Network resource management, or how bandwidth is allocated to flows, tenants, or applications, is a challenging problem. In this paper, we call for using the database abstraction for network resource management. A database provides simple constructs for supporting complex resource management tasks, such as transactions to support bandwidth reservations on multiple links, virtual tables for restricting the view of tenants in a cloud environment, and many others. To highlight the opportunities and challenges in this space, we present a research agenda around new abstractions and policy languages, the necessary data plane support, and potential for domain specific replication and sharding strategies for the resource management database.

Duplication can be a powerful strategy for overcoming stragglers in cloud services, but is often used conservatively because of the risk of overloading the system. We call for making duplication a first-class concept in cloud systems, and make two contributions in this regard. First, we present duplicate-aware scheduling or DAS, an aggressive duplication policy that duplicates every job, but keeps the system safe by providing suitable support (prioritization and purging) at multiple layers of the cloud system. Second, we present the D-Stage abstraction, which supports DAS and other duplication policies across diverse layers of a cloud system (e.g., network, storage, etc.). The D-Stage abstraction decouples the duplication policy from the mechanism, and facilitates working with legacy layers of a system. Using this abstraction, we evaluate the benefits of DAS for two data parallel applications (HDFS, an in-memory workload generator) and a network function (Snort-based IDS cluster). Our experiments on the public cloud and Emulab show that DAS is safe to use, and the tail latency improvement holds across a wide range of workloads.

Existing flow scheduling schemes for data center networks optimize for a specific workload and performance metric. In this paper, we present 2D, a new scheduling policy that offers robustness across performance metrics and changing workloads - a ground existing scheduling policies are unable to cover. 2D combines basic scheduling building blocks of multiplexing and serialization in a principled way, ensuring tail optimal performance across workloads while also improving the average (and lower percentiles) completion times.