68 research outputs found

    Data-Driven Methods for Data Center Operations Support

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    During the last decade, cloud technologies have been evolving at an impressive pace, such that we are now living in a cloud-native era where developers can leverage on an unprecedented landscape of (possibly managed) services for orchestration, compute, storage, load-balancing, monitoring, etc. The possibility to have on-demand access to a diverse set of configurable virtualized resources allows for building more elastic, flexible and highly-resilient distributed applications. Behind the scenes, cloud providers sustain the heavy burden of maintaining the underlying infrastructures, consisting in large-scale distributed systems, partitioned and replicated among many geographically dislocated data centers to guarantee scalability, robustness to failures, high availability and low latency. The larger the scale, the more cloud providers have to deal with complex interactions among the various components, such that monitoring, diagnosing and troubleshooting issues become incredibly daunting tasks. To keep up with these challenges, development and operations practices have undergone significant transformations, especially in terms of improving the automations that make releasing new software, and responding to unforeseen issues, faster and sustainable at scale. The resulting paradigm is nowadays referred to as DevOps. However, while such automations can be very sophisticated, traditional DevOps practices fundamentally rely on reactive mechanisms, that typically require careful manual tuning and supervision from human experts. To minimize the risk of outages—and the related costs—it is crucial to provide DevOps teams with suitable tools that can enable a proactive approach to data center operations. This work presents a comprehensive data-driven framework to address the most relevant problems that can be experienced in large-scale distributed cloud infrastructures. These environments are indeed characterized by a very large availability of diverse data, collected at each level of the stack, such as: time-series (e.g., physical host measurements, virtual machine or container metrics, networking components logs, application KPIs); graphs (e.g., network topologies, fault graphs reporting dependencies among hardware and software components, performance issues propagation networks); and text (e.g., source code, system logs, version control system history, code review feedbacks). Such data are also typically updated with relatively high frequency, and subject to distribution drifts caused by continuous configuration changes to the underlying infrastructure. In such a highly dynamic scenario, traditional model-driven approaches alone may be inadequate at capturing the complexity of the interactions among system components. DevOps teams would certainly benefit from having robust data-driven methods to support their decisions based on historical information. For instance, effective anomaly detection capabilities may also help in conducting more precise and efficient root-cause analysis. Also, leveraging on accurate forecasting and intelligent control strategies would improve resource management. Given their ability to deal with high-dimensional, complex data, Deep Learning-based methods are the most straightforward option for the realization of the aforementioned support tools. On the other hand, because of their complexity, this kind of models often requires huge processing power, and suitable hardware, to be operated effectively at scale. These aspects must be carefully addressed when applying such methods in the context of data center operations. Automated operations approaches must be dependable and cost-efficient, not to degrade the services they are built to improve. i

    Telecommunications Networks

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    This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

    Project BeARCAT : Baselining, Automation and Response for CAV Testbed Cyber Security : Connected Vehicle & Infrastructure Security Assessment

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    Connected, software-based systems are a driver in advancing the technology of transportation systems. Advanced automated and autonomous vehicles, together with electrification, will help reduce congestion, accidents and emissions. Meanwhile, vehicle manufacturers see advanced technology as enhancing their products in a competitive market. However, as many decades of using home and enterprise computer systems have shown, connectivity allows a system to become a target for criminal intentions. Cyber-based threats to any system are a problem; in transportation, there is the added safety implication of dealing with moving vehicles and the passengers within

    Designing and prototyping WebRTC and IMS integration using open source tools

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    WebRTC, or Web Real-time Communications, is a collection of web standards that detail the mechanisms, architectures and protocols that work together to deliver real-time multimedia services to the web browser. It represents a significant shift from the historical approach of using browser plugins, which over time, have proven cumbersome and problematic. Furthermore, it adopts various Internet standards in areas such as identity management, peer-to-peer connectivity, data exchange and media encoding, to provide a system that is truly open and interoperable. Given that WebRTC enables the delivery of multimedia content to any Internet Protocol (IP)-enabled device capable of hosting a web browser, this technology could potentially be used and deployed over millions of smartphones, tablets and personal computers worldwide. This service and device convergence remains an important goal of telecommunication network operators who seek to enable it through a converged network that is based on the IP Multimedia Subsystem (IMS). IMS is an IP-based subsystem that sits at the core of a modern telecommunication network and acts as the main routing substrate for media services and applications such as those that WebRTC realises. The combination of WebRTC and IMS represents an attractive coupling, and as such, a protracted investigation could help to answer important questions around the technical challenges that are involved in their integration, and the merits of various design alternatives that present themselves. This thesis is the result of such an investigation and culminates in the presentation of a detailed architectural model that is validated with a prototypical implementation in an open source testbed. The model is built on six requirements which emerge from an analysis of the literature, including previous interventions in IMS networks and a key technical report on design alternatives. Furthermore, this thesis argues that the client architecture requires support for web-oriented signalling, identity and call handling techniques leading to a potential for IMS networks to natively support these techniques as operator networks continue to grow and develop. The proposed model advocates the use of SIP over WebSockets for signalling and DTLS-SRTP for media to enable one-to-one communication and can be extended through additional functions resulting in a modular architecture. The model was implemented using open source tools which were assembled to create an experimental network testbed, and tests were conducted demonstrating successful cross domain communications under various conditions. The thesis has a strong focus on enabling ordinary software developers to assemble a prototypical network such as the one that was assembled and aims to enable experimentation in application use cases for integrated environments

