A Hierarchical Characterization of a Live Streaming Media Workload

We present what we believe to be the first thorough characterization of live streaming media content delivered over the Internet. Our characterization of over five million requests spanning a 28-day period is done at three increasingly granular levels, corresponding to clients, sessions, and transfers. Our findings support two important conclusions. First, we show that the nature of interactions between users and objects is fundamentally different for live versus stored objects. Access to stored objects is user driven, whereas access to live objects is object driven. This reversal of active/passive roles of users and objects leads to interesting dualities. For instance, our analysis underscores a Zipf-like profile for user interest in a given object, which is to be contrasted to the classic Zipf-like popularity of objects for a given user. Also, our analysis reveals that transfer lengths are highly variable and that this variability is due to the stickiness of clients to a particular live object, as opposed to structural (size) properties of objects. Second, based on observations we make, we conjecture that the particular characteristics of live media access workloads are likely to be highly dependent on the nature of the live content being accessed. In our study, this dependence is clear from the strong temporal correlations we observed in the traces, which we attribute to the synchronizing impact of live content on access characteristics. Based on our analyses, we present a model for live media workload generation that incorporates many of our findings, and which we implement in GISMO [19].

Extending snBench to Provide Concurrency Support in the Sensorium Execution Environment (SXE)

The SNBENCH is a general-purpose programming environment and run-time system targeted towards a variety of Sensor applications such as environmental sensing, location sensing, video sensing, etc. In its current structure, the run-time engine of the SNBENCH namely, the Sensorium Execution Environment (SXE) processes the entities of execution in a single thread of operation. In order to effectively support applications that are time-sensitive and need priority, it is imperative to process the tasks discretely so that specific policies can be applied at a much granular level. The goal of this project was to modify the SXE to enable efficient use of system resources by way of multi-tasking the individual components. Additionally, the transformed SXE offers the ability to classify and employ different schemes of processing to the individual tasks.