Cloud services have shifted from monolithic designs to microservices running on cloud-native infrastructure with monitoring systems to ensure service level agreements (SLAs). However, traditional monitoring systems no longer meet the demands of cloud-native monitoring. In Alibaba’s “double eleven” shopping festival, it is observed that the monitor occupies resources of the monitored infrastructure and even disrupts services. In this paper, we propose a novel monitoring system named for cloud-native monitoring. achieves zero overhead in collecting raw metrics using one-sided remote direct memory access (RDMA) and remedies network congestion by adopting a receiver-driven flow control scheme. also features a priority queue mechanism to meet different quality of service requirements and an efficient batch processing design to relieve CPU occupation. has been deployed and evaluated in four different clusters with heterogeneous RDMA NIC devices and architectures in Alibaba Cloud. Results show that achieves no CPU occupation at the monitored host and supports$1\sim10k$hosts with$0.1\sim1s$sampling interval using a single thread for network I/O. significantly relieves the incast issue and maintains$80\sim95\%$of bandwidth utilization in several clusters when monitoring$1k$hosts. also ensures services with high priority accomplish collecting metrics earlier than low priority ones by at least$400 \mu s$when monitoring$1k$hosts.

In-memory key-value stores (IMKVSes) serve many online applications. They generally adopt the fork-based snapshot mechanism to support data backup. However, this method can result in query latency spikes because the engine is out-of-service for queries during the snapshot. In contrast to existing research optimizing snapshot algorithms, we address the problem from the operating system (OS) level, while keeping the data persistent mechanism in IMKVSes unchanged. Specifically, we first study the impact of the fork operation on query latency. Based on findings in the study, we propose Async-fork, which performs the fork operation asynchronously to reduce the out-of-service time of the engine. Async-fork is implemented in the Linux kernel and deployed into the online Redis database in public clouds. Our experiment results show that Async-fork can significantly reduce the tail latency of queries during the snapshot.

While user-facing services experience diurnal load patterns, co-locating services improve hardware utilization. Prior work on co-locating services on GPUs run queries sequentially, as the latencies of the queries are neither stable nor predictable when running simultaneously. The input sensitiveness and the non-deterministic operator overlap are two primary factors of the latency unpredictability. Hence, We propose Abacus, a runtime system that runs multiple services simultaneously. Abacus enables deterministic operator overlap to enforce latency predictability. Abacus composes of an overlap-aware latency predictor, a headroom-based query controller, and segmental model executors. The predictor predicts the latencies of the deterministic operator overlap. The controller determines the appropriate operator overlap for the QoS guarantee of all the services. The executors run the operators as needed to support the deterministic operator overlap. Our evaluation shows that Abacus reduces 51.3% of the QoS violation and improves the throughput by 29.8% on average compared with state-of-the-art solutions.

Database platform-as-a-service (dbPaaS) is developing rapidly and a large number of databases have been migrated to run on the Clouds for the low cost and flexibility. Emerging Clouds rely on the tenants to provide the resource specification for their database workloads. However, they tend to over-estimate the resource requirement of their databases, resulting in the unnecessarily high cost and low Cloud utilization. A methodology that automatically suggests the “just-enough” resource specification that fulfills the performance requirement of every database workload is profitable.

While microservices that have stringent Quality-of-Service constraints are deployed in the Clouds, the long-term rented infrastructures that host the microservices are under-utilized except peak hours due to the diurnal load pattern. It is resource efficient for Cloud vendors and cost efficient for service maintainers to deploy the microservices in the long-term infrastructure at high load and in the serverless computing platform at low load. However, prior work fails to take advantage of the opportunity, because the contention between microservices on the serverless platform seriously affects their response latencies.Our investigation shows that the load of a microservice, the shared resource contentions on the serverless platform, and its sensitivities to the contention together affect the response latency of the microservice on the platform. To this end, we propose Amoeba, a runtime system that dynamically switches the deployment of a microservice. Amoeba is comprised of a contention-aware deployment controller, a hybrid execution engine, and a multi-resource contention monitor. The deployment controller predicts the tail latency of a microservice based on its load and the contention on the serverless platform, and determines the appropriate deployment of the microservice. The hybrid execution engine enables the quick switch of the two deploy modes. The contention monitor periodically quantifies the contention on multiple types of shared resources. Experimental results show that Amoeba is able to significantly reduce up to 72.9% of CPU usage and up to 84.9% of memory usage compared with the traditional pure IaaS-based deployment, while ensuring the required latency target.

The tenants of public cloud platforms share hard-ware resources on the same node, resulting in the potential for performance interference (or malicious attacks). A tenant is able to degrade the performance of its neighbors on the same node significantly through overuse of the shared memory bus, last level cache (LLC)/memory bandwidth, and power. To eliminate such unfairness we propose Alita, a runtime system consisting of an online interference identifier and adaptive interference eliminator. The interference identifier monitors hardware and system-level event statistics to identify resource polluters. The eliminator improves the performance of normal applications by throttling only the resource usage of polluters. Specifically, Alita adopts bus lock sparsification, bias LLC/bandwidth isolation, and selective power throttling to throttle the resource usage of polluters. Results for an experimental platform and in-production cloud platform with 30,000 nodes demonstrate that Alita significantly improves the performance of co-located virtual machines in the presence of resource polluters based on system-level knowledge.

Database platform-as-a-service (dbPaaS) is developing rapidly and a large number of databases have been migrated to run on the Clouds for the low cost and flexibility. Emerging Clouds rely on the tenants to provide the resource specification for their database workloads. However, they tend to over-estimate the resource requirement of their databases, resulting in the unnecessarily high cost and low Cloud utilization. A methodology that automatically suggests the "just-enough" resource specification that fulfills the performance requirement of every database workload is profitable. To this end, we propose URSA, a capacity planning system for dbPaaS Clouds. Our real system experimental results show that URSA can accurately plan the capacity for dbPaaS.