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Auto-scaling techniques in cloud computing: issues and research directions, 1. introduction, 2. related work, 3. fundamentals of auto-scaling, 4. auto-scaling techniques, 4.1. reactive methods, 4.1.1. threshold rules, 4.1.2. queuing theory, 4.2. proactive methods, 4.2.1. reinforcement learning, 4.2.2. fuzzy learning, 4.2.3. machine learning (ml), 4.2.4. time series analysis, 5. real-world applications, 5.1. smart city platforms, 5.2. sentiment analysis on twitter data, 5.3. telecommunications (telco) cloud infrastructure, 5.4. application of auto-scaling in healthcare, 6. challenges and possible solutions, 6.1. energy efficiency, 6.2. dynamic nature of telco services, 6.3. optimizing big data processing with auto-scaling in the cloud, 6.4. cost-efficient graph processing in clouds, 6.5. enhancing cloud auto-scaling with machine learning-based workload prediction, 7. research directions, 7.1. energy efficiency, 7.2. workload prediction, 7.3. cost optimization, 7.4. combining vertical and horizontal scaling, 7.5. real-time and proactive auto-scaling, 7.6. heterogeneous resource provisioning, 7.7. multi-cloud auto-scaling, 7.8. intelligent resource scaling, 7.9. enhancing security in auto-scaling, 8. conclusions, author contributions, data availability statement, conflicts of interest.
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| Acronym | Definition |
|---|---|
| RL | Reinforcement learning |
| SSCAS | Self-Adaptive cloud auto-scaling system |
| IaaS | Infrastructure as a Service |
| AWS | Amazon Web Services |
| QoS | Quality of service |
| MCS | Mission Critical Services |
| MDP | Markov decision process |
| ML | Machine learning |
| DNN | Deep neural network |
| CNFs | Containerized network functions |
| VNFs | Virtualized network functions |
| VMs | Virtual machines |
| SLAs | Service level agreements |
| HDFS | Hadoop’s Distributed File System |
| Telco | Telecommunications |
| CEAS | Cost-Effective Auto-Scaling |
| SVM | Support Vector Machine |
| OPCR | On-demand provisioning of computing resource |
| IRS | Intelligent resource scaling |
| IoT | Internet of things |
| DT | Digital twin |
| Paper | Objective(s) | Advantages | Limitations |
|---|---|---|---|
| ] | To investigate and analyze existing microservice-based applications that use containers. | Discussed the challenges faced by present solutions and provided future development recommendations. Highlights the strengths and shortcomings of existing microservice-aware auto-scalers. | This study focused on resource allocation in microservice-based applications with containers and did not address other auto-scaling issues, such as cost optimization and performance. |
| ] | To provide an overview of existing container auto-scaling techniques in Kubernetes. | Analyzed auto-scaling methods, such as horizontal, vertical, and hybrid scaling. Identifies the implementation of machine learning for predictive techniques. | This survey does not cover all auto-scaling techniques; it focuses on auto-scaling in Kubernetes. Lack of practical applications and examples. |
| ] | To conduct a methodological survey on auto-scaling techniques for web applications. To identify and summarize the challenges and existing studies related to web application auto-scaling. | Describes the key challenges facing multi-tier web applications in cloud computing. Provides taxonomy to help readers understand different aspects of web application auto-scaling. | This survey covered only web application techniques, excluding other aspects of the scalability of cloud computing. In addition, the review literature excludes recent papers. |
| ] | To review the challenges related to auto-scaling web applications in cloud computing. | The survey covered the weaknesses and strengths of existing auto-scaling techniques deployed by service and infrastructure providers. | Did not cover recent studies conducted after 2019, and the paper focused only on auto-scaling web applications. |
| ] | To review studies on reinforcement learning (RL) and propose taxonomies to compare RL-based auto-scaling approaches. | The survey covered major proposals for RL-based auto-scaling in cloud infrastructure. In addition, this paper discusses related issues and highlights future research directions. | They focused on RL-based auto-scaling and excluded other aspects of auto-scaling in cloud computing. They excluded real-world applications and domains. |
| ] | To summarize existing studies and approaches that used RL in the cloud for workflow auto-scaling applications. | Covers most of the studies in the field and highlight the limitations and gaps of other surveys. The survey also covered the opportunities for advancing learning RL-based workflow in the cloud. | Lack of real-world applications and examples. The authors focused particularly on RL-based workflow in the cloud. |
| ] | To review auto-scaling techniques for container-based virtualization in cloud, edge, and fog computing. | This review provides a comprehensive understanding of challenges related to auto-scalers for container-based applications. | They focused specifically on auto-scaling techniques for container-based virtualization, excluding other auto-scaling techniques. |
