  Content-delivery applications can achieve scalability and reduce wide-area
network traffic using geographically distributed caches. However, each deployed
cache has an associated cost, and under time-varying request rates (e.g., a
daily cycle) there may be long periods when the request rate from the local
region is not high enough to justify this cost. Cloud computing offers a
solution to problems of this kind, by supporting dynamic allocation and release
of resources. In this paper, we analyze the potential benefits from dynamically
instantiating caches using resources from cloud service providers. We develop
novel analytic caching models that accommodate time-varying request rates,
transient behavior as a cache fills following instantiation, and selective
cache insertion policies. Within the context of a simple cost model, we then
develop bounds and compare policies with optimized parameter selections to
obtain insights into key cost/performance tradeoffs. We find that dynamic cache
instantiation can provide substantial cost reductions, that potential
reductions strongly dependent on the object popularity skew, and that selective
cache insertion can be even more beneficial in this context than with
conventional edge caches. Finally, our contributions also include accurate and
easy-to-compute approximations that are shown applicable to LRU caches under
time-varying workloads.
