CRII: CNS: NeTS: Adaptive Cache Dimensioning in Cloud CDNs: Foundations and Practice

The current Internet infrastructure provides a wide range of services such as music and movies delivery, messaging, video-conferencing and software download. The data transmitted across the Internet corresponding to these services is dubbed as “content”. As an increasing number of users desire such services over the Internet, firms called content providers are engaged in developing systems that ensure that the demanded services are available at high quality of experience, i.e., with minimal delay.

This is achieved by the process of “service placement”, which places replicas of popular services near end users at small servers called caches, coupled with additional copies at larger servers deeper in the Internet. If the content requested by a user is available at a cache, it is promptly delivered. Otherwise, the request must be forwarded to servers that are further, hence increasing delay.

However, provisioning these small and large servers is expensive. Under the cloud computing paradigm, cloud providers make server resources available for rent and allow dynamic sizing of caches, referred to as cache dimensioning. This implies that costs for the content provider may be significantly reduced.

This project develops methodologies on cache dimensioning for handling different types of services. A significant challenge lies in the fact that popularity of services changes with time, and hence learning, dimensioning and service placement must happen continually. The solution approach is via machine learning, and the project contributes to the fundamentals of learning from a sequence of samples over time, entitled online learning.

The project also includes the development of educational materials on networking, distributed systems and machine learning.

This project considers the cache dimensioning problem in cloud content distribution networks (CDNs), where the objective is to decide how much storage to place at each location in the network. This project addresses key issues essential to developing theoretical foundations, practical online algorithms and low-complexity implementation for providing adaptive cache dimensioning differentiated services in cloud CDNs.

This requires the conjunction of several mathematical tools to analyze online algorithms, leading to systems development to make the algorithms a reality. This project develops a social welfare maximization-based framework for providing adaptive cache dimensioning differentiated service in cloud CDNs. The project is organized into three interdependent thrusts.

The first thrust focuses on a Time-to-Live (TTL) approximation analogy-based analysis to decouple the behaviors of different contents by means of dynamically adapting the timer values to maximize the social welfare. The second thrust focuses on online optimization-based analysis by leveraging online learning to design new online reactive algorithms that are aware of non-stationary popularity and traffic variations.

The third thrust focuses on implementation and evaluation on public cloud infrastructure. An immediate impact of this project is to help design next-generation cloud CDNs leading to greater enterprise productivity and user satisfaction. The impact is enhanced by specific minority inclusion activities, an education plan focusing on caching and machine learning, as well as outreach in the form of summer camps for high school students.

At the same time the project develops fundamental theories that pertain to the area of machine learning, specifically to online learning.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.