AngelCast: cloud-based peer-assisted live streaming using optimized multi-tree construction

Increasingly, commercial content providers (CPs) offer streaming solutions using peer-to-peer (P2P) architectures, which promises significant scalabil- ity by leveraging clients’ upstream capacity. A major limitation of P2P live streaming is that playout rates are constrained by clients’ upstream capac- ities – typically much lower than downstream capacities – which limit the quality of the delivered stream. To leverage P2P architectures without sacri- ficing quality, CPs must commit additional resources to complement clients’ resources. In this work, we propose a cloud-based service AngelCast that enables CPs to complement P2P streaming. By subscribing to AngelCast, a CP is able to deploy extra resources (angel), on-demand from the cloud, to maintain a desirable stream quality. Angels do not download the whole stream, nor are they in possession of it. Rather, angels only relay the minimal fraction of the stream necessary to achieve the desired quality. We provide a lower bound on the minimum angel capacity needed to maintain a desired client bit-rate, and develop a fluid model construction to achieve it. Realizing the limitations of the fluid model construction, we design a practical multi- tree construction that captures the spirit of the optimal construction, and avoids its limitations. We present a prototype implementation of AngelCast, along with experimental results confirming the feasibility of our service.Supported in part by NSF awards #0720604, #0735974, #0820138, #0952145, #1012798 #1012798 #1430145 #1414119. (0720604 - NSF; 0735974 - NSF; 0820138 - NSF; 0952145 - NSF; 1012798 - NSF; 1430145 - NSF; 1414119 - NSF

AngelCast: cloud-based peer-assisted live streaming using optimized multi-tree construction

Increasingly, commercial content providers (CPs) offer streaming and IPTV solutions that leverage an underlying peer-to-peer (P2P) stream distribution architecture. The use of P2P protocols promises significant scalability and cost savings by leveraging the local resources of clients -- specifically, uplink capacity. A major limitation of P2P live streaming is that playout rates are constrained by the uplink capacities of clients, which are typically much lower than downlink capacities, thus limiting the quality of the delivered stream. Thus, to leverage P2P architectures without sacrificing the quality of the delivered stream, CPs must commit additional resources to complement those available through clients. In this paper, we propose a cloud-based service--AngelCast--that enables CPs to elastically complement P2P streaming "as needed". By subscribing to AngelCast, a CP is able to deploy extra resources ("angels"), on-demand from the cloud, to maintain a desirable stream (bit-rate) quality. Angels need not download the whole stream (they are not "leachers"), nor are they in possession of it (they are not "seeders"). Rather, angels only relay (download once and upload as many times as needed) the minimal possible fraction of the stream that is necessary to achieve the desirable stream quality, while maximally utilizing available client resources. We provide a lower bound on the minimum amount of angel capacity needed to maintain a certain bit-rate to all clients, and develop a fluid model construction that achieves this lower bound. Realizing the limitations of the fluid model construction--namely, susceptibility to potentially arbitrary start-up delays and significant degradation due to churn--we present a practical multi-tree construction that captures the spirit of the optimal construction, while avoiding its limitations. In particular, our AngelCast protocol achieves near optimal performance (compared to the fluid-model construction) while ensuring a low startup delay by maintaining a logarithmic-length path between any client and the provider, and while gracefully dealing with churn by adopting a flexible membership management approach. We present the blueprints of a prototype implementation of AngelCast, along with experimental results confirming the feasibility and performance potential of our AngelCast service when deployed on Emulab and PlanetLab.National Science Foundation (0720604, 0735974, 0820138, 0952145, 1012798

Optimizing on-demand resource deployment for peer-assisted content delivery (PhD thesis)

Increasingly, content delivery solutions leverage client resources in exchange for service in a peer-to-peer (P2P) fashion. Such peer-assisted service paradigms promise significant infrastructure cost reduction, but suffer from the unpredictability associated with client resources, which is often exhibited as an imbalance between the contribution and consumption of resources by clients. This imbalance hinders the ability to guarantee a minimum service fidelity of these services to the clients. In this thesis, we propose a novel architectural service model that enables the establishment of higher fidelity services through (1) coordinating the content delivery to optimally utilize the available resources, and (2) leasing the least additional cloud resources, available through special nodes (angels) that join the service on-demand, and only if needed, to complement the scarce resources available through clients. While the proposed service model can be deployed in many settings, this thesis focuses on peer-assisted content delivery applications, in which the scarce resource is typically the uplink capacity of clients. We target three applications that require the delivery of fresh as opposed to stale content. The first application is bulk-synchronous transfer, in which the goal of the system is to minimize the maximum distribution time -- the time it takes to deliver the content to all clients in a group. The second application is live streaming, in which the goal of the system is to maintain a given streaming quality. The third application is Tor, the anonymous onion routing network, in which the goal of the system is to boost performance (increase throughput and reduce latency) throughout the network, and especially for bandwidth-intensive applications. For each of the above applications, we develop mathematical models that optimally allocate the already available resources. They also optimally allocate additional on-demand resource to achieve a certain level of service. Our analytical models and efficient constructions depend on some simplifying, yet impractical, assumptions. Thus, inspired by our models and constructions, we develop practical techniques that we incorporate into prototypical peer-assisted angel-enabled cloud services. We evaluate those techniques through simulation and/or implementation. (Major Advisor: Azer Bestavros

Optimizing on-demand resource deployment for peer-assisted content delivery

Increasingly, content delivery solutions leverage client resources in exchange for services in a pee-to-peer (P2P) fashion. Such peer-assisted service paradigm promises significant infrastructure cost reduction, but suffers from the unpredictability associated with client resources, which is often exhibited as an imbalance between the contribution and consumption of resources by clients. This imbalance hinders the ability to guarantee a minimum service fidelity of these services to clients especially for real-time applications where content can not be cached. In this thesis, we propose a novel architectural service model that enables the establishment of higher fidelity services through (1) coordinating the content delivery to efficiently utilize the available resources, and (2) leasing the least additional cloud resources, available through special nodes (angels) that join the service on-demand, and only if needed, to complement the scarce resources available through clients. While the proposed service model can be deployed in many settings, this thesis focuses on peer-assisted content delivery applications, in which the scarce resource is typically the upstream capacity of clients. We target three applications that require the delivery of real-time as opposed to stale content. The first application is bulk-synchronous transfer, in which the goal of the system is to minimize the maximum distribution time - the time it takes to deliver the content to all clients in a group. The second application is live video streaming, in which the goal of the system is to maintain a given streaming quality. The third application is Tor, the anonymous onion routing network, in which the goal of the system is to boost performance (increase throughput and reduce latency) throughout the network, and especially for clients running bandwidth-intensive applications. For each of the above applications, we develop analytical models that efficiently allocate the already available resources. They also efficiently allocate additional on-demand resource to achieve a certain level of service. Our analytical models and efficient constructions depend on some simplifying, yet impractical, assumptions. Thus, inspired by our models and constructions, we develop practical techniques that we incorporate into prototypical peer-assisted angel-enabled cloud services. We evaluate these techniques through simulation and/or implementation

Supporting Heterogeneity and Congestion Control in Peer-to-Peer Multicast Streaming

Abstract — We consider the problem of supporting bandwidth heterogeneity and congestion control in the context of P2P multicast streaming. We identify several challenges peculiar to the P2P setting including robustness concerns arising from peer unreliability and the ambiguity of packet loss as an indicator of congestion. We propose a hybrid parent- and child-driven bandwidth adaptation protocol that is designed in conjunction with a framework for robustness and that exploits application-level knowledge. I