Jiayi Wang

Bio: Jiayi Wang is an academic researcher from Saint Louis University. The author has contributed to research in topics: Engineering & Network management. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

A Behavior-Driven Approach to Intent Specification for Software-Defined Infrastructure Management

Abstract: One of the goals of Software-Defined Networking (SDN) is to allow users to specify high-level policies into lower level network rules. Managing a network and decide what policy set is appropriate requires, however, expertise and low level know-how. An emerging SDN paradigm is to allow higher-level network level decisions wishes in the form of "intents". Despite its importance in simplifying network management, intent specification is not yet standardized. In this work, we propose a northbound interface (NBI) for intent declaration, based on Behavior-Driven Development. In our approach, intents are specified in plain English and translated by our system into pre-compiled network policies, that are in turn, converted into low-level rules by the software-defined infrastructure e.g. an SDN controller. We demonstrated our behavior-driven approach with two practical use cases: service function chaining deployed on OpenStack, supported by both ONOS and Ryu controllers, and dynamic firewall programming. We also measured the overhead and response time of our NBI. We believe that our approach is far more general and paves the way for a more expressive and simplified northbound interface for intent-driven networking.

Strategic Defense of Feedback-Controlled Parallel Queues against Reliability and Security Failures

Abstract: Parallel traffic service systems such as transportation, manufacturing, and computer systems typically involve feedback control (e.g., dynamic routing) to ensure stability and to improve throughput. Such control relies on connected cyber components for computation and communication. These components are susceptible to random malfunctions and malicious attacks, which motivates the design of strategic defense that are both traffic-stabilizing and cost-efficient under reliability/security failures. In this paper, we consider a parallel queuing system with dynamic routing subject to such failures. For the reliability setting, we consider an infinite-horizon Markov decision process where the system operator strategically activates the protection mechanism upon each job arrival based on the traffic state. We use Hamilton-Jacobi-Bellman equation to show that the optimal protection strategy is a deterministic threshold policy. For the security setting, we extend the model to an infinite-horizon stochastic game where the attacker strategically manipulates routing assignment. We show that a Markov perfect equilibrium of this game always exists and that both players follow a threshold strategy at each equilibrium. For both settings, we also consider the stability of the traffic queues in the face of failures. Finally, we develop approximate dynamic programming algorithms to compute the optimal/equilibrium policies and present numerical examples for validation and illustration.

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.

Intent-based zero-touch service chaining layer for software-defined edge cloud networks

Abstract: Edge Computing, along with Software Defined Networking and Network Function Virtualization, are causing network infrastructures to become as distributed clouds extended to the edge with services provided as dynamically established sequences of virtualized functions (i.e., dynamic service chains) thereby elastically addressing different processing requirements of application data flows. However, service operators and application developers are not inclined to deal with descriptive configuration directives to establish and operate services, especially in case of service chains. Intent-based Networking is emerging as a novel approach that simplifies network management and automates the implementation of network operations required by applications. This paper presents an intent-based zero-touch service chaining layer that provides the programmable provision of service chain paths in edge cloud networks. In addition to the dynamic and elastic deployment of data delivery services, the intent-based layer offers an automated adaptation of the service chains paths according to the application’s goals expressed in the intent to recover from sudden congestion events in the SDN network. Experiments have been carried out in an emulated network environment to show the feasibility of the approach and to evaluate the performance of the intent layer in terms of network resource usage and adaptation overhead.

When NLP meets SDN : an application to Global Internet eXchange Network

Abstract: Software-Defined Networking (SDN) and its extension Intent-Based Networking (IBN) are network paradigms that enable dynamic, programmatically efficient network configuration. IBN allows network operators to express an outcome or business objective without the low-level configurations necessary to program the network to achieve these demands. Existing research proposals for IBN introduce several systems to translate users intents into network infrastructure configurations. Despite the positive aspects of these proposals, they still suffer from many drawbacks. Some require users to learn a new intent definition language. Some others may lack the appropriate grammar to make these frameworks recognize the intent correctly. In this paper, we introduce a framework leveraging the capabilities of Natural Language Processing (NLP) for network management from an operator utterances. In order to understand natural language, our framework uses the sequence-to-sequence (seq2seq) learning model based on recurrent neural networks (LSTM). The model has been improved by using word embedding and user feedback. As a proof of concept, we implement our framework for network management in a Global Internet eXchange Network and evaluate its practicality regarding NLP accuracy and network performance.

SLA Management in Intent-Driven Service Management Systems: A Taxonomy and Future Directions

Abstract: Traditional, slow and error-prone human-driven methods to configure and manage Internet service requests are proving unsatisfactory. This is due to an increase in Internet applications with stringent quality of service (QoS) requirements. Which demands faster and fault-free service deployment with minimal or without human intervention. With this aim, intent-driven service management (IDSM) has emerged, where users express their service level agreement (SLA) requirements in a declarative manner as intents. With the help of closed control-loop operations, IDSM performs service configurations and deployments, autonomously to fulfill the intents. This results in a faster deployment of services and reduction in configuration errors caused by manual operations, which in turn reduces the SLA violations. This paper is an attempt to provide a systematic review of How the IDSM systems manage and fulfill the SLA requirements specified as intents. As an outcome, the review identifies four intent management activities which are performed in a closed-loop manner. For each activity, a taxonomy is proposed and used to compare the existing techniques for SLA management in IDSM systems. A critical analysis of all the considered research articles in the review and future research directions are presented in the conclusion.

NLP4: An Architecture for Intent-Driven Data Plane Programmability

Abstract: Translating high-level policies to lower-level network rules is one of the main goals of control or data plane network programmability. To further abstract requirements and propel automation in networking, several industries have proposed the paradigm of “network intent”. However, the translation from intents to low-level policies is considered critical to program data planes and other network elements, especially when dealing with P4-enabled switches. In this paper, we present NLP4, an architecture that helps translate intents, in the form of human language, into data-plane programs, in the form of P4 rules. In particular, NLP4 uses Natural Language Processing (NLP) techniques to translate high-level human-language intents, a MultiLayer Perceptron (MLP) model for processing the NLP output and converting it into mid-level policy. An API then uses this information, which separates the intent from the network to generate commands readable by P4-enabled switches. Our initial prototype on a network emulator validates our architecture for a specific case: load profiling, demonstrating how even users with limited P4 expertise may customize their networks by merely specifying intents.