Compact Oblivious Routing

Oblivious routing is an attractive paradigm for large distributed systems in which centralized control and frequent reconfigurations are infeasible or undesired (e.g., costly). Over the last almost 20 years, much progress has been made in devising oblivious routing schemes that guarantee close to optimal load and also algorithms for constructing such schemes efficiently have been designed. However, a common drawback of existing oblivious routing schemes is that they are not compact: they require large routing tables (of polynomial size), which does not scale. This paper presents the first oblivious routing scheme which guarantees close to optimal load and is compact at the same time - requiring routing tables of polylogarithmic size. Our algorithm maintains the polylogarithmic competitive ratio of existing algorithms, and is hence particularly well-suited for emerging large-scale networks

Feasibility Analysis of the Algorithms: Secured and Efficient Routing Path Update in Software Defined Networking (SDN)

Software-defined networking is the talk of the town in today’s networking industry. Because of the limitations of traditional networking, SDN is getting more popular every year. Lots of researches are taking place to improve the efficiency and overcome the challenges of SDN though it has many advantages. Hence one key problem of SDN is the network update. If the route update does not perform well, it causes congestion and inconsistencies in the network system whereas bandwidth utilization and security is our main concern. We have compared two pre-built algorithms especially for routing path update and proposed a new algorithm with maximum security and loop-free network

Distributed Consistent Network Updates in SDNs: Local Verification for Global Guarantees

While SDNs enable more flexible and adaptive network operations, (logically) centralized reconfigurations introduce overheads and delays, which can limit network reactivity. This paper initiates the study of a more distributed approach, in which the consistent network updates are implemented by the switches and routers directly in the data plane. In particular, our approach leverages concepts from local proof labeling systems, which allows the data plane elements to locally check network properties, and we show that this is sufficient to obtain global network guarantees. We demonstrate our approach considering three fundamental use cases, and analyze its benefits in terms of performance and fault-tolerance.Comment: Appears in IEEE NCA 201

Determination of Complete Sequence Mutation of Myostatin Gene in Fast- and Slow-Growing Chicken

Myostatin plays a role in inhibiting skeletal muscle growth in vertebrates. This study aimed to investigate the full sequence of the myostatin gene in fast-growing and slow-growing chickens. Fast- and slow-growing chicken models were produced from F2 Kampung x broiler. The full sequence of the myostatin gene was identified using 24 pairs of primers covering about 8,000 bp. mRNA expression analysis of muscle tissue was performed to examine whether the expression levels of myostatin are affected by chicken lines, sex, or muscle type. The results showed 170 mutations in fast- and slow-growing chickens. One hundred and sixty-one of them are novel mutations. A total of five and twenty-two alleles were specific alleles found only in the fast-growing and slow-growing groups of chickens, respectively. There were no differences in amino acids and gene expression levels of myostatin between the fast- and slow-growing chickens. In summary, the results of this study showed that specific alleles for the fast-growing or slow-growing chicken groups were found, suggesting that these specific alleles potentially be used as genetic markers for muscle growth in chickens

AllSynth: A BDD-Based Approach for Network Update Synthesis

The increasingly stringent dependability requirements on communication networks as well as the need to render these networks more adaptive to improve performance, demand for more automated approaches to operate networks. We present AllSynth, a symbolic synthesis tool for updating communication networks in a provably correct and efficient manner. AllSynth automatically synthesizes network update schedules which transiently ensure a wide range of policy properties expressed using linear temporal logic (LTL). In particular, in contrast to existing approaches, AllSynth symbolically computes and compactly represents all feasible and cost-optimal solutions. At its heart, AllSynth relies on a novel parameterized use of binary decision diagrams (BDDs) which greatly improves performance. Indeed, AllSynth not only provides formal correctness guarantees and outperforms existing state-of-the-art tools in terms of generality, but also in terms of runtime as documented by experiments on a benchmark of real-world network topologies

Hybrid SDN Evolution: A Comprehensive Survey of the State-of-the-Art

Software-Defined Networking (SDN) is an evolutionary networking paradigm which has been adopted by large network and cloud providers, among which are Tech Giants. However, embracing a new and futuristic paradigm as an alternative to well-established and mature legacy networking paradigm requires a lot of time along with considerable financial resources and technical expertise. Consequently, many enterprises can not afford it. A compromise solution then is a hybrid networking environment (a.k.a. Hybrid SDN (hSDN)) in which SDN functionalities are leveraged while existing traditional network infrastructures are acknowledged. Recently, hSDN has been seen as a viable networking solution for a diverse range of businesses and organizations. Accordingly, the body of literature on hSDN research has improved remarkably. On this account, we present this paper as a comprehensive state-of-the-art survey which expands upon hSDN from many different perspectives

Control designs and reinforcement learning-based management for software defined networks

In this thesis, we focus our investigations around the novel software defined net- working (SDN) paradigm. The central goal of SDN is to smoothly introduce centralised control capabilities to the otherwise distributed computer networks. This is achieved by abstracting and concentrating network control functionalities in a logically centralised control unit, which is referred to as the SDN controller. To further balance between centralised control, scalability and reliability considerations, distributed SDN is introduced to enable the coexistence of multiple physical SDN controllers. For distributed SDN, networking elements are grouped together to form various domains, with each domain managed by an SDN controller. In such a distributed SDN setting, SDN controllers of all domains synchronise with each other to maintain logically centralised network views, which is referred to as controller synchronisation. Centred on the problem of SDN controller synchronisation, this thesis specifically aims at addressing two aspects of the subject as follows. First, we model and analyse the performance enhancements brought by controller synchronisation in distributed SDN from a theoretical perspective. Second, we design intelligent controller synchronisation policies by leveraging existing and creating new Reinforcement Learning (RL) and Deep Learning (DL)-based approaches. In order to understand the performance gains of SDN controller synchronisation from a fundamental and analytical perspective, we propose a two-layer network model based on graphs to capture various characteristics of distributed SDN net- works. Then, we develop two families of analytical methods to investigate the performance of distributed SDN in relationship to network structure and the level of SDN controller synchronisation. The significance of our analytical results is that they can be used to quantify the contribution of controller synchronisation level, in improving the network performance under different network parameters. Therefore, they serve as fundamental guidelines for future SDN performance analyses and protocol designs. For the designs of SDN controller synchronisation policies, most existing works focus on the engineering-centred system design aspect of the problem for ensuring anomaly-free synchronisation. Instead, we emphasise on the performance improvements with respect to (w.r.t.) various networking tasks for designing controller synchronisation policies. Specifically, we investigate various scenarios with diverse control objectives, which range from routing related performance metric to other more sophisticated optimisation goals involving communication and computation resources in networks. We also take into consideration factors such as the scalability and robustness of the policies developed. For this goal, we employ machine learning techniques to assist our policy designs. In particular, we model the SDN controller synchronisation policy as serial decision-making processes and resort to RL-based techniques for developing the synchronisation policy. To this end, we leverage a combination of various RL and DL methods, which are tailored for handling the specific characteristics and requirements in different scenarios. Evaluation results show that our designed policies consistently outperform some already in-use controller synchronisation policies, in certain cases by considerable margins. While exploring existing RL algorithms for solving our problems, we identify some critical issues embedded within these algorithms, such as the enormity of the state-action space, which can cause inefficiency in learning. As such, we propose a novel RL algorithm to address these issues, which is named state action separable reinforcement learning (sasRL). Therefore, the sasRL approach constitutes another major contribution of this thesis in the field of RL research.Open Acces