More Like this

1. FLASH: Fast Model Adaptation in ML-Centric Cloud Platforms.

   Qiu, H ; Mao, W ; Patke, A ; Cui, S ; Wang, C ; Franke, H ; Kalbarczyk, Z ; Başar, T ; Iyer, R ( September 2024 , MLSys)

   Gibbons, Phillip B ; Gennady, P ; De_Sa, Christopher (Ed.)

   The emergence of ML in various cloud system management tasks (e.g., workload autoscaling and job scheduling) has become a core driver of ML-centric cloud platforms. However, there are still numerous algorithmic and systems challenges that prevent ML-centric cloud platforms from being production-ready. In this paper, we focus on the challenges of model performance variability and costly model retraining, introduced by dynamic workload patterns and heterogeneous applications and infrastructures in cloud environments. To address these challenges, we present FLASH, an extensible framework for fast model adaptation in ML-based system management tasks. We show how FLASH leverages existing ML agents and their training data to learn to generalize across applications/environments with meta-learning. FLASH can be easily integrated with an existing ML-based system management agent with a unified API. We demonstrate the use of FLASH by implementing three existing ML agents that manage (1) resource configurations, (2) autoscaling, and (3) server power. Our experiments show that FLASH enables fast adaptation to new, previously unseen applications/environments (e.g., 5.5× faster than transfer learning in the autoscaling task), indicating significant potential for adopting ML-centric cloud platforms in production. 

   more » « less
2. AWARE: Automate Workload Autoscaling with Reinforcement Learning in Production Cloud Systems

   Qiu, Haoran ; Mao, Weichao ; Wang, Chen ; Franke, Hubertus ; Yousseff, Alaa ; Kalbarczyk, Zbigniew T. ; Iyer, Ravishankar K ( July 2023 , 2023 USENIX Annual Technical Conference (USENIX ATC 23))

   Workload autoscaling is widely used in public and private cloud systems to maintain stable service performance and save resources. However, it remains challenging to set the optimal resource limits and dynamically scale each workload at runtime. Reinforcement learning (RL) has recently been proposed and applied in various systems tasks, including resource management. In this paper, we first characterize the state-of-the-art RL approaches for workload autoscaling in a public cloud and point out that there is still a large gap in taking the RL advances to production systems. We then propose AWARE, an extensible framework for deploying and managing RL-based agents in production systems. AWARE leverages meta-learning and bootstrapping to (a) automatically and quickly adapt to different workloads, and (b) provide safe and robust RL exploration. AWARE provides a common OpenAI Gym-like RL interface to agent developers for easy integration with different systems tasks. We illustrate the use of AWARE in the case of workload autoscaling. Our experiments show that AWARE adapts a learned autoscaling policy to new workloads 5.5x faster than the existing transfer-learning-based approach and provides stable online policy-serving performance with less than 3.6% reward degradation. With bootstrapping, AWARE helps achieve 47.5% and 39.2% higher CPU and memory utilization while reducing SLO violations by a factor of 16.9x during policy training. 

   more » « less
3. Mu: An Efficient, Fair and Responsive Serverless Framework for Resource-Constrained Edge Clouds

   https://doi.org/10.1145/3472883.3487014  

   Mittal, Viyom ; Qi, Shixiong ; Bhattacharya, Ratnadeep ; Lyu, Xiaosu ; Li, Junfeng ; Kulkarni, Sameer G. ; Li, Dan ; Hwang, Jinho ; Ramakrishnan, K. K. ; Wood, Timothy ( November 2021 , Proceedings of the ACM Symposium on Cloud Computing)

   Serverless computing platforms simplify development, deployment, and automated management of modular software functions. However, existing serverless platforms typically assume an over-provisioned cloud, making them a poor fit for Edge Computing environments where resources are scarce. In this paper we propose a redesigned serverless platform that comprehensively tackles the key challenges for serverless functions in a resource constrained Edge Cloud. Our Mu platform cleanly integrates the core resource management components of a serverless platform: autoscaling, load balancing, and placement. Each worker node in Mu transparently propagates metrics such as service rate and queue length in response headers, feeding this information to the load balancing system so that it can better route requests, and to our autoscaler to anticipate workload fluctuations and proactively meet SLOs. Data from the Autoscaler is then used by the placement engine to account for heterogeneity and fairness across competing functions, ensuring overall resource efficiency, and minimizing resource fragmentation. We implement our design as a set of extensions to the Knative serverless platform and demonstrate its improvements in terms of resource efficiency, fairness, and response time. Evaluating Mu, shows that it improves fairness by more than 2x over the default Kubernetes placement engine, improves 99th percentile response times by 62% through better load balancing, reduces SLO violations and resource consumption by pro-active and precise autoscaling. Mu reduces the average number of pods required by more than ~15% for a set of real Azure workloads. 

   more » « less
4. Mitigating Large Response Time Fluctuations through Fast Concurrency Adapting in Clouds

   https://doi.org/10.1109/IPDPS47924.2020.00046  

   Liu, Jianshu ; Zhang, Shungeng ; Wang, Qingyang ; Wei, Jinpeng ( May 2020 , 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS))

   null (Ed.)

   Dynamically reallocating computing resources to handle bursty workloads is a common practice for web applications (e.g., e-commerce) in clouds. However, our empirical analysis on a standard n-tier benchmark application (RUBBoS) shows that simply scaling an n-tier application by reallocating hardware resources without fast adapting soft resources (e.g., server threads, connections) may lead to large response time fluctuations. This is because soft resources control the workload concurrency of component servers in the system: adding or removing hardware resources such as Virtual Machines (VMs) can implicitly change the workload concurrency of dependent servers, causing either under- or over-utilization of the critical hardware resource in the system. To quickly identify the optimal soft resource allocation of each server in the system and stabilize response time fluctuation, we propose a novel Scatter-Concurrency-Throughput (SCT) model based on the monitoring of each server's real-time concurrency and throughput. We then implement a Concurrency-aware system Scaling (ConScale) framework which integrates the SCT model to fast adapt the soft resource allocations of key servers during the system scaling process. Our experiments using six realistic bursty workload traces show that ConScale can effectively mitigate the response time fluctuations of the target web application compared to the state-of-the-art cloud scaling strategies such as EC2-AutoScaling. 

   more » « less
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. 

   more » « less