International Conference on Computer and Knowledge Engineering

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A Review on Machine Learning Methods for Workload Prediction in Cloud Computing

Authors :

Mohammad Yekta¹ Hadi Shahriar Shahhoseini²

1- School of Electrical Engineering, Iran University of Science and Technology 2- School of Electrical Engineering, Iran University of Science and Technology

Keywords :

Workload Prediction،Cloud Computing،Resource Provisioning،Machine Learning،Deep Learning

Abstract :

As technology advances, the volume of data and computing requirements has increased significantly. Personal computers are no longer sufficient to meet the computational and storage needs of users' data. This has led to the emergence of cloud computing, a new technology that transfers computational and data-processing tasks from PCs to data centers. Cloud computing offers resources in the form of infrastructure, platforms, and software as services, all accessible to users through the Internet. Resource provisioning is a critical aspect of cloud computing. To effectively allocate resources, accurate prediction of cloud workloads is essential. Workload prediction plays a crucial role in enhancing efficiency, reducing costs, optimizing cloud performance, maintaining a high level of quality of service, and minimizing energy consumption. In this paper, we conduct a comprehensive review of the most significant machine learning algorithms used for workload prediction. The algorithms are categorized and discussed, while also highlighting open issues and challenges that could serve as potential directions for future research.

Papers List

List of archived papers

An Effective Connectomics Approach for Diagnosing ADHD using Eyes-open Resting-state MEG

Nastaran Hamedi - Ali Khadem - Sajjad Vardast - Mehdi Delrobaei - Abbas Babajani-Feremi

Simulating Human Visual Cortex and Recall System with Convolutional Neural Networks

Sina Saadati - Abdolah Sepahvand

Introducing Meta-Contrastive Adaptive Autoencoder to Tackle Cold-Start Challenges in Sparse Domains

Hossein Rashid - Erfan Arzhmand - Fatemeh Hosseini

Using Deep Learning for Classification of Lung Cancer on CT Images in Ardabil Province

Mohammad Ali Javadzadeh Barzaki - Jafar Abdollahi - Mohammad Negaresh - Maryam Salimi - Hadi Zolfeghari - Mohsen Mohammadi - Asma Salmani - Rona Jannati - Firouz Amani

YOLOatt-Med: YOLO-Based Attention Mechanism for Medical Image Classification

Fatemeh Naserizadeh - Erfan Akbarnezhad Sany - Parsa Sinichi - Seyyed Abed Hosseini

A Deep CNN Model Based Ensemble Approach for Semantic and Instance Segmentation of Indoor Environment

Sajad Rezaei - Jafar Tanha - Zahra Jafari - SeyedEhsan Roshan - Mohammad-Amin Memar Kochebagh

Damage Detection After the Earthquake Using Sentinel-1 and 2 Images and Machine Learning Algorithms (Case Study: Sarpol-e Zahab Earthquake)

Niloofar Alizadeh - Behnam Asghari Beirami - Mehdi Mokhtarzade

Improving Machine Learning Classification of Heart Disease Using the Graph-Based Techniques

Abolfazl Dibaji - Sadegh Sulaimany

Mitochondrial Segmentation in Microscopy Images Using UNet-VGG19

Zerek Sediq Hossein - Rojiar Pir Mohammadiani - Saadat Izadi

Enhancing Lighter Neural Network Performance with Layer-wise Knowledge Distillation and Selective Pixel Attention

Siavash Zaravashan - Sajjad Torabi - Hesam Zaravashan