International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
A REVIEW OF COMPARATIVE STUDY OF SERVERLESS COMPUTING PLATFORMS: AWS LAMBDA vs. GOOGLE CLOUD FUNCTIONS vs. AZURE FUNCTIONS
Iqrar Nisar1, Dipti Ranjan Tiwari2
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 - FaaS (Function-as-a-Service) and serverless computing have made it possible to build cloud applications by managing infrastructure on a needed basis and reacting to events. In this review, we compare the three top serverless platforms AWS Lambda, Google Cloud Functions (GCF) and Azure Functions thoroughly. We examine peerreviewed research (2020–2024), company materials and real-world results to assess both technical and cost factors, as well as how mature the technology is. AWS Lambda manages to reach 32,000 requests per minute, offers a good variety of options to trigger functions, but cold starts take the most time. GCF Gen2 gives users the fastest cold starts (one-eighth of a second) along with the flexibility of containers and a 40% discount on processing costs per request for stream data. Azure Functions allows hybrid hosting and the Premium option has the next-highest cold start duration (a little over one second). Durable Functions from Azure allow developers to use stateful workflows. Certain problems remain the same regardless of the platform such as VPC making cold starts slower by at least 1.2 or as many as 4.8 seconds, vendor lock-in reduces the ability to port applications and issues with observability make distributed tracing more challenging. Because of this structure, we discover that Lambda saves money for tasks that happen now and then, GCF is efficient for handling a lot of data and Azure is the right match for programs with recurring changes of state. Based on the review, there are several urgent areas where research is required such as benchmarking, standardizing security and designing serverless systems sustainably.
Key Words: Serverless Computing, Function-as-a-Service (FaaS),ComparativeAnalysis,AWS Lambda,GoogleCloud Functions (GCF), Azure Functions, Performance Evaluation.
1. INTRODUCTION
1.1. Background: Evolution of Cloud Computing
The process of providing resources for computing and deploying applications has changed from using (onpremises) servers to using cloud services. This growth included Infrastructure as a Service (IaaS) that offered
virtualizedresources,shiftedcapitalspendingtooperating expenses, yet required users to oversee and control the newsystems.ThroughPaaS,infrastructurewasmadeeven more abstract and developers were provided ready resourcesandtoolsforprogramming,sotheyhadtofocus less on OS and middleware management. Now, serverless computing, especially through services called Function as a Service (FaaS), is the newest development on this continuum,makingit even easier to break down resource management and task execution into smaller pieces. Serverless automates the management of servers such as provisioning, scaling, patching and planning for capacity which is a big change for creating and deploying cloudnativeapplications.
1.2. Serverless Computing Defined
Serverless computing refers mainly to the Function-as-aService(FaaS)model,asseeninthisreview.Functionsare based on Event-Driven Execution which means functions are only executed in response to specific events like an HTTP request; they are managed by the cloud without userinvolvement,includingautomaticscaling;chargesare only made according to the amount of resources needed for function execution; and this happens quickly, automatically,withzerointerferencefromtheuser.While the terms Function as a Service (FaaS) and serverless are commonly mixed, this paper is about FaaS rather than all otherserverlessproducts.
1.3. Benefits of Serverless
Choosinga serverlessFaaS model providesmany benefits that are helping it gain wide interest. Operational overhead should be cut down significantly, since developers escape from managing servers, updating the OS, keeping things running and handling infrastructure issues, giving them more time to build the essential parts of the application. For this reason, delivery of products and updates gets quicker as challenges in infrastructure and deployment are resolved. For apps that often have Sporadic or Bursty Workloads, the Pay-per-Use model saves a lot of money because you only pay for the time your code is active. The platform has Inherent, Granular
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net
Scalability;itautomaticallyadjustsitsresourcesaccording to event load and overhead, removing the need for managers to worry about setting extra resources to meet peak demand. All of these benefits have made serverless useful in microservices, event processing pipelines, APIs andautomationtasks.
1.4. Challenges & Considerations
Althoughtherearemanybenefitstoserverlesscomputing, some new problems need to be studied. Launching a functioninstance(especiallyafterabreakinuse)cantake too much time, making certain applications less effective unless specific plans are put in place. Vendor Lock-in relevant to functions-as-a-service is an architectural risk, as tightly binding applications to one provider’s solution, tooling and various services can make it complex and costly to switch platforms. Since serverless architectures use many short-lived functions and triggered services, debugging, monitoring and observability become more complicated than in traditional monoliths. Due to constantly changing security needs, developers should fully understand the shared responsibility model, make sure their code and dependencies are secure, handle secrets safely and organize access controls for multiple triggers. In addition, how much time a function can use andtheamountofmemoryitcanaccessarelimitedbythe platform,sosome workloadsmaynotfitFaaSand call for different designs. How many resources are available affectshowmuchitcoststobuild.
