Christina Delimitrou and Christos Kozyrakis.In Proceedings of the Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) (Houston, TX, USA). Paragon: QoS-Aware Scheduling for Heterogeneous Datacenters. A practical algorithm for balancing the max-min fairness and throughput objectives in traffic engineering. Emilie Danna, Subhasree Mandal, and Arjun Singh.In Proceedings of the 16th USENIX Conference on Networked Systems Design and Implementation (NSDI'19). Hydra: A Federated Resource Manager for Data-Center Scale Analytics. Fumarola, Botong Huang, Kishore Chaliparambil, Arun Suresh, Young Chen, Solom Heddaya, Roni Burd, Sarvesh Sakalanaga, Chris Douglas, Bill Ramsey, and Raghu Ramakrishnan. Carlo Curino, Subru Krishnan, Konstantinos Karanasos, Sriram Rao, Giovanni M.In Proceedings of the ACM Symposium on Cloud Computing (SOCC '14). Reservation-Based Scheduling: If You're Late Don't Blame Us!. Difallah, Chris Douglas, Subru Krishnan, Raghu Ramakrishnan, and Sriram Rao. In Proceedings of the 26th ACM Symposium on Operating Systems Principles. Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini.In Proceedings of the 27th ACM Symposium on Operating Systems Principles (SOSP '19). Taiji: Managing Global User Traffic for Large-Scale Internet Services at the Edge. David Chou, Tianyin Xu, Kaushik Veeraraghavan, Andrew Newell, Sonia Margulis, Lin Xiao, Pol Mauri Ruiz, Justin Meza, Kiryong Ha, Shruti Padmanabha, Kevin Cole, and Dmitri Perelman.In Proceedings of the 11th USENIX Conference on Operating Systems Design and Implementation (OSDI'14). Apollo: Scalable and Coordinated Scheduling for Cloud-Scale Computing. Eric Boutin, Jaliya Ekanayake, Wei Lin, Bing Shi, Jingren Zhou, Zhengping Qian, Ming Wu, and Lidong Zhou.In Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation. In Proceedings of the ACM SIGCOMM 2012 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication (SIGCOMM '12). Surviving Failures in Bandwidth-Constrained Datacenters. Peter Bodík, Ishai Menache, Mosharaf Chowdhury, Pradeepkumar Mani, David A.In IEEE INFOCOM 2008 - The 27th Conference on Computer Communications. Towards Optimal Resource Allocation in Partial-Fault Tolerant Applications. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20). Providing SLOs for Resource-Harvesting VMs in Cloud Platforms. Pradeep Ambati, Inigo Goiri, Felipe Frujeri, Alper Gun, Ke Wang, Brian Dolan, Brian Corell, Sekhar Pasupuleti, Thomas Moscibroda, Sameh Elnikety, Marcus Fontoura, and Ricardo Bianchini.How to Use Alibaba Cloud Reserved Instances to Reduce Costs. In Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation (NSDI'10). Volley: Automated Data Placement for Geo-Distributed Cloud Services. Sharad Agarwal, John Dunagan, Navendu Jain, Stefan Saroiu, Alec Wolman, and Harbinder Bhogan.Local search in combinatorial optimization. Emile Aarts, Emile HL Aarts, and Jan Karel Lenstra.We describe the design of RAS and share our experience of deploying it at scale. 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 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. RAS uses a capacity abstraction called reservation to represent a set of servers dynamically assigned to a logical cluster. In this paper, we describe how Facebook's region-scale Resource Allowance System (RAS) addresses these issues and provides guaranteed capacity. However, no prior work provides capacity reservation with SLO guarantees that takes into account random and correlated hardware failures, datacenter maintenance, and heterogeneous hardware. Capacity reservation is a common offering in public clouds and on-premise infrastructure.

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