A Review of Cost-Effective Resource Management in Cloud Computing using AI- Based Forecasting

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN:2395-0072

A Review of Cost-Effective Resource Management in Cloud Computing using AIBased Forecasting

Mohammad Shahbaz1, Deepshikha2

1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India

2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India

Abstract - Formoderncomputing,thecloudcomputingis imperative, it provides scalable and on demand availability of the resources. One such challenge is resource management for efficient workloads that fluctuate, demand that is unpredictable and costs bounded. The problem with traditional allocation methods is that they can over provisionorunderprovisionandthatcomesatahighercost or poor performance. However, with the rise in the race for faster deployment, AI based forecasting has significantly proven itself as a viable option in solving the issue of optimizing resource utilization by precisely forecasting future workload demands. This review studies the role of AI driven forecasting in cost efficient cloud resource management and discusses AI methods such as Machine Learning (ML), Deep Learning (DL) as well as Reinforcement Learning (RL). The strategies that this explorestosavecostsare:predictivescaling,intelligentload balancing, and optimal pricing models. Challenges such as model accuracy, data privacy, and integration with increasingly popular tech such as edge computing and IoT are also reviewed in the review. Through a discussion of recent progress and case studies, it provides an example of how AI forecasting could help improve cloud efficiency, sustainabilityandscalability.

Key Words: CloudComputing,ResourceManagement,AIBased Forecasting, Cost Optimization, Machine Learning, PredictiveScaling,LoadBalancing

1.INTRODUCTION

1.1.BackgroundonCloudComputing

The access to computing resources has been revolutionized by cloud computing, which gives scalable, ondemand,servicesover thecloud, whichis the internet, without the requirement to own any additional costly hardware.Deployingapplications,storagesandcomputing power is given flexibility. Infrastructure as a Service (IaaS), such as Amazon EC2, Microsoft Azure VMs; Platform as a Service (PaaS), like development environment, Google App Engine, AWS Elastic Beanstalk; software as a Service (SaaS) are the main models of this category. Nevertheless, managing the resources still remains a challenge, as workloads fluctuate, demand is

unpredictable and there is a tradeoff between cost and performance.

1.2.IMPORTANCE OF COST-EFFECTIVE RESOURCE MANAGEMENT

CostStructure:Cloudservicesoffersitsownpayasyougo pricing model and User has to pay on resource consumption basis.Suchinefficientresourcemanagement canleadto overprovisioning,where extra resourcesthan whatisrequiredareallocatedandthisleadstoadditional operationalcost,overprovisioningcanalsoleadtoapoor user experience, because there are not enough resources tosufficetherequirementsandthisresultsinpoorservice performance, and finally, idle resource, where resources arenotbeingusedorbeingusedinefficient.Costeffective resource management aims to allocate the computational resources to the actual demand at least cost. This approach incorporates dynamic scaling, workload prediction, and cost awareness scheduling to use the resourcesproperly.

1.3.ROLE OF AI IN FORECASTING AND OPTIMIZATION

Techniques such as Machine Learning (ML), Deep Learning(DL),ReinforcementLearning(RL)amongothers improveArtificialIntelligence(AI)toboostcloudresource management. Preproactive scaling powered by AI driven modelsandreducewastageofpower.Servesautoscaling, dynamicresourceadjustment,aswellasloadbalancingto gain improved performance. Also, AI enables cost optimization by advice on best to go pricing strategy, comparable to spot instances and cast plans. Through integration of AI, organizations overcome costs, better resourceallocation,andperformanceofcloud.

1.4.OBJECTIVEOFTHEREVIEWPAPER

This review paper aims to give a comprehensive analysis on AI based cost effective forecasting techniques in cloud computing.Thepaperaimsto:

 Examine the problems that arise in the context of traditionalcloudresourcemanagement.

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN:2395-0072

 Different AI techniques for workload prediction and optimizationareevaluated.

 Discuss various techniques of cost savings with help ofAIdrivenfeatures.

 Identify gaps in existing literature and propose potential routes for future enhancement of AI based cloudmanagementsolutions.

Thispaperreviewsrecentthingsandsucceedcasestudies, highlightinghowAIcanchangecloudassetadministration forbetterscalability,effectiveness,andcostproficiency.