| ] | To analyze existing studies on Self-Aware and Self-Adaptive cloud auto-scaling systems (SSCAS) | Presented a detailed analysis of open challenges in SSCAS approaches. The paper identified promising directions for further advancement of SSCAS. | The survey excludes auto-scaling concepts and techniques. This study focused on the SSCAS but did not cover other aspects of cloud computing. |
| ] | To determine the current issues of load balancing and understand the challenges related to cloud-based container microservices. | Adds valuable insight into existing challenges faced in load balancing and auto-scaling. | Lack of auto-scaling techniques information. The survey focused on auto-scaling and load balancing in cloud-based container microservices. |
| ] | To investigate and compare the two techniques of auto-scaling: fixed threshold-based and adaptive threshold-based. | Provides comprehensive insights into fixed threshold-based and adaptive threshold-based techniques and how they can optimize resource allocation. | The paper does not cover challenges related to auto-scaling techniques. They do not address possible solutions and research directions for auto-scaling. |
| To explore the concept of auto-scaling in cloud computing, algorithms predominantly employed in auto-scaling, to address the challenges associated with auto-scaling in cloud computing and present potential solutions. | Covers recent studies in auto-scaling techniques. Provides research directions that address future challenges. Focuses on auto-scaling techniques (reactive, proactive) and provides detailed discussion about each method. | - |
| Cloud Providers | Auto-Scaling Feature | Auto-Scaling Supported (Yes/No) |
|---|---|---|
| AMAZON | Automatically scales number of EC2 instances for different applications. | Yes |
| WINDOWS AZURE | Provides auto-scaling feature manually based on the applications. | Yes |
| GOOGLE APP | Owns auto-scaling technology. | Yes |
| ENGINE | Google applications. | Yes |
| GOGRID | Supports auto-scaling technique in programmatic way and does not implement it. | Yes/No |
| FLEXISCALE | Provides auto-scaling mechanism with high performance and availability. | Yes |
| ANEKA | Application management service through cloud peer service. | Yes |
| NIMBUS | Open-source cloud provided by resource manager and Python modules. | Yes |
| EUCALYPTUS | Open-source cloud which provides wrapper service for various applications. | Yes |
| OPEN NEBULA | Open-source cloud which provides OpenNebula Service Management Project. | Yes |
| Issue | Possible Research Direction |
|---|---|
| Energy efficiency | Energy consumption is a major concern in auto-scaling and some of the existing work tried to minimize energy utilization. |
| Workload prediction | Using algorithms such as support vector machines, deep learning, and time series analysis enhanced the accuracy. |
| Cost optimization | Spot instances have emerged recently as a promising solution introduced by AWS that minimizes the cost of resources |
| Combining vertical and horizontal scaling | Hybrid scaling provides more flexible solutions to enhance resource usage. |
| Real-time and proactive auto-scaling | Real-time auto-scaling minimizes the response time and enhances system performance. |
| Heterogeneous resource provisioning | Researchers are investigating auto-scaling techniques that efficiently manage the allocation of different resources. |
| Multi-cloud auto-scaling | With the significant increase in the adopting of multi-cloud platforms, there is a growing need for auto-scaling algorithms. |
| Intelligent resource scaling | IRS is the dynamic allocation of computing resources to meet the system’s evolving needs. |
| Enhancing security in auto-scaling | Developing advanced techniques that improve the security of auto-scaling applications is a promising research direction. |
| The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Share and Cite
Alharthi, S.; Alshamsi, A.; Alseiari, A.; Alwarafy, A. Auto-Scaling Techniques in Cloud Computing: Issues and Research Directions. Sensors 2024 , 24 , 5551. https://doi.org/10.3390/s24175551
Alharthi S, Alshamsi A, Alseiari A, Alwarafy A. Auto-Scaling Techniques in Cloud Computing: Issues and Research Directions. Sensors . 2024; 24(17):5551. https://doi.org/10.3390/s24175551
Alharthi, Saleha, Afra Alshamsi, Anoud Alseiari, and Abdulmalik Alwarafy. 2024. "Auto-Scaling Techniques in Cloud Computing: Issues and Research Directions" Sensors 24, no. 17: 5551. https://doi.org/10.3390/s24175551
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From Grids to Cloud Federations
WIDESim: A Toolkit for Simulating Resource Management Techniques Of Scientific Workflows in Distributed Environments with Graph Topology
- Mohammad Amin Rayej
- Mohammad Izadi
CMK: Enhancing Resource Usage Monitoring across Diverse Bioinformatics Workflow Management Systems
- Robert Nica
- Stefan Götz
- Germán Moltó
Resource Utilization Based on Hybrid WOA-LOA Optimization with Credit Based Resource Aware Load Balancing and Scheduling Algorithm for Cloud Computing
- Abhikriti Narwal
Energy-Constrained DAG Scheduling on Edge and Cloud Servers with Overlapped Communication and Computation
Resource allocation using deep deterministic policy gradient-based federated learning for multi-access edge computing, optimizing resource consumption and reducing power usage in data centers, a novel mathematical vm replacement model and efficient algorithm.
