Donald E. Smith

IP TV Bandwidth Demand: Multicast and Channel Surfing

IP networks may soon become a delivery mechanism for broadcast television content. Multicast can reduce the steady state bandwidth demand on network links from one stream per viewer to one stream per watched program. However, channel surfing at commercial breaks can periodically increase the bandwidth demand. In the channel change mechanism we study, surfers leave multicast groups and receive unicast streams at higher than usual bandwidth. This paper builds a mathematical model to determine the net bandwidth demand of multicast and surfing during commercial breaks. In one example, we find that the peak demand during a commercial break is twice the steady state multicast demand.

How big is too big? Trading off the economies of scale of larger telecommunications network elements against the risk of larger outages

Abstract This paper builds a probabilistic model to analyze the risk–reward tradeoffs that larger telecommunications network elements present. Larger machines offer rewards in the form of cost savings due to economies of scale. But large machines are riskier because they affect more customers when they fail. Our model translates the risk of outages into dollar costs, which are random variables. This step enables us to combine the deployment cost and outage cost into a total cost. Once we express the decision makers’ preferences via a utility function, we can find the machine size that minimizes the total cost’s expected utility, thereby achieving an optimal tradeoff between reward and risk. The expected utility answers the question “how big is too big?”.

Managing home and network storage of television recordings: "I filled my DVR again! Now what?"

In this paper we study the recording and watching patterns of DVR users using real traces. Many DVR users complain about running out of space. Thus, we propose the idea of adding extra storage on the network. Storing content in the network would create a new challenge, as most of users use their DVR during prime-time resulting in congesting the already strained network with unicast streams. We develop statistical models that learn the behavior of users and are most likely to make watched programs readily available for users each day. We develop a simple caching technique that captures the dominant factors of user behavior. Our verification, using real traces, shows that this technique performs as efficiently as more advanced statistical models, while requiring only a small state to be maintained.