Load Balancing plays a vital role in cloud data centers to distribute traffic among instances of network functions or services. State-of-the-art load balancers dispatch traffic obliviously without considering the real-time utilization of service instances and therefore can lead to uneven load distribution and sub-optimal performance. In this article, we design and implement Spotlight, a scalable and distributed load balancing architecture that maintains connection-to-instance mapping consistency at the edge of data center networks. Spotlight uses a new stateful flow dispatcher which periodically polls instances’ load and dispatches incoming connections to instances in proportion to their available capacity. Our design utilizes a distributed control plane and in-band flow dispatching; thus, it scales horizontally in data center networks. Through extensive flow-level simulation and packet-level experiments on a testbed with HTTP traffic on unmodified Linux kernel, we demonstrate that compared to existing methods Spotlight distributes traffic more efficiently and has near-optimum performance in terms of overall service utilization. Compared to existing solutions, Spotlight improves aggregated throughput and average flow completion time by at least 20 percent with infrequent control plane updates. Moreover, we show that Spotlight scales horizontally as it updates the switches at O(100ms) and is resilient to lack of control plane convergence.

Advances in software-defined networking (SDN) enable a wave of innovation in a wide selection of networks ranging from data center networks to WAN. While existing standard bodies for mobile networks define stringent requirements, they too are embracing the flexibility of SDN in defining the specifications of the next generation of mobile networks. Mobile edge computing (MEC), in particular, is an emerging architecture to bring virtualized network functions and programmable network devices closer to the user. For instance, delay-sensitive or bandwidth-hungry computing resources are moved to the edge of the radio access network (RAN) to provide low latency computation and/or content for users while alleviating the backhaul pressure for network operators. In this paper, we propose an edge gateway (EGW) in the MEC that enables offloading of computation and storage resources to the edge of mobile networks. The EGW is backward compatible with components and protocols of LTE networks and does not require any modification in the user equipment, LTE software, or offloaded resources. We have designed and implemented the EGW using P4 language and verified its operation on a small testbed using a low-end P4 target and a reference LTE protocol stack.

Enterprise data centers (EDCs) are critical infrastructure to large enterprises, government agencies, research institutions, etc. They are used to support a variety of off-the-shelf and customized services. EDCs are different from cloud data centers (CDCs) in two major aspects. Firstly, an EDC is usually built over time and consists of old and new equipment. Secondly, the type of services and applications in EDCs are quite different from those in CDCs. Therefore, we expect that the traffic characteristics in EDCs would also be different from those in CDCs. While most existing data center measurements were from CDCs, we performed extensive traffic measurement and analysis in an EDC that provided multiple services to over a million users. We present the data center architecture, measurement methodology, measurement results, and analysis. The results include traffic matrix, traffic distribution, flow characteristics, and TCP characteristics. Our research reveals that the traffic characteristics in the EDC are indeed quite different from the reported results in CDCs. For example, the traffic matrix tends to be sparse rather than all-to-all. Based on the analysis we provide a few guidelines for EDC design, optimization, and anomaly detection. As the first most extensive study on EDC traffic, our work provides valuable information to future EDC design and implementation, and also helps researchers develop insights into the differences and similarities between EDCs and CDCs.