- Reza Rabieyan
- Ramin Yahyapour
- Patrick Jahnke
EQGSA-DPW: A Quantum-GSA Algorithm-Based Data Placement for Scientific Workflow in Cloud Computing Environment
- Zaki Brahmi
- Rihab Derouiche
Towards Enhanced Energy Aware Resource Optimization for Edge Devices Through Multi-cluster Communication Systems
- Yingying Ma
- Yinghui Xie
Evaluation of Storage Placement in Computing Continuum for a Robotic Application
- Zeinab Bakhshi
- Guillermo Rodriguez-Navas
- Radu Prodan
An Effective Prediction of Resource Using Machine Learning in Edge Environments for the Smart Healthcare Industry
Deep learning based entropy controlled optimization for the detection of covid-19.
- Abdullah Alshammari
- Romi Fadillah Rahmat
HRNN: Hypergraph Recurrent Neural Network for Network Intrusion Detection
Joint task dispatching and bandwidth allocation with hard deadlines in distributed serverless edge computing systems, correction: enabling configurable workflows in smart environments with knowledge-based process fragment reuse.
- Mouhamed Gaith Ayadi
- Haithem Mezni
A Hybrid Discrete Grey Wolf Optimization Algorithm Imbalance-ness Aware for Solving Two-dimensional Bin-packing Problems
- Saeed Kosari
- Mirsaeid Hosseini Shirvani
- Danial Javaheri
Enabling Configurable Workflows in Smart Environments with Knowledge-based Process Fragment Reuse
Enhancing service offloading for dense networks based on optimal stopping theory in virtual mobile edge computing, signature-based adaptive cloud resource usage prediction using machine learning and anomaly detection.
- Piotr Nawrocki
Exploring the Synergy of Blockchain, IoT, and Edge Computing in Smart Traffic Management across Urban Landscapes
Micro frontend based performance improvement and prediction for microservices using machine learning.
- Neha Kaushik
- Harish Kumar
CIA Security for Internet of Vehicles and Blockchain-AI Integration
- Muammer Aksoy
- Senthilkumar Mohan
On the Joint Design of Microservice Deployment and Routing in Cloud Data Centers
Improving performance of smart education systems by integrating machine learning on edge devices and cloud in educational institutions, cost-efficient workflow as a service using containers.
- Kamalesh Karmakar
- Anurina Tarafdar
- Sunirmal Khatua
Adaptive Scheduling Framework of Streaming Applications based on Resource Demand Prediction with Hybrid Algorithms
- Hongjian Li
- Xiaolin Duan
Multi-Agent Systems for Collaborative Inference Based on Deep Policy Q-Inference Network
- Shangshang Wang
Dueling Double Deep Q Network Strategy in MEC for Smart Internet of Vehicles Edge Computing Networks
- Haotian Pang
- Zhanwei Wang
Work Scheduling in Cloud Network Based on Deep Q-LSTM Models for Efficient Resource Utilization
Dynamic multi-resource fair allocation with elastic demands, joint task offloading based on distributed deep reinforcement learning-based genetic optimization algorithm for internet of vehicles.
