Design, Implementation, and Evaluation of Join and Split Strategy for Transmission control protocol running on Software Defined Networks

Software Defined Networks (SDN)-enabled switches of today can be empowered to intelligently forward as well as elastically steer the network traffic. In this work, we focus on developing a SDN-based framework to provide improved delivery performance (of applications) in the network. This dissertation proposed a new TCP join and split proxy on SDN platform. The proposed framework allowed part of TCP (Transmission Control Protocol) optimization to migrate from the application server to the proxy. Therefore, with a control plane built between SDN controller and proxy, the SDN controller can further improve the TCP delivery performance. The proxy (join-proxy) joins all TCP flows at the beginning of the shared path into one long TCP flow. At the end of the shared path, the proxy (split-proxy) splits the long flow for each joined client with the same TCP session state. With the help of centralized controller of SDN and customized SDN switch, the new design simplifies the TCP session synchronization between proxies. Also, this dissertation developed Linked-ACK ((Acknowledgement) to maintain the end-to-end semantic and limit the buffer size in each proxy by coupling the ACK of three TCP flows separated by the join and split proxy. At the last, this dissertation shows that the proposed proxy can well integrate with wireless network and MPTCP (Multi-Path TCP) proxy [1] The extensions of the proposed TCP Join and Split platform are applied to Smart Grid network for improving fairness, WiFi network for reducing gaming traffic delay, and Data Center network for addressing Virtual Machine (VM) live migration problem. First, the proposed TCP Join and Split platform can be applied to Smart Grid network to provide better fairness on the application layer. The latest research in Smart Grid communications has advocated the aggregation of multiple traffic flows in order to achieve an improved throughput. While aggregation improves the overall throughput, the individual flows still suffer from unfair throughput performance. As a result, the enablers for time sensitive Smart Grid services, such as load-shedding which requires a timely report of data, are mostly affected. This dissertation proposed a novel SDN-based framework to provide fairness among smart-meters (SMs) through flow aggregation and scheduling. By exploring the SDN’s flow-level manageability features, for the first time in this paper, we present an implementation-based architecture to perform effective aggregation-and-scheduling of traffic flows. The proposed framework ensures fairness (among the smart-meters) as well as improve the throughput performance. Our extensive experimental results validate the efficacy of our proposed framework. Second, the proposed TCP Join and Split platform can be applied to WiFi network to reduce the gaming traffic delay. WiFi users typically expect different performance requirements for various types of applications. For instance, users expect 'better and consistent throughput' for Internet video consumption, and 'minimal delay' for local network gaming applications. The wireless access substrate (at the consumer-end), typically being the bottleneck in these networks, causes different users (in the same WiFi coverage) to experience unfair and fluctuating network performance. To combat such unfair situations, we need approaches to effectively control and steer the applications’ traffic in the shared WiFi medium. However, a network that deals with a crowd or private end-users (such as gaming multiplayers or the Internet content distributors), encounters a major challenge in controlling the traffic without involvement or modification at the end-host application devices. In this dissertation, we propose a SDN-based seamless traffic steering and control strategy in order to provide effective application-specific delivery services, such as reduced delay (for gaming traffic) and improved throughput (for video consumption). Unlike simulation-based solutions, our approach is production-ready, as we have implemented our framework on a real network testbed environment. With extensive performance study and sufficient mathematical insight, we demonstrate the prowess of our proposed framework. Last but not the least, the proposed TCP Join and Split platform can be applied to Data Center network to optimize the VM live migration. With the growth of data volumes and a variety of Internet applications, virtualization has become commonplace in modern data centers and an effective solution to provide better management flexibility, lower cost, scalability, better resources utilization, and energy efficiency. One of the powerful features provided by virtualization is Virtual Machine (VM) live migration, which facilitates moving workloads within the infrastructure with negligible downtime and minimal impact on workload. However, the performance of running applications is likely to be negatively affected during a live VM migration. The objective of this paper is to optimize the total performance degradation of concurrent VM live migration in the data center network by exploiting the SDN platform. The problem is modeled using mixed integer linear programming(MILP) for VM live migration with a fixed path and VM live migration with path selection. To provide a practical optimization, the greedy algorithm is proposed. Numerical study results show that a significant decrease occur in performance degradation in MILP model and greedy algorithm when the number of VMs increases. The proposed greedy algorithm cannot yield the optimum solution as the problem become harder, but it provides better solution than MILP model in terms of the time constrain exhibited in case of large problems

Progressive Network Deployment, Performance, and Control with Software-defined Networking

