Mouna Allani

A Gambling Approach to Scalable Resource-Aware Streaming

In this paper, we propose a resource-aware solution to achieving reliable and scalable stream diffusion in a probabilistic model, i.e., where communication links and processes are subject to message losses and crashes, respectively. Our solution is resource-aware in the sense that it limits the memory consumption, by strictly scoping the knowledge each process has about the system, and the bandwidth available to each process, by assigning a fixed quota of messages to each process. We describe our approach as gambling in the sense that it consists in accepting to give up on a few processes sometimes, in the hope to better serve all processes most of the time. That is, our solution deliberately takes the risk not to reach some processes in some executions, in order to reach every process in most executions. The underlying stream diffusion algorithm is based on a tree-construction technique that dynamically distributes the load of forwarding stream packets among processes, based on their respective available bandwidths. Simulations show that this approach pays off when compared to traditional gossiping, when the latter faces identical bandwidth constraints.

Streamline: An Architecture for Overlay Multicast

We propose Streamline, a two-layered architecture designed for media streaming in overlay networks. The first layer is a generic, customizable and lightweight protocol which is able to construct and maintain different types of meshes, exhibiting different properties. We discuss two types of overlay networks and explain how the first layer protocol builds these networks in a distributed manner. The second layer is responsible for data propagation to the nodes in the mesh by constructing an optimized diffusion tree. In order to cover the vulnerabilities of the diffusion tree, we propose a masking mechanism which enables the nodes to instantly switch to alternative data paths when necessary. Our simulations reveal that, the structure and properties of the underlying mesh are key to the performance of the system and Streamline can tolerate high node churn without degrading delivery rate.

QuoCast: A Resource-Aware Algorithm for Reliable Peer-to-Peer Multicast

This paper presents QuoCast, a resource-aware protocol for reliable stream diffusion in unreliable environments, where processes may crash and communication links may lose messages. QuoCast is resource-aware in the sense that it takes into account memory, CPU, and bandwidth constraints. Memory constraints are captured by the limited knowledge each process has of its neighborhood. CPU and bandwidth constraints are captured by a fixed quota on the number of messages that a process can use for streaming. Both incoming and outgoing traffic are accounted for. QuoCast maximizes the probability that each streamed packet reaches all consumers while respecting their incoming and outgoing quotas. The algorithm is based on a tree-construction technique that dynamically distributes the forwarding load among processes and links, based on their reliabilities and on their available quotas. The evaluation results show that the adaptiveness of QuoCast to several contraints provides better reliability when compared to other adaptive approaches.

Chams: Churn-aware overlay construction for media streaming

Overlay networks support a wide range of peer-to-peer media streaming applications on the Internet. The user experience of such applications is affected by the churn resilience of the system. When peers disconnect from the system, streamed data may be delayed or lost due to missing links in the overlay topology. In this paper, we explore a proactive strategy to create churn-aware overlay networks that reduce the potential of disruptions caused by churn events. We describe Chams, a middleware for constructing overlay networks that mitigates the impact of churn. Chams uses a “hybrid” approach—it implicitly defines an overlay topology using a gossip-style mechanism, while taking the reliability of peers into account. Unlike systems for overlay construction, Chams supports a variety of topologies used in media streaming systems, such as trees, multi-trees and forests. We evaluate Chams with different topologies and show that it reduces the impact of churn, while imposing only low computational and message overheads.

Application Layer Multicast

An increasing number of Peer-to-Peer (P2P) Internet applications rely today on data dissemination as their cornerstone, e.g., audio or video streaming, multi-party games. These applications typically depend on some support for multicast communication, where peers interested in a given data stream can join a corresponding multicast group. As a consequence, the efficiency, scalability, and reliability guarantees of these applications are tightly coupled with that of the underlying multicast mechanism.