Table 1: Key Serverless Challenges and Implications.
S.No Challenge Description
10 ColdStarts Latencyoninitialinvocationdueto environmentsetup
2.0 VendorLockin Tightcouplingtoprovider-specific APIs,services,triggers,andtooling.
30 Debugging& Observability Difficultytracingrequests& monitoringephemeral,distributed functionexecutions.
40 Security Model Sharedresponsibility,securing numeroustriggers,functioncode, secrets,accesscontrols
50 Resource Limits Maximumexecutionduration(eg., 15mins)andmemoryallocation perfunctioninstance
1.5. Rationale for Comparison
Amazon Web Services (AWS) Lambda, Google Cloud Functions (GCF) and Microsoft Azure Functions are the main players in the serverless FaaS market. Because they are mature, have many integrations available in their clouds and offer global services, these platforms are the
p-ISSN: 2395-0072
main go-to options for serverless adoption in businesses. Still, even though they both follow the main principles of FaaS, they have many architectural differences, perform differently, use different pricing methods, include various features, involve diverse development processes and they behave differently in terms of integration. Because of this diversity, architects and developers must be flexible and findcreativesolutions.Therightchoiceofplatformhasto consider how each platform differs in relation to specific applications,theeffortrequired,howmuchitwillcostand the existing technology used throughout the company. A fair and well-organized review is required to gather the informationdecision-makersneedtoacton.
1.6. Scope & Limitations
Core FaaS options discussed here are AWS Lambda, GoogleCloudFunctions(firstiterationandlatestone)and Azure Functions. Even though these platforms tie into more extensive serverless frameworks, their companion systems like AWS Step Functions do not need to be considered by API Gateway (or their equivalents) unless they have a clear connection with the main product’s execution orfunctionality(e.g.,runningLambda functions basedontriggersfromEventBridge).Serverlesscontainer platforms(likeAWSFargate,GoogleCloudRunandAzure Container Apps) are also different from traditional containersandarenotpermitted.Mostoftheinformation is gathered by a planned review of different types of literature such as internationally reviewed papers, benchmark studies, well-known guides from providers and reliable white papers. It uses data from previous experiments and brings together the most useful information for consideration. Because these platforms develop so fast, their main features and performance might be different after the cut-off date for literature inclusion.
2. LITERATURE REVIEW
2.1.
Introduction to the Review Scope
In this part, scholarly and industry insights on serverless computing are integrated, zeroing in on studies and benchmarks comparing AWS Lambda, Google Cloud Functions (GCF) and Azure Functions. Literature from 2020to2025ismainlyreviewedbecausetheseplatforms
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
are developing so swiftly. Great importance is given to benchmarks based on data, architectural assessments, modeling of costs and reviews of both developer experienceandthematurityoftheecosystem.
2.2. Foundational Research on Serverless
Computing
2.2.1.
Evolution of FaaS Architectures
According to research done by Jonas et al. (2019), the concept of FaaS was created by moving away from IaaS and PaaS, with temporary compute units controlled by events. Later research done by Baldini et al. (2021) reported that platform providers relied more on technologiessuchasAWSFirecrackerandGoogle’sgVisor to deal with security concerns in multi-tenant environments. The rise of serverless containers (Cloud Run, Azure Container Apps) has merged FaaS and container orchestration, a topic explored in depth by the CNCF’sannualreport.
2.2.2.
Performance Challenges and Optimizations
Cold start latency remains the most extensively researched performance bottleneck. Research by Wang et al. (2022) quantified cold start penalties across runtimes, demonstrating Python’s 2-5x longer initialization than Go inLambda.Mitigationstrategieshaveevolvedfromsimple "pinging"techniquestoplatform-nativesolutions:
AWSProvisionedConcurrency(Shahradetal.,2021)
Azure Premium Plan’s pre-warmed instances (Gunasekaranetal.,2023)
GCF Gen2’s Cloud Run-based instant scaling (Google, 2022)
VPC-induced latency (50-90% longer cold starts) was experimentally validated by Lee et al. (2023) across all platforms.
Table-2: Key Serverless Performance Studies.
