Graph-PHPA: Graph-based Proactive Horizontal Pod Autoscaling for Microservices using LSTM-GNN

• URL: http://arxiv.org/abs/2209.02551v1
• Date: Tue, 6 Sep 2022 14:57:53 GMT
• Title: Graph-PHPA: Graph-based Proactive Horizontal Pod Autoscaling for Microservices using LSTM-GNN
• Authors: Hoa X. Nguyen, Shaoshu Zhu, Mingming Liu
• Abstract summary: Graph-PHPA is a graph-based proactive autoscaling strategy for allocating cloud resources. We demonstrate the efficacy of Graph-PHPA by comparing it with the rule-based resource allocation scheme in as our baseline.
• Score: 4.4345763263216895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Microservice-based architecture has become prevalent for cloud-native applications. With an increasing number of applications being deployed on cloud platforms every day leveraging this architecture, more research efforts are required to understand how different strategies can be applied to effectively manage various cloud resources at scale. A large body of research has deployed automatic resource allocation algorithms using reactive and proactive autoscaling policies. However, there is still a gap in the efficiency of current algorithms in capturing the important features of microservices from their architecture and deployment environment, for example, lack of consideration of graphical dependency. To address this challenge, we propose Graph-PHPA, a graph-based proactive horizontal pod autoscaling strategy for allocating cloud resources to microservices leveraging long short-term memory (LSTM) and graph neural network (GNN) based prediction methods. We evaluate the performance of Graph-PHPA using the Bookinfo microservices deployed in a dedicated testing environment with real-time workloads generated based on realistic datasets. We demonstrate the efficacy of Graph-PHPA by comparing it with the rule-based resource allocation scheme in Kubernetes as our baseline. Extensive experiments have been implemented and our results illustrate the superiority of our proposed approach in resource savings over the reactive rule-based baseline algorithm in different testing scenarios.

Related papers

• Novel Representation Learning Technique using Graphs for Performance Analytics [0.0]
  We propose a novel idea of transforming performance data into graphs to leverage the advancement of Graph Neural Network-based (GNN) techniques. In contrast to other Machine Learning application domains, such as social networks, the graph is not given; instead, we need to build it. We evaluate the effectiveness of the generated embeddings from GNNs based on how well they make even a simple feed-forward neural network perform for regression tasks.
  arXiv  Detail & Related papers  (2024-01-19T16:34:37Z)
• A Microservices Identification Method Based on Spectral Clustering for Industrial Legacy Systems [5.255685751491305]
  We propose an automated microservice decomposition method for extracting microservice candidates based on spectral graph theory. We show that our method can yield favorable results even without the involvement of domain experts.
  arXiv  Detail & Related papers  (2023-12-20T07:47:01Z)
• Online Network Source Optimization with Graph-Kernel MAB [62.6067511147939]
  We propose Grab-UCB, a graph- kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks. We describe the network processes with an adaptive graph dictionary model, which typically leads to sparse spectral representations. We derive the performance guarantees that depend on network parameters, which further influence the learning curve of the sequential decision strategy.
  arXiv  Detail & Related papers  (2023-07-07T15:03:42Z)
• Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services [23.408109000977987]
  Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures. We present Fograph, a novel distributed real-time GNN inference framework that leverages diverse and dynamic resources of multiple fog nodes in proximity to IoT data sources. Prototype-based evaluation and case study demonstrate that Fograph significantly outperforms the state-of-the-art cloud serving and fog deployment by up to 5.39x execution speedup and 6.84x throughput improvement.
  arXiv  Detail & Related papers  (2023-07-04T12:30:01Z)
• Bures-Wasserstein Means of Graphs [60.42414991820453]
  We propose a novel framework for defining a graph mean via embeddings in the space of smooth graph signal distributions. By finding a mean in this embedding space, we can recover a mean graph that preserves structural information. We establish the existence and uniqueness of the novel graph mean, and provide an iterative algorithm for computing it.
  arXiv  Detail & Related papers  (2023-05-31T11:04:53Z)
• A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction [4.572330678291241]
  We develop a unified active learning framework specializing in software performance prediction. We investigate the impact of using different levels of information for active and passive learning. Our approach aims to improve the investment in AI models for different software performance predictions.
  arXiv  Detail & Related papers  (2023-04-06T14:00:48Z)
• Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks [58.720142291102135]
  Large-scale machine learning models are bringing advances to a broad range of fields. Many of these models are too large to be trained on a single machine, and must be distributed across multiple devices. We show that maximum parallelisation is sub-optimal in relation to user-critical metrics such as throughput and blocking rate.
  arXiv  Detail & Related papers  (2023-01-31T17:41:07Z)
• EGRC-Net: Embedding-induced Graph Refinement Clustering Network [66.44293190793294]
  We propose a novel graph clustering network called Embedding-Induced Graph Refinement Clustering Network (EGRC-Net) EGRC-Net effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance. Our proposed methods consistently outperform several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-19T09:08:43Z)
• GraphMI: Extracting Private Graph Data from Graph Neural Networks [59.05178231559796]
  We present textbfGraph textbfModel textbfInversion attack (GraphMI), which aims to extract private graph data of the training graph by inverting GNN. Specifically, we propose a projected gradient module to tackle the discreteness of graph edges while preserving the sparsity and smoothness of graph features. We design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
  arXiv  Detail & Related papers  (2021-06-05T07:07:52Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)
• Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation [37.742218733235084]
  We propose a novel graph-based active learning algorithm for the task of node classification in attributed graphs. Our algorithm uses graph cognizant logistic regression, equivalent to a linearized graph convolutional neural network (GCN) for the prediction phase and maximizes the expected error reduction in the query phase. We conduct experiments on five public benchmark datasets, demonstrating a significant improvement over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-07-09T18:00:53Z)

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.