2.CLOUD COMPUTING AND RESOURCE MANAGEMENT

2.1 Definition and Key Characteristics of Cloud Computing

In essence, cloud computing is a technology which allows for the utilization of shared and dramatic amount of computing resources (such as servers, storage, databases, networking,software,andanalytics)whilebeingdelivered over the internet. It does not require physical infrastructure and makes it possible for us to rent our computing power and services from cloud providers. According to the National Institute of Standards and Technology (NIST), cloud computing model is that the serviceofferson-demandnetworkaccesstoasharedpool ofconfigurablecomputingresources(e.g.,servers,storage, applications, and services) that may be provisioned and releasedwithminimalmanagementeffort.

2.2 TYPES OF CLOUD SERVICES (IAAS, PAAS,

SAAS)

Cloudcomputingiscategorizedintothreeprimaryservice models,eachcateringtodifferentneeds:

2.2.1InfrastructureasaService(IaaS)

IaaS (Infrastructure as a Service) is the virtualized computing infrastructure including virtual machines (VMs), storage and networking resources that are provided to the user to deploy and to manage the applications without significant hardware investment. IaaS allows business to scale its IT resources as per their needs in terms of cost and performance. There are many popular examples of IaaS providers including: Amazon Elastic Compute Cloud (EC2), Google Compute Engine (GCE), and Microsoft Azure Virtual Machines. Some of the common use cases for IaaS are hosting web applications, big data analytics, fulfilling backup and disaster recovery needs,etc.

2.2.2.PlatformasaService(PaaS)

The Platform as a Service (PaaS) is a development and deployment environment with complete framework, database and many tools provided to the developing application over eliminating the under platform. Popular examples of PaaS include Google App Engine, Microsoft Azure App Services, and AWS Elastic Beanstalk. PaaS is widely employed in web and mobile application development, API management, and DevOps automation allowing developers to solely put in efforts on coding and innovation while the application flexes on scalability, safety,andinfrastructuremanagement.

2.2.3SoftwareasaService(SaaS)

Software as a Service (SaaS) actually refers to fully functional software application that can be accessed throughtheinternetwithoutinstallationonthedeviceitis beingusedon.Google Workspace(Docs,Drive),Microsoft 365, Salesforce, Dropbox are examples of SaaS. Services such as email, customer relationship management (CRM), and collaboration tools are the kinds of services that businesses use SaaS for, which helps provide the convenience,scalability,andcost-savingbusinesssolution. There are three different cloud service models IaaS, PaaS, or SaaS and each one provides a unique function to solve specific computing requirements, technical competencies,andfinancialbudgets.

2.3.CHALLENGES IN CLOUD RESOURCE MANAGEMENT

Cloud computing is effective in managing resources such as performance, scalability and cost efficiency. Nonetheless, there are several obstacles to optimal resourceallocation.

2.3.1.DynamicWorkloadVariability

The resource requirements of such cloud workloads are often variable because they must adjust to the changing

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user demand. An over-provisioning practice is the allocationofmoreresourcesthanwhatistrulyneededfor an application for the purpose of minimizing server resource usage, while in turn causing wasted resources and higher costs. Conversely, in the case of underprovisioning where resources are insufficient, performance degrades and users' experience, as well as operation efficiency, is affected negatively. However, the allocation of resources has to be balanced to meet demandsfluctuatingwithoutoverorunderprivision.

2.3.2.Auto-ScalingComplexity

Real time scaling mechanisms should be implemented whichdemandfluctuationsareforecastedaccuratelywith sophisticated algorithms. These algorithms enable systemstochangetheirresourcesinadynamicmannerin accordance to the changing workloads. But, traditional rule based scaling methodologies often fail to accommodate sudden spikes or sudden slow down in incoming demand as they depend on pre defined conditions and do not keep the ability to adeptly react to the unforeseen changes. Accordingly, adaptive real time scalingforcloudsystemsis necessary,inordertoachieve goodperformanceandmakebestuseofresources.

2.4.IMPORTANCE OF COST EFFICIENCY IN CLOUD COMPUTING

Thepricingofthecloudcomputingiscontractualbasedon the principle of pay as you go or subscription. Effective resource management is needed to avoid wasting resourcesandhenceunnecessaryexpenseswithoutlosing performance.