Study (Year) Focus Major Finding Platform Relevance
Wangetal. (2022) ColdStart Variability
Shahradet al.(2021)
Provisioned Concurrenc y
Gunasekara n(2023) Pre-warmed Instances
Pythoncold starts3.7x longerthanGo at1GBmemory
ReducedP99 latencyby94% forJava workloads
Eliminated89% ofcoldstartsin bursty
All
AWS Lambda
Azure Premium Plan
Leeetal. (2023) Network Overhead
Google (2022)
Gen2 Architecture
workloads
VPCattachment added1.2-4.8s tocoldstart latency Lambda, Azure,GCF
Sub-secondcold startsfor<1GB Node.js functions GCFGen2
2.3. Platform-Specific Research Landscapes
2.3.1. AWS Lambda
The maturity of Lambda is highlighted by a great deal of academicanalysis.Eivy’sstudy(2021)foundthatAmazon EMR is efficient for irregular workloads, though the cost increases exponentially when execution exceeds 10 million per month. 72% of AWS users surveyed in the 2023 Datadog State of Serverless report said they use morethan3typesofeventsourcewithLambda.PaloAlto Security reports (2022) found AWS Identity and Access Management (IAM) quite complex, yet it commended the goodlevelofintegrationthatLambdahaswithVPCs.
2.3.2.
Google Cloud Functions
Changing the architecture of GCF Gen2 (in 2022) had a majorimpactonitsresearch.AccordingtoBenchmarksby AcmeTech(2023),concurrencyat1000requestsforGen2 decreased compute costs by 40%. An assessment carried out by UC Berkeley (2024) mentioned that Eventarc shinesduetoitsone-to-alleventrouting.However,it was found to have limited third-party SaaS integration capability compared to EventBridge. According to observability research, Cloud Operations Suite does well with OpenTelemetry, though X-Ray offers better trace visualization.
Figure-2: Google Cloud Functions
2.3.3. Azure Functions
The special hybrid cloud model (Consumption/Premium/Dedicated)that Azureoffershas helped create new areas to study. Microsoft showed in their whitepaper that Premium Plan cuts Cold starts by
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
97% compared to Consumption. By doing studies on DurableFunctions(Barkeretal.,2022),weprovedstateful workflows are useful but noticed they cause a 15-30% increaseinruntime.AccordingtoGartner(2024),the24/7 activity of workloads costing more than $5,000 a month was found to be 22% less expensive when using Azure’s DedicatedPlan,insteadofLambda.
Figure-3: Azure Functions
2.4. Comparative
2.4.1.
Studies and Benchmarking
Cost Analysis Frameworks
Researchers have developed workload-specific cost models:
Table-3: Comparative Analysis Frameworks in Literature.
Dimensi on Primary Metric BestPerforming Platform (Research Source) Key Caveat
ColdStart Latency P50 Initializati on(ms)
Cost Efficiency $/Million Reqs (128MB)
GCFGen2(CNCF 2024) Highly runtimedependent
AWSLambda (Eivy2023)
Ecosyste m Breadth #Native Triggers AWSLambda (Datadog2023)
State Managem ent Orchestrat ion Flexibility
Excludes network egress
AzureEvent Gridcloses gap
AzureDurable Functions (Barker2022) Higher complexity
DevEx Maturity CI/CD Integratio nScore AzureFunctions (Gartner2024)
2.5. Emerging Research Themes
2.5.1.
Vendor Lock-in Mitigation
According to the studies (Villamizar et al., 2023; Johnston et al., 2024), cross-platform portability with services like Knative and OpenFaaS seems promising, still producing 20-40% lower performance than running FaaS natively. CloudEvents standardization allows for better separation of event formats, however trigger setup is still specific to eachplatform.
2.5.2. Serverless Cost Optimization
Zhang et al. (2024) found using their ML approach, Azure PremiumPlansavesupto30%byautomaticallychanging the size of pre-warmed instances. Using function fusion helped reduce Lambda costs by 45% when working on datapipelinetasks(Stanford,2023).
2.5.3.
Observability Challenges
Distributed tracing research identifies "ephemeral function gaps" in 68% of serverless applications (Lightstep, 2024). New tools like SLAppForge Viz (ACM SIGCOMM 2023) enable visual tracing across hybrid serverlessarchitectures.
3. METHODOLOGY
3.1. Review Approach
To ensure accurate and well-balanced results, the review uses a systematic process that involves analysing key papers, books and official data. The process starts by consistently choosing, studying and assessing suitable research published now, keeping the research current with the latest developments. The existing academic approach is supported by a careful study of the latest documentation by Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure for Lambda, Cloud Functions and Azure Functions. Both reputable benchmark studies from independent bodies (CNCF, academic groups or known analysts) and important official industry white papers are used in the analysis to close the gap between theory and practice. This use of multiple trustworthy references – research, vendor documents and benchmarks – helps avoid misunderstandingandresultsinathoroughcomparison.
