Mu: An Efficient, Fair and Responsive Serverless Framework for Resource-Constrained Edge Clouds
Mu: An Efficient, Fair and Responsive Serverless Framework for Resource-Constrained Edge Clouds
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Mu：针对资源受限边缘云的高效、公平、响应灵敏的无服务器框架
DOI：
10.1145/3472883.3487014
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Wood, Timothy
中科院分区：
文献类型：
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作者：
Mittal, Viyom;Qi, Shixiong;Bhattacharya, Ratnadeep;Lyu, Xiaosu;Li, Junfeng;Kulkarni, Sameer G.;Li, Dan;Hwang, Jinho;Ramakrishnan, K. K.;Wood, Timothy
无服务器计算平台简化了模块化软件功能的开发、部署和自动化管理。然而，现有的无服务器平台通常采用过度配置的云，这使得它们不太适合资源稀缺的边缘计算环境。在本文中，我们提出了一种重新设计的无服务器平台，全面解决了资源受限的边缘云中无服务器功能的关键挑战。我们的 Mu 平台干净地集成了无服务器平台的核心资源管理组件：自动扩展、负载平衡和放置。 Mu 中的每个工作节点透明地传播响应标头中的服务速率和队列长度等指标，将此信息提供给负载平衡系统，以便它可以更好地路由请求，并提供给我们的自动缩放器以预测工作负载波动并主动满足 SLO。然后，放置引擎使用来自自动缩放器的数据来考虑竞争功能之间的异构性和公平性，确保整体资源效率并最大限度地减少资源碎片。我们将我们的设计实现为 Knative 无服务器平台的一组扩展，并展示了其在资源效率、公平性和响应时间方面的改进。评估 Mu，表明它比默认 Kubernetes 放置引擎将公平性提高了 2 倍以上，通过更好的负载平衡将 99% 的响应时间提高了 62%，通过主动和精确的自动扩展减少了 SLO 违规和资源消耗。 Mu 将一组实际 Azure 工作负载所需的平均 Pod 数量减少了约 15% 以上。
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.
DOI：
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发表时间：
2018-07
期刊：
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影响因子：
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作者：
Liang Wang;Mengyuan Li;Yinqian Zhang;Thomas Ristenpart;M. Swift
通讯作者：
Liang Wang;Mengyuan Li;Yinqian Zhang;Thomas Ristenpart;M. Swift
HALO：异构感知负载平衡
DOI：
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发表时间：
2015
期刊：
2015 IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
影响因子：
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作者：
Anshul Gandhi;Xi Zhang;Naman Mittal
通讯作者：
Naman Mittal
无服务器计算：影响微服务性能的因素调查
DOI：
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发表时间：
2018
期刊：
2018 IEEE International Conference on Cloud Engineering (IC2E)
影响因子：
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作者：
W. Lloyd;S. Ramesh;Swetha Chinthalapati;Lan Ly;S. Pallickara
通讯作者：
S. Pallickara