An online learning model based on episode mining for workload prediction in cloud

Amiri, Maryam; Khanli, Leyli Mohammad; Mirandola, Raffaela

doi:10.1016/j.future.2018.04.044

Cited by 35 publications

(14 citation statements)

References 45 publications

(106 reference statements)

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“…In 2018, Amiri et al sought to improve POSITING online learning capability after improving their proposed algorithm [12]. To measure the power of offline patterns to predict the current behavior of the program, they defined a conformance criterion.…”

Section: Forecast-based Approachesmentioning

confidence: 99%

A Novel Resource Productivity Based on Granular Neural Network in Cloud Computing

2021

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In recent years, due to the growing demand for computational resources, particularly in cloud computing systems, the data centers’ energy consumption is continually increasing, which directly causes price rise and reductions of resources’ productivity. Although many energy-aware approaches attempt to minimize the consumption of energy, they cannot minimize the violation of service-level agreements at the same time. In this paper, we propose a method using a granular neural network, which is used to model data processing. This method identifies the physical hosts’ workloads before the overflow and can improve energy consumption while also reducing violation of service-level agreements. Unlike the other techniques that use a single criterion, namely, worked on the basis of the history of using the processor, we simultaneously use all the productivity rates criteria, that is, processor productivity rates, main memory, and bandwidth. Extensive real-world simulations using the CloudSim simulator show the high efficiency of the proposed algorithm.

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Section: Forecast-based Approachesmentioning

confidence: 99%

A Novel Resource Productivity Based on Granular Neural Network in Cloud Computing

2021

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“…To deal with these challenges, many prediction techniques based on machine learning are widely used and have proven to be superior to the mathematical algorithms above [6], [8], [9], [23], [24]. Most of them require a training phase based on large-scale historical data.…”

Section: Related Workmentioning

confidence: 99%

“…The most important metric on systems workload prediction models is accuracy, which is evaluated by the difference between predicted results and actual values [9]. Generally, the closer the predicted value is to the actual value, the better the model is.…”

Section: Introductionmentioning

confidence: 99%

“…The other important metric for systems workload prediction models is real-time effectiveness. This measures the ability of the systems resource management to carve out enough time to optimize its resources according to future workload prediction, which should be produced in a reasonable and effective time frame [9]. For instance, in 5 minutes, the Supercomputers Tianhe-2A will perform financial Big data analysis and processing systems requiring 12400 computing nodes.…”

Section: Introductionmentioning

confidence: 99%

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Large-Scale Computing Systems Workload Prediction Using Parallel Improved LSTM Neural Network

Tang

2019

IEEE Access

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In recent years, large-scale computing systems have been widely used as an important part of the computing infrastructure. Resource management based on systems workload prediction is an effective way to improve application efficiency. However, accuracy and real-time functionalities are always the key challenges that perplex the systems workload prediction model. In this paper, we first investigate the dependence on historical workload in large-scale computing systems and build a day and time two-dimensional time-series workload model. We then design a two-dimensional long short-term memory (LSTM) neural network cell structure. Based on this, we propose an improved LSTM prediction model providing its mathematical description and an error back propagation method. Furthermore, to achieve systems resource management real-time requirement, we provide a parallel improved LSTM algorithm that uses a hidden layer week-based dependence and weights parallelization algorithm. The comparative studies, based on the actual workload of the Shanghai Supercomputer Center, demonstrate that our proposed improved LSTM neural network prediction model can achieve higher accuracy and real-time performance in large-scale computing systems. Workload prediction, computing systems, LSTM, neural network, parallel. INDEX TERMS

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“…An effective and proactive approach must therefore accurately predict the future resources needed to achieve QoE. The most important measure of system workload prediction models is accuracy, which is measured by the difference between predicted and actual results [16]. In general, the closer the predicted value is to the actual value, the better the model.…”

Section: Introductionmentioning

confidence: 99%

Fractional Rider Deep Long Short Term Memory Network for Workload Prediction-Based Distributed Resource Allocation Using Spark in Cloud Gaming

Désiré¹,

Francis²,

Kouassi³

et al. 2021

ENG

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The modern development in cloud technologies has turned the idea of cloud gaming into sensible behaviour. The cloud gaming provides an interactive gaming application, which remotely processed in a cloud system, and it streamed the scenes as video series to play through network. Therefore, cloud gaming is a capable approach, which quickly increases the cloud computing platform. Obtaining enhanced user experience in cloud gaming structure is not insignificant task because user anticipates less response delay and high quality videos. To achieve this, cloud providers need to be able to accurately predict irregular player workloads in order to schedule the necessary resources. In this paper, an effective technique, named as Fractional Rider Deep Long Short Term Memory (LSTM) network is developed for workload prediction in cloud gaming. The workload of each resource is computed based on developed Fractional Rider Deep LSTM network. Moreover, resource allocation is performed by fractional Rider-based Harmony Search Algorithm (Rider-based HSA). This Fractional Rider-based HSA is developed by combining Fractional calculus (FC), Rider optimization algorithm (ROA) and Harmony search algorithm (HSA). Moreover, the developed Fractional Rider Deep LSTM is developed by integrating FC and Rider Deep LSTM. In addition, the multi-objective parameters, namely gaming experience loss QE, Mean Opinion Score (MOS), Fairness, energy, network parameters, and predictive load are considered for efficient resource allocation and workload prediction. Additionally, the developed workload prediction model achieved better performance using various parameters, like fairness, MOS, QE, energy and delay.

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An online learning model based on episode mining for workload prediction in cloud

Cited by 35 publications

References 45 publications

A Novel Resource Productivity Based on Granular Neural Network in Cloud Computing

A Novel Resource Productivity Based on Granular Neural Network in Cloud Computing

Large-Scale Computing Systems Workload Prediction Using Parallel Improved LSTM Neural Network

Fractional Rider Deep Long Short Term Memory Network for Workload Prediction-Based Distributed Resource Allocation Using Spark in Cloud Gaming

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