Multicast Mobility in Mobile IP Version 6 (MIPv6) : Problem Statement and Brief Survey

Publisher PD

Measurement-Driven Algorithm and System Design for Wireless and Datacenter Networks

The growing number of mobile devices and data-intensive applications pose unique challenges for wireless access networks as well as datacenter networks that enable modern cloud-based services. With the enormous increase in volume and complexity of traffic from applications such as video streaming and cloud computing, the interconnection networks have become a major performance bottleneck. In this thesis, we study algorithms and architectures spanning several layers of the networking protocol stack that enable and accelerate novel applications and that are easily deployable and scalable. The design of these algorithms and architectures is motivated by measurements and observations in real world or experimental testbeds. In the first part of this thesis, we address the challenge of wireless content delivery in crowded areas. We present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast. AMuSe is based on accurate receiver feedback and incurs a small control overhead. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes which periodically send information about the channel quality to the multicast sender. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe's feedback to optimally tune the physical layer multicast rate. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. We implemented the AMuSe system on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi nodes. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions. Further, our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. As an example application, MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality. Next, we specifically focus on ensuring high Quality of Experience (QoE) for video streaming over WiFi multicast. We formulate the problem of joint adaptation of multicast transmission rate and video rate for ensuring high video QoE as a utility maximization problem and propose an online control algorithm called DYVR which is based on Lyapunov optimization techniques. We evaluated the performance of DYVR through analysis, simulations, and experiments using a testbed composed of Android devices and o the shelf APs. Our evaluation shows that DYVR can ensure high video rates while guaranteeing a low but acceptable number of segment losses, buffer underflows, and video rate switches. We leverage the lessons learnt from AMuSe for WiFi to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance. DyMo employs eMBMS for broadcasting instructions which indicate the reporting rates as a function of the observed Quality of Service (QoS) for each UE. This simple feedback mechanism collects very limited QoS reports which can be used for network optimization. We evaluated the performance of DyMo analytically and via simulations. DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. In the second part of the thesis, we study datacenter networks which are key enablers of the end-user applications such as video streaming and storage. Datacenter applications such as distributed file systems, one-to-many virtual machine migrations, and large-scale data processing involve bulk multicast flows. We propose a hardware and software system for enabling physical layer optical multicast in datacenter networks using passive optical splitters. We built a prototype and developed a simulation environment to evaluate the performance of the system for bulk multicasting. Our evaluation shows that the optical multicast architecture can achieve higher throughput and lower latency than IP multicast and peer-to-peer multicast schemes with lower switching energy consumption. Finally, we study the problem of congestion control in datacenter networks. Quantized Congestion Control (QCN), a switch-supported standard, utilizes direct multi-bit feedback from the network for hardware rate limiting. Although QCN has been shown to be fast-reacting and effective, being a Layer-2 technology limits its adoption in IP-routed Layer 3 datacenters. We address several design challenges to overcome QCN feedback's Layer- 2 limitation and use it to design window-based congestion control (QCN-CC) and load balancing (QCN-LB) schemes. Our extensive simulations, based on real world workloads, demonstrate the advantages of explicit, multi-bit congestion feedback, especially in a typical environment where intra-datacenter traffic with short Round Trip Times (RTT: tens of s) run in conjunction with web-facing traffic with long RTTs (tens of milliseconds)

\STATMOND: A Peer-To-Peer Status And Performance Monitor For Dynamic Resource Allocation On Parallel Computers

This thesis presents a decentralized tool STATMOND - to monitor the status of a peer-to-peer network. STATMOND provides an accurate measurement scheme for parameters such as CPU load and memory utilization on Linux clusters. The services of STATMOND are ubiquitous in that each computer measures and for- wards its data over the network and also maintains the data of other nodes in memory. The data are periodically updated, and users on any node can ‘see‘ the status and performance of the network based on these parameters. This thesis describes the problems confronting cluster computing, the necessity of monitoring tools and how STATMOND can be a step towards better allocation of resources for dynamic computing

Agent Organization and Request Propagation in the Knowledge Plane

In designing and building a network like the Internet, we continue to face the problems of scale and distribution. In particular, network management has become an increasingly difficult task, and network applications often need to maintain efficient connectivity graphs for various purposes. The knowledge plane was proposed as a new construct to improve network management and applications. In this proposal, I propose an application-independent mechanism to support the construction of application-specific connectivity graphs. Specifically, I propose to build a network knowledge plane and multiple sub-planes for different areas of network services. The network knowledge plane provides valuable knowledge about the Internet to the sub-planes, and each sub-plane constructs its own connectivity graph using network knowledge and knowledge in its own specific area. I focus on two key design issues: (1) a region-based architecture for agent organization; (2) knowledge dissemination and request propagation. Network management and applications benefit from the underlying network knowledge plane and sub-planes. To demonstrate the effectiveness of this mechanism, I conduct case studies in network management and security

Scalable and Reliable File Transfer for Clusters Using Multicast.

A cluster is a group of computing resources that are connected by a single computer network and are managed as a single system. Clusters potentially have three key advantages over workstations operated in isolation—fault tolerance, load balancing and support for distributed computing. Information sharing among the cluster’s resources affects all phases of cluster administration. The thesis describes a new tool for distributing files within clusters. This tool, the Scalable and Reliable File Transfer Tool (SRFTT), uses Forward Error Correction (FEC) and multiple multicast channels to achieve an efficient reliable file transfer, relative to heterogeneous clusters. SRFTT achieves scalability by avoiding feedback from the receivers. Tests show that, for large files, retransmitting recovery information on multiple multicast channels gives significant performance gains when compared to a single retransmission channel

PIM-SM extension for Source-Specific Multicast through non multicast networks

Barneko ikerkuntza-txostenaDeployment of multicast in the open Internet is stagnated, mainly as a result of service provider policies and network limitations. To skip the lack of multicast connectivity between receivers and networks that carry traffic generated by multicast sources, the IETF has developed a proposal, called Automatic Multicast Tunnelling (AMT), supported in routers at least from 2011. Even so, it has not brought the necessary momentum to the expansion of multicast. In this report a similar but simpler than AMT proposal to skip the non-multicast gap is described. The basic idea in the proposal is to remove from multicast routing architecture some elements imposed by ASM model, those elements that are not needed for the SSM applications (e.g. Internet TV), but make multicast an 'all-or-nothing' technology.The University of the Basque Country (UPV/EHU