A review of flow conflicts and solutions in software defined networks (SDN)

Software Defined Networks (SDN) are a modern networking technology introduced to simplify network management via the separation of the data and control planes. Characteristically, flow entries are propagated between the control plane layer and application or data plane layers respectively while following flow table instructions through an OpenFlow protocol. More often than not, conflicts in flows occur as a result of traffic load and priority of instructions in the data plane. Several research works have been conducted on flow conflicts in SDN to reduce their adverse effect. Solutions to flow conflict in SDN have three main limitations. First, the OpenFlow table may still cause a defect in the security module according to the priority and action matching in the OpenFlow of the control plane. Second, flow conflict detection requires more time due to flow tracking and incremental update, whereas in such a case, delay affects the efficiency of SDN. Besides, the SDN algorithm and mechanism have substantially high memory requirement for instruction and proper functioning. Third, most of the available algorithms and detection methods used to avoid flow conflicts have not fully covered the security model policy. This study reviews these limitations and suggest solutions for future research directions

SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning

Software-defined networks (SDNs) have the capabilities of controlling the efficient movement of data flows through a network to fulfill sufficient flow management and effective usage of network resources. Currently, most data center networks (DCNs) suffer from the exploitation of network resources by large packets (elephant flow) that enter the network at any time, which affects a particular flow (mice flow). Therefore, it is crucial to find a solution for identifying and finding an appropriate routing path in order to improve the network management system. This work proposes a SDN application to find the best path based on the type of flow using network performance metrics. These metrics are used to characterize and identify flows as elephant and mice by utilizing unsupervised machine learning (ML) and the thresholding method. A developed routing algorithm was proposed to select the path based on the type of flow. A validation test was performed by testing the proposed framework using different topologies of the DCN and comparing the performance of a SDN-Ryu controller with that of the proposed framework based on three factors: throughput, bandwidth, and data transfer rate. The results show that 70% of the time, the proposed framework has higher performance for different types of flows.</jats:p

A Machine Learning Enhanced Scheme for Intelligent Network Management

The versatile networking services bring about huge influence on daily living styles while the amount and diversity of services cause high complexity of network systems. The network scale and complexity grow with the increasing infrastructure apparatuses, networking function, networking slices, and underlying architecture evolution. The conventional way is manual administration to maintain the large and complex platform, which makes effective and insightful management troublesome. A feasible and promising scheme is to extract insightful information from largely produced network data. The goal of this thesis is to use learning-based algorithms inspired by machine learning communities to discover valuable knowledge from substantial network data, which directly promotes intelligent management and maintenance. In the thesis, the management and maintenance focus on two schemes: network anomalies detection and root causes localization; critical traffic resource control and optimization. Firstly, the abundant network data wrap up informative messages but its heterogeneity and perplexity make diagnosis challenging. For unstructured logs, abstract and formatted log templates are extracted to regulate log records. An in-depth analysis framework based on heterogeneous data is proposed in order to detect the occurrence of faults and anomalies. It employs representation learning methods to map unstructured data into numerical features, and fuses the extracted feature for network anomaly and fault detection. The representation learning makes use of word2vec-based embedding technologies for semantic expression. Next, the fault and anomaly detection solely unveils the occurrence of events while failing to figure out the root causes for useful administration so that the fault localization opens a gate to narrow down the source of systematic anomalies. The extracted features are formed as the anomaly degree coupled with an importance ranking method to highlight the locations of anomalies in network systems. Two types of ranking modes are instantiated by PageRank and operation errors for jointly highlighting latent issue of locations. Besides the fault and anomaly detection, network traffic engineering deals with network communication and computation resource to optimize data traffic transferring efficiency. Especially when network traffic are constrained with communication conditions, a pro-active path planning scheme is helpful for efficient traffic controlling actions. Then a learning-based traffic planning algorithm is proposed based on sequence-to-sequence model to discover hidden reasonable paths from abundant traffic history data over the Software Defined Network architecture. Finally, traffic engineering merely based on empirical data is likely to result in stale and sub-optimal solutions, even ending up with worse situations. A resilient mechanism is required to adapt network flows based on context into a dynamic environment. Thus, a reinforcement learning-based scheme is put forward for dynamic data forwarding considering network resource status, which explicitly presents a promising performance improvement. In the end, the proposed anomaly processing framework strengthens the analysis and diagnosis for network system administrators through synthesized fault detection and root cause localization. The learning-based traffic engineering stimulates networking flow management via experienced data and further shows a promising direction of flexible traffic adjustment for ever-changing environments

Slicing of Concurrent Programs and its Application to Information Flow Control

This thesis presents a practical technique for information flow control for concurrent programs with threads and shared-memory communication. The technique guarantees confidentiality of information with respect to a reasonable attacker model and utilizes program dependence graphs (PDGs), a language-independent representation of information flow in a program

Tools and Algorithms for the Construction and Analysis of Systems

This open access two-volume set constitutes the proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021, which was held during March 27 – April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The total of 41 full papers presented in the proceedings was carefully reviewed and selected from 141 submissions. The volume also contains 7 tool papers; 6 Tool Demo papers, 9 SV-Comp Competition Papers. The papers are organized in topical sections as follows: Part I: Game Theory; SMT Verification; Probabilities; Timed Systems; Neural Networks; Analysis of Network Communication. Part II: Verification Techniques (not SMT); Case Studies; Proof Generation/Validation; Tool Papers; Tool Demo Papers; SV-Comp Tool Competition Papers