Study on proactive auto scaling for instance through the prediction of ...

proactive autoscaling for edge computing systems - arXiv

The proposed PPA is able to forecast workloads in advance with multiple user-defined/customized metrics and to scale edge computing applications ...

How to Choose the Best Way to Scale EC2 Instances

The difference is that you also get predictive scaling. To do so, AWS Auto Scaling takes the past 14 days into account to predict the next two ...

Proactive auto scaling for optimal cost and availability - awsstatic.com

AWS Auto Scaling: Best Practices and Strategies - CloudExpat

It's crucial to set specific parameters that dictate when and how to scale. For instance, you can configure a policy to add new instances when ...

A proactive auto-scaling scheme with latency guarantees for multi ...

To simplify the auto-scaling problem, we develop an adaptive algorithm to divide the NFV cloud into persistent and temporary layers. Our ...

A Study And Analysis Of Forecasts In Resource Allocation Using ...

Keywords: Auto Scaling, Time Series, Workload Prediction. 1. Introduction ... The future work is to tackle such factors associating the proactive scaling strategy ...

AWS Auto Scaling: Intro, Strategies & Tips to Keep in Mind

Predictive scaling optimizes for a metric specified in the scaling plan as well. However, it differs from dynamic scaling in that it uses ...

Proactive Auto Scaling Based on Marginal Request Change ...

Auto scaling dynamically adjusts resource allocation based on application metrics to optimize service performance and resource efficiency. In.

A Proactive Formal Approach For Microservice-based Applications ...

HLPNs and PPNs, in order to construct a proactive compromise auto-scaling and illustrated through the example of the email pipeline processing. We describe ...

Auto-scaling Techniques for Elastic Applications in Cloud ...

Therefore, proactive or prediction-based resource scaling is required in ... Therefore, research on auto-scaling techniques should focus on ...

Proactive Service Auto-Scaling in Edge Environments - OUCI

R Shariffdeen, Adaptive workload prediction for proactive auto scaling in ... S Wang, A computing resources prediction approach based on ensemble learning ...

Cloud Optimization for Data Processing | Restackio

Predictive Auto-scaling: Utilizing machine learning algorithms to analyze historical workload patterns allows for proactive resource ...

Efficient Auto Scaling Mechanism in the Cloud Environment Using ...

[3] F´abio Morais, Raquel Lopes, Francisco Brasileiro , In Instance Type Selection in Proactive Horizontal Auto-Scaling[3] , The goal is to predict the ...

Journal of Critical Reviews

A SURVEY ON WORKLOAD PREDICTION MODELS IN CLOUD BASED ON SPOT INSTANCES FOR PROACTIVE AUTO SCALING STRATEGY (2020). R. Yuvha Secaran, E. Sathiyamoorthy

Proactive and autonomic IoT service auto-scaling in ... - OATD

Service auto-scaling is an interesting solution that improves resource utilization by dynamically adjusting the number of service instances to match the ...

Design and Evaluation of a Proactive, Application-Aware Auto-Scaler

In this tutorial paper, we guide the reader step-by- step through the design and evaluation of a proactive and application-aware auto-scaling mechanism. First, ...

Auto scaling using cloud formation according to Request count

We are using cloud formation for auto scaling according based upon the load balancer RequestCount metric. Currently we scale up an instance if the request is ...

Self-tuning serverless task farming using proactive elasticity control

This enables these applications to benefit from elastic auto-scaling in a straightforward manner. ... Prediction model instance \mathcal {P}_5 is ...

Application Auto Scaling on AWS – Options and Impact on ...

EC2 instances capacity can be scaled ahead of traffic changes. Predictive Auto Scaling is based on the trained Machine Learning (ML) Algorithm ...

Transforming reactive auto-scaling into proactive auto-scaling

Typically research into predictive elasticity techniques is based on machine learning and control theory [4]. The elasticity controller ...