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 Comparative Study of Serverless Computing and Virtual Machines in Cloud Environments
Isha Srivastava1 , 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 - Twoimportantparadigmsofcloudcomputing areserverlesscomputingandvirtualmachines(VMs),which have advantages and tradeoffs to deploy modern applications. A systematic, comparative analysis from the aspects of architecture, performance, cost-efficiency, scalability, security and workload suitability for serverless computing and VMs in cloud environment is conducted in this review paper. We first provide definition and illustration of serverless computing (such as eventdriven Function-as-a-Service models) and VMs (such as hypervisorbased virtualization) along with description of theirownstrengthsandweaknesses.Majordiscoveriespoint out that serverless computing presents itself best for high scalability scenarios, low management overheads, and pay per use cost models and is fit for event driven microservices and workloads with short duration. On the other hand, VMs offer greater control of the infrastructure, and good compatibility with legacy systems and consistent performances for long running applications, at a cost of increasedresourcecostandhighermanagementcomplexity. The paper presents context specific tradeoffs and use case through real world case studies (e.g., AWS Lambda vs EC2 instance). Serverless cold starts, VM resource underutilization and vendor lock in are been critically examined. Future directions are discussed with emerging trends such as hybrid architectures, and AI driven resource optimization. Finally, the review makes actionable recommendations for practitioners on how to choose an optimal model among the offered models based on the workload characteristics and specific cost and scalability demands. Motivated by this goal, we present a synthesis of information to help practitioners in the clouds, and the researchers that are beginning to study them, understand the potential roles that this emerging technology may play in the near future, and what those roles imply for the developmentofnextgenerationapplications.
Key Words: Serverless computing, Virtual Machines (VMs), Cloud computing, FaaS, IaaS, Scalability, Costefficiency,Comparativeanalysis.
1.INTRODUCTION
1.1 Background and Context
The cloud computing has disrupted businesses by allowing them to leverage scalable and on demand
servicesovertheinternetinsteadofrelyingonexistingon premises infrastructures. Cloud computing started from the turn of the century when virtualization technologies poweredtheevolutionofcloudcomputingaswellthrough the use of virtual machines (VM). As the time progressed, cloud models like Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) were developed based on different level of abstraction and control. Today, serverless computing (Function as a Service, or FaaS) has arrived –infrastructuremanagementbeingabstractedexactlywhile developers are not allowed to concentrate on the execution of code. In cloud, resource management and deployment plays an important role, economic cost, scalability, and operational efficiency directly depend on it.
Problem Statement
Manualscalingandmaintenanceofthesetraditionalcloud computing models such as VM based architectures cause resource underuse, over provisioning as well as high operational overhead. On the one hand, VMs are flexible but tend to find fulfilling the needs of dynamic, event drivenworkloadsdifficult.However,serverlesscomputing claimstoalleviatethehassleofscalingthroughautomatic scaling as well as cost efficiency; but casts limitations includinglimitedcontrolofruntime,andlatencyconcerns (e.g, starting from cold). To help organizations select the best of the solutions for different workloads, it is imperativetoevaluatetheseparadigmssystematically.
Figure-1: Server vs VMs vs Serverless.
1.2
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
1.3 Objectives of the Study
Theaimofthispaperistocompareserverlesscomputing, with virtual machines (VM) in cloud environment on architectural, performance metrics, cost models and scalability mechanisms. In this way, it will analyze each approach’s strengths, for instance, serverless computing’s pay per use pricing and the ability to control infrastructure in VMs, as well as its weaknesses, like serverlesscoldstartsandVMresourcewaste.Further,the paper will investigate various use cases for when each model is most appropriate, e.g. serving microservices usingserverlesscomputingorVMsforlegacyapplications.
1.4.Scope of the Paper
Thestudyfocusesonfourkeyevaluationcriteria:
Performance: Latency, throughput, and resource utilization
Cost: Pricing models (e.g., pay-per-use vs. reserved instances).
Scalability: Horizontal vs. vertical scaling, autoscalingefficiency.
Ease of Use: Developer experience, deployment complexity,andmonitoringtools.
Aspects like security and compliance are acknowledged butnotdeeplyexplored.