2.4.1.FactorsAffectingCloudCosts

There are several critical factors pertaining to using the cloud resources: resource utilization, scaling strategies, workload distribution and cloud pricing models. In the case of underutilized virtual machines (VMs) and idle resources, this will only increase the costs unnecessarily. The same extends to unscalable implementation, as well, which can lead to instances being paid for that are not being used, thus adding to expenses. Uneven resource usage can be the result if workloads are not distributed poorly and can impair performance and impact the cost. Similarly, choosing an inappropriate cloud pricing model, for example, on demand, reserved or spot instances, can lead to financial losses as its a very costly business if not planned and optimized properly depending upon the organization’srequirementandpatternsofusage.

2.4.2.Cost-Effective

Resource Management Strategies

Given this, organizations take several AI driven strategies to minimize the costs while ensuring the optimal

performance of the system. With predictive scaling using AI, we can predict future workload demands and adjust our resources accordingly, awoing us to have timely allocationofresources,aswellaspreventingitfrombeing overly provisioned. Workloads are equally spread among availableresourcestokeepbottlenecksdownandincrease efficiency by intelligent load balancing. In addition, AI helpsto optimizeinstance selection,identifying most cost effectivecloudpricingmodel(spot,reserved,hybridcloud instance) based on usage patterns. Furthermore, automatedresourceschedulingpoweredwithAIallocates workloads in low cost time periods, and as a result leverage cheaper price modelsin order to furthercontain costsandincreaseresourceutilization.

3.AI-BASED FORECASTING IN CLOUD RESOURCE MANAGEMENT

Accurate forecasting is needed for efficient utilization of computing power,storage and network resource in Cloud resource management so as to keep costs to a minimum. Currentforecastingmethodsrelyonstaticrulesorsimple statistical models which lack the ability to adapt to the dynamic nature of cloud workloads. Machine Learning (ML), Deep Learning (DL) and Reinforcement Learning (RL)areusedbyAIbasedforecastingtopredictworkload patterns and to minimize resource allocation. It goes through AI techniques, predictive analytics for cloud workloads,importantforecastingmodels,andthebenefits ofusingAIdrivenmethodsovertraditionalapproaches.

3.1.Overview of AI Techniques in Cloud Resource Management

Algorithms that must be used for AI based forecasting in cloudcomputingareasfollows

3.1.1.MachineLearning(ML)

InMachineLearning(ML),algorithmsaretrainedtolearn patterns off a historical cloud workload data and predict

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future resource utilization. There are a few categories of MLtechniques.InSupervisedLearning,trainedmodelsfor workload prediction are used where the model is trained from labeled datasets; for this purpose one can train regression models to be used to predict CPU or memory utilization based on past utilization trends. Clustering algorithmswillclusteranunlabeledworkloadpatternsfor optimization purposes, whereas identifying anomalies in resource utilization by unsupervised Learning in unlabeleddata.Inotherwords,Semi-SupervisedLearning consists of using both labeled and unlabeled data to improve forecast accuracy. Reinforcement Learning takes on an agent based approach whereby models learn optimal resource allocation strategies through trial and error focused on providing continuous refinement of the allocation decisions based on real time feedback and outcomes. ML techniques maximize prediction accuracy and improve decision making on cloud resource management.

3.1.2.Deep

Learning (DL)

Machine Learning (ML), a subset of Deep Learning (DL), has great capacity in managing cloud data at large scale and complex. Artificial neural networks (ANNs) are used to build a DL model, which scan through workload fluctuations and make accurate predictions. Long Short Term Memory (LSTM) networks, a class of Recurrent NeuralNetworks(RNN)withspecialdesignfortimeseries forecasting,aregenerallyusedforDLwhicharesuitableto be used for predicting fluctuating cloud workloads. While usedforimagerecognitionmorecommonly,Convolutional Neural Networks (CNN) are also applicable to find patterns in the resource utilization trends. Furthermore, Transformer Models like GPT or BERT are advanced models which are good at processing large amount of datasets, providing powerful predictive analytics in cloud environment to enhancethe resourceallocation and raise the performance. DL techniques are therefore suitable for the analysis of complex cloud data and efficient cloud operation.

