Optimizing horn solvers for network repair

Automatic program repair modifies a faulty program to make it correct with respect to a specification. Previous approaches have typically been restricted to specific programming languages and a fixed set of syntactical mutation techniques-e.g., changing the conditions of if statements. We present a more general technique based on repairing sets of unsolvable Horn clauses. Working with Horn clauses enables repairing programs from many different source languages, but also introduces challenges, such as navigating the large space of possible repairs. We propose a conservative semantic repair technique that only removes incorrect behaviors and does not introduce new behaviors. Our proposed framework allows the user to request the best repairs-it constructs an optimization lattice representing the space of possible repairs, and uses a novel local search technique that exploits heuristics to avoid searching through sub-lattices with no feasible repairs. To illustrate the applicability of our approach, we apply it to problems in software-defined networking (SDN), and illustrate how it is able to help network operators fix buggy configurations by properly filtering undesired traffic. We show that interval and Boolean lattices are effective choices of optimization lattices in this domain, and we enable optimization objectives such as modifying the minimal number of switches. We have implemented a prototype repair tool, and present preliminary experimental results on several benchmarks using real topologies and realistic repair scenarios in data centers and congested networks.

Growing concern for reduced power dissipation, cost and latency demands in next generation Data Centers (DC) motivates us to revisit header optimizations. Headers contribute to about 30–40% of DC traffic and is responsible for equal proportion of consumed power. This amounts to significant overhead on per byte transfer of payload. In the past, highly inflexible switches have limited the focus of header optimizations primarily on end-host or edge network routers. Further, strict compliance to protocols by switches/routers along the path have limited the segments of headers that can be optimized. Recent evolution of Software Defined Network (SDN) brings in several opportunities to control over switches. We believe that if SDN's finer handle to the switching devices is exploited, it can help deal with header information. In this paper, we exploit the capabilities of SDN and introduce a new functionality that can effectively replace the redundant and repetitive header information with shorter unique identity. We present Scissor that trims the headers lower in the protocol stack. As a replacement for routing, we introduce the notion of Flow-ID, where all packets belonging to a flow are identified using this unique Flow-ID. We leverage the capabilities of the SDN to dynamically allow switching devices to route the packets based on the Flow-IDs. Our approach of trimming header at the switching devices leaves the hosts unmodified making it highly adoptable for DC environment. We show that our approach can decrease switch fabric power consumption by a factor of 2.5 compared to an existing L2 switch and reduce the latency by about 30% for a significant fraction (around 30–50%) of the network traffic.

Healthcare experts and researchers have been promoting the need for IoT-based remote health monitoring systems that take care of the health of elderly people. However, such systems may generate large amounts of data, which makes the security and privacy of such data to become imperative. This paper studies the security and privacy concerns of the existing Healthcare Monitoring System (HMS) and proposes a reference architecture (security integration framework) for managing IoT-based healthcare monitoring systems that ensures security, privacy, and reliable service delivery for patients and elderly people to reduce and avoid health related risks. Our proposed framework will be in the form of state-of-the-art Security Platform, for HMS, using the emerging Software Defined Network (SDN) networking paradigm. Our proposed integration framework eliminates the dependency on specific Software or vendor for different security systems, and allows for the benefits from the functional and secure applications, and services provided by the SDN platform.

As the infrastructure of cloud computing and big data, data centers have been deployed widely. Then, how to make full use of the computing and storage resources in data centers will be the focus. Data center networks are considered important solution for the problem above, which include intra-data center and inter-data center networks. Both of them will depend on the optical networking due to its advantages, such as high bandwidth, low latency, and low energy consumption. Data center interconnected by flexi-grid optical networks is a promising scenario to allocate spectral resources for applications in a dynamic, tunable and efficient control manner. Compared with inter-data center, optical interconnect in intra-data center networks is a more pressing need and promising scenario to accommodate these applications in a dynamic, flexible and efficient manner. OpenFlow based Software Defined Networking (SDN) is considered as a good technology, which is very suitable for data center networks. This paper mainly focuses on the data center optical networks based on software defined networking (SDN), which can control the heterogeneous networks with unified resource interface. Architecture and experimental demonstration of OpenFlow-based optical interconnects in intra-data center Networks and OpenFlow-based flexi-grid optical networks for inter-data center are presented in the paper respectively. Some future works are listed finally.

Combining edge processing (at data capture site) with analysis carried out while data is enroute from the capture site to a data center offers a variety of different processing models. Such in-transit nodes include network data centers that have generally been used to support content distribution (providing support for data multicast and caching), but have recently started to offer user-defined programmability, through Software Defined Networks (SDN) capability, e.g., OpenFlow and Network Function Visualization (NFV). We demonstrate how this multi-site computational capability can be aggregated to support video analytics, with Quality of Service and cost constraints (e.g., latency-bound analysis). The use of SDN technology enables separation of the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage SDN capability to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. Using a number of scenarios, we demonstrate the benefits and limitations of this approach for video analysis, comparing this with the baseline scenario of undertaking all such analysis at a data center located at the core of the infrastructure.