2.OVERVIEW OF SERVERLESS COMPUTING
2.1
Definition and Key Concepts
Serverless computing is a cloud execution model where servers do not need to be managed by the developer; insteadcoderunningintheformofindividualfunctionsis deployed on the cloud. Although the term 'serverless' is a bit misleading as the servers are still involved, the cloud provider fully abstracts the provisionning, scaling, and maintenance of these servers. Serverless computing key conceptsare:Event-DrivenArchitecturewhichmeansthat functions are triggered based on events coming from HTTP requests, database update, uploaded file, or messages from a message queue and Function-as-aService (FaaS) to allow code execution in stateless, ephemeral containers. In FaaS platforms such as AWS Lambda and Azure Functions, the users get charged only forthecomputetimespentwhileexecuting.
2.2.Architecture of Serverless Computing
Any serverless architecture would be made up of three primarycomponents,theAPIGateway,theFaaSplatform, and the Backend services. It receives external requests as entry point, routes them to the appropriate functions
while handling onboarding, throttling, and logging. Functions can be hosted and executed on the FaaS Platformontopofdynamicallyallocatedresources,scaled instances, and idle containers can be terminated as needed. The functions call upon some backend services (e.g., DynamoDB databases, S3 storage, Kafka messaging queues) to perform their work. A serverless function, for example,maybetriggeredwhenanimageisuploadedtoa cloud storage bucket and it generates a thumbnail that is storedinanother(different)bucket.
of Serverless Computing
2.3.Advantages
Since serverless computing is essentially a means to run your ba see on someone else's servers, it is scalable, cost efficient, and much less operational overhead (compared to a traditional app). With automatic horizontal scaling, it automatically scales horizontally at high scale with minimal or no intervention whether you receive a few dozen requests to a few thousand requests per second. Pay per execution pricing model is very cost effective as there are no costs in idle periods and best suited for sporadicorunpredictableworkloads.However,serverless computing also declines the operational overhead as developers write code only for the what needs to be written, while the cloud provider does the rest, provisioningservers,patching,andtheyarealwayson.
2.4.Limitations
However, vendor lock in, no control over the infrastructure and cold start issues are some of the drawbacks of serverless computing. Depending on the function, there can also be a cold start delay, when the function has been inactive at some point and hence the platformneedstoprovisionresourcesbeforeexecution of these functions. Vendor lock in is also an issue due to tighterintegrationfromtheecosystemoftheprovider(for example, AWS specific triggers) which make it hard to migratetootherplatforms.Inaddition,thereisn’tmucha developer can do to influence the underlying infrastructure to such an extent where OS configurations,
Figure-2:Architecture
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
runtime versions, and hardware can be better tuned to maketheworkloadsoptimizedforspecificusecases.
3.OVERVIEW OF VIRTUAL MACHINES (VMS)
3.1 Definition and Key Concepts
VirtualMachines(VM) isasoftwareemulationofphysical computers which allows us to run multiple individual separateOSonsamephysicalhost.Thisispossibledue to the process called virtualization which takes the physical hardware resource like CPU, memory, and storage and allocates them in virtual environments. The hypervisors are the key concepts in VM technology, because they use software or firmware to create, manage and allocate the resourcesfortheVMs.Thetwohypervisortypesavailable are:Type1(BareMetal)hypervisor thatrunsonthehost hardware (i.e., VMware ESXi, Microsoft Hyper-V), and Type 2 (Hosted) type of hypervisor that runs on top of a host OS (i.e., Oracle VirtualBox, VMware Workstation). In addition, Infrastructure as a Service (IaaS) is the cloud modelwhereserviceproviders(e.g.,AWSEC2,AzureVMs) offervirtualizedcomputingserver,virtualizedCPU(vCPU) to the users, VMs on demand, and will pay the cost associated with the resources that are allocated (RAM, vCPU,Storage).
3.2.Architecture
of VMs
The rest of this paper describes a VM architecture composed of three core components which include the hypervisor, guest OS, and the resource allocation. The hypervisor allocates the physical host resources among the virtualized pools and then carefully partitions them into isolated pools of resources for VMs, also selectively managing the affiliation between them to prevent interference.OperatingsystemofeachVM,independentof host OS or other VM, the operating system the guest OS, which applications run in a self contained environment. Resourceallocationmeansthatthehypervisorcanprovide dynamic resource allocation for CPU cores, memory, disk space to the VM, based on specific configurations or current demand. A physical server with 4 CPU cores and 16GB of RAM would be able to run 4 VMs, using 4 cores and16GBofRAMperVM.IneachVM,adifferentOSsuch asUbuntu,CentOSorWindowsServercouldberun,andit couldbesupportingdifferentapplications.