The inflexible nature of traditional computer networks has led to tightly-integrated systems that are inherently difficult to manage and secure. New designs move low-level network control into software creating software-defined networks (SDN). Augmenting an existing network with these enhancements can be expensive and complex. This research investigates solutions to these problems. It is hypothesized that an add-on device, or shim could be used to make a traditional switch behave as an OpenFlow SDN switch while maintaining reasonable performance. A design prototype is found to cause approximately 1.5% reduction in throughput for one ow and less than double increase in latency, showing that such a solution may be feasible. It is hypothesized that a new design built on event-loop and reactive programming may yield a controller that is higher-performing and easier to program. The library node-openflow is found to have performance approaching that of professional controllers, however it exhibits higher variability in response rate. The framework rxdn is found to exceed performance of two comparable controllers by at least 33% with statistical significance in latency mode with 16 simulated switches, but is slower than the library node-openflow or professional controllers (e.g., Libfluid, ONOS, and NOX). Collectively, this work enhances the tools available to researchers, enabling experimentation and development toward more sustainable and secure infrastructur

Local data plane event handling in software-defined networking

Software-defined networking is a rising technology for handling traffic in large networks. To ensure a high flexibility, software-defined networking separates the control plane from the data plane. The data plane forwards packets while the control plane defines the forwarding rules. In case packets or events need to be processed in a way that is not covered by these rules, the packets or events have to be forwarded to the control plane. This imposes latency to the processing of network traffic and events. This master’s thesis proposes a concept for generic handling of local events in software-defined networks, using local data plane applications directly on the switch devices. During this thesis, a concept for local data plane event handling is developed and implementation details are discussed. The evaluation shows that processing of events directly on the data plane improves network performance and saves resources on the switch devices.Software-defined Networking ist eine aufkommende Technologie zur Verarbeitung von Netzwerkverkehr in großen Netzwerken. Um eine höhere Flexibilität zu ermöglichen, sieht Software-defined Networking eine Trennung der Kontroll- und der Weiterleitungsschicht vor. Die Weiterleitungsschicht übernimmt das Verteilen von Paketen während die Kontrollschicht definiert, mit welchen Regeln dies zu erfolgen hat. Sofern man Pakete oder Ereignisse verarbeiten möchte, welche nicht mit diesen Regeln ausgedrückt werden können, müssen diese Pakete und Ereignisse an die Kontrollschicht weitergeleitet werden. Durch Latenzen in Netzwerken wird jedoch die benötigte Verarbeitungszeit der Ereignisse und Pakete erhöht. In dieser Masterarbeit wird ein Konzept zur lokalen Behandlung von Ereignissen in der Weiterleitungsschicht beschrieben. Diese lokalen Ereignisse werden mittels Anwendungen auf Netzwerkswitches behandelt. Weiterhin wird auf die Details der Implementierung eingegangen. Die Evaluation zeigt, dass das lokale Verarbeiten von Ereignissen die Performanz erhöht und zusätzlich die Ressourcen auf den Netzwerkswitches schont

View on 5G Architecture: Version 2.0

The 5G Architecture Working Group as part of the 5GPPP Initiative is looking at capturing novel trends and key technological enablers for the realization of the 5G architecture. It also targets at presenting in a harmonized way the architectural concepts developed in various projects and initiatives (not limited to 5GPPP projects only) so as to provide a consolidated view on the technical directions for the architecture design in the 5G era. The first version of the white paper was released in July 2016, which captured novel trends and key technological enablers for the realization of the 5G architecture vision along with harmonized architectural concepts from 5GPPP Phase 1 projects and initiatives. Capitalizing on the architectural vision and framework set by the first version of the white paper, this Version 2.0 of the white paper presents the latest findings and analyses with a particular focus on the concept evaluations, and accordingly it presents the consolidated overall architecture design

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas

Linux XIA: an interoperable meta network architecture

With the growing number of clean-slate redesigns of the Internet, the need for a medium that enables all stakeholders to participate in the realization, evaluation, and selection of these designs is increasing. We believe that the missing catalyst is a meta network architecture that welcomes most, if not all, clean-state designs on a level playing field, lowers deployment barriers, and leaves the final evaluation to the broader community. This thesis presents the eXpressive Internet (Meta) Architecture (XIA), itself a clean-slate design, as well as Linux XIA, a native implementation of XIA in the Linux kernel, as a candidate. As a meta network architecture, XIA is highly flexible, leaving stakeholders to choose an expressive set of network principals to instantiate a given network architecture within the XIA framework. Central to XIA is its novel, non-linear network addressing format, from which derive key architectural features such as evolvability, intrinsically secure identifiers, and a low degree of principal isolation. XIP, the network layer protocol of XIA, forwards packets by navigating these structured addresses and delegating the decision-making and packet processing to appropriate principals, accordingly. Taken together, these mechanisms work in tandem to support a broad spectrum of interoperable principals. We demonstrate how to port four distinct and unrelated network architectures onto Linux XIA, none of which were designed for interoperability with this platform. We then show that, notwithstanding this flexibility, Linux XIA's forwarding performance remains comparable to that of the more mature legacy TCP/IP stack implementation. Moreover, the ported architectures, namely IP, Serval, NDN, and ANTS, empower us to present a deployment plan for XIA, to explore design variations of the ported architectures that were impossible in their original form due to the requirement of self-sufficiency that a standalone network architecture bears, and to substantiate the claim that XIA readily supports and enables network evolution. Our work highlights the benefits of specializing network designs that XIA affords, and comprises instructive examples for the network researcher interested in design and implementation for future interoperability

