In this paper we consider the problem of estimating the difficulty of parking at a particular time and place; this problem is a critical sub-component for any system providing parking assistance to users. We describe an approach to this problem that is currently in production in Google Maps, providing inferences in cities across the world. We present a wide range of features intended to capture different aspects of parking difficulty and study their effectiveness both alone and in combination. We also evaluate various model architectures for the prediction problem. Finally, we present challenges faced in estimating parking difficulty in different regions of the world, and the approaches we have taken to address them.

On multi-core processors, applications are run sharing the cache. This paper presents optimization theory to co-locate applications to minimize cache interference and maximize performance. The theory precisely specifies MRC-based composition, optimization, and correctness conditions. The paper also presents a new technique called footprint symbiosis to obtain the best shared cache performance underfair CPU allocation as well as a new sampling technique which reduces the cost of locality analysis. When sampling and optimization are combined, the paper shows that it takes less than 0.1 second analysis per program to obtain a co-run that is within 1.5 percent of the best possible performance. In an exhaustive evaluation with 12,870 tests, the best prior work improves co-run performance by 56 percent on average. The new optimization improves it by another 29 percent. Without single co-run test, footprint symbiosis is able to choose co-run choices that are just 8 percent slower than the best co-run solutions found with exhaustive testing.

On multicore processors, applications are run sharing the cache. This paper presents online optimization to collocate applications to minimize cache interference to maximize performance. The paper formulates the optimization problem and solution, presents a new sampling technique for locality analysis and evaluates it in an exhaustive test of 12,870 cases. For locality analysis, previous sampling was two orders of magnitude faster than full-trace analysis. The new sampling reduces the cost by another two orders of magnitude. The best prior work improves co-run performance by 56% on average. The new optimization improves it by another 29%. When sampling and optimization are combined, the paper shows that it takes less than 0.1 second analysis per program to obtain a co-run that is within 1.5% of the best possible performance.

When a cache is shared by multiple cores, its space may be allocated either by sharing, partitioning, or both. We call the last case partition-sharing. This paper studies partition-sharing as a general solution, and presents a theory an technique for optimizing partition-sharing. We present a theory and a technique to optimize partition sharing. The theory shows that the problem of partition-sharing is reducible to the problem of partitioning. The technique uses dynamic programming to optimize partitioning for overall miss ratio, and for two different kinds of fairness. Finally, the paper evaluates the effect of optimal cache sharing and compares it with conventional solutions for thousands of 4-program co-run groups, with nearly 180 million different ways to share the cache by each co-run group. Optimal partition-sharing is on average 26% better than free-for-all sharing, and 98% better than equal partitioning. We also demonstrate the trade-off between optimal partitioning and fair partitioning.

Cache sharing among multiple computing units on chip is common in today's multi-core processors, and a lot of research has focused on the effective management of shared cache. A software management method called page coloring is commonly used to divide the cache among different applications competing for the same cache entries. Both static and dynamic cache partition mechanism have been implemented in operating system or user-space level. However, few efforts have been made under virtualization environments. Our previous work has provided a static cache partition method based on page coloring in Xen, following that, a dynamic cache partition mechanism called Colored Page Migration (CoPaM) is presented in this paper.