A SURVEY ON WORKLOAD PREDICTION MODELS IN CLOUD BASED ON SPOT INSTANCES FOR PROACTIVE AUTO SCALING STRATEGY

Paper: Scientific and Web applications are major sources of Internet traffic that requires resources such as Memory ,CPU and Network are on demand. Cloud computing and virtualization are the boons for such resource demand applications from various users. Service models of cloud computing provide a platform for many applications to use resources as pay per use model. In Cloud, Auto-scaling with manage Service Level Agreement (SLA) of resources is one of the main challenges to meet the current demand for resources. To maintain the performance of the cloud, which provision resources based on a heuristic for workload prediction is prime importance. In this paper, we address auto-scaling as a problem to forecast near-future demand of resource using a KNN machine learning methods suggest the optimized model for the dynamic variation of CPU utilization

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Two-Stage Adaptive Classification Cloud Workload Prediction Based on Neural Networks

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2019040101 ◽

2019 ◽

Vol 11 (2) ◽

pp. 1-23 ◽

Cited By ~ 2

Author(s):

Lei Li ◽

Yilin Wang ◽

Lianwen Jin ◽

Xin Zhang ◽

Huiping Qin

Keyword(s):

Neural Networks ◽

Prediction Models ◽

Network Models ◽

Classification Model ◽

Neural Network Models ◽

Two Stage ◽

Workload Prediction ◽

Random Mutation ◽

Prediction Result ◽

Adaptive Classification

Workload prediction is important for automatic scaling of resource management, and a high accuracy of workload prediction can reduce the cost and improve the resource utilization in the cloud. But, the task request is usually random mutation, so it is difficult to achieve more accurate prediction result for single models. Thus, to improve the prediction result, the authors proposed a novel two-stage workload prediction model based on artificial neural networks (ANNs), which is composed of one classification model and two prediction models. On the basis of the first-order gradient feature, the model can categorize the workload into two classes adaptively. Then, it can predict the workload by using the corresponding prediction neural network models according to the classification results. The experiment results demonstrate that the suggested model can achieve more accurate workload prediction compared with other models.

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Adaptive workload prediction for proactive auto scaling in PaaS systems

2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech) ◽

10.1109/cloudtech.2016.7847713 ◽

2016 ◽

Cited By ~ 6

Author(s):

R.S. Shariffdeen ◽

D.T.S.P. Munasinghe ◽

H.S. Bhathiya ◽

U.K.J.U. Bandara ◽

H.M.N. Dilum Bandara

Keyword(s):

Workload Prediction ◽

Auto Scaling

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Workload Prediction on Google Cluster Trace

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2014070103 ◽

2014 ◽

Vol 6 (3) ◽

pp. 34-52 ◽

Cited By ~ 4

Author(s):

Md. Rasheduzzaman ◽

Md. Amirul Islam ◽

Rashedur M. Rahman

Keyword(s):

Resource Utilization ◽

Prediction Models ◽

Mean Squared Error ◽

Experimental Result ◽

Percentage Error ◽

Support Vector ◽

Workload Prediction ◽

Inference System ◽

Squared Error ◽

Efficient Resource

Workload prediction in cloud systems is an important task to ensure maximum resource utilization. So, a cloud system requires efficient resource allocation to minimize the resource cost while maximizing the profit. One optimal strategy for efficient resource utilization is to timely allocate resources according to the need of applications. The important precondition of this strategy is obtaining future workload information in advance. The main focus of this analysis is to design and compare different forecasting models to predict future workload. This paper develops model through Adaptive Neuro Fuzzy Inference System (ANFIS), Non-linear Autoregressive Network with Exogenous inputs (NARX), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR). Public trace data (workload trace version II) which is made available by Google were used to verify the accuracy, stability and adaptability of different models. Finally, this paper compares these prediction models to find out the model which ensures better prediction. Performance of forecasting techniques is measured by some popular statistical metric, i.e., Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Sum of Squared Error (SSE), Normalized Mean Squared Error (NMSE). The experimental result indicates that NARX model outperforms other models, e.g., ANFIS, ARIMA, and SVR.