Extending Provenance For Deep Diagnosis Of Distributed Systems

Diagnosing and repairing problems in complex distributed systems has always been challenging. A wide variety of problems can happen in distributed systems: routers can be misconfigured, nodes can be hacked, and the control software can have bugs. This is further complicated by the complexity and scale of today’s distributed systems. Provenance is an attractive way to diagnose faults in distributed systems, because it can track the causality from a symptom to a set of root causes. Prior work on network provenance has successfully applied provenance to distributed systems. However, they cannot explain problems beyond the presence of faulty events and offer limited help with finding repairs. In this dissertation, we extend provenance to handle diagnostics problems that require deeper investigations. We propose three different extensions: negative provenance explains not just the presence but also the absence of events (such as missing packets); meta provenance can suggest repairs by tracking causality not only for data but also for code (such as bugs in control plane programs); temporal provenance tracks causality at the temporal level and aims at diagnosing timing-related faults (such as slow requests). Compared to classical network provenance, our approach tracks richer causality at runtime and applies more sophisticated reasoning and post-processing. We apply the above techniques to software-defined networking and the border gateway protocol. Evaluations with real world traffic and topology show that our systems can diagnose and repair practical problems, and that the runtime overhead as well as the query turnarounds are reasonable

Foundations for practical network verification

Computer networks are large and complex and the often manual process of configuring such systems is error-prone, leading to network outages and breaches. This has ignited research into network verification tools that given a set of operator intents, automatically check whether the configured network satisfies the intents. In this dissertation, we argue that existing works in this area have important limitations that prevent their widespread adoption in the real world. We set to address these limitations by revisiting the main aspects of network verification: verification framework, intent specification, and network modeling. First, we develop #PEC, a symbolic packet header analysis framework that resolves the tension between expressiveness and efficiency in previous works. We provide an extensible library of efficient match-types that allows encoding and analyzing more types of forwarding rules (e.g. Linux iptables) compared to most previous works. Similar to the state-of-the-art, #PEC partitions the space of packet headers into a set of equivalence classes (PECs) before the analysis. However, it uses a lattice-based approach to do so, refraining from using computationally expensive negation and subtraction operations. Our experiments with a broad range of real-world datasets show that #PEC is 10× faster than similarly expressive state-of-the-art. We also demonstrate how empty PECs in previous works lead to unsound/incomplete analysis and develop a counting-based method to eliminate empty PECs from #PEC that outperforms baseline approaches by 10 − 100×. Next, we note that network verification requires formal specifications of the intents of the network operator as a starting point, which are almost never available or even known in a complete form. We mitigate this problem by providing a framework to utilize existing low-level network behavior to infer the high-level intents. We design Anime, a system that given observed packet forwarding behavior, mines a compact set of possible intents that best describe the observations. Anime accomplishes this by applying optimized clustering algorithms to a set of observed network paths, encoded using path features with hierarchical values that yield a way to control the precision-recall tradeoff. The resulting inferred intents can be used as input to verification/synthesis tools for continued maintenance. They can also be viewed as a summary of network behavior, and as a way to find anomalous behavior. Our experiments, including data from an operational network, demonstrate that Anime produces higher quality (F-score) intents than past work, can generate compact summaries with minimal loss of precision, is resilient to imperfect input and policy changes, scales to large networks, and finds actionable anomalies in an operational network. Finally, we turn our attention to modeling networking devices. We envision basing data plane analysis on P4 as the modeling language. Unlike most tools, we believe P4 analysis must be based on a precise model of the language rather than its informal specification. To this end, we develop a formal operational semantics of the P4 language during the process of which we have identified numerous issues with the design of the language. We then provide a suite of formal analysis tools derived directly from our semantics including an interpreter, a symbolic model checker, a deductive program verifier, and a program equivalence checker. Through a set of case studies, we demonstrate the use of our semantics beyond just a reference model for the language. This includes applications for the detection of unportable code, state-space exploration, search for bugs, full functional verification, and compiler translation validation

Software-Defined Networking: A Comprehensive Survey

peer reviewedThe Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this - ew paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms—with a focus on aspects such as resiliency, scalability, performance, security, and dependability—as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment