Cooperative caching versus proactive replication for location dependent request patterns
Today's trend to create and share personal content, such as digital photos and digital movies, results in an explosive growth of a user's personal content archive. Managing such an often distributed collection becomes a complex and time consuming task, indicating the need for a personal content management system that provides storage space transparently, is quality-aware, and is available at any time and at any place to end-users. A key feature of such a Personal Content Storage Service (PCSS) is the ability to search worldwide through the dataset of personal files. Due to the extremely large size of the dataset of personal content, a centralized solution is no longer feasible and an interesting approach for an efficient distributed PCSS implementation is to use a structured Peer-to-Peer network, and more in particular a Distributed Hash Table (DHT), providing a logarithmic lookup performance in the number of network nodes. In order to further increase the lookup performance, a caching layer is typically used between the application layer and the DHT. These caching strategies are location neutral, and usually do not exploit location dependence of request patterns. In this article we present our cooperative caching framework and introduce the cooperative Request Times Distance (RTD) caching algorithm. Since, lookup patterns in a PCSS typically have a power law popularity distribution and exhibit location dependent requests patterns, the proposed caching solution takes into account popularity and distance metrics. To enable cooperation between caches we introduce an update protocol, which only occasionally introduces one hop delay for a lookup operation. We present a systematic analysis of the caching framework and compare the cooperative caching algorithm to the state-of-the-art Beehive replication strategy. The cooperative RTD caching solution shows that when request patterns are more localized, the increase in lookup performance through cooperation is significantly better than Beehive.
personal content distributed hash table caching strategy performance analysis scalability