3.1.3.Reinforcement Learning (RL)

Reinforcement Learning (RL) is a dynamic AI technique that automates the task of optimizing cloud resource allocation via continuous interactions with the environment. RL agents make decisions by maximizing theirrewardfunction,forinstancetominimizecostat the cost of optimal performance. RL has a series of key applications in cloud computing. RL models can learn when to scale resources up or down depending on fluctuating demand with auto-scaling. RL for energy efficient scheduling allows predicting and adjusting energy needs in order to optimize power consumption. Moreover, RL would help in load balancing by adjusting the serverload distribution, thereby increasing the whole system efficiency. As such, these applications enable

simplification of running of cloud operations, minimizing expensesandoptimalexecution.

4. COST-EFFECTIVE STRATEGIES FOR CLOUD RESOURCEMANAGEMENT

Flexibility, flexibility, and flexibility are some of the features of cloud computing, but lack of optimal resource management may result in a lot of unnecessary expenses. There is a need for organization to embrace cost effective strategiessuitableinresourcesallocationandatthesame time ensuring that performance and reliability do not deteriorate. In this section we take a closer look at some strategies, like dynamic scaling, load balancing, cloud pricing models and cost aware scheduling algorithms, which through AI driven forecast are attempting to maximizeefficiency.

Figure-3: Cloud resource management framework based on DRL

4.1.DynamicScalinginCloudComputing

DynamicScalingisdonesothatcloudresourcesarescaled in real time depending upon the supply. It helps in avoidingoverprovisioning(resourcewastage&increased cost) as well as under provisioning (performance issues duetoresourceshortage).

4.1.1.Auto-Scaling

Auto scaling is a mechanism that automatically scales resourcesaccordingtosomerulesandactualrequirement. ThethreemaintypesareReactiveAuto-Scalingthatreacts based on certain metrics to take a scaling action (e.g., if CPU usage exceeds a certain threshold), but with a delay asittriggersonlyafteraspikeofworkloads,andhencethe system suffers from delay and performance degradation before the scaling comes into effect. Auto-Scaling

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Scheduled adjusts resources based on set schedules so that resources are available during peak times and inactive during off-peak hours improving the cost efficiency in the mean time. LSTM, ARIMA, machine learning models, are used to predict future demand. PredictiveAuto-Scaling,proactivelyscalesupresourcesin advance to prevent future bottlenecks. For instance, AWS AutoScalingappliespredictivescalingbasedonhistoryto determine a way to pre adjust in order to optimize cloud operation.

Table-1: Dynamic Scaling in Cloud Computing

Auto-Scaling Type Trigger Pros Cons

Reactive Real-time resource usage Simpleto implement Delayed responseto workload spikes

Scheduled Predefined time-based rules

Predictable andcosteffective Cannot handle unexpected traffic

Predictive AI-based workload forecasting Mostefficient andcosteffective RequiresAI modelsand training

4.2.LoadBalancingTechniques

Load balancing is essential for distributing workloads acrossmanyresourcessobottlenecksdonotoccur.

4.2.1.StaticLoadBalancing

However, assignments of requests in Round Robin are done in such a way that each server gets its fair share of requests on a round robin basis. However, Least Connection sends requests to the server with the least number of active connections in order to optimize the distribution of the load. But both methods lack a time changing server load into consideration that can result in suboptimal resource usage and performance for dynamic workloads.

4.2.2.DynamicLoadBalancing

LoadbalancingwithAIisbasedonrealdataandadvanced AIalgorithmstooptimizeresourceutilization,resultingin better efficiency and performance. AI Based Load Balancing uses reinforcement learning (RL) and deep Qnetworks (DQN) to fill the gaps of the current classical load balancers using the current server environment. Google’s Load Balancer provides an example using AI to shift workloads as needed. Auto-Scaling Integrated Load Balancingdynamicallyscalesinstancesbytheactualtraffic pattern and the workload spike as being PAT along with predictive scaling to improve the cost efficiency. Content-

Aware Load Balancing considers factors such as application type, request size, server capacity before distributing workloads. Such approach is used by examples like NGINX and HAProxy for optimizing web application performance by utilizing resources properly dependingonworkloadrequirements.

4.3.PricingModelsinCloudComputing

Different pricing models are provided by Cloud providers tooptimizecostmanagement.Inmostcases,choosingthe rightmodelcancutcloudexpensesbyabigmargin.

