A Comparative Analysis of Live Migration Techniques for Load Management in Cloud

Chakrabarti, Koyela; Majumder, Koushik; Sarkar, Subhanjan; Sing, Mihir; Chatterjee, Santanu

doi:10.1007/978-981-15-2043-3_30

Cited by 1 publication

(1 citation statement)

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“…Prominent applications within this domain include: the automated dynamic scaling of resources which aims to reduce over-and under-provisioning, minimizing operating costs and preventing possible reductions in the service quality [6]- [9]; The live migration of functionality/state across the network where cluster metrics are collected, processed, and scanned for anomalies which might reveal performance degradations and/or signs of component failure [10]- [12]; and the automatic system tuning where dynamic runtime adjustment of system configurations are performed in order to improve overall system availability and reliability [13]- [15]. The majority of these approaches rely on coarse-grained metrics to reactively make remediation decisions.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Load Prediction Techniques for Distributed Stream Processing

Gontarska¹,

Geldenhuys²,

Scheinert³

et al. 2021

Preprint

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Distributed Stream Processing (DSP) systems enable processing large streams of continuous data to produce results in near to real time. They are an essential part of many data-intensive applications and analytics platforms. The rate at which events arrive at DSP systems can vary considerably over time, which may be due to trends, cyclic, and seasonal patterns within the data streams. A priori knowledge of incoming workloads enables proactive approaches to resource management and optimization tasks such as dynamic scaling, live migration of resources, and the tuning of configuration parameters during run-times, thus leading to a potentially better Quality of Service.In this paper we conduct a comprehensive evaluation of different load prediction techniques for DSP jobs. We identify three use-cases and formulate requirements for making load predictions specific to DSP jobs. Automatically optimized classical and Deep Learning methods are being evaluated on nine different datasets from typical DSP domains, i.e. the IoT, Web 2.0, and cluster monitoring. We compare model performance with respect to overall accuracy and training duration. Our results show that the Deep Learning methods provide the most accurate load predictions for the majority of the evaluated datasets.

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