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Cold start delays are a main pain point for today’s FaaS (Function-as-a-Service) platforms. A widely used mitigation strategy is keeping recently invoked function containers alive in memory to enable warm starts with minimal overhead. This paper identifies new challenges that state-of-the-art FaaS keep-alive policies neglect. These challenges are caused by concurrent function invocations, a common FaaS workload behavior. First, concurrent requests present a trade-off between reusing busy containers (delayed warm starts) versus cold-starting containers. Second, concurrent requests cause imbalanced evictions of containers that will be reused shortly thereafter. To tackle the challenges, we propose a novel serverless function container orchestration algorithm called CIDRE. CIDRE makes informed decisions to speculatively choose between a delayed warm start and a cold start under concurrency-driven function scaling. CIDRE uses both fine-grained container-level and coarse-grained concurrency information to make balanced eviction decisions. We evaluate CIDRE extensively using two production FaaS workloads. Results show that CIDRE reduces the cold start ratio and the average invocation overhead by up to 75.1% and 39.3% compared to state-of-the-art function keep-alive policies. 

In metropolitan areas with heavy transit demands, electric vehicles (EVs) are expected to be continuously driving without recharging downtime. Wireless Power Transfer (WPT) provides a promising solution for in-motion EV charging. Nevertheless, previous works are not directly applicable for the deployment of in-motion wireless chargers due to their different charging characteristics. The challenge of deploying in-motion wireless chargers to support the continuous driving of EVs in a metropolitan road network with the minimum cost remains unsolved. We propose CatCharger to tackle this challenge. By analyzing a metropolitan-scale dataset, we found that traffic attributes like vehicle passing speed, daily visit frequency at intersections (i.e., landmarks) and their variances are diverse, and these attributes are critical to in-motion wireless charging performance. Driven by these observations, we first group landmarks with similar attribute values using the entropy minimization clustering method, and select candidate landmarks from the groups with suitable attribute values. Then, we use the Kernel Density Estimator (KDE) to deduce the expected vehicle residual energy at each candidate landmark and consider EV drivers’ routing choice behavior in charger deployment. Finally, we determine the deployment locations by formulating and solving a multi-objective optimization problem, which maximizes vehicle traffic flow at charger deployment positions while guaranteeing the continuous driving of EVs at each landmark. Trace-driven experiments demonstrate that CatCharger increases the ratio of driving EVs at the end of a day by 12.5% under the same deployment cost. 

The key to optimizing the performance of an anycast-based sys- tem (e.g., the root DNS or a CDN) is choosing the right set of sites to announce the anycast prefix. One challenge here is predicting catchments. A naïve approach is to advertise the prefix from all subsets of available sites and choose the best-performing subset, but this does not scale well. We demonstrate that by conducting pairwise experiments between sites peering with tier-1 networks, we can predict the catchments that would result if we announce to any subset of the sites. We prove that our method is effective in a simplified model of BGP, consistent with common BGP routing policies, and evaluate it in a real-world testbed. We then present AnyOpt, a system that predicts anycast catchments. Using AnyOpt, a network operator can find a subset of anycast sites that minimizes client latency without using the naïve approach. In an experiment using 15 sites, each peering with one of six transit providers, AnyOpt predicted site catchments of 15,300 clients with 94.7% accuracy and client RTTs with a mean error of 4.6%. AnyOpt identified a subset of 12 sites, announcing to which lowers the mean RTT to clients by 33ms compared to a greedy approach that enables the same number of sites with the lowest average unicast latency. 

This report is based on activities supported by the National Science Foundation under award number 2006409. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.