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1. Gemini: Learning to Manage CPU Power for Latency-Critical Search Engines

   https://doi.org/10.1109/MICRO50266.2020.00059  

   Zhou, Liang ; Bhuyan, Laxmi N. ; Ramakrishnan, K. K. ( October 2020 , 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO))

   null (Ed.)

   Saving energy for latency-critical applications like web search can be challenging because of their strict tail latency constraints. State-of-the-art power management frameworks use Dynamic Voltage and Frequency Scaling (DVFS) and Sleep states techniques to slow down the request processing and finish the search just-in-time. However, accurately predicting the compute demand of a request can be difficult. In this paper, we present Gemini, a novel power management framework for latency- critical search engines. Gemini has two unique features to capture the per query service time variation. First, at light loads without request queuing, a two-step DVFS is used to manage the CPU power. Our two-step DVFS selects the initial CPU frequency based on the query specific service time prediction and then judiciously boosts the initial frequency at the right time to catch-up to the deadline. The determination of boosting time further relies on estimating the error in the prediction of individual query’s service time. At high loads, where there is request queuing, only the current request being executed and the critical request in the queue adopt a two-step DVFS. All the other requests in-between use the same frequency to reduce the frequency transition overhead. Second, we develop two separate neural network models, one for predicting the service time and the other for the error in the prediction. The combination of these two predictors significantly improves the power saving and tail latency results of our two-step DVFS. Gemini is implemented on the Solr search engine. Evaluations on three representative query traces show that Gemini saves 41% of the CPU power, and is better than other state-of-the-art techniques. 

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2. RackSched: A Microsecond-Scale Scheduler for Rack-Scale Computers

   Zhu, Hang ; Kaffes, Kostis ; Chen, Zixu ; Liu, Zhenming ; Kozyrakis, Christos ; Stoica, Ion ; Jin, Xin ( November 2020 , 14th USENIX Symposium on Operating Systems Design and Implementation)

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   Low-latency online services have strict Service Level Objectives (SLOs) that require datacenter systems to support high throughput at microsecond-scale tail latency. Dataplane operating systems have been designed to scale up multi-core servers with minimal overhead for such SLOs. However, as application demands continue to increase, scaling up is not enough, and serving larger demands requires these systems to scale out to multiple servers in a rack. We present RackSched, the first rack-level microsecond-scale scheduler that provides the abstraction of a rack-scale computer (i.e., a huge server with hundreds to thousands of cores) to an external service with network-system co-design. The core of RackSched is a two-layer scheduling framework that integrates inter-server scheduling in the top-of-rack (ToR) switch with intra-server scheduling in each server. We use a combination of analytical results and simulations to show that it provides near-optimal performance as centralized scheduling policies, and is robust for both low-dispersion and high-dispersion workloads. We design a custom switch data plane for the inter-server scheduler, which realizes power-of-k- choices, ensures request affinity, and tracks server loads accurately and efficiently. We implement a RackSched prototype on a cluster of commodity servers connected by a Barefoot Tofino switch. End-to-end experiments on a twelve-server testbed show that RackSched improves the throughput by up to 1.44x, and scales out the throughput near linearly, while maintaining the same tail latency as one server until the system is saturated. 

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3. FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices

   Qiu, Haoran ; Banerjee, Subho S. ; Jha, Saurabh ; Kalbarczyk, Zbigniew T. ; Iyer, Ravishankar K. ( November 2020 , Proceedings of The 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI ‘20))

   null (Ed.)

   User-facing latency-sensitive web services include numerous distributed, intercommunicating microservices that promise to simplify software development and operation. However, multiplexing of compute resources across microservices is still challenging in production because contention for shared resources can cause latency spikes that violate the service level objectives (SLOs) of user requests. This paper presents FIRM, an intelligent fine-grained resource management framework for predictable sharing of resources across microservices to drive up overall utilization. FIRM leverages online telemetry data and machine-learning methods to adaptively (a) detect/localize microservices that cause SLO violations, (b) identify low-level resources in contention, and (c) take actions to mitigate SLO violations via dynamic reprovisioning. Experiments across four microservice benchmarks demonstrate that FIRM reduces SLO violations by up to 16Å~ while reducing the overall requested CPU limit by up to 62%. Moreover, FIRM improves performance predictability by reducing tail latencies by up to 11Å~. 

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4. InferLine: latency-aware provisioning and scaling for prediction serving pipelines

   https://doi.org/10.1145/3419111.3421285  

   Crankshaw, Daniel ; Sela, Gur-Eyal ; Mo, Xiangxi ; Zumar, Corey ; Stoica, Ion ; Gonzalez, Joseph ; Tumanov, Alexey ( January 2020 , SoCC '20: Proceedings of the 11th ACM Symposium on Cloud Computing)

   null (Ed.)

   Serving ML prediction pipelines spanning multiple models and hardware accelerators is a key challenge in production machine learning. Optimally configuring these pipelines to meet tight end-to-end latency goals is complicated by the interaction between model batch size, the choice of hardware accelerator, and variation in the query arrival process. In this paper we introduce InferLine, a system which provisions and manages the individual stages of prediction pipelines to meet end-to-end tail latency constraints while minimizing cost. InferLine consists of a low-frequency combinatorial planner and a high-frequency auto-scaling tuner. The low-frequency planner leverages stage-wise profiling, discrete event simulation, and constrained combinatorial search to automatically select hardware type, replication, and batching parameters for each stage in the pipeline. The high-frequency tuner uses network calculus to auto-scale each stage to meet tail latency goals in response to changes in the query arrival process. We demonstrate that InferLine outperforms existing approaches by up to 7.6x in cost while achieving up to 34.5x lower latency SLO miss rate on realistic workloads and generalizes across state-of-the-art model serving frameworks. 

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5. MArk: Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving

   Zhang, Chengliang ; Yu, Minchen ; Wang, Wei ; Yan, Feng ( May 2019 , Proceedings of the USENIX Conference)

   The advances of Machine Learning (ML) have sparked a growing demand of ML-as-a-Service: developers train ML models and publish them in the cloud as online services to provide low-latency inference at scale. The key challenge of ML model serving is to meet the response-time Service-Level Objectives (SLOs) of inference workloads while minimizing the serving cost. In this paper, we tackle the dual challenge of SLO compliance and cost effectiveness with MArk (Model Ark), a general-purpose inference serving system built in Amazon Web Services (AWS). MArk employs three design choices tailor-made for inference workload. First, MArk dynamically batches requests and opportunistically serves them using expensive hardware accelerators (e.g., GPU) for improved performance-cost ratio. Second, instead of relying on feedback control scaling or over-provisioning to serve dynamic workload, which can be too slow or too expensive for inference serving, MArk employs predictive autoscaling to hide the provisioning latency at low cost. Third, given the stateless nature of inference serving, MArk exploits the flexible, yet costly serverless instances to cover the occasional load spikes that are hard to predict. We evaluated the performance of MArk using several state-of-the-art ML models trained in popular frameworks including TensorFlow, MXNet, and Keras. Compared with the premier industrial ML serving platform SageMaker, MArk reduces the serving cost up to 7.8× while achieving even better latency performance. 

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