CloudKit is Apple's cloud backend service and application development framework that provides strongly-consistent storage for structured data and makes it easy to synchronize data across user devices or share it among multiple users. Launched more than 3 years ago, CloudKit forms the foundation for more than 50 Apple apps, including many of our most important and popular applications such as Photos, iCloud Drive, Notes, Keynote, and News, as well as many third-party apps. To deliver this at large scale, CloudKit explicitly leverages multi-tenancy at the application level as well as at the user level to guide efficient data placement and distribution. By using CloudKit application developers are free to focus on delivering the application front-end and logic while relying on CloudKit for scale, consistency, durability and security. CloudKit manages petabytes of data and handles hundreds of millions of users around the world on a daily basis.

Scheduling of large-scale, distributed topology-aware applications requires that not only the properties of the requested machines be considered, but also the properties of the machines' interconnections. This requirement severely complicates the scheduling process, as even a matching between a single multi-processors task and available machines in a single time slot becomes an NP-complete problem with no polynomial approximation. In this paper we propose a complete scheduling framework for multi-cluster, heterogeneous environments that provides, in practice, an efficient solution for the scheduling of topology-aware applications. The proposed framework is very flexible as it is composed of pluggable components and can be easily configured to support a variety of scheduling policies. W e also describe three novel scheduling and coallocation algorithms that were developed and plugged into the framework. The proposed scheduling framework was integrated into the QosCosGrid system, where it is used as the main decision-making module.

The concept of topology-aware grid applications is derived from parallelized computational models of complex systems that are executed on heterogeneous resources, either because they require specialized hardware for certain calculations, or because their parallelization is flexible enough to exploit such resources. Here we describe two such applications, a multi-body simulation of stellar evolution, and an evolutionary algorithm that is used for reverse-engineering gene regulatory networks. We then describe the topology-aware middleware we have developed to facilitate the "modeling-implementing-executing" cycle of complex systems applications. The developed middleware allows topology-aware simulations to run on geographically distributed clusters with or without firewalls between them. Additionally, we describe advanced coallocation and scheduling techniques that take into account the applications topologies. Results are given based on running the topology-aware applications on the Grid'5000 infrastructure.