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In this paper, we present Novasky, a real-world Video-on-Demand (VoD) system capable of delivering cinematicquality video streams to end users. The foundation of the Novasky design is a peer-to-peer (P2P) storage cloud, storing and refreshing media streams in a decentralized fashion using local storage spaces of end users. We present our design objectives in Novasky, and how these objectives are achieved using a collection of unique mechanisms, with respect to caching strategies, coding mechanisms, and the maintenance of the supplydemand relationship when it comes to media availability in the P2P storage cloud. The production Novasky system has been implemented with over 100,000 lines of code. It has been deployed in the Tsinghua University campus network, operational since September 2009, attracting 10,000 users to date, and providing over 1,000 cinematic-quality video streams with bit rates of 1-2 Mbps. Based on real-world traces collected over 6 months, we show that Novasky can achieve rapid startups within 4-9 seconds, and extremely short seek latencies within 3 seconds. Our empirical experiences with Novasky may bring valuable insights to future designs of production-quality P2P storage cloud systems.

As outsourcing data centers emerge to host applications and services from many different organizations, it is critical for data center owners to isolate different applications while dynamically and optimally allocate sharable resources among them. To address this issue, we propose a virtual-appliance-based autonomic resource provisioning framework for large virtualized data centers. We present the architecture of the data center with enriched autonomic features. We define a non-linear constrained optimization model for dynamic resource provisioning and present a novel analytic solution. Key factors, including virtualization overhead and reconfiguration delay, are incorporated into the model. Experimental results based on a prototype demonstrate that the system-level performance has been greatly improved by taking advantage of fine-grained server consolidation, and the whole system exhibits flexible adaptation in failure scenarios. Experiments with the impact of switching delay also show the efficiency of the framework due to significantly reduced provisioning time.

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