Network Optimizations for Distributed Storage Networks

Distributed file systems enable the reliable storage of exabytes of information on thousands of servers distributed throughout a network. These systems achieve reliability and performance by storing three or more copies of data in different locations across the network. The management of these copies of data is commonly handled by intermediate servers that track and coordinate the placement of data in the network. This introduces potential network bottlenecks, as multiple transfers to fast storage nodes can saturate the network links connecting intermediate servers to the storage. The advent of open Network Operating Systems presents an opportunity to alleviate this bottleneck, as it is now possible to treat network elements as intermediate nodes in this distributed file system and have them perform the task of replicating data across storage nodes. In this thesis, we propose a new design paradigm for distributed file systems, driven by a new fundamental component of the system which runs on network elements such as switches or routers. We describe the component’s architecture and how it can be integrated into existing distributed file systems to increase their performance. To measure this performance increase over current approaches, we emulate a distributed file system by creating a block-level storage array distributed across multiple iSCSI targets presented in a network. Furthermore we emulate more complicated redundancy schemes likely to be used in distributed file systems in the future to determine what effect this approach may have on those systems and what benefits it offers. We find that this new component offers a decrease in request latency proportional to the number of storage nodes involved in the request. We also find that the benefits of this approach are limited by the ability of switch hardware to process incoming data from the request, but that these limitations can be surmounted through the proposed design paradigm

Network Optimizations for Distributed Storage Networks

Distributed file systems enable the reliable storage of exabytes of information on thousands of servers distributed throughout a network. These systems achieve reliability and performance by storing three or more copies of data in different locations across the network. The management of these copies of data is commonly handled by intermediate servers that track and coordinate the placement of data in the network. This introduces potential network bottlenecks, as multiple transfers to fast storage nodes can saturate the network links connecting intermediate servers to the storage. The advent of open Network Operating Systems presents an opportunity to alleviate this bottleneck, as it is now possible to treat network elements as intermediate nodes in this distributed file system and have them perform the task of replicating data across storage nodes. In this thesis, we propose a new design paradigm for distributed file systems, driven by a new fundamental component of the system which runs on network elements such as switches or routers. We describe the component’s architecture and how it can be integrated into existing distributed file systems to increase their performance. To measure this performance increase over current approaches, we emulate a distributed file system by creating a block-level storage array distributed across multiple iSCSI targets presented in a network. Furthermore we emulate more complicated redundancy schemes likely to be used in distributed file systems in the future to determine what effect this approach may have on those systems and what benefits it offers. We find that this new component offers a decrease in request latency proportional to the number of storage nodes involved in the request. We also find that the benefits of this approach are limited by the ability of switch hardware to process incoming data from the request, but that these limitations can be surmounted through the proposed design paradigm

Adaptive and Scalable Controller Placement in Software-Defined Networking

Software-defined networking (SDN) revolutionizes network control by externalizing and centralizing the control plane. A critical aspect of SDN is Controller Placement (CP), which involves identifying the ideal number and location of controllers in a network to fulfill diverse objectives such as latency constraints (node-to-controller and controller-controller delay), fault tolerance, and controller load. Existing optimization techniques like Multi-Objective Particle Swarm Optimisation (MOPSO), Adapted Non-Dominating Sorting Genetic Algorithm-III (ANSGA-III), and Non-Dominating Sorting Genetic Algorithm-II (NSGA-II) struggle with scalability (except ANSGA-III), computational complexity, and inability to predict the required number of controllers. This thesis proposes two novel approaches to address these challenges. First, an enhanced version of NSGA-III with a repair operator-based approach (referred to as ANSGA-III) is introduced, enabling efficient CP in SD-WAN by optimizing multiple conflicting objectives simultaneously. Second, a Stochastic Computational Graph Model with Ensemble Learning (SCGMEL) is developed, overcoming scalability and computational inefficiency associated with existing methods. SCGMEL employs stochastic gradient descent with momentum, a learning rate decay, a computational graph model, a weighted sum approach, and the XGBoost algorithm for optimization and machine learning. The XGBoost predicts the number of controllers needed and a supervised classification algorithm called Learning Vector Quantization (LVQ) is used to predict the optimal locations of controllers. Additionally, this research introduces the Improved Switch Migration Decision Algorithm (ISMDA) as part of the holistic contribution. ISMDA is implemented on each controller to ensure even load distribution throughout the controllers. It functions as a plug-and-play module, periodically checking if the load surpasses a certain limit. ISMDA improves controller throughput by approximately 7.4% over CAMD and roughly 1.1% over DALB. ISMDA also outperforms DALB and CAMD with a decrease of 5.7% and 1%, respectively, in terms of controller response time. Additionally, ISMDA outperforms DALB and CAMD with a decrease of 1.7% and 5.6%, respectively, in terms of the average frequency of migrations. The established framework results in fewer switch migrations during controller load imbalance. Finally, ISMDA proves more efficient than DALB and CAMD, with an estimated 1% and 6.4% lower average packet loss, respectively. This efficiency is a result of the proposed migration efficiency strategy, allowing ISMDA to handle higher loads and reject fewer packets. Real-world experiments were conducted using the Internet Zoo topology dataset to evaluate the proposed solutions. Six objective functions, including worst-case switch-to-controller delay, load balancing, reliability, average controller-to-controller latency, maximum controller-to-controller delay, and average switch-to-controller delay, were utilized for performance evaluation. Results demonstrated that ANSGA-III outperforms existing algorithms in terms of hypervolume indicator, execution time, convergence, diversity, and scalability. SCGMEL exhibited exceptional computational efficiency, surpassing ANSGA-III, NSGA-II, and MOPSO by 99.983%, 99.985%, and 99.446% respectively. The XGBoost regression model performed significantly better in predicting the number of controllers with a mean absolute error of 1.855751 compared to 3.829268, 3.729883, and 1.883536 for KNN, linear regression, and random forest, respectively. The proposed LVQ-based classification method achieved a test accuracy of 84% and accurately predicted six of the seven controller locations. To culminate, this study presents a refined and intelligent framework designed to optimize Controller Placement (CP) within the context of SD-WAN. The proposed solutions effectively tackle the shortcomings associated with existing algorithms, addressing challenges of scalability, intelligence (including the prediction of optimal controller numbers), and computational efficiency in the pursuit of simultaneous optimization of multiple conflicting objectives. The outcomes underscore the supremacy of the suggested methodologies and underscore their potential transformative influence on SDN deployments. Notably, the findings validate the efficacy of the proposed strategies, ANSGA-III and SCGMEL, in enhancing the optimization of controller placement within SD-WAN setups. The integration of the XGBoost regression model and LVQ-based classification technique yields precise predictions for both optimal controller quantities and their respective positions. Additionally, the ISMDA algorithm emerges as a pivotal enhancement, enhancing controller throughput, mitigating packet losses, and reducing switch migration frequency—collectively contributing to elevated standards in SDN deployments

Leveraging ONOS SDN Controllers for OF@TEIN SD-WAN Experiments

In this paper, we discuss on-going experiments at OF@TEIN Playground to evaluate the feasibility of SD-WAN (software-defined wide area network) solutions by leveraging open-source ONOS (open network operating systems) SDN controller. In order to explore efficient solutions in building SDN-enabled WAN, we utilize distributed cloud-based resources to inter-connect them by employing legacy-friendly L3 routing instead of L2-focused overlay tunnels