3.3
Advantages
Its virtual machines provide full control environment, flexibility and compatibility advantages. Users have total control over the OS configurations; they can install the programs, adjust hardware settings, such as CPU prioritization, and memory allocation. Just like VMS, the VMs are also very flexible and can support a wide variety of workloads, from legacy apps that need a specific OS version or a proprietary software environment. VMs also
emulate standard x86/ARM architectures for compatibility and ease of moving (lift and shift) onprem workloadstothecloud.
4.COMPARATIVE ANALYSIS
In this section, six dimensions performance, cost, scalability, ease of use & security are compared between serverlesscomputingandvirtualmachines(VMs).
4.1.Performance
Latency,throughputand resource utilization aretheclear differences when comparing serverless computing with Virtual Machines (VMs). Cold start latency: Serverless computing’slatencycomesfromfunctioninstancestaking anywhere from hundreds of milliseconds to seconds to start, which exacerbates the delay after non-activity by that amount of time, although from there the response times are much faster. But while VMs have a consistent latency since they stay running, they still cold starts, so differentthanalivesystem ina real worldtopologythere would be some with variability, other times network or evenstoragebottlenecksinsharedphysicalhosts.Another performance consideration serverless bringsis horizontal scaling: since instant you can scale up the number of instancesthatareusedtoexecuteyourfunction,however, there are limits to the amount of throughput you can obtain from a function instance (e.g., AWS Lambda limits its functions to 15 minutes of execution). Long running tasks, such as batch processing, benefit better from VMs, however,VMsneedtobemanuallyscaledtohandletraffic spikes, while on the other hand VMs provide higher sustained throughput and better isolation. Serverless computing efficiently utilizesthe resource by dynamically allocating compute power during the function execution time and thus so less idle time. However, far from achieving efficiency, VMs very often suffer from resource underutilization, where allocated resources such as 8GB RAM is only used a part (e.g., 2GB), wasting the excess capacity.
4.2.Cost
Regardingpricingmodels,serverlesscomputingemploysa pay-per-execution model where resources consume and arebilledpercomputesecondandallocatedmemory(e.g. AWS Lambda charges on a per millisecond of compute), thus regarding total cost of ownership becomes an important factor. It would be the perfect model for sporadicworkloads.However,VMstendtofollowapayas you go or reserved instance pricing model, where users are charged for the allocated resources such as CPU and memory on the hourly basis (e.g AWS EC2 charges per hour irrespective of usage). For low-to-moderate traffic applications(APIsoreventtriggers)serverlesscomputing iseconomical,butforhighthroughput,longrunningtasks for instance video encoding there is high cost. However,
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
VMs are not cost effective when workloads are predictable, constant, for example a database, but are not costeffectiveduetooversupplyingresourcesforirregular demand.InthecaseofaweatherAPIserving10,000daily requests, serverless can cost $5 per month for the API versus$50foraVMrunningallthetime.
4.3.Scalability
Serverlesscomputingautomaticallyscaleshorizontallyby creating many parallel function instances to fulfill fluctuatingworkloadsbutnotvertically,thechangeofCPU or memory behavior at runtime. Thanks to VMs, vertical andhorizontalscalingisautomatic,butmanualhorizontal scaling is needed by means of load balancers or autoscalinggroups,andverticalscalinginvolvesupgradingthe VMspecs,whichinmostcasesimpliesdowntime.Onautoscaling, serverless’ scaling services are granular and built intotheplatformforexampleperrequestscalingonAzure Functions so adding workload can be scaled in real time without interruption. Serverless are VMs, too, but they offer auto scaling (AWS EC2 Auto Scaling) which is brought up through configuration and it may lag behind theactualdemandtime.
4.4.Security
Serverless computing isolates functions into containers but sharing of backend services (such as databases) is a shared tenancy, which brings risks. However, unlike VMs, hypervisors in VMs offer strong isolation in the form of virtual machines wherein the risk of cross VM attacks is greatly reduced. Serverless inherently trusts provider’s securitymeasures(AWSIAMroles)anditsdataisexposed through payloads of event. Conversely, while VMs give us full control over the encryption, firewalls, and access policies there is a greater level of security compared to ISVs.Butwhereserverlesshassomelimitedvisibilityinto the compliance of the underlying infrastructure with standards such as HIPAA or GDPR is in terms of compliance.WhereasVMsaremoreintelligibleandcanbe more easily audited as well as customised to conform to specific regulatory needs, thus more control on the compliance.
5.CASE STUDIES AND REAL-WORLD APPLICATIONS
In this section we study practical implementations of serverless computing and VMs in big cloud vendors and analyzetheirpracticalresults.
