Cloud Analytics for Capacity Planning and Instant VM Provisioning

Jiang, Yexi; Perng, Chang-Shing; Li, Tao; Chang, Rcc

doi:10.1109/tnsm.2013.051913.120278

Cited by 82 publications

(13 citation statements)

References 19 publications

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“…VNF forwarding graph topology information is exploited by a neural network (NN) based algorithm to predict future resource metrics for each VNF component [15]. Ensemble prediction mechanisms are presented in [16] to turn off and assign VMs smartly to maintain SLA compliant QoS level. Autoregressive integrated moving average (ARIMA) based future resource demand prediction is adopted in [17] to improve consolidation and migration efficiency for the cloud servers employing local storage.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Multi-Target Classification Based Automatic Virtual Resource Allocation Scheme

Muktadir

Miyazawa

Martínez-Juliá

et al. 2019

IEICE Trans. Inf. & Syst.

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In this paper, we propose a method for automatic virtual resource allocation by using a multi-target classification-based scheme (MTCAS). In our method, an Infrastructure Provider (InP) bundles its CPU, memory, storage, and bandwidth resources as Network Elements (NEs) and categorizes them into several types in accordance to their function, capabilities, location, energy consumption, price, etc. MTCAS is used by the InP to optimally allocate a set of NEs to a Virtual Network Operator (VNO). Such NEs will be subject to some constraints, such as the avoidance of resource over-allocation and the satisfaction of multiple Quality of Service (QoS) metrics. In order to achieve a comparable or higher prediction accuracy by using less training time than the available ensemble-based multi-target classification (MTC) algorithms, we propose a majority-voting based ensemble algorithm (MVEN) for MTCAS. We numerically evaluate the performance of MTCAS by using the MVEN and available MTC algorithms with synthetic training datasets. The results indicate that the MVEN algorithm requires 70% less training time but achieves the same accuracy as the related ensemble based MTC algorithms. The results also demonstrate that increasing the amount of training data increases the efficacy of MTCAS, thus reducing CPU and memory allocation by about 33% and 51%, respectively.

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Section: Related Work and Contributionsmentioning

confidence: 99%

Multi-Target Classification Based Automatic Virtual Resource Allocation Scheme

Muktadir

Miyazawa

Martínez-Juliá

et al. 2019

IEICE Trans. Inf. & Syst.

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“…The RL is realized by using parallel learning; that is, the authors intend to speed up agent's learning process of approximated model by learning in parallel, without visiting every state-action pair in a given environment. The approaches that rely on demand prediction (e.g., the Autoflex [25] and PRESS [80,129,42,60,90]) are also regarded as implicit search. This is because the autoscaling decision is directly predicted by the demand models, without the needs of reasoning and optimization.…”

Section: Search-based Optimizationmentioning

confidence: 99%

A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling Systems

2018

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Autoscaling system can recon gure cloud-based services and applications, through various con gurations of cloud so ware and provisions of hardware resources, to adapt to the changing environment at runtime. Such a behavior o ers the foundation for achieving elasticity in modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, cloud autoscaling system has been engineered as one of the most complex, sophisticated and intelligent artifacts created by human, aiming to achieve self-aware, self-adaptive and dependable runtime scaling. Yet, existing Self-aware and Self-adaptive Cloud Autoscaling System (SSCAS) is not mature to a state that it can be reliably exploited in the cloud. In this article, we survey the state-of-the-art research studies on SSCAS and provide a comprehensive taxonomy for this eld. We present detailed analysis of the results and provide insights on open challenges, as well as the promising directions that are worth investigated in the future work of this area of research. Our survey and taxonomy contribute to the fundamentals of engineering more intelligent autoscaling systems in the cloud.

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“…[2,3,10,17,19,24,26]) and data center consolidation (e.g. [1,5,13,14,16]) and with respect to our previous results [4] we introduce the following novelties:…”

Section: Research Contributionmentioning

confidence: 99%

“…While Horizontal scaling, vertical scaling and configutation tuning approaches are somentime mixed, optimal placement (e.g. [1,5,13,14,16]) is never considered in combination with the other adaptation strategies.…”

Section: Related Workmentioning

confidence: 99%

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

2017

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Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra virtual data centers (VDCs). This paper address the problem of energy efficient autoscaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: Opt, LocalOpt and LocalOpt-H. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. LocalOpt consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energyaware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely BestFit and BestFit-H. at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures.

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Cloud Analytics for Capacity Planning and Instant VM Provisioning

Cited by 82 publications

References 19 publications

Multi-Target Classification Based Automatic Virtual Resource Allocation Scheme

Multi-Target Classification Based Automatic Virtual Resource Allocation Scheme

A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling Systems

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

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