Reducing Inconsistency between Software-Defined Networking Controllers

Software-defined networks (SDNs) are novel networking architectures that decouple the network control and forwarding functions from the data plane. Unlike traditional networking, the control logic of SDNs is implemented in a logically centralized controller which provides a global network view and open programming interface to the applications. While SDNs have become a hot topic among both academia and industry in recent years, little attention has been paid on the security aspect. In this paper, we introduce a novel attack, namely, packet injection attack, in SDNs. By maliciously injecting manipulated packets into SDNs, attackers can affect the services and networking applications in the control plane, and largely consume the resources in the data plane. The consequences could be the disruption of applications built on the top of the topology manager service and rest API, as well as a huge consumption of network resources, such as the bandwidth of the OpenFlow channel. To defend against the packet injection attack, we present PacketChecker, a lightweight extension module on SDN controllers to effectively detect and mitigate the flooding of falsified packets. We implement a prototype of PacketChecker in floodlight controller and conduct experiments to evaluate the efficiency of the defense mechanism. The evaluation shows that the PacketChecker module can effectively mitigate the attack with a minor overhead to the SDN controller.

Software-Defined Networking (SDN) has been proposed as a promising way for its centralized network control and management. However, the latest SDN research focuses on smaller network environments such as data centers and enterprises, which easily lead to single point of failure and unbalanced network load in large-scale network environments. One effective way to solve this problem is to establish a standardized mechanism between network entities such as data centers, enterprises and Internet service providers (ISPs). In this paper, we propose WECAN, an efficient West-East Control Associated Network for enabling communication between different SDN entities. WECAN has three complementary modules: Network Information Collection (NIC) module, Cross-domain Management (CDM) module and Controller Selection Management(CSM) module. NIC collects network information from a different set of controllers and generates a domain-wide network view. CDM collects domain information from other domains to generate a global network view. Base on the domain-wide network view and global network view, CSM selects the most efficient controller for each network flow in the network. To test WECAN, we develop a prototype system. Our experimental results show that WECAN can effectively control network entities to communicate, and WECAN has greatly improved network latency, network throughput and network reliability compared to a single controller-controlled network. Moreover, WECAN is very easy to use.

Software Defined Networking (SDN) is a new networking paradigm which decouples control plane from the forwarding plane. Even though the control plane is assumed to be logically centralized, multiple physical controllers are essential to handle the issues such as fault tolerance and load balancing, especially in wireless networks. However, the physical constraints restrict the control plane to have a global network view among the controllers, which leads to the generation of inconsistent flow-rules and, thereby, a flow-deadlock. In this paper, we identify the inconsistencies resulting from controller mismatch in a flow-path and resolve them using packet_in relay mechanism through east-west interface. We modify Optimized Link State Routing (OLSR) protocol to advertise the switch information to controllers. The performance of our scheme is tested using a wireless mesh network test-bed consisting of static and mobile nodes. The results show that our scheme provides delivery ratio greater than 0.8 in the presence of network view inconsistencies at the controller nodes, with a slight increase in controller response time.

Efficient switch clustering for distributed controllers of OpenFlow network with bi-connectivity

Service composition is an aggregate of services often leveraged to automate the enterprise business processes. While Service Oriented Architecture (SOA) has been a forefront of service composition, services can be realized as efficient distributed and parallel constructs such as MapReduce, which are not typically exploited in service composition. With the advent of Software-Defined Networking (SDN), global view and control of the entire network is made available to the networking controller, which can further be leveraged in the application level. This paper presents FIRM, an approach for Software-Defined Service Composition by leveraging SDN and MapReduce. FIRM comprises Find, Invoke, Return, and Manage, as the core procedures in achieving a QoS-Aware Service Composition.

Software-Defined Networking (SDN) is now a well-established approach in 5G, Internet of Things (IoT) and Cloud Computing. The primary idea behind its immense popularity is the separation of its underlying intelligence from the data-carrying components like routers and switches. The intelligence of the SDN-based networks lies in the central point, popularly known as the SDN controller. It is the central control hub of the SDN-based network, which has full privileges and a global view over the entire network. Providing security to SDN controllers is one such important task. Whenever one wishes to implement SDN into their data center or network, they are required to provide the website to SDN controllers. Several attacks are becoming a hurdle in the exponential growth of SDN, and among all one such attack is a Distributed Denial of Service (DDoS) attack. In a couple of years, several new SDN controllers will be available. Among many, Open Networking Operating System (ONOS) and OpenDayLight (ODL) are two popular SDN controllers laying the foundation for many other controllers. These SDN controllers are now being used by numerous businesses, including Cisco, Juniper, IBM, Google, etc. In this paper, vulnerability analysis is carried out against DDoS attacks on the latest released versions of both ODL and ONOS SDN controllers in real-time cloud data centers. For this, we have considered distributed SDN controllers (located at different locations) on two different clouds (AWS and Azure). These controllers are connected through the Internet and work on different networks. DDoS attacks are bombarded on the distributed SDN controllers, and vulnerability is analyzed. It was observed with experimentation that, under five different scenarios (malicious traffic generated), ODL-3 node cluster controller had performed better than ONOS. In these five different scenarios, the amount of malicious traffic was incregradually increased. It also observed that, in terms of disk utilization, memory utilization, and CPU utilization, the ODL 3-node cluster was way ahead of the SDN controller.

