Capacity reservation is a common offering in public clouds and on-premise infrastructure. However, no prior work provides capacity reservation with SLO guarantees that takes into account random and correlated hardware failures, datacenter maintenance, and heterogeneous hardware. In this paper, we describe how Facebook's region-scale Resource Allowance System (RAS) addresses these issues and provides guaranteed capacity. RAS uses a capacity abstraction called reservation to represent a set of servers dynamically assigned to a logical cluster. We take a two-level approach to scale resource allocation to all datacenters in a region, where a mixed-integer-programming solver continuously optimizes server-to-reservation assignments off the critical path, and a traditional container allocator does real-time placement of containers on servers in a reservation. As a relatively new component of Facebook's 10-year old cluster manager Twine, RAS has been running in production for almost two years, continuously optimizing the allocation of millions of servers to thousands of reservations. We describe the design of RAS and share our experience of deploying it at scale.

Many networked applications that run in the background on a mobile device incur significant energy drains when using the cellular radio interface for communication. This is mainly due to the radio-tail, where the cellular radio remaining in a high energy state for up to 20s after each communication spurt. In order to cut down energy consumption, many recent devices employ fast dormancy, a feature that forces the client radio to quickly go into a low energy state after a fixed short idle period. However, aggressive idle timer values for fast dormancy can increase signaling overhead due to frequent state transitions, which negatively impacts the network. In this work, we have designed and implemented RadioJockey, a system that uses program execution traces to predict the end of communication spurts, thereby accurately invoking fast dormancy without increasing network signaling load. We evaluate RadioJockey on a broad range of background applications and show that it achieves 20-40\% energy savings with negligible increase in signaling overhead compared to fixed idle timer-based approaches.