Proceedings ArticleDOI
AlphaR: Learning-Powered Resource Management for Irregular, Dynamic Microservice Graph
Xiaofeng Hou,Chao Li,Jiacheng Liu,Lu Zhang,Shaolei Ren,Jingwen Leng,Quan Chen,Minyi Guo +7 more
- pp 797-806
TLDR
In this paper, a learning-powered resource management system tailored to the microservice environment is proposed, which can improve the mean and p95 response time by up to 80% and 77.5% respectively compared with conventional schemes.Abstract:
The microservice architecture is a hot trend which proposes to transform the traditional monolith application into massive dynamic and irregular small services. To boost the overall throughput and ensure the guaranteed latency, it is desirable to process massive service requests in parallel with efficient resource sharing in data centers. However, the disaggregation nature of microservice unavoidably upscales the design space of resource management and increases its complexity. In this paper, we propose AlphaR, a learning-powered resource management system tailored to the microservice environment. The basic idea of AlphaR is to generate microservice-specific resource management policies for improving efficiency. Specifically, we take the first step to use bipartite graph as a convenient abstraction for application built with microservices. Based on this, we devise a bipartite feature inference approach named Bi-GNN to extract the temporal characteristics of microservices. Furthermore, we implement a policy network to select appropriate resource allocation choices for maximizing the performance in resource-constrained data centers. AlphaR can improve the mean and p95 response time by up to 80% and 77.5% respectively compared with conventional schemes.read more
Citations
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Journal ArticleDOI
Adaptive Resource Efficient Microservice Deployment in Cloud-Edge Continuum
TL;DR: Nautilus as mentioned in this paper is a runtime system that effectively deploys microservice-based user-facing services in cloud-edge continuum, which is comprised of a communication-aware microservice mapper, a contention-aware resource manager and an IO-sensitive and load aware microservice migration scheduler.
Proceedings ArticleDOI
Practical Efficient Microservice Autoscaling with QoS Assurance
TL;DR: PEMA (Practical Efficient Microservice Autoscaling), a lightweight microservice resource manager that finds efficient resource allocation through opportunistic resource reduction and enables novel workload-aware and adaptive resource management.
Proceedings ArticleDOI
Layer-aware Collaborative Microservice Deployment toward Maximal Edge Throughput
TL;DR: This paper investigates the problem of how to collaboratively deploy microservices by incorporating both intra-server and inter-server layer sharing to maximize the edge throughput by proposing a randomized rounding based heuristic algorithm, and conducting formal analysis on the guaranteed approximation ratio.
Journal Article
A Lightweight Workload-Aware Microservices Autoscaling with QoS Assurance
TL;DR: PEMA (Practical EcientMicroserviceAutoscaling), a lightweight microservice resource manager that nds ecient resource allocation through opportunistic resource reduction, enables novel workload-aware and adaptive resource management.
Journal ArticleDOI
A Survey on Graph Neural Networks for Microservice-Based Cloud Applications
TL;DR: In this paper , the authors provide a comprehensive review of recent studies leveraging GNNs for microservice-based applications, identifying the key areas in which GNN-based solutions can be applied and how they can be designed to address the challenges in specific areas found in the literature.
References
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Usability Engineering
TL;DR: This guide to the methods of usability engineering provides cost-effective methods that will help developers improve their user interfaces immediately and shows you how to avoid the four most frequently listed reasons for delay in software projects.
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Graph Attention Networks
TL;DR: Graph Attention Networks (GATs) as mentioned in this paper leverage masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Posted Content
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou,Ganqu Cui,Shengding Hu,Zhengyan Zhang,Cheng Yang,Zhiyuan Liu,Lifeng Wang,Changcheng Li,Maosong Sun +8 more
TL;DR: A detailed review over existing graph neural network models is provided, systematically categorize the applications, and four open problems for future research are proposed.
Journal ArticleDOI
The tail at scale
Jeffrey Dean,Luiz Andre Barroso +1 more
TL;DR: Software techniques that tolerate latency variability are vital to building responsive large-scale Web services.
Proceedings ArticleDOI
Dominant resource fairness: fair allocation of multiple resource types
TL;DR: Dominant Resource Fairness (DRF), a generalization of max-min fairness to multiple resource types, is proposed, and it is shown that it leads to better throughput and fairness than the slot-based fair sharing schemes in current cluster schedulers.
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