COVID-19 pandemic created unexpected demand for our physical infrastructure. We increased our computing supply by growing our infrastructure footprint as well as expanded existing capacity by using various techniques among those DVFS boosting. This paper describes our experience in deploying DVFS boosting to expand capacity.

Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to determine which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose a novel algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN's cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai's CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region. The paper also reports extensive analyses of the RL-Cache sensitivity to its features and hyperparameter values. The analyses validate the made design choices and reveal interesting insights into the RL-Cache behavior.

Modern CDNs cache and deliver a highly-diverse set of traffic classes, including web pages, images, videos and software downloads. It is economically advantageous for a CDN to cache and deliver all traffic classes using a shared distributed cache server infrastructure. However, such sharing of cache resources across multiple traffic classes poses significant cache provisioning challenges that are the focus of this paper.

In this paper, we propose multicast protection trees that provide instantaneous failure recovery from single node failures. For a given node v, the multicast protection tree spans all the neighbors v and does not include v. Thus, when node v fails, its neighbors are connected through the multicast protection tree instead of node v, and the neighbors of node v forward the traffic over this tree. The multicast protection trees are constructed a priori, without the knowledge of the multicast traffic in the network. This facilitates protocol independent single node failure recovery in multicast networks. These trees are used when a new multicast tree is being formed after a node failure has occurred. We analyze the effectiveness of the proposed fast rerouting technique using three practical networks.