A Configurable Transport Layer for CAF

The message-driven nature of actors lays a foundation for developing scalable and distributed software. While the actor itself has been thoroughly modeled, the message passing layer lacks a common definition. Properties and guarantees of message exchange often shift with implementations and contexts. This adds complexity to the development process, limits portability, and removes transparency from distributed actor systems. In this work, we examine actor communication, focusing on the implementation and runtime costs of reliable and ordered delivery. Both guarantees are often based on TCP for remote messaging, which mixes network transport with the semantics of messaging. However, the choice of transport may follow different constraints and is often governed by deployment. As a first step towards re-architecting actor-to-actor communication, we decouple the messaging guarantees from the transport protocol. We validate our approach by redesigning the network stack of the C++ Actor Framework (CAF) so that it allows to combine an arbitrary transport protocol with additional functions for remote messaging. An evaluation quantifies the cost of composability and the impact of individual layers on the entire stack

Evolving SDN for Low-Power IoT Networks

Software Defined Networking (SDN) offers a flexible and scalable architecture that abstracts decision making away from individual devices and provides a programmable network platform. However, implementing a centralized SDN architecture within the constraints of a low-power wireless network faces considerable challenges. Not only is controller traffic subject to jitter due to unreliable links and network contention, but the overhead generated by SDN can severely affect the performance of other traffic. This paper addresses the challenge of bringing high-overhead SDN architecture to IEEE 802.15.4 networks. We explore how traditional SDN needs to evolve in order to overcome the constraints of low-power wireless networks, and discuss protocol and architectural optimizations necessary to reduce SDN control overhead - the main barrier to successful implementation. We argue that interoperability with the existing protocol stack is necessary to provide a platform for controller discovery and coexistence with legacy networks. We consequently introduce {\mu}SDN, a lightweight SDN framework for Contiki, with both IPv6 and underlying routing protocol interoperability, as well as optimizing a number of elements within the SDN architecture to reduce control overhead to practical levels. We evaluate {\mu}SDN in terms of latency, energy, and packet delivery. Through this evaluation we show how the cost of SDN control overhead (both bootstrapping and management) can be reduced to a point where comparable performance and scalability is achieved against an IEEE 802.15.4-2012 RPL-based network. Additionally, we demonstrate {\mu}SDN through simulation: providing a use-case where the SDN configurability can be used to provide Quality of Service (QoS) for critical network flows experiencing interference, and we achieve considerable reductions in delay and jitter in comparison to a scenario without SDN

Simulation study for wireless sensor networks and load sharing routing protocol to increase network life and connectivity

LSU SensorSimulator is a framework for simulating wireless sensor networks. It is a customizable and extendible simulator, which allows testing and analyzing software for wireless sensor networks. The users can subclass the framework classes and customize the behavior of various network layers. This subclassing gives a way to the developers an opportunity to analyze and investigate, phenomenological, networking, robustness and scaling issues, to explore arbitrary algorithms for distributed sensors, independent of hardware constraint. The results are compared against the simulation results for ns-2 for routing protocols Directed Diffusion and GEAR. Through the comparison of results for scalability, performance and memory utilization it is observed that LSU SensorSimulator performs much better. Buddy load sharing routing protocol is a routing protocol which can be combined with any geographically aware routing protocol to increase the network life and connectivity. The performance of Buddy load sharing algorithm for network life, and it is found that for a very negligible overhead the network life and connectivity and be improved by buddy load sharing

The Push Model in Web-Based Network Management

The management of IP networks is currently based on the SNMP protocol, and the use of expensive network management platforms designed according to the manager/agent paradigm of the SNMP framework. It uses two different schemes to transfer management data: a request/response protocol for data collection and network monitoring (data polling), and unsolicited push to deliver SNMP notifications. This design is exposed to a number of problems, with regards to the time-to-market of vendor-specific management software, versioning, protocol efficiency, security, etc. In this paper, we propose a novel approach to network management based on the push model. This model is well-known in software engineering, and encountered a large success on the Web recently with the push technologies. It relies on the publish/subscribe/distribute paradigm, and uses a single scheme to transfer all management data. We describe why it is more efficient, in terms of network and systems resources, than the traditional pull model. We also explain in detail how to implement this model with Web technologies to deliver SNMP notifications, to handle events, and to distribute MIB data for network monitoring and data collection

A machine learning-based framework for preventing video freezes in HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) represents the dominant technology to deliver videos over the Internet, due to its ability to adapt the video quality to the available bandwidth. Despite that, HAS clients can still suffer from freezes in the video playout, the main factor influencing users' Quality of Experience (QoE). To reduce video freezes, we propose a network-based framework, where a network controller prioritizes the delivery of particular video segments to prevent freezes at the clients. This framework is based on OpenFlow, a widely adopted protocol to implement the software-defined networking principle. The main element of the controller is a Machine Learning (ML) engine based on the random undersampling boosting algorithm and fuzzy logic, which can detect when a client is close to a freeze and drive the network prioritization to avoid it. This decision is based on measurements collected from the network nodes only, without any knowledge on the streamed videos or on the clients' characteristics. In this paper, we detail the design of the proposed ML-based framework and compare its performance with other benchmarking HAS solutions, under various video streaming scenarios. Particularly, we show through extensive experimentation that the proposed approach can reduce video freezes and freeze time with about 65% and 45% respectively, when compared to benchmarking algorithms. These results represent a major improvement for the QoE of the users watching multimedia content online