RASM: A Reliable Algorithm for Scalable Multicast

Recently there has been an effort to build scalable and reliable application-level multicast solutions that combine the resilience of pure gossip-based with the efficiency of tree-based schemes. However, such solutions assume that participants have unlimited resources, for instance, that they can send an unbounded number of messages to mask network omissions. Such scenario is not realistic, specially for streaming protocols, where messages can be transmitted at a very high rate and have a small temporal validity. In this paper, we propose RASM, a scalable distributed protocol for application-level multicast. Our protocol is based on the combination of gossip-based and tree-based multicast schemes. Unlike previous approaches, which strive to combine gossip-based and tree-based schemes, our solution takes into consideration the reliability of components: nodes and communication links can fail, unexpectedly, ceasing their operation and dropping messages, respectively. Experimental results show that our scheme offers better reliability than previous solutions with low overhead.

Hyphen: a hybrid protocol for generic overlay construction in P2P environments

Overlay networks form the core part of peer-to-peer (P2P) applications such as application-level multicast, content distribution and media streaming. To ease development, middleware solutions and toolkit libraries have been proposed in the past to help with the implementation of overlay networks. Existing solutions, however, are either too generic by only providing low-level communication abstractions, requiring developers to implement algorithms for overlay networks from scratch, or too restrictive by only supporting a particular overlay topology with fixed properties. In this paper, we argue that it is possible to find a middle ground between these two extremes. We describe Hyphen, a middleware for overlay construction and maintenance that supports a range of overlay topologies with custom properties, and show how it can replace topology construction for a variety of application-level multicast systems. Unlike previous efforts, Hyphen can construct and maintain a range of overlay topologies such as trees and forests with specific optimisation goals such as low latency or high bandwidth. By using a gossip-based mechanism to define topologies implicitly, Hyphen can scale to many peers and achieve low construction overhead. Our experimental evaluation with Bullet and Splitstream, two P2P streaming systems, shows that Hyphen can construct a bandwidth-optimised tree for Bullet that achieves a higher streaming rate than the original Bullet implementation, and that it can construct a more reliable forest for Splitstream by taking individual peer reliability into account.

ScaleStream - An Adaptive Replication Algorithm for Scalable Multimedia Streaming

In this paper, we propose Scale Stream, a new algorithm to support scalable multimedia streaming in peer-to-peer networks, under strict resources constraints. With the growth of multimedia consumption over the Internet, achieving scalability in a resource-constrained environment is indeed becoming a critical requirement. Intuitively, our approach consists in dynamically replicating the multimedia content being streamed, based on bandwidth and memory constraints. Our replication management strategy maximizes the number of consumers being served concurrently, while minimizing memory usage, under strict bandwidth constraints.

Reliable Communication Infrastructure for Adaptive Data Replication

Apparatus for determining in-blow % carbon content and/or the First Turn Down Carbon of a BOF heat includes a light sensor housed within a temperature regulated case having a sighting window including air wipe means to shield the light sensor from steelmaking dust and fume. The apparatus also includes a means to generate a signal that corresponds to the amount of oxygen blown into the BOF during a heat, and a programmable logic controller. The logic controller is programmed to continuously process oxygen blown signals from the signal generating means, and light intensity signals received from the light sensor. The program calculates continuous in-blow % carbon content of the heat based upon the difference in light intensity from a point of maximum light intensity emitted from the BOF vessel in relation to the amount of oxygen blown into the BOF vessel during the same period of time.

Resource-Aware Multimedia Content Delivery

In this paper, we propose a resource-aware solution to achieving reliable and scalable stream diffusion in a probabilistic model, i.e. where communication links and processes are subject to message losses and crashes, respectively. Our solution is resource-aware in the sense that it limits the memory consumption, by strictly scoping the knowledge each process has about the system, and the bandwidth available to each process, by assigning a fixed quota of messages to each process. We describe our approach as gambling in the sense that it consists in accepting to give up on a few processes sometimes, in the hope of better serving all processes most of the time. That is, our solution deliberately takes the risk not to reach some processes in some executions, in order to reach every process in most executions. The underlying stream diffusion algorithm is based on a tree-construction technique that dynamically distributes the load of forwarding stream packets among processes, based on their respective available bandwidths. Simulations show that this approach pays off when compared to traditional gossiping, when the latter faces identical bandwidth constraints.