Optimizing TCP Performance in Multi-AP Residential Broadband Connections via Minislot Access

The high bandwidth demand of Internet applications has recently driven the need of increasing the residential download speed. A practical solution to the problem has been proposed aggregating the bandwidth of 802.11 Access Points (APs) backhauls in range via 802.11 connections. Since 802.11 devices are usually single-radio, the communication to multiple APs on different radio-channels requires the introduction of a time-division multiple access (TDMA) policy at the client station. Current investigation in this area supposes that there is a sufficient number of TCP flows to saturate the Asymmetric Digital Subscriber Line (ADSL) behind the APs. However, this may be not guaranteed according to the user traffic pattern. As a consequence, a TDMA policy introduces additional delays in the end-to-end transmissions that will cause degradation of the TCP throughput and an under-utilization of the AP backhauls. In this paper, we first perform an in-depth experimental analysis with a customized 802.11 driver of how the usage of multi-AP TDMA affects the observed Round-Trip-Time (RTT) of TCP flows. Then, we introduce a simple analytical model that accurately predicts the TCP RTT when accessing the wireless medium with a Multi-AP TDMA policy. Based on this model, we propose a resource allocation algorithm that runs locally at the station and it greatly reduces the observed TCP RTT with a very low computational cost. Our proposed scheme can improve up to 1:5 times the aggregate throughput observed by the station compared to state-of-the-art multi-AP TDMA allocations. We also show that the throughput performance of the algorithm is very close to the theoretical upper-bound in key simulation scenarios

Simplifying data path processing in next-generation routers

ABSTRACT Customizable packet processing is an important aspect of next-generation networks. Packet processing architectures using multi-core systems on a chip can be difficult to program. In our work, we propose a new packet processor design that simplifies packet processing by managing packet contexts in hardware. We show how such a design scales to large systems. Our results also show that the management of such a system is feasible with the proposed mapping algorithm

The Power of Choice in Data-Aware Cluster Scheduling

Abstract Providing timely results in the face of rapid growth in data volumes has become important for analytical frameworks. For this reason, frameworks increasingly operate on only a subset of the input data. A key property of such sampling is that combinatorially many subsets of the input are present. We present KMN, a system that leverages these choices to perform data-aware scheduling, i.e., minimize time taken by tasks to read their inputs, for a DAG of tasks. KMN not only uses choices to co-locate tasks with their data but also percolates such combinatorial choices to downstream tasks in the DAG by launching a few additional tasks at every upstream stage. Evaluations using workloads from Facebook and Conviva on a 100-machine EC2 cluster show that KMN reduces average job duration by 81% using just 5% additional resources

Improving the performance of software-defined networks using dynamic flow installation and management techniques

As computer networks evolve, they become more complex, introducing several challenges in the areas of performance and management. Such problems can lead to stagnation in network innovation. Software Defined Networks (SDN) framework could be one of the best candidates for improving and revolutionising networking by giving the full control to the network administrators to implement new management and performance optimisation techniques. This thesis examines performance issues faced in SDN due to the introduction of the SDN Controller. These issues include the extra delay due to the round-trip time between the switch and the controller as well as the fact that some packets arrive at the destination out-of-order. We propose a novel dynamic flow installation and management algorithm (OFPE) using the SDN protocol OpenFlow, which preserves the controller to a non-overloaded CPU state and allow it to dynamically add and adjust flow table rules to reduce packet delay and out-of-order packets. In addition, we propose OFPEX, an extension to OFPE algorithm that includes techniques for managing multi-switch environments as well as methods that make use of the packets interarrival time in categorising and serving packet flows. Such techniques allow topology awareness, helping the controller to install flow table rules in such a way to form optimal routes for high priority flows thus increasing network performance. For the performance evaluation of the proposed algorithms, both hardware testbed as well as emulation experiments have been conducted. The performance results indicate that OFPE algorithm achieves a significant enhancement in performance in the form of reduced delay by up to 92.56% (depending on the scenario), reduced packet loss by up to 55.32% and reduced out-of-order packets by up to 69.44%. Furthermore, we propose a novel placement algorithm for distributed Mininet implementations which uses weights in order to distribute the experiment components to the appropriately distributed machines. The proposed algorithm uses static code analysis in order to examine the experimental code as well as it measures the capabilities of physical components in order to create a weights table which is then used to distribute the experiment components properly. The performance results of the proposed algorithm evaluation indicated reductions in delay and packet loss of up to 65.51% and 86.35% respectively, as well as a decrease in the standard deviation of CPU usage by up to 88.63%. These results indicate that the proposed algorithm distributes the experiment components evenly across the available resources. Finally, we propose a series of Benchmarking tests that can be used to rate all the available SDN experimental platforms. These tests allow the selection of the appropriate experimental platform according to the scenario needs as well as they indicate the resources needed by each platform

