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Infrastructure-as-a-Service cloud providers sell virtual machines that are only specified in terms of number of CPU cores, amount of memory, and I/O throughput. Performance-critical aspects such as cache sizes and memory latency are missing or reported in ways that make them hard to compare across cloud providers. It is difficult for users to adapt their application’s behavior to the available resources. In this work, we aim to increase the visibility that cloud users have into shared resources on public clouds. Specifically, we present CacheInspector , a lightweight runtime that determines the performance and allocated capacity of shared caches on multi-tenant public clouds. We validate CacheInspector ’s accuracy in a controlled environment, and use it to study the characteristics and variability of cache resources in the cloud, across time, instances, availability regions, and cloud providers. We show that CacheInspector ’s output allows cloud users to tailor their application’s behavior, including their output quality, to avoid suboptimal performance when resources are scarce. 

Cloud applications are increasingly relying on hundreds of loosely-coupled microservices to complete user requests that meetan application’s end-to-end QoS requirements. Communication time between services accounts for a large fraction of the end-to-endlatency and can introduce performance unpredictability and QoS violations. This work presents our early work onDagger, a hardwareacceleration platform for networking, designed specifically with the unique qualities of microservices in mind. The Dagger architecturerelies on an FPGA-based NIC, closely coupled with the processor over a configurable memory interconnect, designed to offload andaccelerate RPC stacks. Unlike the traditional cloud systems that use PCIe links as the NIC I/O interface, we leverage memory-interconnectedFPGAs as networking devices to provide the efficiency, transparency, and programmability needed for fine-grained microservices. We showthat this considerably improves CPU utilization and performance for cloud RPCs. 

Cloud applications are increasingly shifting from large monolithic services, to complex graphs of loosely-coupled microservices. Despite their advantages, microservices also introduce cascading QoS violations in cloud applications, which are difficult to diagnose and correct. We present Sage, a ML-driven root cause analysis system for interactive cloud microservices. Sage leverages unsupervised learning models to circumvent the overhead of trace labeling, determines the root cause of unpredictable performance online, and applies corrective actions to restore performance. On experiments on both dedicated local clusters and large GCE clusters we show that Sage achieves high root cause detection accuracy and predictable performance.