DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning

• URL: http://arxiv.org/abs/2309.00859v1
• Date: Sat, 2 Sep 2023 08:22:21 GMT
• Title: DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning
• Authors: Chunyang Meng, Shijie Song, Haogang Tong, Maolin Pan, Yang Yu
• Abstract summary: This paper presents DeepScaler, a deep learning-based holistic autoscaling approach. It focuses on coping with service dependencies to optimize service-level agreements (SLA) assurance and cost efficiency. Experimental results demonstrate that our method implements a more effective autoscaling mechanism for microservice.
• Score: 4.128665560397244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or de-provisioning resources for cloud software services and applications without human intervention to adapt to workload fluctuations. However, autoscaling microservice is challenging due to various factors. In particular, complex, time-varying service dependencies are difficult to quantify accurately and can lead to cascading effects when allocating resources. This paper presents DeepScaler, a deep learning-based holistic autoscaling approach for microservices that focus on coping with service dependencies to optimize service-level agreements (SLA) assurance and cost efficiency. DeepScaler employs (i) an expectation-maximization-based learning method to adaptively generate affinity matrices revealing service dependencies and (ii) an attention-based graph convolutional network to extract spatio-temporal features of microservices by aggregating neighbors' information of graph-structural data. Thus DeepScaler can capture more potential service dependencies and accurately estimate the resource requirements of all services under dynamic workloads. It allows DeepScaler to reconfigure the resources of the interacting services simultaneously in one resource provisioning operation, avoiding the cascading effect caused by service dependencies. Experimental results demonstrate that our method implements a more effective autoscaling mechanism for microservice that not only allocates resources accurately but also adapts to dependencies changes, significantly reducing SLA violations by an average of 41% at lower costs.

Related papers

• Leveraging Interpretability in the Transformer to Automate the Proactive Scaling of Cloud Resources [1.1470070927586018]
  We develop a model that captures the relationship between an end-to-end latency, requests at the front-end level, and resource utilization. We then use the developed model to predict the end-to-end latency. We demonstrate the merit of a microservice-based application and provide a roadmap to deployment.
  arXiv  Detail & Related papers  (2024-09-04T22:03:07Z)
• TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features [0.5161531917413706]
  Temporal Prediction is essential to identify a suitable service over time. Recent methods hardly achieved desired accuracy due to various limitations. This paper proposes a scalable strategy for Temporal Prediction using Multi-source Collaborative-Features.
  arXiv  Detail & Related papers  (2023-03-30T06:49:53Z)
• PBScaler: A Bottleneck-aware Autoscaling Framework for Microservice-based Applications [6.453782169615384]
  We propose PBScaler, a bottleneck-aware autoscaling framework for microservice-based applications. We show that PBScaler outperforms existing approaches while conserving resources efficiently.
  arXiv  Detail & Related papers  (2023-03-26T04:20:17Z)
• Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning [53.17258888552998]
  This work proposes an exploration variant of the basic $Q$-learning protocol with linear function approximation. We show that the performance of the algorithm degrades very gracefully under a novel and more permissive notion of approximation error.
  arXiv  Detail & Related papers  (2022-06-01T23:26:51Z)
• A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud [10.970391043991363]
  We propose an end-to-end predictive meta model-based RL algorithm, aiming to optimally allocate resource to maintain a stable CPU utilization level. Our algorithm not only ensures the predictability and accuracy of the scaling strategy, but also enables the scaling decisions to adapt to the changing workloads with high sample efficiency.
  arXiv  Detail & Related papers  (2022-05-31T13:54:04Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Energy-Efficient Multi-Orchestrator Mobile Edge Learning [54.28419430315478]
  Mobile Edge Learning (MEL) is a collaborative learning paradigm that features distributed training of Machine Learning (ML) models over edge devices. In MEL, possible coexistence of multiple learning tasks with different datasets may arise. We propose lightweight algorithms that can achieve near-optimal performance and facilitate the trade-offs between energy consumption, accuracy, and solution complexity.
  arXiv  Detail & Related papers  (2021-09-02T07:37:10Z)
• Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks [88.15736037284408]
  We study a new model of multiple federated learning services at the multi-access edge computing server. We propose a joint resource optimization and hyper-learning rate control problem, namely MS-FEDL. Our simulation results demonstrate the convergence performance of our proposed algorithms.
  arXiv  Detail & Related papers  (2020-11-25T01:29:41Z)
• Dif-MAML: Decentralized Multi-Agent Meta-Learning [54.39661018886268]
  We propose a cooperative multi-agent meta-learning algorithm, referred to as MAML or Dif-MAML. We show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting.
  arXiv  Detail & Related papers  (2020-10-06T16:51:09Z)
• AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments [0.0]
  Serverless computing has emerged as a compelling new paradigm of cloud computing models in recent years. A common approach among both commercial and open source serverless computing platforms is workload-based auto-scaling. In this paper we investigate the applicability of a reinforcement learning approach to request-based auto-scaling in a serverless framework.
  arXiv  Detail & Related papers  (2020-05-29T06:18:39Z)
• A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service [68.84245063902908]
  This paper introduces a novel distributed architecture for deep-learning-as-a-service. It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
  arXiv  Detail & Related papers  (2020-03-30T15:12:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.