Seer

Gan, Yu; Zhang, Yanqi; Hu, Kelvin; Cheng, Dailun; He, Yuan; Pancholi, Meghna; Delimitrou, Christina

doi:10.1145/3297858.3304004

Cited by 184 publications

(3 citation statements)

References 65 publications

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“…Machine learning for resource management. A number of works have leveraged ML to optimize server applications and cloud infrastructures [2,10,11,16,18,25]. We focus here on the most closely related to our work.…”

Section: Related Workmentioning

confidence: 99%

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Ofc

Mvondo

Bacou

Nguetchouang

et al. 2021

Proceedings of the Sixteenth European Conference on Computer Systems

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Cloud applications based on the "Functions as a Service" (FaaS) paradigm have become very popular. Yet, due to their stateless nature, they must frequently interact with an external data store, which limits their performance. To mitigate this issue, we introduce OFC, a transparent, vertically and horizontally elastic in-memory caching system for FaaS platforms, distributed over the worker nodes. OFC provides these benefits cost-effectively by exploiting two common sources of resource waste: (i) most cloud tenants overprovision the memory resources reserved for their functions because their footprint is non-trivially input-dependent and (ii) FaaS providers keep function sandboxes alive for several minutes to avoid cold starts. Using machine learning models adjusted for typical function input data categories (e.g., multimedia formats), OFC estimates the actual memory resources required by each function invocation and hoards the remaining capacity to feed the cache. We build our OFC prototype based on enhancements to the OpenWhisk FaaS platform, the Swift persistent object store, and the RAM-Cloud in-memory store. Using a diverse set of workloads, we show that OFC improves by up to 82 % and 60 % respectively the execution time of single-stage and pipelined functions. CCS Concepts: • Computer systems organization → Cloud computing; • Software and its engineering → n-tier architectures. * A part of work done while Jinho Hwang was at IBM Research.

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Section: Related Workmentioning

confidence: 99%

“…Seer [16] uses deep learning and monitoring to infer the cause of QoS violations in microservices-based applications. For issues attributed to memory capacity, Seer resizes the resources of the corresponding container.…”

Section: Related Workmentioning

confidence: 99%

Ofc

Mvondo

Bacou

Nguetchouang

et al. 2021

Proceedings of the Sixteenth European Conference on Computer Systems

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“…Another use case for profiled VNF data is anomaly detection in complex VNF topologies. In [24], an online cloud performance debugging system is based on a deep learning model. The input values correspond to network queue depths, and the output values to the probability for a given VNF to initiate a performance violation.…”

Section: Related Workmentioning

confidence: 99%

VNF Performance modelling: From stand-alone to chained topologies

Rossem¹,

Tavernier²,

Colle³

et al. 2020

Computer Networks

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One of the main incentives for deploying network functions on a virtualized or cloud-based infrastructure, is the ability for on-demand orchestration and elastic resource scaling following the workload demand. This can also be combined with a multi-party service creation cycle: the service provider sources various network functions from different vendors or developers, and combines them into a modular network service. This way, multiple virtual network functions (VNFs) are connected into more complex topologies called service chains. Deployment speed is important here, and it is therefore beneficial if the service provider can limit extra validation testing of the combined service chain, and rely on the provided profiling results of the supplied single VNFs. Our research shows that it is however not always evident to accurately predict the performance of a total service chain, from the isolated benchmark or profiling tests of its discrete network functions. To mitigate this, we propose a two-step deployment workflow: First, a general trend estimation for the chain performance is derived from the stand-alone VNF profiling results, together with an initial resource allocation. This information then optimizes the second phase, where online monitored data of the service chain is used to quickly adjust the estimated performance model where needed. Our tests show that this can lead to a more efficient VNF chain deployment, needing less scaling iterations to meet the chain performance specification, while avoiding the need for a complete proactive and timeconsuming VNF chain validation.

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Cloud Elasticity of Microservices‐Based Applications: A Survey

Fourati,

Marzouk,

Jmaiel

2024

Concurrency and Computation

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Elasticity is an essential treatment in Cloud environment employed in academic and industrial contexts. The main purpose of elasticity is to reduce the deployment cost while optimizing computing resources. Multiple studies were conducted to tackle classic applications using monolithic architecture deployed with virtual machines (VMs). However, with the spread of microservice pattern, recent studies have been investigating this new trend using containers. This paper classifies and discusses existing approaches dealing with cloud elasticity. It provides a novel taxonomy for elastic approaches while focusing on microservices‐based solutions. We additionally specify the strength and the shortcomings of each class of works. As a conclusion, we report the challenges for microservices‐based applications elasticity and provide requirements for future investigations.

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Seer

Cited by 184 publications

References 65 publications

Ofc

Ofc

VNF Performance modelling: From stand-alone to chained topologies

Cloud Elasticity of Microservices‐Based Applications: A Survey

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