New and emerging use cases, such as the interconnection of geographically distributed data centers (DCs), are drawing attention to the requirement for dynamic end-to-end service provisioning, spanning multiple and heterogeneous optical network domains. This heterogeneity is, not only due to the diverse data transmission and switching technologies, but also due to the different options of control plane techniques. In light of this, the problem of heterogeneous control plane interworking needs to be solved, and in particular, the solution must address the specific issues of multi-domain networks, such as limited domain topology visibility, given the scalability and confidentiality constraints. In this article, some of the recent activities regarding the Software-Defined Networking (SDN) orchestration are reviewed to address such a multi-domain control plane interworking problem. Specifically, three different models, including the single SDN controller model, multiple SDN controllers in mesh, and multiple SDN controllers in a hierarchical setting, are presented for the DC interconnection network with multiple SDN/ OpenFlow domains or multiple OpenFlow/ Generalized Multi-Protocol Label Switching (GMPLS) heterogeneous domains. In addition, two concrete implementations of the orchestration architectures are detailed, showing the overall feasibility and procedures of SDN orchestration for the end-to-end service provisioning in multi-domain data center optical networks.

The fundamental role of the software defined networks (SDNs) is to decouple the data plane from the control plane, thus providing a logically centralized visibility of the entire network to the controller. This enables the applications to innovate through network programmability. To establish a centralized visibility, a controller is required to discover a network topology of the entire SDN infrastructure. However, discovering a network topology is challenging due to: 1) the frequent migration of the virtual machines in the data centers; 2) lack of authentication mechanisms; 3) scarcity of the SDN standards; and 4) integration of security mechanisms for the topology discovery. To this end, in this paper, we present a comprehensive survey of the topology discovery and the associated security implications in SDNs. This survey provides discussions related to the possible threats relevant to each layer of the SDN architecture, highlights the role of the topology discovery in the traditional network and SDN, presents a thematic taxonomy of topology discovery in SDN, and provides insights into the potential threats to the topology discovery along with its state-of-the-art solutions in SDN. Finally, this survey also presents future challenges and research directions in the field of SDN topology discovery.

Data centers consume vast amounts of energy, making them a significant indirect source of carbon emissions. The use of renewable energy sources to power data centers is essential to reduce these emissions and is a key strategy to achieve zero-carbon goals. Redesigning data centers to integrate renewable energy has become increasingly important. One approach is to collocate data centers with renewable energy sources, and, when necessary, combine multiple multisite data centers into a single larger entity. We propose the Green Multisite Data Center (GMDC) network infrastructure architecture to replace expensive electrical power transmission lines with network connections. The proposed architecture improves GMDC resiliency by leveraging network features like all-active paths, multi-homing, redundancy, and load balancing capabilities. In addition, it offers a pay-as-you-go model for owners and investors in green data center facilities. The GMDC network is an energy-aware infrastructure that integrates renewable energy sources while mitigating the risks related to their time and location variability. The design prioritizes minimizing initial investment costs while ensuring long-term economic sustainability. In our solution, which combines Software-Defined Networking (SDN) with Ethernet VPN/Virtual eXtensible LAN (EVPN/VXLAN), we simulate the SDN forwarding layer to manage multisite inter-data center traffic. The primary focus is improving performance metrics, particularly total data transfer time, which is vital to establish network infrastructure across geographically dispersed data centers. This paper simulates the data plane and presents strategies for minimizing total workload migration times using Data Plane Development Kit (DPDK) libraries. Testing results indicate a 4.24% reduction in the overall workload migration time between green data centers in a multisite setup.

Software Defined Networking (SDN) and Network Function Virtualization (NFV) are transforming Data Center (DC), Telecom, and enterprise networking. The programmability offered by P4 enables SDN to be more protocol-independent and flexible. Data Centers are increasingly adopting SmartNICs (sNICs) to accelerate packet processing that can be leveraged to support packet processing pipelines and custom Network Functions (NFs). However, there are several challenges in integrating and deploying P4 based SDN control as well as host and sNIC-based programmable NFs. These include configuration and management of the data plane components (Host and sNIC P4 switches) for the SDN control plane and effective utilization of data plane resources. P4NFV addresses these concerns and provides a unified P4 switch abstraction framework to simplify the SDN control plane, reducing management complexities, and leveraging a host-local SDN Agent to improve the overall resource utilization. The SDN agent considers the network-wide, host, and sNIC specific capabilities and constraints. Based on workload and traffic characteristics, P4NFV determines the partitioning of the P4 tables and optimal placement of NFs (P4 actions) to minimize the overall delay and maximize resource utilization. P4NFV uses Mixed Integer Linear Programming (MILP) based optimization formulation and achieves up to 2. 5X increase in system capacity while minimizing the delay experienced by flows. P4NFV considers the number of packet exchanges, flow size, and state dependency to minimize the delay imposed by data transmission over PCI Express interface.

