Shahid Akhtar

HiFi: A Hierarchical Filtering Algorithm for Caching of Online Video

Online video presents new challenges to traditional caching with over a thousand fold increase in number of assets, rapidly changing popularity of assets and much higher throughput requirements. We propose a new hierarchical filtering algorithm for caching online video-HiFi. Our algorithm is designed to optimize hit-rate, replacement rate and cache throughput. It has an associated implementation complexity comparable to that of LRU. Our results show that under typical operator conditions, HiFi can increase edge cache byte hit-rate by 5-24% over an LRU policy, but more importantly can increase RAM or memory byte hit-rate by 80% to 200% and reduce replacement rate by 90%! These two factors combined can dramatically increase throughput for most caches. If SSDs are used for storage, the much lower replacement rate may also allow substitution of lower cost MLC based SSDs instead of SLC based SSDs. We extend previous multi-tier analytical models for LRU caches to caches with filtering. We develop a realistic simulation environment for online video using statistics from operator traces. We show that HiFi performs within a few percentage points from the optimal solution which was simulated by Beladys MIN algorithm under typical operator conditions.

Interaction of AQM schemes and adaptive streaming with Internet traffic on access networks

We tested AQM techniques on their ability to influence end-user QoE, especially HTTP Adaptive Streaming (HAS) based video traffic on fixed access networks. Using ns-2 we built a set of simulation scenarios for realistic Internet traffic. Based on data from recent Internet measurements, there are three major types of traffic flowing through the Internet: HTTP web traffic; HAS video traffic and progressive download (PD) video traffic. We modeled HTTP web traffic using Internet statistics published by Google. We generated dynamic HAS traffic by implementing a HAS client. We modeled PD video traffic using statistics from Youtube. We used published research to convert data from simulation traces into QoE metrics. We used realistic mixes of the three types of traffic with different loading conditions to test Random Early Detection (RED), CoDel and Tail-drop queuing on DSL access networks. We also tested various options in HAS for their impact on its QoE. Most AQM configurations improved the QoE metrics of HAS and web traffic. They improved fairness among HAS streams, avoided HAS underflow in a majority of the cases where it would occur with Tail-drop, and tangibly reduced the latency for web page downloads. Sensitivity analysis with RED parameters confirmed that the improved QoE of HAS and web traffic was relatively insensitive to the exact RED parameter set.