    Autonomic Overload Management For Large-Scale Virtualized Network Functions

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    The explosion of data traffic in telecommunication networks has been impressive in the last few years. To keep up with the high demand and staying profitable, Telcos are embracing the Network Function Virtualization (NFV) paradigm by shifting from hardware network appliances to software virtual network functions, which are expected to support extremely large scale architectures, providing both high performance and high reliability. The main objective of this dissertation is to provide frameworks and techniques to enable proper overload detection and mitigation for the emerging virtualized software-based network services. The thesis contribution is threefold. First, it proposes a novel approach to quickly detect performance anomalies in complex and large-scale VNF services. Second, it presents NFV-Throttle, an autonomic overload control framework to protect NFV services from overload within a short period of time, allowing to preserve the QoS of traffic flows admitted by network services in response to both traffic spikes (up to 10x the available capacity) and capacity reduction due to infrastructure problems (such as CPU contention). Third, it proposes DRACO, to manage overload problems arising in novel large-scale multi-tier applications, such as complex stateful network functions in which the state is spread across modern key-value stores to achieve both scalability and performance. DRACO performs a fine-grained admission control, by tuning the amount and type of traffic according to datastore node dependencies among the tiers (which are dynamically discovered at run-time), and to the current capacity of individual nodes, in order to mitigate overloads and preventing hot-spots. This thesis presents the implementation details and an extensive experimental evaluation for all the above overload management solutions, by means of a virtualized IP Multimedia Subsystem (IMS), which provides modern multimedia services for Telco operators, such as Videoconferencing and VoLTE, and which is one of the top use-cases of the NFV technology

    DEPENDABILITY BENCHMARKING OF NETWORK FUNCTION VIRTUALIZATION

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    Network Function Virtualization (NFV) is an emerging networking paradigm that aims to reduce costs and time-to-market, improve manageability, and foster competition and innovative services. NFV exploits virtualization and cloud computing technologies to turn physical network functions into Virtualized Network Functions (VNFs), which will be implemented in software, and will run as Virtual Machines (VMs) on commodity hardware located in high-performance data centers, namely Network Function Virtualization Infrastructures (NFVIs). The NFV paradigm relies on cloud computing and virtualization technologies to provide carrier-grade services, i.e., the ability of a service to be highly reliable and available, within fast and automatic failure recovery mechanisms. The availability of many virtualization solutions for NFV poses the question on which virtualization technology should be adopted for NFV, in order to fulfill the requirements described above. Currently, there are limited solutions for analyzing, in quantitative terms, the performance and reliability trade-offs, which are important concerns for the adoption of NFV. This thesis deals with assessment of the reliability and of the performance of NFV systems. It proposes a methodology, which includes context, measures, and faultloads, to conduct dependability benchmarks in NFV, according to the general principles of dependability benchmarking. To this aim, a fault injection framework for the virtualization technologies has been designed and implemented for the virtualized technologies being used as case studies in this thesis. This framework is successfully used to conduct an extensive experimental campaign, where we compare two candidate virtualization technologies for NFV adoption: the commercial, hypervisor-based virtualization platform VMware vSphere, and the open-source, container-based virtualization platform Docker. These technologies are assessed in the context of a high-availability, NFV-oriented IP Multimedia Subsystem (IMS). The analysis of experimental results reveal that i) fault management mechanisms are crucial in NFV, in order to provide accurate failure detection and start the subsequent failover actions, and ii) fault injection proves to be valuable way to introduce uncommon scenarios in the NFVI, which can be fundamental to provide a high reliable service in production

    Infrastructure sharing of 5G mobile core networks on an SDN/NFV platform

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    When looking towards the deployment of 5G network architectures, mobile network operators will continue to face many challenges. The number of customers is approaching maximum market penetration, the number of devices per customer is increasing, and the number of non-human operated devices estimated to approach towards the tens of billions, network operators have a formidable task ahead of them. The proliferation of cloud computing techniques has created a multitude of applications for network services deployments, and at the forefront is the adoption of Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV). Mobile network operators (MNO) have the opportunity to leverage these technologies so that they can enable the delivery of traditional networking functionality in cloud environments. The benefit of this is reductions seen in the capital and operational expenditures of network infrastructure. When going for NFV, how a Virtualised Network Function (VNF) is designed, implemented, and placed over physical infrastructure can play a vital role on the performance metrics achieved by the network function. Not paying careful attention to this aspect could lead to the drastically reduced performance of network functions thus defeating the purpose of going for virtualisation solutions. The success of mobile network operators in the 5G arena will depend heavily on their ability to shift from their old operational models and embrace new technologies, design principles and innovation in both the business and technical aspects of the environment. The primary goal of this thesis is to design, implement and evaluate the viability of data centre and cloud network infrastructure sharing use case. More specifically, the core question addressed by this thesis is how virtualisation of network functions in a shared infrastructure environment can be achieved without adverse performance degradation. 5G should be operational with high penetration beyond the year 2020 with data traffic rates increasing exponentially and the number of connected devices expected to surpass tens of billions. Requirements for 5G mobile networks include higher flexibility, scalability, cost effectiveness and energy efficiency. Towards these goals, Software Defined Networking (SDN) and Network Functions Virtualisation have been adopted in recent proposals for future mobile networks architectures because they are considered critical technologies for 5G. A Shared Infrastructure Management Framework was designed and implemented for this purpose. This framework was further enhanced for performance optimisation of network functions and underlying physical infrastructure. The objective achieved was the identification of requirements for the design and development of an experimental testbed for future 5G mobile networks. This testbed deploys high performance virtualised network functions (VNFs) while catering for the infrastructure sharing use case of multiple network operators. The management and orchestration of the VNFs allow for automation, scalability, fault recovery, and security to be evaluated. The testbed developed is readily re-creatable and based on open-source software
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