provided by multiple Mobile Service Providers (MSPs) in enabled. Due to the dynamic lifecycle of network slices there is a strong demand for efficient algorithms for the so-called Network Slice Embedding (NSE) problem. Efficient and reliable resource provisioning for Network Slicing as a Service, requires resource allocation based on a mapping of virtual network slice elements on the serving physical mobile network infrastructure. In this thesis, first of all, a formal Network Slice Instance Admission (NSIA) process is presented, based on the 3GPP standardization. This process allows to give fast feedback to a network operator or tenant on the feasibility of embedding incoming Network Slice Instance Requests (NSI-Rs). In addition, corresponding services for NSIA and feasibility checking services are defined in the context of the ETSI ZSM Reference Architecture Framework. In the main part of this work, a mathematical model for solving the NSE Problem formalized as a standardized Linear Program (LP) is presented. The presented solution provides a nearly optimal embedding. This includes the optimal subset of Network Slice Instances (NSIs) to be selected for embedding, in terms of network slice revenue and costs, and the optimal allocation of associated network slice applications, functions, services and communication links on the 5G end-to-end mobile network infrastructure. It can be used to solve the online as well as the offline NSIA problem automatically in different variants. In particular, low latency network slices require deployment of their services and applications, including Network Functions (NFs) close to the user, i.e., at the edge of the mobile network. Since the users of those services might be widely distributed and mobile, multiple instances of the same application are required to be available on numerous distributed edge clouds. A holistic approach for tackling the problem of NSE with edge computing is provided by our so-called Multiple Application Instantiation (MAI) variant of the NSE LP solution. It is capable of determining the optimal number of application instances and their optimal deployment locations on the edge clouds, even for multiple User Equipment (UE) connectivity scenarios. In addition to that multi-path, also referred to as path-splitting, scenarios with a latency sensitive objective function, which guarantees the optimal network utilization as well as minimum latency in the network slice communication, is included. Resource uncertainty, as well as reuse and overbooking of resources guaranteed by Service Level Agreements (SLAs) are discussed in this work. There is a consensus that over-provisioning of mobile communication bands is economically infeasible and certain risk of network overload is accepted for the majority of the 5G use cases. A probabilistic variant of the NSE problem with an uncertainty-aware objective function and a resource availability confidence analysis are presented. The evaluation shows the advantages and the suitability of the different variants of the NSE formalization, as well as its scalability and computational limits in a practical implementation

Towards critical event monitoring, detection and prediction for self-adaptive future Internet applications

The Future Internet (FI) will be composed of a multitude of diverse types of services that offer flexible, remote access to software features, content, computing resources, and middleware solutions through different cloud delivery models, such as IaaS, PaaS and SaaS. Ultimately, this means that loosely coupled Internet services will form a comprehensive base for developing value added applications in an agile way. Unlike traditional application development, which uses computing resources and software components under local administrative control, FI applications will thus strongly depend on third-party services. To maintain their quality of service, those applications therefore need to dynamically and autonomously adapt to an unprecedented level of changes that may occur during runtime. In this paper, we present our recent experiences on monitoring, detection, and prediction of critical events for both software services and multimedia applications. Based on these findings we introduce potential directions for future research on self-adaptive FI applications, bringing together those research directions

Runtime model checking for sla compliance monitoring and qos prediction

Sophisticated workflows, where multiple parties cooperate towards the achievement of a shared goal are today common. In a market-oriented setup, it is key that effective mechanisms be available for providing accountability within the business process. The challenge is to be able to continuously monitor the progress of the business process, ideally,anticipating contract breaches and triggering corrective actions. In this paper we propose a novel QoS prediction approach which combines runtime monitoring of the real system with probabilistic model-checking on a parametric system model. To cope with the huge amount of data generated by the monitored system, while ensuring that parameters are extracted in a timing fashion, we relied on big data analytics solutions. To validate the proposed approach, a prototype of the QoS prediction framework has been developed, and an experimental campaign has been conducted with respect to a case study in the field of Smart Grids