Radhika Niranjan Mysore

Scale-Out Networking in the Data Center

Scale-out architectures supporting flexible, incremental scalability are common for computing and storage. However, the network remains the last bastion of the traditional scale-up approach, making it the data centers weak link. Through the UCSD Triton network architecture, the authors explore issues in managing the network as a single plug-and-play virtualizable fabric scalable to hundreds of thousands of ports and petabits per second of aggregate bandwidth.

FasTrak: enabling express lanes in multi-tenant data centers

The shared nature of multi-tenant cloud networks requires providing tenant isolation and quality of service, which in turn requires enforcing thousands of network-level rules, policies, and traffic rate limits. Enforcing these rules in virtual machine hypervisors imposes significant computational overhead, as well as increased latency. In FasTrak, we seek to exploit temporal locality in flows and flow sizes to offload a subset of network virtualization functionality from the hypervisor into switch hardware freeing up the hypervisor. FasTrak manages the required hardware and hypervisor rules as a unified set, moving rules back and forth to minimize the overhead of network virtualization, and focusing on flows (or flow aggregates) that are either most latency sensitive or exhibit the highest packets-per-second rates.

ALIAS: scalable, decentralized label assignment for data centers

Modern data centers can consist of hundreds of thousands of servers and millions of virtualized end hosts. Managing address assignment while simultaneously enabling scalable communication is a challenge in such an environment. We present ALIAS, an addressing and communication protocol that automates topology discovery and address assignment for the hierarchical topologies that underlie many data center network fabrics. Addresses assigned by ALIAS interoperate with a variety of scalable communication techniques. ALIAS is fully decentralized, scales to large network sizes, and dynamically recovers from arbitrary failures, without requiring modifications to hosts or to commodity switch hardware. We demonstrate through simulation that ALIAS quickly and correctly configures networks that support up to hundreds of thousands of hosts, even in the face of failures and erroneous cabling, and we show that ALIAS is a practical solution for auto-configuration with our NetFPGA testbed implementation.

TritonSort: A Balanced and Energy-Efficient Large-Scale Sorting System

We present TritonSort, a highly efficient, scalable sorting system. It is designed to process large datasets, and has been evaluated against as much as 100TB of input data spread across 832 disks in 52 nodes at a rate of 0.938TB/min. When evaluated against the annual Indy GraySort sorting benchmark, TritonSort is 66% better in absolute performance and has over six times the per-node throughput of the previous record holder. When evaluated against the 100TB Indy JouleSort benchmark, TritonSort sorted 9703 records/Joule. In this article, we describe the hardware and software architecture necessary to operate TritonSort at this level of efficiency. Through careful management of system resources to ensure cross-resource balance, we are able to sort data at approximately 80% of the disks’ aggregate sequential write speed. We believe the work holds a number of lessons for balanced system design and for scale-out architectures in general. While many interesting systems are able to scale linearly with additional servers, per-server performance can lag behind per-server capacity by more than an order of magnitude. Bridging the gap between high scalability and high performance would enable either significantly less expensive systems that are able to do the same work or provide the ability to address significantly larger problem sets with the same infrastructure.

Condor: Better Topologies Through Declarative Design

The design space for large, multipath datacenter networks is large and complex, and no one design fits all purposes. Network architects must trade off many criteria to design cost-effective, reliable, and maintainable networks, and typically cannot explore much of the design space. We present Condor, our approach to enabling a rapid, efficient design cycle. Condor allows architects to express their requirements as constraints via a Topology Description Language (TDL), rather than having to directly specify network structures. Condor then uses constraint-based synthesis to rapidly generate candidate topologies, which can be analyzed against multiple criteria. We show that TDL supports concise descriptions of topologies such as fat-trees, BCube, and DCell; that we can generate known and novel variants of fat-trees with simple changes to a TDL file; and that we can synthesize large topologies in tens of seconds. We also show that Condor supports the daunting task of designing multi-phase network expansions that can be carried out on live networks.

Brief announcement: a randomized algorithm for label assignment in dynamic networks

In large-scale networking environments, such as data centers, a key difficulty is the assignment of labels to network elements. Labels can be assigned statically, e.g. MAC-addresses in traditional Layer 2 networks, or by a central authority as in DHCP in Layer 3 networks. On the other hand, networks requiring a dynamic solution often use a Consensus-based state machine approach. While designing Alias [2], a protocol for automatically assigning hierarchically meaningful addresses in data center networks, we encountered an instance of label assignment with entirely different requirements. In this case, the rules for labels depend on connectivity, and connectivity (and hence, labels) changes over time. Thus, neither static assignment nor a state machine approach is ideal.