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.

We design and implement a distributed algorithm for balanced k -way hypergraph partitioning that minimizes fanout, a fundamental hypergraph quantity also known as the communication volume and ( k - 1)-cut metric, by optimizing a novel objective called probabilistic fanout. This choice allows a simple local search heuristic to achieve comparable solution quality to the best existing hypergraph partitioners. Our algorithm is arbitrarily scalable due to a careful design that controls computational complexity, space complexity, and communication. In practice, we commonly process hypergraphs with billions of vertices and hyperedges in a few hours. We explain how the algorithm's scalability, both in terms of hypergraph size and bucket count, is limited only by the number of machines available. We perform an extensive comparison to existing distributed hypergraph partitioners and find that our approach is able to optimize hypergraphs roughly 100 times bigger on the same set of machines. We call the resulting tool Social Hash Partitioner , and accompanying this paper, we open-source the most scalable version based on recursive bisection.

Graph reordering is a powerful technique to increase the locality of the representations of graphs, which can be helpful in several applications. We study how the technique can be used to improve compression of graphs and inverted indexes.