4.3.1.On-Demand

Pricing

The pay-as-you-go model enables users to pay less without paying for the resources they do not consume, resulting in flexibility and cost efficiency for different usage patterns. This is specifically intended for short termsworkloadsorunpredictabletrafficwherebydemand can be fluctuating. On the positive side, it usually means paying more than reserved or spot instances, especially forsustainedorcertainresourceneeds.

4.3.2.ReservedInstances

With up to 75% savings, users committocloud resources for1to3yearsforreservedinstancemodel.Itworkswell for predictableworkloadsthatneed tohave a stablestate foralongperiodoftimeandcanprovidemoreeconomical savingsoveralongerperiodoftime.Thedownsideisthat it provides less flexibility, users have to pay for the reserved capacity even when the resources aren’t fully used, which means loss of cost if the demand suddenly changes.

4.3.4.HybridPricingStrategy

It was a hybrid cloud pricing model, which is mostly on demand, though it incorporates reserved and spot instances to get savings and optimized utilization of the resource.Forexample,lowprioritytasksthatcantolerate interruptions should use spot instances whereas critical applications that require flexibility and need to be scaled instantly should use on demand instances and reserved instances are good for stable workloads that require long term capacity. This allows there to be cost efficiency with providingthedifferentworkloadstheirneeds.

5. AI Applications in Cost-Effective Resource Management

With the advancement of computer systems and manufacturers, Artificial Intelligence (AI) is campaigning cloud computing by optimizing resource allocation, minimizing costs, and enhancing efficiency. Workload prediction, load balancing, and scaling automation is performed based on various AI techniques – Machine

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Learning (ML), Deep Learning (DL) and Reinforcement Learning (RL) with enabling large cost savings. In this section,welookthroughrealworldusecasesofAIdriven cloud resource management, the comparison of different AI technology for cost reduction, and limitations and challengesofusingAIforcloudcomputing.

5.1.Case Studies of AI-Based Resource Management

Successful implementation of AI driven resource management is done in industries such as e commerce, finance, healthcare etc. Some examples of the use of cost effective AI strategies in cloud computing are as shown below.

5.1.1.Google DeepMind: AI for Energy-Efficient DataCenters

Google was challenged to cool massive data centers that used a huge amount of power. DeepMind’s AI models worked with historical cooling data with deep learning, and with Reinforcement Learning (RL) to optimize the operationofthecoolingsystem.ThecombinationoftheAI driven approach has resulted to a 40% reduction in energy usage for cooling, which has also significantly reduced operation cost. It also contributed to sustainability, demonstrating how AI can improve efficiency and lower environmental impact of massive datacenteroperations.

5.2.Comparison of Different AI Techniques in CostReduction

While different techniques exist to handle cost effective resource management, they also come with their own shortcomings. A comparison of Machine Learning (ML), DeepLearning(DL),ReinforcementLearning(RL)incloud computingisgivenbythefollowingtable.

Table-2: Comparison of Different AI Techniques in Cost Reduction

AI Technique Use Case Advantages Limitations Machine Learning (ML) Predictive workload analysis Fastand efficientfor structured data Lessaccurate forhighly complex workloads

Deep Learning (DL) (LSTM, CNN, ANN) Complex resource prediction andscaling Handles large datasetsand non-linear patterns

Requireslarge amountsof dataand computing power

Reinforcement Learning (RL) Autoscaling, load balancing Dynamic decisionmaking, learnsfrom High computational cost,long trainingtimes

Hybrid AI Models (LSTM + ARIMA, RL + ML)

real-time data

Cost-aware scheduling Combines benefitsof multiple models Morecomplex toimplement

5.3.Challenges and Limitations of AI-Driven Approaches

Even though AI is capable of handling cloud resource management,thefollowingchallengesexist:

5.3.1.HighComputationalCosts

The considerable processing power and large datasets needed for deep learning and reinforcement learning are too costly for small businesses to implement. With that, deploying AI based resource management solutions is difficult as these models can be just too expensive. A simpler solution is to use simple AI models like lightweightmachinelearningalgorithmsforsimplertasks. By employing this approach, the computational demands and cost associated becomes more feasible for smaller organizationtoutilizeAIforresourcemanagement.