5.1.Serverless Computing in Industry
Major companies use AWS Lambda, Azure Functions and Google Cloud Functions for many use cases. AWS Lambda isbeingusedbyNetflixforrealtimeeventprocessingsuch as encoding validation and personalised
recommendations, and save 80% operational cost from using EC2 instances. This was however mitigated with provisioned concurrency in all but cold starts during low traffic times. For IoT based supply chain monitoring, Maersk usesAzureFunctionstoprocesssensor data from a shipping container, triggering alerts with scalability to receive over 10,000 concurrent events during peak operations. Otherwise, debugging distributed functions wasachallengetheyovercamebywayofintegrationwith Application Insights. Twitter (now X), uses Google Cloud Functions for auto moderation of the user generated content, saving money by using pay per use pricing compared to dedicated VMs. Functions were deployed regionally to solve the latency spikes challenge during viralevents.
Figure-3: Serverless Computing in Industry
5.2.Virtual Machines in Industry
Having major companies employ AWS EC2, Azure VMs, and Google Compute Engine (GCE) to run critical applications is incredibly widespread. For its core reservation system and PostgreSQL databases, Airbnb usesEC2instanceswhichit deploys reservedinstancesto guaranteeperformanceonthehighavailabilityworkloads. A full setup control over database tuning, such as query optimization,orindexing,howeverwithover-provisioning we were getting 30% of the resources before using AWS Auto Scaling. Azure VMs, the GPU optimized NVv4 series instances, are used by GE Healthcare for processing medical imaging and means of running AI models on MRI/CTscansandprovidesbenefitfromHIPAAcompliant VM’s. As a consequence, a partial migration to Azure KubernetesService(AKS)wasdonepartlybecauseofhigh costs for 24/7 uptime for VMs. Spotify deploys its music recommendationengineontopofGoogleComputeEngine, and uses preemptible VMs to run batch processing over the user listening patterns. While interruptible instances yieldcostsavingsof70%,theyrequirejobrestartsincase of VM preemptions, which necessitates the implementation of checkpointing workflows for job continuity.
Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072
5.3.Comparative Analysis of Case Studies
Furthermore, key insights from the implementations of serverless computing and VMs focus on the major differencesintermsofperformance,cost,scalability,level of operational complexity and the use of the hybrid approach.Serverlessperformswell for event driven tasks that is just what Netflix and Maersk saw but it struggled with latency critical workflows such as Twitter. However, while VMs offered very stable performance for longrunning processes like Airbnb reservation system or GE Healthcare’smedicalimaging,theywereinefficientinhow theyallocatedresources.Serverlesswasaffordableforthe fits and starts of Netflix’s encoding checks but expensive for the high throughput of Twitter’s peak traffic moderation. Spotify’s batch jobs were VMs which were economicalinasteadystate,butcausedresourcewastein the absence of auto-scaling as with Airbnb’s early setup. Serverless had no trouble with scaling traffic spikes automatically (Maersk’s IoT events), whereas VMs requiredmanualinterventiontodynamicscaling(Airbnb); or hybrid architectures (GE Healthcare migrating its VM applicationstoAKS).ServerlessreducedtheDevOpseffort as in Netflix but it brought the debugging issue with it, similar to Maersk’s distributed functions. Spotify’s couponing needed expertise in infrastructure management as its VMs, whereas GE Healthcare’s HIPAA compliance needed granular control as its VMs. Finally, Hybrid Approaches: companies like Airbnb and GE Healthcare followed a hybrid approach, with serverless being used for edge tasks (e.g. for log analysis Lambda is used)andVMsforcoresystems(e.g.,databasesarerunon EC2).
6.CONCLUSION
Comparing serverless computing and virtual machines (VMs) in the cloud discloses complementing as well as contrastingqualitiesinherentinbothconcepts.Serverless computing specifically suits event-driven architectures, microservices, and shorten lived tasks since it excels at rapidscalability,costefficiencytosporadicworkloadsand lower overhead. Nevertheless, cold start latency, lack of runtime control, and vendor lock in can hamper its application in latency sensitive or long running application. However, VMs offer complete flexibility, complete control over infrastructure and compatibility withlegacysystems,whicharewellsuitedforsteadystate workloads, for high performance computing, or for regulated environments. However, without manual intervention, VMs are often resource underutilized, constant operational costs, and scalability suffer. Ultimately,whichoption serverlessorVMs presents the correct approach depends on the characteristics of workload, the cost considerations and organizational priorities.
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