- Yong-Guk Kim
- Yonglong Xu
Decentralized AI-Based Task Distribution on Blockchain for Cloud Industrial Internet of Things
- Amir Javadpour
- Arun Kumar Sangaiah
- HamidReza Ahmadi
Employing RNN and Petri Nets to Secure Edge Computing Threats in Smart Cities
- Jinpeng Wang
A Probabilistic Deadline-aware Application Offloading in a Multi-Queueing Fog System: A Max Entropy Framework
- Naveen Chauhan
- Rajeev Agrawal
Edge Computing Empowered Smart Healthcare: Monitoring and Diagnosis with Deep Learning Methods
- Kemeng Wang
- Shurui Kong
Dynamic Resource Management in MEC Powered by Edge Intelligence for Smart City Internet of Things
- Xucheng Wan
Joint Task Offloading and Multi-Task Offloading Based on NOMA Enhanced Internet of Vehicles in Edge Computing
- Ahmed M. El-Sherbeeny
Dependent Task Scheduling Using Parallel Deep Neural Networks in Mobile Edge Computing
- Jimmy Huang
An IoT-based Covid-19 Healthcare Monitoring and Prediction Using Deep Learning Methods
- Jianjia Liu
Smart City Transportation: A VANET Edge Computing Model to Minimize Latency and Delay Utilizing 5G Network
- Mengqi Wang
- Jiayuan Mao
Marine Goal Optimizer Tuned Deep BiLSTM-Based Self-Configuring Intrusion Detection in Cloud
- Sanchika Abhay Bajpai
- Archana B. Patankar
A Combined Approach of PUF and Physiological Data for Mutual Authentication and Key Agreement in WMSN
- Shanvendra Rai
- Rituparna Paul
E-Commerce Logistics and Supply Chain Network Optimization for Cross-Border
A federated deep reinforcement learning-based low-power caching strategy for cloud-edge collaboration.
- Xinyu Zhang
Automated Pallet Racking Examination in Edge Platform Based on MobileNetV2: Towards Smart Manufacturing
Hybridized black widow-honey badger optimization: swarm intelligence strategy for node localization scheme in wsn.
- K Johny Elma
- Praveena Rachel Kamala S
- Saraswathi T
DRL-based Task and Computational Offloading for Internet of Vehicles in Decentralized Computing
- Ziyang Zhang
Joint Autoscaling of Containers and Virtual Machines for Cost Optimization in Container Clusters
- Joaquín Entrialgo
- Manuel García
- José Luis Díaz
On-Chain and Off-Chain Data Management for Blockchain-Internet of Things: A Multi-Agent Deep Reinforcement Learning Approach
- Y. P. Tsang
- C. K. M. Lee
Intrusion Detection using Federated Attention Neural Network for Edge Enabled Internet of Things
- Xiedong Song
3D Lidar Target Detection Method at the Edge for the Cloud Continuum
- Xuelian Liu
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A Review Paper on Grid Computing
- Mr.Ramesh Prajapati , Dr.Samrat Khanna
- Published 2013
- Computer Science, Engineering
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One Citation
Grid computing: review and s.w.o.t analysis, 17 references, a computational economy for grid computing and its implementation in the nimrod-g resource brok, challenges of grid computing, architectural models for resource management in the grid, security for grid services, introduction to grid computing, modifying modern power systems quality by integrating grid computing technology, a survey on wireless grid computing, grid characteristics and uses: a grid definition, security for grids, the grid: blueprint for a new computing infrastructure, related papers.
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Environmental mapping serves as a crucial element in Simultaneous Localization and Mapping (SLAM) algorithms, playing a pivotal role in ensuring the accurate representation necessary for autonomous robot navigation guided by SLAM. Current SLAM systems predominantly rely on grid-based map representations, encountering challenges such as measurement discretization for cell fitting and grid map ...
In the dynamic world of cloud computing, auto-scaling stands as a beacon of efficiency, dynamically aligning resources with fluctuating demands. This paper presents a comprehensive review of auto-scaling techniques, highlighting significant advancements and persisting challenges in the field. First, we overview the fundamental principles and mechanisms of auto-scaling, including its role in ...
The grid computing is a ne xt generation computing technology with the focus of. combining several weak and smaller networks in order to make a strong processing power and. storage resource (Wang ...
The Journal of Grid Computing explores an emerging technology that enables large-scale resource sharing problem solving within distributed, loosely coordinated groups sometimes termed "virtual organizations". Coverage includes protocols, security, scaling and more. Although the advantages of this technology for classes of applications have been acknowledged, research in a variety of ...
The power of sharing resources and information is to huge to ignore. Grid computing has promised to enable the sharing particularly by scientific research and academic communities. The objective of this paper is to lend some understanding on grid in terms of its architecture, development and evolution. Review was conducted on some grid models, common feature and characteristics, architecture ...
