ATP: a Datacenter Approximate Transmission Protocol

Many datacenter applications such as machine learning and streaming systems do not need the complete set of data to perform their computation. Current approximate applications in datacenters run on a reliable network layer like TCP. To improve performance, they either let sender select a subset of data and transmit them to the receiver or transmit all the data and let receiver drop some of them. These approaches are network oblivious and unnecessarily transmit more data, affecting both application runtime and network bandwidth usage. On the other hand, running approximate application on a lossy network with UDP cannot guarantee the accuracy of application computation. We propose to run approximate applications on a lossy network and to allow packet loss in a controlled manner. Specifically, we designed a new network protocol called Approximate Transmission Protocol, or ATP, for datacenter approximate applications. ATP opportunistically exploits available network bandwidth as much as possible, while performing a loss-based rate control algorithm to avoid bandwidth waste and re-transmission. It also ensures bandwidth fair sharing across flows and improves accurate applications' performance by leaving more switch buffer space to accurate flows. We evaluated ATP with both simulation and real implementation using two macro-benchmarks and two real applications, Apache Kafka and Flink. Our evaluation results show that ATP reduces application runtime by 13.9% to 74.6% compared to a TCP-based solution that drops packets at sender, and it improves accuracy by up to 94.0% compared to UDP

QUALITY-OF-SERVICE PROVISIONING FOR SMART CITY APPLICATIONS USING SOFTWARE-DEFINED NETWORKING

In the current world, most cities have WiFi Access Points (AP) in every nook and corner. Hence upraising these cities to the status of a smart city is a more easily achievable task than before. Internet-of-Things (IoT) connections primarily use WiFi standards to form the veins of a smart city. Unfortunately, this vast potential of WiFi technology in the genesis of smart cities is somehow compromised due to its failure in meeting unique Quality-of-Service (QoS) demands of smart city applications. Out of the following QoS factors; transmission link bandwidth, packet transmission delay, jitter, and packet loss rate, not all applications call for the all of the factors at the same time. Since smart city is a pool of drastically unrelated services, this variable demand can actually be advantageous to optimize the network performance. This thesis work is an attempt to achieve one of those QoS demands, namely packet delivery latency. Three algorithms are developed to alleviate traffic load imbalance at APs so as to reduce packet forwarding delay. Software-Defined Networking (SDN) is making its way in the network world to be of great use and practicality. The algorithms make use of SDN features to control the connections to APs in order to achieve the delay requirements of smart city services. Real hardware devices are used to imitate a real-life scenario of citywide coverage consisting of WiFi devices and APs that are currently available in the market with neither of those having any additional requirements such as support for specific roaming protocol, running a software agent or sending probe packets. Extensive hardware experimentation proves the efficacy of the proposed algorithms

Secure Multi-Path Selection with Optimal Controller Placement Using Hybrid Software-Defined Networks with Optimization Algorithm

The Internet's growth in popularity requires computer networks for both agility and resilience. Recently, unable to satisfy the computer needs for traditional networking systems. Software Defined Networking (SDN) is known as a paradigm shift in the networking industry. Many organizations are used SDN due to their efficiency of transmission. Striking the right balance between SDN and legacy switching capabilities will enable successful network scenarios in architecture networks. Therefore, this object grand scenario for a hybrid network where the external perimeter transport device is replaced with an SDN device in the service provider network. With the moving away from older networks to SDN, hybrid SDN includes both legacy and SDN switches. Existing models of SDN have limitations such as overfitting, local optimal trapping, and poor path selection efficiency. This paper proposed a Deep Kronecker Neural Network (DKNN) to improve its efficiency with a moderate optimization method for multipath selection in SDN. Dynamic resource scheduling is used for the reward function the learning performance is improved by the deep reinforcement learning (DRL) technique. The controller for centralised SDN acts as a network brain in the control plane. Among the most important duties network is selected for the best SDN controller. It is vulnerable to invasions and the controller becomes a network bottleneck. This study presents an intrusion detection system (IDS) based on the SDN model that runs as an application module within the controller. Therefore, this study suggested the feature extraction and classification of contractive auto-encoder with a triple attention-based classifier. Additionally, this study leveraged the best performing SDN controllers on which many other SDN controllers are based on OpenDayLight (ODL) provides an open northbound API and supports multiple southbound protocols. Therefore, one of the main issues in the multi-controller placement problem (CPP) that addresses needed in the setting of SDN specifically when different aspects in interruption, ability, authenticity and load distribution are being considered. Introducing the scenario concept, CPP is formulated as a robust optimization problem that considers changes in network status due to power outages, controller’s capacity, load fluctuations and changes in switches demand. Therefore, to improve network performance, it is planned to improve the optimal amount of controller placements by simulated annealing using different topologies the modified Dragonfly optimization algorithm (MDOA)

Intelligent Management and Efficient Operation of Big Data

This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources, the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services, and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies

High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G networks. HTS are mainly designed to provide higher data rates and capacities. Fueled by technological advancements including beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electronically steerable antennas, HTS have emerged as a fundamental component for future network generation. This paper offers a comprehensive state-of-the-art of HTS systems, with a focus on standardization, patents, channel multiple access techniques, routing, load balancing, and the role of software-defined networking (SDN). In addition, we provide a vision for next-satellite systems that we named as extremely-HTS (EHTS) toward autonomous satellites supported by the main requirements and key technologies expected for these systems. The EHTS system will be designed such that it maximizes spectrum reuse and data rates, and flexibly steers the capacity to satisfy user demand. We introduce a novel architecture for future regenerative payloads while summarizing the challenges imposed by this architecture