We study the content placement problem for cache delivery video-on-demand systems under static random network topologies with fixed heavy-tailed video demand. The performance measure is the amount of server load; we wish to minimize the total download rate for all users from the server and maximize the rate from caches. Our approach reduces the analysis for multiple videos to consideration of decoupled systems with a single video each. For each placement policy, insights gained from the single video analysis carry back to the original multiple video content placement problem. Finally, we propose a hybrid placement technique that achieves near optimal performance with low complexity.
Wu and Li @cite_3 studied the optimal cache replacement algorithm and suggested that the simplest heuristics perform as well as the optimal algorithms, with very insignificant differences. While our formulation assumes random but static video requests, we also look for a simple suboptimal alternative to the optimal content placement algorithm. In this direction, we decompose the analysis of the content placement problem from the scale of the entire system with multiple videos into decoupled systems, each with only a single video of given popularity. @cite_2 formulated the adaptive whole storage placement problem as a mixed integer program (MIP) subject to a storage constraint and link bandwidth constraint. The adaptive whole storage placement problem is solved approximately by MIP. In our work, we derive an upper and a lower bound on the performance of the single video adaptive whole storage placement policy using analytical and heuristic approaches. Zhang @cite_6 used MDS codes to relax the integer constraint and converted the integer program into a convex, adaptive fractional storage placement problem that can be solved exactly. This result provides an upper bound on the performance of any content placement policy, including all single video placement policies.
@cite_6 @cite_3 @cite_2
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Peer-assisted Video-on-Demand (VoD) systems have not only received substantial recent research attention, but also been implemented and deployed with success in large-scale real- world streaming systems, such as PPLive. Peer-assisted Video- on-Demand systems are designed to take full advantage of peer upload bandwidth contributions with a cache on each peer. Since the size of such a cache on each peer is limited, it is imperative that an appropriate cache replacement algorithm is designed. There exists a tremendous level of flexibility in the design space of such cache replacement algorithms, including the simplest alternatives such as Least Recently Used (LRU). Which algorithm is the best to minimize server bandwidth costs, so that when peers need a media segment, it is most likely available from caches of other peers? Such a question, however, is arguably non-trivial to answer, as both the demand and supply of media segments are stochastic in nature. In this paper, we seek to construct an analytical framework based on optimal control theory and dynamic programming, to help us form an in-depth understanding of optimal strategies to design cache replacement algorithms. With such analytical insights, we have shown with extensive simulations that, the performance margin enjoyed by optimal strategies over the simplest algorithms is not substantial, when it comes to reducing server bandwidth costs. In most cases, the simplest choices are good enough as cache replacement algorithms in peer-assisted VoD systems. IPTV service providers offering Video-on-Demand currently use servers at each metropolitan office to store all the videos in their library. With the rapid increase in library sizes, it will soon become infeasible to replicate the entire library at each office. We present an approach for intelligent content placement that scales to large library sizes (e.g., 100Ks of videos). We formulate the problem as a mixed integer program (MIP) that takes into account constraints such as disk space, link bandwidth, and content popularity. To overcome the challenges of scale, we employ a Lagrangian relaxation-based decomposition technique combined with integer rounding. Our technique finds a near-optimal solution (e.g., within 1-2 ) with orders of magnitude speedup relative to solving even the LP relaxation via standard software. We also present simple strategies to address practical issues such as popularity estimation, content updates, short-term popularity fluctuation, and frequency of placement updates. Using traces from an operational system, we show that our approach significantly outperforms simpler placement strategies. For instance, our MIP-based solution can serve all requests using only half the link bandwidth used by LRU or LFU cache replacement policies. We also investigate the trade-off between disk space and network bandwidth.