The worldwide expansion of the network devices led to difficulties with network management and routing mechanisms related to dynamic adapting to the network needs. SDN provides a centralized global network view, maintenance, and management of network devices with vendor-specific configurations. Its main focus is routing the network traffic, load balancing the network, and improving the QoS. This paper proposes a Simple Routing algorithm with Link Discovery between source and destination hosts in SDN networks.

Failures are inevitable in an operational network. They can happen anytime in different sizes and components of a network. They impact the network economics regarding CAPEX (Capital Expenditure), OPEX (Operational Expenditure), revenue lost due to service provisioning cut and so on. In order to mitigate the damages resulting from these failures, reactions of network architectures and designs are crucial for the future of the network. Recently, Software Defined Networking (SDN) has got the attention of researchers from both academia and industry as a means in order to increase network availability and reliability due to features, such as centralized automated control and global network view, it promises in networking. To this end, we explore the effects of programmable network architectures, i.e. SDN technology, and traditional network architectures, i.e. MPLS (Multiprotocol Label Switching) technology, on network economics by exploiting Number of Satisfied Service Requests and our predefined Unit Service Cost Scalability metrics under network failure scenarios: i) a random single data plane link failure and ii) a random controller (i.e. control plane) failure. To the best of our knowledge, this study is the first to consider a comparison of a programmable network architecture, i.e. SDN, along with different control plane models, Centralized (Single) Control Plane (CCP), Distributed (Flat) Control Plane (DCP), and Hierarchical Control Plane (HCP), and a non-programmable network architecture, i.e. MPLS, regarding network economics in case of network failures.

Diabetic kidney disease (DKD) has become the major contributor to end-stage renal disease with high incidence and mortality. The functional roles and exact mechanisms of long noncoding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) network in DKD are still largely unknown. This study sought to discover novel potential biomarkers and ceRNA network for DKD.The candidate differentially expressed genes (DEGs), lncRNAs and microRNAs (miRNAs) in human glomerular and tubular tissues derived from Gene Expression Omnibus database were systematically selected and analyzed. Functional enrichment analysis and protein-protein interaction network analysis were conducted to identify hub genes and reveal their regulatory mechanisms involved in DKD. Following this, the integrated ceRNA network was constructed by bioinformatics methods.A total of 164 DEGs, 6 lncRNAs and 18 miRNAs correlated with DKD were finally filtered and identified. It is noteworthy that the global lncRNA-associated ceRNA network related to DKD was constructed, among which lnc-HIST2H2AA4-1, VCAN-AS1 and MAGI2-AS1 were identified as the 3 key lncRNAs, and VCAN, FN1, CCL2, and KNG1 were identified as the predominant genes. Consistent with that observed in the training set, 3 of the key genes also showed significant differences in the 2 validation datasets. Integrating with functional enrichment analysis results, these key genes in the ceRNA network were mainly enriched in the immune and inflammation-related pathways.This study first identified key lncRNAs, miRNAs and their targets, and further revealed a global view of lncRNA-associated ceRNA network involved in DKD by using whole gene transcripts analysis.

Recommender systems have become an important area of research with numerous applications on e-commerce. This chapter introduces a joint statistical model for user preferences and item features that can serve as the basis for a recommendation about recently published scientific papers. The model is constructed using ideas from the literature on Bayesian nonparametric mixture modeling. More specifically, user preferences are modeled using an Infinite Relational Model (IRM) in which both users and items are independently partitioned into homogeneous groups, while item features are modeled using a topic model, which also partitions items into groups with homogenous features. Information is shared across both components of the model through a common partition of items. Hence, the model is a hybrid system that combines ideas from collaborative and content-based filtering. The chapter discusses three different computational strategies, including a Markov chain Monte Carlo algorithm for full posterior inference, an iterated conditional maximization algorithm, and a mean-field variational algorithm for point estimation and prediction in large datasets where Markov chain Monte Carlo approaches might not be practical. The model is illustrated through simulation studies and by analyzing data from CiteULike.

