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Practical Efficient Microservice Autoscaling
ACM SIGMETRICS Performance Evaluation Review, 2023Motivation. In recent years, the adoption of microservices in production systems has been steadily growing. With their loosely-coupled and lightweight components, microservices are easier to manage than traditional monolithic applications. However, microservices introduce new challenges towards efficient resource management because their high number of
Md Rajib Hossen, Mohammad A. Islam 0001
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Autoscaling for Hadoop Clusters
2016 IEEE International Conference on Cloud Engineering (IC2E), 2016Unforeseen events such as node failures and resource contention can have a severe impact on the performance of data processing frameworks, such as Hadoop, especially in cloud environments where such incidents are common. SLA compliance in the presence of such events requires the ability to quickly and dynamically resize infrastructure resources ...
Anshul Gandhi +4 more
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COPA: A Combined Autoscaling Method for Kubernetes
2021 IEEE International Conference on Web Services (ICWS), 2021Autoscaling is one of the major features of Cloud Computing aiming to improve the Quality-of-Service(QoS) in response to fluctuating workloads. Existing state-of-the-art autoscaling methods for Kubernetes focus on single scaling mode, that is, only horizontal scaling and only vertical scaling.
Zhijun Ding, Qichen Huang
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Optimal autoscaling in a IaaS cloud
Proceedings of the 9th international conference on Autonomic computing, 2012An application provider leases resources (i.e., virtual machine instances) of variable configurations from a IaaS provider over some lease duration (typically one hour). The application provider (i.e., consumer) would like to minimize their cost while meeting all service level obligations (SLOs).
Hamoun Ghanbari +4 more
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Towards Autoscaling with Guarantees on Kubernetes Clusters
2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), 2021Autoscaling is used by cloud providers, microser-vices, and edge computing applications to respond to dynamic load fluctuations. A critical direction of research has focused on providing guarantees under uncertainty that the auto scaling system will work as intended-both at design-time and more importantly, at runtime.
Stephen Burroughs +6 more
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Autoscaling Performance Measurement Tool
Companion of the 2018 ACM/SPEC International Conference on Performance Engineering, 2018More companies are shifting focus to adding more layers of virtualization for their cloud applications thus increasing the flexibility in development, deployment and management of applications. Increase in the number of layers can result in additional overhead during autoscaling and also in coordination issues while layers may use the same resources ...
Anshul Jindal +2 more
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Predicting CPU usage for proactive autoscaling
Proceedings of the 1st Workshop on Machine Learning and Systems, 2021Private and public clouds require users to specify requests for resources such as CPU and memory (RAM) to be provisioned for their applications. The values of these requests do not necessarily relate to the application's run-time requirements, but only help the cloud infrastructure resource manager to map requested resources to physical resources.
Thomas Wang, Simone Ferlin, Marco Chiesa
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Autoscaling tiered cloud storage in Anna
The VLDB Journal, 2019In this paper, we describe how we extended a distributed key-value store called Anna into an autoscaling, multi-tier service for the cloud. In its extended form, Anna is designed to overcome the narrow cost-performance limitations typical of current cloud storage systems.
Chenggang Wu 0001 +2 more
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