Grid Computing is basically an infrastructure which provides high computational capacity to the distributed system by making use of widely geographically distributed resources. The resources in grid are owned by different organizations which has their own policies, computation capability, framework, and cost and access model. The last fifteen years have observed a growth in computer and ...
1 - Introduction. Grid computing has been promoted for more than 10 years as the Advanced Computational Infrastructure of the future, see Figure 1. Many scientists and others have considered grid computing as one of main sources of the impact that scientific and technological changes have made on the economy and society.
A Survey on Reliability in Grid Computing Systems. Grid computing is a new technology the objective of which is to share resources including processor, hard disk, software etc. in a dynamic, heterogeneous, and large-scale environment. This sharing is mainly employed to solve computing... more. Download. by Dr. Sobhan Esmaeili.
This paper presents a state-of-the-art review of wireless grid computing. while the resources of these devices can be processor, memory, bandwidth, code repositories, softwares, etc. (5).
Smart grid is a new vision of the conventional power grid to integrate green and renewable technologies. Smart grid (SG) has become a hot research topic with the development of new technologies, such as IoT, edge computing, artificial intelligence, big data, 5G, and so on.
PDF | On Jun 30, 2017, Matthew N.O. Sadiku and others published Grid Computing | Find, read and cite all the research you need on ResearchGate
Research 19 January 2024 Article: 14. Part of 1 collection: Machine Learning at the Edge for the Cloud Continuum. 1. 2. …. 17. Next. The Journal of Grid Computing explores an emerging technology that enables large-scale resource sharing problem solving within distributed, loosely ...
The research paper also investigates emerging trends in the modern grid computing technology. Grid computing is one the stat of the art technologies being used for high speed computing. Reference ...
Conclusions and future directions. The purpose of this review was to provide an extensive literature survey of current research in the area of security in grid computing, and to identify areas of grid computing security in which more extensive research is needed. More importantly, this paper contributes to the overall body of research ...
marizes the research outcomes of the most recent papers, highlights their suggestions for utilizing QC techniques for various smart grid applications, and further identifies the potential smart grid applications. Several real-world QC case studies in various research fields besides power and energy systems are demonstrated.
This paper compares and contrast the models of Grid and Cloud Computing, and discusses their essential characteristics by reviewing a handful of research papers based on the two technologies. The present competitive world is characterized by individuals and businesses constantly trying to adapt themselves to progressive technological innovations for competitive advantage to stay ahead of the race.
Grid computing has evolved into an important discipline within the computer industry by differentiating itself from distributed computing through an increased focus on resource sharing, coordination, manageability and high performance. The concept of grid has emerged as a new approach to high performance distributed computing infrastructure. In general, Grids represent a new way of managing ...
In this paper, architecture of grid computing is defined as shown inFigure-1. The structure partitions the computing service system into two sub services: service for the computing providers, and service for the computing consumers. Figure- 1. Web service of grid computing based on multi agents. Each resource of grid computing provides local
This paper strives to compare and contrast Cloud Computing with Grid Computing from various angles and give insights into the essential characteristics of both. Grids and Clouds Overview. Grid ...
A Review Paper on Grid Computing. Mr.Ramesh Prajapati, Dr.Samrat Khanna. Published 2013. Computer Science, Engineering. TLDR. A detailed survey on the challenges and characteristics of the grid and how to manage the resources in the grid environment is given and the security issues related to grid are dealt with. Expand.
GRID. computing [1, 2] is a technology for coordinating large. scale resource sharing and problem solving among various autonomous group. Grid technologies are currently distinct from other major technical trends such as internet, enterprise distributed networks and peer to peer computing.
We take scheduling in Grid as a topic of research and try to provide concise understanding of all important concepts pertaining to scheduling of jobs on Grid computing infrastructure. Main objective of this paper is to provide various understandings related to scheduling in Grid at a single space, i.e., in this paper.
Grid computing have received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational in e-Science and businesses. The objective of the journal is to serve as both the premier venue for presenting foremost research results in the area and as a forum for introducing and ...
A resource management system matches requests to resources, schedules the matched resources, and executes the requests using scheduled resources. Scheduling in the grid environment depends upon the characteristics of the tasks, machines and network connectivity. The paper provides a brief overview of resource management in grid computing ...
disadvantages; these were reviewed during this research. The remainder of this paper is arranged as follows: in section 2 some review of the literature is presented; section 3 shows the research methodology used; section 4 starts with cluster computing; in section 5 grid computing is explored; section 6 discusses cloud computing.