This article presents a linear programming formulation to solve the network design problem using the link transmission model (LTM) as the underlying traffic flow model. The original LTM was adapted by incorporating link-sending and receiving flows using linear inequalities. Furthermore, route choice was relaxed, and transfer flow variables were used to model vehicles’ routing decisions within the network. The objective function of the linear program aimed to minimize the total difference between the cumulative vehicle numbers (CVN) at the upstream and at the downstream boundaries of each link subject to flow-conservation and budget constraints. CVN were represented using transfer flows from connected links. The resulting formulation is a linear program that represents a dynamic system optimum traffic flow pattern, embedding the LTM’s network loading procedure. In contrast to the single-destination system optimum dynamic traffic assignment, based on the cell transmission model, the proposed formulation requires considerably fewer decision variables, thus potentially providing a more scalable approach. The proposed formulation was implemented on an example network to illustrate the behavior of the model, and was compared with the cell-based formulation. We show that the model describes free-flow and congested traffic flow states accurately in terms of shock-wave propagation, queuing of vehicles, and optimal total system travel time.

Urban mobility becomes one of the most challenging issues in large urban centers, since traffic congestion is a daily problem. In order to address this issue, a number of researchers, from both academia and industry, have studied several Traffic Management Systems (TMS) approaches to improve urban mobility. However, the existing approaches do not consider an essential factor: the population information. Within this context, this work proposes a new approach, called APOLO, that employs historical knowledge of mobility patterns of the drivers to obtain a global view of the road network. APOLO is different from others research approaches that need constant information exchange among the vehicles and the central server in order to obtain a global view of road traffic condition. These existing approaches can lead to network overload and have a high data processing cost in real-time. Results show that APOLO improves vehicles' mobility compared to well-known approaches, which indicates that APOLO could be a potential alternative for providing TMS services with valuable mobility knowledge.

In this paper we consider sequential SNP systems where the sequentiality of the system is induced by the max-spike: the neuron with the maximum number of spikes out of the neurons that can spike at one step will fire. This corresponds to a global view of the whole network that makes the system sequential. We continue the study in the direction of max-spike and show that systems with 132 neurons are universal. This improves a recent result in the area.

Peanut embryo development is easily affected by a variety of nutrient elements in the soil, especially the calcium level. Peanut produces abortive embryos in calcium-deficient soil, but underlying mechanism remains unclear. Thus, identifying key transcriptional regulators and their associated regulatory networks promises to contribute to a better understanding of this process. In this study, cellular biology and gene expression analyses were performed to investigate peanut embryo development with the aim to discern the global architecture of gene regulatory networks underlying peanut embryo abortion under calcium deficiency conditions. The endomembrane systems tended to disintegrate, impairing cell growth and starch, protein and lipid body accumulation, resulting in aborted seeds. RNA-seq analysis showed that the gene expression profile in peanut embryos was significantly changed under calcium deficiency. Further analysis indicated that multiple signal pathways were involved in the peanut embryo abortion. Differential expressed genes (DEGs) related to cytoplasmic free Ca2+ were significantly altered. DEGs in plant hormone signaling pathways tended to be associated with increased IAA and ethylene but with decreased ABA, gibberellin, cytokinin, and brassinosteroid levels. Certain vital genes, including apoptosis-inducing factor, WRKYs and ethylene-responsive transcription factors, were up-regulated, while key regulators of embryo development, such as TCP4, WRI1, FUS3, ABI3, and GLK1 were down-regulated. Weighted gene co-expression network analysis (WGCNA) identified 16 significant modules associated with the plant hormone signaling, MAPK signaling, ubiquitin mediated proteolysis, reserve substance biosynthesis and metabolism pathways to decipher regulatory network. The most significant module was darkolivegreen2 and FUS3 (AH06G23930) had the highest connectivity among this module. Importantly, key transcription factors involved in embryogenesis or ovule development including TCP4, GLK1, ABI3, bHLH115, MYC2, etc., were also present in this module and down regulated under calcium deficiency. This study presents the first global view of the gene regulatory network involved in peanut embryo abortion under calcium deficiency conditions and lays foundation for improving peanut tolerances to calcium deficiency by a targeted manipulation of molecular breeding.

FOG computing provides real-time service for analyzing data sensed by massive wireless embedded devices. However, an attacker can get a global view of an event-driven sensor network through the contextual information collected in FOG. We present an attack model that uses such information to analyze traffic and infer the source location. To resist traffic analysis, dummy message injection approaches have been widely applied. In previous studies, a method in which all nodes in a global scope send packets periodically gains perfect privacy but fails to provide energy-efficient and low-latency service. We propose a novel scheme, DLSA (Dark-Light Stripe Alternation). Compared with state-of-the-art techniques, our methods can not only maintain perfect privacy but also reduce the communication overhead by more than 50% and the end-to-end latency by more than 40%.