Many proposals have demonstrated the efficiency advantages of software-defined networking (SDN) in managing data center networks. Common practices employ centralized traffic engineering (TE) in the SDN control plane to optimize load balancing and throughput. Meanwhile, for high availability purposes, the control plane is partitioned to ensure the impact of a single faulty controller is contained. However, the interaction between these two aspects is often overlooked. In particular, we show that the current control plane partitioning approach leads to imbalanced link loads and degraded application performance. To address this issue, we proposevirtual slicing, a new control plane partitioning scheme. Virtual slicing achieves desirable traffic engineering performance while retaining the availability guarantees from the current approach. Virtual slicing is implemented and evaluated with real-world and synthetic traffic traces on production spine-free data center networks. Results show that virtual slicing reduces tail link utilizations by up to 28.4%, and improves flow completion times by up to 36%.

Many proposals have demonstrated the efficiency advantages of software-defined networking (SDN) in managing data center networks. Common practices employ centralized traffic engineering (TE) in the SDN control plane to optimize load balancing and throughput. Meanwhile, for high availability purposes, the control plane is partitioned to ensure the impact of a single faulty controller is contained. However, the interaction between these two aspects is often overlooked. In particular, we show that the current control plane partitioning approach leads to imbalanced link loads and degraded application performance. To address this issue, we propose virtual slicing, a new control plane partitioning scheme. Virtual slicing achieves desirable traffic engineering performance while retaining the availability guarantees from the current approach. Virtual slicing is implemented and evaluated with real-world and synthetic traffic traces on production spine-free data center networks. Results show that virtual slicing reduces tail link utilizations by up to 28.4 %, and improves flow completion times by up to 36 %.

Cache management is critical for Content Delivery Networks (CDNs), impacting their performance and operational costs. Most production CDNs apply static, hand-tuned caching policy parameters at cache servers, such as admission frequency or size thresholds for the Hot Object Caches (HOC) of their system. However, these static policies fall short when a server is faced with unpredictable traffic pattern changes, even when policies employ multiple control parameters/knobs. Recent approaches have proposed learning-based solutions to dynamically adjust policy parameters, but they are limited in action space, caching objectives, or impose high overhead. We propose Darwin, a CDN cache management system that is robust to traffic pattern changes and can flexibly optimize different caching objectives with unrestricted action spaces. Darwin employs a three-stage pipeline involving traffic pattern feature collection, unsupervised clustering for classification, and neural bandit expert selection to choose the optimal caching policy. Through extensive simulations, experiments using an Apache Traffic Server (ATS)-based prototype, and theoretical analysis, we show that Darwin achieves significant performance gain w.r.t. different objectives such as maximizing object hit rates and minimizing disk writes, while simultaneously adapting to traffic pattern shifts. Darwin imposes negligible overhead and achieves high throughput compared to the state-of-the-art.