Gianluca Davoli

Bio: Gianluca Davoli is an academic researcher from University of Bologna. The author has contributed to research in topics: Service (systems architecture) & Chaining. The author has an hindex of 7, co-authored 17 publications receiving 112 citations. Previous affiliations of Gianluca Davoli include Istituto Nazionale di Fisica Nucleare.

Intent-based management and orchestration of heterogeneous openflow/IoT SDN domains

Abstract: One of the main challenges in delivering end-toend service chains across multiple Software Defined Networking (SDN) and Network Function Virtualization (NFV) domains is to achieve unified management and orchestration functions. A very critical aspect is the definition of an open, vendoragnostic, and interoperable northbound interface (NBI) that should be as abstracted as possible from domain-specific data and control plane technologies. In this paper we propose a reference architecture and an intent-based NBI for end-to-end service orchestration across multiple technological domains. In particular, we consider the use case of an Internet of Things (IoT) infrastructure deployment and the corresponding cloudbased data collection, processing, and publishing services with quality differentiation.We also report the experimental validation of the proposed architecture over a heterogeneous OpenFlow/IoT SDN test bed.

Intent-based service management for heterogeneous software-defined infrastructure domains

Abstract: Summary One of the main challenges in delivering end‐to‐end service chains across multiple software‐defined networking (SDN) and network function virtualization (NFV) domains is to achieve unified ...

Enabling Industrial IoT as a Service with Multi-Access Edge Computing

Abstract: Industrial IoT coupled with emerging cloud computing architectures shows high potential in transforming the way industrial processes are managed and carried out. This potential can be further enhanced by enabling on-demand deployment of IoT services located very close to the factory premises. This article proposes an architecture, based on the ETSI multi-access edge computing (MEC) framework, for the automated deployment of Industrial IoT applications “as a service,” taking advantage of proximity computing platforms such as edge and fog environments. A proof-of-concept implementation is reported to demonstrate that transforming Industrial IoT applications into MEC-based services running over multiple technological domains is not only feasible, but can achieve full service deoployment in a matter of a few seconds.

Implementation of service function chaining control plane through OpenFlow

Abstract: This papers describes a proof-of-concept implementation of the Service Function Chaining Control Plane, exploiting the IETF Network Service Header approach. The proposed implementation combines the OpenFlow protocol to control and configure the network nodes and the NSH method to adapt the service requirements to the transport technology. The manuscript shows that the result of this combination is a very general architecture that may be used to implement any sort of Service Function Chain with great flexibility.

Demonstration of Latency-Aware and Self-Adaptive Service Chaining in 5G/SDN/NFV infrastructures

Abstract: This paper will demonstrate an orchestration system for 5G infrastructure supporting latency-minimized and self-adaptive service chaining over geographically distributed edge clouds interconnected through SDN. The demo will show an orchestration system that comprises dynamic virtual function selection and intent-based traffic steering control functionalities to provide optimized service chains in terms of end-to-end latency and adjustable with respect to the context (e.g., service dynamics, network status).

Test Driven Development: By Example

Abstract: From the Book: “Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons: Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). In Test-Driven Development, you: Write new code only if you first have a failing automated test.Eliminate duplication. Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy The two rules imply an order to the tasks ofprogramming: 1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first 2. Green—make the test work quickly, committing whatever sins necessary in the process 3. Refactor—eliminate all the duplication created in just getting the test to work Red/green/refactor. The TDD’s mantra. Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications: If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage. Courage Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects: Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and: Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet. The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test. Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately. That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress. There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code. Once you are finished reading this book, you should be ready to: Start simplyWrite automated testsRefactor to add design decisions one at a time This book is organized into three sections. An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples. I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference. Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read. A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

An In-Depth Analysis of IoT Security Requirements, Challenges, and Their Countermeasures via Software-Defined Security

Abstract: Internet of Things (IoT) is transforming everyone’s life by providing features, such as controlling and monitoring of the connected smart objects. IoT applications range over a broad spectrum of services including smart cities, homes, cars, manufacturing, e-healthcare, smart control system, transportation, wearables, farming, and much more. The adoption of these devices is growing exponentially, that has resulted in generation of a substantial amount of data for processing and analyzing. Thus, besides bringing ease to the human lives, these devices are susceptible to different threats and security challenges, which do not only worry the users for adopting it in sensitive environments, such as e-health, smart home, etc., but also pose hazards for the advancement of IoT in coming days. This article thoroughly reviews the threats, security requirements, challenges, and the attack vectors pertinent to IoT networks. Based on the gap analysis, a novel paradigm that combines a network-based deployment of IoT architecture through software-defined networking (SDN) is proposed. This article presents an overview of the SDN along with a thorough discussion on SDN-based IoT deployment models, i.e., centralized and decentralized. We further elaborated SDN-based IoT security solutions to present a comprehensive overview of the software-defined security (SDSec) technology. Furthermore, based on the literature, core issues are highlighted that are the main hurdles in unifying all IoT stakeholders on one platform and few findings that emphases on a network-based security solution for IoT paradigm. Finally, some future research directions of SDN-based IoT security technologies are discussed.

A Survey on Intent-Driven Networks

Abstract: Software defined network and network function visualization enhance the network flexibility and management agility, which increase network fragility and complexity. However, the vast majority of network parameters are manually configured, which makes the configuration failures still inevitable. Future networks should be self-configuring, self-managing, and self-optimizing. Intent-driven network (IDN) is a self-driving network that uses decoupling network control logic and closed-loop orchestration techniques to automate application intents. At present, a unified definition of IDN has not yet been presented, and the research background and current status of IDN are not clear. Considering the emerging applications and research of IDN, in this article, we survey existing technologies, clarify definitions, and summarize features for IDN. Specifically, we discuss the basic architecture and key technologies of IDN. In addition, diversity gains and challenges are analyzed briefly. Finally, some future work is highlighted and wider applications of IDN are provided for further research.

Intent based network configuration

Abstract: An embodiment device includes a network interface, a non-transitory computer readable medium having executable instructions thereon, and a processor coupled to the network interface and the computer readable medium. The executable instructions cause the processor to receive an Intent representing requirements for data traffic on a network having a plurality of endpoints, with the Intent specifying one or more traffic parameters identifying one or more of the endpoints, and and at least one first service. The executable instructions also include instructions to generate one or more networking commands identifying the at least one first service according to the traffic parameters, send the networking commands to one or more network devices on the network and cause the network devices to perform the at least one first service on a first data transmission in response to parameters of the data transmission satisfying the one or more networking commands.