5.3.2.DataQualityandAvailability

InorderforeffectivetrainingofanAImodel,accurateand clean datasets are very important, and can lead to incorrectworkloadpredictions,inefficiencieswithscaling, and poor resources management. To resolve this issue, one should apply the data preprocessing techniques and anomalydetectionmodelsinthepipelinepriortotraining theAI.Byusingthesemethods,itcanhelpverifythedata being used to train are accurate and not obese so that predictionresultscanbemorereliableandscaleplanning canbemoreefficient.

6. FUTURE DIRECTIONS AND RESEARCH CHALLENGES

With continuous progression of the cloud computing, the roleofAIbasedresourcemanagementwillbeallthemore important in helping bring more cost, performance and efficiency to our consumption. We still have a few challenges left, such as raising the accuracy of AI model, securing and preserving privacy, conflating AI with a variety of cutting-edge technologies like the edge computing and IoT, and cultivating sustainability in cloud operations.

6.1ImprovingAIModelAccuracyandReliability

For resource forecasting, auto-scaling, as well as cost optimization,AI modelsusedforcloud management must be highly accurate and reliable. Nevertheless, existing AI techniquesarelimitedinthefollowingrespects:

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6.1.1EnhancingAIModelGeneralization

The AI models trained on the historical data may have difficulties in generalizing to the unseen workload or that has highly dynamic workloads and under allocation the resources. Thus, this poor generalization causes the resultingincreasesinthecostsandthedegradationofthe performance. Transfer learning will be needed to fix this, and this will allow AI models trained on one dataset to adapt to another dataset. It can also be used to leverage online learning, where AI models use online data to continuouslygetupdatesandlearnaboutnewdatarather than relying only on offline historical data sets, which wouldgivebetterandrealtimeresourcemanagement.

6.1.2 Handling Unpredictable Workload Variations

Trafficsurgesincloudcomputingenvironmentsarehighly dynamic, i.e., during Black Friday and during live stream events.Theunpredictabilityofwearparametersrepresent an adversarial condition for AI based forecasting models, representing a variable which the AI model cannot take into account, leading to: a miss of sudden spikes, which willleadtounderprovision(degradationof performance) oroverprovision(increaseofcost).Thiscanbeaddressed byusinghybridAImodelthatcombinedeeplearningwith existing statistical model like ARIMA for accurate forecasting. Furthermore, real time anomaly detection algorithms can be enforced to detect sudden workload pattern changes that otherwise requires more time to fix andmanagetheresourcewell.

6.1.3 Reducing AI Model Complexity and ComputationalCosts

If the underlying models used are deep learning models suchasLSTMasinthecaseofgenerativemodels,CNN,or Transformers, these models require more computational resources, and this fact tilts the balance to something different, namely, having to invest more on computation than what you’re saving by using AI driven cloud optimization.However,smallbusinessesandstartupswill face this challenge because owning AI infrastructure in itself consumes high costs for the implementation of AI based resource management. To overcome this, lightweight AI models that need smaller amounts of computationalresourcescanbeworkedout.Finally,some quantization and model compression techniques can be utilized to shrink the size of AI models, without the accuracy loss, in order to make AI resource management moreaccommodatingandcheaper.

7.CONCLUSION

Resource management in cloud computing using AI has proventobeadisruptivesolutionthatoptimizescostand efficiency as well as performance. Main technological AI

approaches from machine learning, deep learning, and reinforcement learning applied for predictive workload forecasting, auto-scaling, load balancing, and cost-aware scheduling were discussed during this review. Real world examples were shown through case studies brought forth from Google, Netflix, and AWS as to how as AI did help reduceoperationalcostsandoptimizeresourceutilization. Nevertheless, it faces challenges of significant computationalcosts,ensuringdatasecurity,etc.,aswellas model interpretability in AI and adaptability to dynamic workloads.IntegratingAIforcloudprovidersmeansmore energy efficient use, lower operation expenses and smarter workload management, and for users, lower cost andbetterservicequality.Inthefuture,futureinnovations in AI like privacy preserving AI using federated learning, integrating the cloud with edge computing and working towards AI driven sustainability will become the factors whichwilldefinenextlevelofthecloudcomputing.Cloud computingcanbecomemoreintelligent,costeffectiveand environmentally sustainable with the help of emerging AI drivenstrategiesifwecanovercomecurrentlimitations.

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