From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey

Context data is in demand more than ever with the rapid increase in the development of many context-aware Internet of Things applications. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when responding to large number of context queries. Context Management Platforms that infer and deliver context to applications measure this problem using Quality of Service (QoS) parameters. Although caching is a proven way to improve QoS, transiency of context and features such as variability, heterogeneity of context queries pose an additional real-time cost management problem. This paper presents a critical survey of state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies. We comprehensively survey a large number of research publications and evaluate, compare, and contrast different techniques, policies, approaches, and schemes in adaptive caching. Our critical analysis is motivated by the focus on adaptively caching context as a core research problem. A formal definition for adaptive context caching is then proposed, followed by identified features and requirements of a well-designed, objective optimal adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys Journal at this time of publishing in arxiv.or

Automated Formal Analysis of Internet Routing Configurations

Today\u27s Internet interdomain routing protocol, the Border Gateway Protocol (BGP), is increasingly complicated and fragile due to policy misconfigurations by individual autonomous systems (ASes). To create provably correct networks, the past twenty years have witnessed, among many other efforts, advances in formal network modeling, system verification and testing, and point solutions for network management by formal reasoning. On the conceptual side, the formal models usually abstract away low-level details, specifying what are the correct functionalities but not how to achieve them. On the practical side, system verification of existing networked systems is generally hard, and system testing or simulation provide limited formal guarantees. This is known as a long standing challenge in network practice --- formal reasoning is decoupled from actual implementation. This thesis seeks to bridge formal reasoning and actual network implementation in the setting of the Border Gateway Protocol (BGP), by developing the Formally Verifiable Routing (FVR) toolkit that combines formal methods and programming language techniques. Starting from the formal model, FVR automates verification of routing models and the synthesis of faithful implementations that carries the correctness property. Conversely, starting from large real-world BGP systems with arbitrary policy configurations, automates the analysis of Internet routing configurations, and also includes a novel network reduction technique that scales up existing techniques for automated analysis. By developing the above formal theories and tools, this thesis aims to help network operators to create and manage BGP systems with correctness guarantee

Facilitating dynamic network control with software-defined networking

This dissertation starts by realizing that network management is a very complex and error-prone task. The major causes are identified through interviews and systematic analysis of network config- uration data on two large campus networks. This dissertation finds that network events and dynamic reactions to them should be programmatically encoded in the network control program by opera- tors, and some events should be automatically handled for them if the desired reaction is general. This dissertation presents two new solutions for managing and configuring networks using Software- Defined Networking (SDN) paradigm: Kinetic and Coronet. Kinetic is a programming language and central control platform that allows operators to implement traffic control application that reacts to various kinds of network events in a concise, intuitive way. The event-reaction logic is checked for correction before deployment to prevent misconfigurations. Coronet is a data-plane failure recovery service for arbitrary SDN control applications. Coronet pre-plans primary and backup routing paths for any given topology. Such pre-planning guarantees that Coronet can perform fast recovery when there is failure. Multiple techniques are used to ensure that the solution scales to large networks with more than 100 switches. Performance and usability evaluations show that both solutions are feasible and are great alternative solutions to current mechanisms to reduce misconfigurations.Ph.D

Host and Network Optimizations for Performance Enhancement and Energy Efficiency in Data Center Networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows

A Map-algebra-inspired Approach for Interacting With Wireless Sensor Networks, Cyber-physical Systems or Internet of Things

The typical approach for consuming data from wireless sensor networks (WSN) and Internet of Things (IoT) has been to send data back to central servers for processing and analysis. This thesis develops an alternative strategy for processing and acting on data directly in the environment referred to as Active embedded Map Algebra (AeMA). Active refers to the near real time production of data, and embedded refers to the architecture of distributed embedded sensor nodes. Network macroprogramming, a style of programming adopted for wireless sensor networks and IoT, addresses the challenges of coordinating the behavior of multiple connected devices through a high-level programming model. Several macroprogramming models have been proposed, but none to date has adopted a comprehensive spatial model. This thesis takes the unique approach of adapting the well-known Map Algebra model from Geographic Information Science to extend the functionality of WSN/IoT and the opportunities for user interaction with WSN/IoT. As an inherently spatial model, the Map Algebra-inspired metaphor supports the types of computation desired from a network of geographically dispersed WSN nodes. The AeMA data model aligns with the conceptual model of GIS layers and specific layer operations from Map Algebra. A declarative query and network tasking language, based on Map Algebra operations, provides the basis for operations and interactions. The model adds functionality to calculate and store time series and specific temporal summary-type composite objects as an extension to traditional Map Algebra. The AeMA encodes Map Algebra-inspired operations into an extensible Virtual Machine Runtime system, called MARS (Map Algebra Runtime System) that supports Map Algebra in an efficient and extensible way. Map algebra-like operations are performed in a distributed manner. Data do not leave the network but are analyzed and consumed in place. As a consequence, collected information is available in-situ to drive local actions. The conceptual model and tasking language are designed to direct nodes as active entities, able to perform some actions on their environment. This Map Algebra inspired network macroprogramming model has many potential applications for spatially deployed WSN/IoT networks. In particular the thesis notes its utility for precision agriculture applications