Search for: All records

LEO satellite networks possess highly dynamic topologies, with satellites moving at 27,000 km/hour to maintain their orbit. As satellites move, the characteristics of the satellite network routes change, triggering rerouting events. Frequent rerouting can cause poor performance for path-adaptive algorithms (e.g., congestion control). In this paper, we provide a thorough characterization of route variability in LEO satellite networks, focusing on route churn and RTT variability. We show that high route churn is common, with most paths used for less than half of their lifetime. With some paths used for just a few seconds. This churn is also unnecessary with rerouting leading to marginal gains in most cases (e.g., less than a 15% reduction in RTT). Moreover, we show that the high route churn is harmful to network utilization and congestion control performance. By examining RTT variability, we find that the smallest achievable RTT between two ground stations can increase by 2.5x as satellites move in their orbits. We show that the magnitude of RTT variability depends on the location of the communicating ground stations, exhibiting a spatial structure. Finally, we show that adding more satellites, and providing more routes between stations, does not necessarily reduce route variability. Rather, constellation configuration (i.e., the number of orbits and their inclination) plays a more significant role. We hope that the findings of this study will help with designing more robust routing algorithms for LEO satellite networks. 

Modern datacenter applications are concurrent, so they require synchronization to control access to shared data. Requests can contend for different combinations of locks, depending on application and request state. In this paper, we show that locks, especially blocking synchronization, can squander throughput and harm tail latency, even when the CPU is underutilized. Moreover, the presence of a large number of contention points, and the unpredictability in knowing which locks a request will require, make it difficult to prevent contention through overload control using traditional signals such as queueing delay and CPU utilization. We present Protego, a system that resolves these problems with two key ideas. First, it contributes a new admission control strategy that prevents compute congestion in the presence of lock contention. The key idea is to use marginal improvements in observed throughput, rather than CPU load or latency measurements, within a credit-based admission control algorithm that regulates the rate of incoming requests to a server. Second, it introduces a new latency-aware synchronization abstraction called Active Synchronization Queue Management (ASQM) that allows applications to abort requests if delays exceed latency objectives. We apply Protego to two real-world applications, Lucene and Memcached, and show that it achieves up to 3.3x more goodput and 12.2x lower 99th percentile latency than the state-of-the-art overload control systems while avoiding congestion collapse.