Hybrid software-defined networking (SDN) networks are intermediates between traditional networks and SDN networks. However, since the hybrid control planes are structurally different from the single control plane, updating the hybrid SDN networks lacks effective techniques. In this paper, by utilizing the segment routing (SR) mechanism to update hybrid SDN networks and formalize the application of SR during updates, we propose a cooperative consistency update algorithm that can coordinate SR, two-phase commit techniques. The algorithm is capable of preserving consistency with low overhead and excellent applicability. When a network requires updating, the algorithm leverages the SR mechanism to stitch a new path using the available path segments; thus, flows can use the new path immediately. If the update cannot be completed with the SR mechanism alone, the algorithm can utilize the two-phase commit technique to update the nodes through duplication. Notably, our experiments based on real topologies indicate that, compared to prior techniques, the proposed algorithm can greatly reduce the TCAM overhead.

Due to the need to agilely cope with more complex and virtualized applications, integrating Software-Defined Networking (SDN) switches and OpenStack software platform into the data center has becomes an important enabling technology. However, the SDN controller and OpenStack have their own management interface, such as Horizon, which may not be able to meet the user's requirement on managing both platforms in one data center. For example, although the deployment of switches of a data center usually follows a layering hierarchy, the Graphical User Interface (GUI) of management system of the SDN controller, such as Ryu, shows the topology of switches in a random and messy manner. Furthermore, tightly integration of SDN and OpenStack is required for agile and real-time management. Thus, in this paper, we design and implement a management system which tightly integrates SDN and OpenStack. In the proposed management system, the topology displays SDN switches, Open vSwitches (OVS), as well as virtual machines in hierarchy of multi-rooted trees. In order to display millions of virtual machines and thousands of switches, the GUI of the proposed management system provides a scalable view for users to zoom in or zoom out the topology. The proposed management system adopts modular design and provides web-based user interface and RESTful API for interoperability. The prototype of the proposed management system is shown to demonstrate the usability and scalability of the proposed system.

Network emulations are widely used for testing novel network protocols and routing algorithms in realistic scenarios. Up to now, there is no emulation tool that is able to emulate large software-defined data center networks that consist of several thousand nodes. Mininet is the most common tool to emulate Software-Defined Networks of several hundred nodes. We extend Mininet to span an emulated network over several physical machines, making it possible to emulate networks of several thousand nodes on just a handful of physical machines. This enables us to emulate, e.g., large data center networks. To test this approach, we additionally introduce a traffic generator for data center traffic. Since there are no data center traffic traces publicly available we use the results of two recent traffic studies to create synthetic traffic. We show the design and discuss some challenges we had in building our traffic generator. As a showcase for our work we emulated a data center consisting of 3200 hosts on a cluster of only 12 physical machines. We show the resulting workloads and the trade-offs involved.

Online assignment of non-SDN virtual network nodes to a physical SDN

Live migration is a key technique for virtual machine (VM) management in data center networks, which enables flexibility in resource optimization, fault tolerance, and load balancing. Despite its usefulness, the live migration still introduces performance degradations during the migration process. Thus, there has been continuous efforts in reducing the migration time in order to minimize the impact. From the network's perspective, the migration time is determined by the amount of data to be migrated and the available bandwidth used for such transfer. In this paper, we examine the problem of how to schedule the migrations and how to allocate network resources for migration when multiple VMs need to be migrated at the same time. We consider the problem in the Software-defined Network (SDN) context since it provides flexible control on routing. More specifically, we propose a method that computes the optimal migration sequence and network bandwidth used for each migration. We formulate this problem as a mixed integer programming, which is NP-hard. To make it computationally feasible for large scale data centers, we propose an approximation scheme via linear approximation plus fully polynomial time approximation, and obtain its theoretical performance bound. Through extensive simulations, we demonstrate that our fully polynomial time approximation (FPTA) algorithm has a good performance compared with the optimal solution and two state-of-the-art algorithms. That is, our proposed FPTA algorithm approaches to the optimal solution with less than 10% variation and much less computation time. Meanwhile, it reduces the total migration time and the service downtime by up to 40% and 20% compared with the state-of-the-art algorithms, respectively.

SummaryWith the development of cloud computing and big data, the internal communication business in data center has increased dramatically, and then the traffic in data center has also significantly increased. The bandwidth of data center is difficult to meet the bandwidth requirements of those intensive applications, and data center is facing a risk of network congestion. Under the background of the development of network intelligence, software‐defined network (SDN) should demonstrate its intelligence as a future network architecture. In this paper, we introduce reinforcement learning into the SDN data center to implement congestion control based on flow. We improve the Q‐learning and Sarsa algorithms and propose two methods of congestion control based on the algorithms. Test results show that these two congestion control methods can control congestion effectively. And Sarsa method has a better performance of link utilization. The average link utilization of the Sarsa method is 2.4% higher than the Q‐learning method and is 4.48% higher than the on‐demand method.