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Workload prediction of cloud computing based on SVM and BP neural networks

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-191266 ◽

2020 ◽

Vol 39 (3) ◽

pp. 2861-2867

Author(s):

Qiong Sun ◽

Zhiyong Tan ◽

Xiaolu Zhou

Keyword(s):

Neural Networks ◽

Cloud Computing ◽

Prediction Models ◽

Back Propagation ◽

Average Error ◽

Support Vector ◽

Bp Neural Networks ◽

Workload Prediction ◽

Physical Machine ◽

Study Support

In this study, support vector machine (SVM) and back-propagation (BP) neural networks were combined to predict the workload of cloud computing physical machine, so as to improve the work efficiency of physical machine and service quality of cloud computing. Then, the SVM and BP neural network was simulated and analyzed in MATLAB software and compared with SVM, BP and radial basis function (RBF) prediction models. The results showed that the average error of the SVM and BP based model was 0.670%, and the average error of SVM, BP and RBF was 0.781%, 0.759% and 0.708%, respectively; in the multi-step prediction, the prediction accuracy of SVM, BP, RBF and SVM + BP in the first step was 89.3%, 94.6%, 96.3% and 98.5%, respectively, the second step was 87.4%, 93.1%, 95.2% and 97.8%, respectively, the third step was 83.5%, 90.3%, 93.1% and 95.7%, the fourth step was 79.1%, 87.4%, 90.5% and 93.2%, respectively, the fifth step was 75.3%, 81.3%, 85.9% and 91.1% respectively, and the sixth step was 71.1%, 76.6%, 82.1% and 89.4%, respectively.

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4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi

Sir Syed Research Journal of Engineering & Technology ◽

10.33317/ssurj.v1i1.64 ◽

2013 ◽

Vol 1 (1) ◽

pp. 13

Author(s):

Javaria Manzoor Shaikh ◽

JaeSeung Park

Keyword(s):

Pattern Recognition ◽

Prediction Models ◽

Uv Light ◽

Healing Process ◽

Space Syntax ◽

Depth Map ◽

Routine Tasks ◽

Hyper Plane ◽

First Time ◽

Learning Analysis

Usually elongated hospitalization is experienced byBurn patients, and the precise forecast of the placement of patientaccording to the healing acceleration has significant consequenceon healthcare supply administration. Substantial amount ofevidence suggest that sun light is essential to burns healing andcould be exceptionally beneficial for burned patients andworkforce in healthcare building. Satisfactory UV sunlight isfundamental for a calculated amount of burn to heal; this delicaterather complex matrix is achieved by applying patternclassification for the first time on the space syntax map of the floorplan and Browder chart of the burned patient. On the basis of thedata determined from this specific healthcare learning technique,nurse can decide the location of the patient on the floor plan, hencepatient safety first is the priority in the routine tasks by staff inhealthcare settings. Whereas insufficient UV light and vitamin Dcan retard healing process, hence this experiment focuses onmachine learning design in which pattern recognition andtechnology supports patient safety as our primary goal. In thisexperiment we lowered the adverse events from 2012- 2013, andnearly missed errors and prevented medical deaths up to 50%lower, as compared to the data of 2005- 2012 before this techniquewas incorporated.In this research paper, three distinctive phases of clinicalsituations are considered—primarily: admission, secondly: acute,and tertiary: post-treatment according to the burn pattern andhealing rate—and be validated by capable AI- origin forecastingtechniques to hypothesis placement prediction models for eachclinical stage with varying percentage of burn i.e. superficialwound, partial thickness or full thickness deep burn. Conclusivelywe proved that the depth of burn is directly proportionate to thedepth of patient’s placement in terms of window distance. Ourfindings support the hypothesis that the windowed wall is mosthealing wall, here fundamental suggestion is support vectormachines: which is most advantageous hyper plane for linearlydivisible patterns for the burns depth as well as the depth map isused.

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