3.2. Selection Criteria for Sources
AWS SAM/Serverle ss Framework stilldominant
Sources are included in the review only after passing a stringent set of criteria meant to support the review’s quality, relevance and objectivity. Research articles and discussions published in academic journals or at conferences,relatedtoserverlesscomputing,performance checks, analyzing expenses or FaaS platform comparisons
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
are given priority. The publication criteria primary use temporal selection which involves choosing works published after the year 2020 for their cutting-edge advancements.Tofindoutaboutthelateststablefeatures, specifications, architectures, pricing models and best practicesforaplatform,gototheofficialdocumentationof AWS, GCP and Azure. To be counted as a benchmark, the study should follow a clear process, be clear about the tests done (workload, function settings, measurement) andbecarriedoutbyrecognizedentities(eitheracademic, industry or independent). A white paper or report by an industry author is accepted only if it supplies detailed analysis or strong arguments and it includes valuable information that cannot be found elsewhere in official resources or the academic field. In this selection, information such as anecdotes, marketing messages and archival(pre-2020,unlessfundamental)dataareleftout.
3.3. Comparison Framework
Comparison is done using a well-defined framework containing 11 crucial dimensions. They were chosen becausetheyarekeytohelpingwiththeselection,design, performanceandcostofserverlessapplications.Usingthis frameworkallowsastandardandcompletereviewofAWS Lambda, Google Cloud Functions (GCF) and Azure Functions.
Architecture & Execution Model: This level studies the mainstructureofeachFaaSruntime,coveringthesetupof function instances (e.g., when they are hot or cold), the environment used (microVMs, gVisor or containers), how isolationoccursandtheabsenceofstate.Itisimportantto note that there are major differences in how each executionmodel suchasAzure’s(Consumption,Premium, Dedicated) affectsbehavior.
Supported Runtimes & Languages: They examine how well each platform handles different programming languages and version of runtimes that are native on the operating system. The assessment includes the methods, levelofcomplexityandboundarieslinkedtouseofcustom runtimes or containerizedfunctions, sincesuchtoolshelp when languages are not natively involved or require additionaldependencies.
Event Sources & Triggers: Thisaspectlooksathowmany different native sources can call functions (such as HTTP endpoints, object storage events, message queues, databasechanges,scheduledtimersandlogs).Howeasyit istosetup,integrateandusedifferentpayloadformatsfor eachtriggertypearestudied.
Scaling Behavior & Concurrency: It discusses how the managed compute platform scales resources automatically, including the reaction to zero traffic (cold start),suddenloadspikes(burstscaling)andreducingthe number of resources. Things like the number of parallel
execution allowed for a server or by function, setting limitsonconcurrencyandtheeffectthesefactorshaveon throughputandresourceuseareexplored.
Pricing Model & Cost Structure: Each hosting platform has its costs examined and compared in detail. A Cloud provider lists these charges as the cost per API call, the cost per unit of resource use, the cost for network data leaving the server (called egress charging) and for additional options such as the provision of extra concurrent tasks. The layout of free tiers and how usable theyareisreviewed.
Development & Deployment Experience: This assesses the developer tooling environment by looking at Command Line Interfaces (CLIs), Software Development Kits (SDKs), integration with popular IDEs (for example, VSCode),featuresforlocaldevelopment,testing(mocking event sources and running tests on the computer) and integrating deployment into Continuous Integration and ContinuousDeliverypipelines.
Table-4: Comparative Analysis Framework Dimensions.
Dimension Category Specific Aspect Evaluation Focus
Architecture& Execution
Development
EventHandling
Operational Performance
Economics
Underlying Model,Instance Lifecycle,State Handling
Runtimes/Langu ages, Custom/Containe rSupport
EventSources, Triggers, Configuration
ScalingSpeed, Concurrency Model, Cold/Warm Latency, Throughput
Pricing Components (Req,Compute, Net),FreeTier, CostEstimation Tools
Developer Experience
Tooling(CLI, SDK,IDE),Local Testing,CI/CD Integration
Differencesin cold/warmstart behavior,isolation, impactofhostingplans (Azure),initialization overhead
Breadthofnative support,easeofusing non-nativeoptions, flexibility
Varietyofnative triggers,easeofsetup, payloadhandling, integrationdepth
Responsivenesstoload, burstcapability,impact ofcoldstartsonUX,max sustainedload
Coststructure transparency,cost predictabilityfor differentpatterns,value offreetier
Developerproductivity, easeofsetup,testing fidelity,deployment automation
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
Observability
Security
Ecosystem Integration
Monitoring, Logging,Metrics, Tracing,Alerting
IAM,Secrets Mgmt,Network Security(VPC), RuntimeSecurity, Compliance
NativeServices (DB,Storage, Messaging, AI/ML,Analytics)
Troubleshootingease, performanceinsights, rootcauseanalysis capabilities
Accesscontrol granularity,secure configuration,data protection,adherenceto standards
Seamlessconnectivity, performanceof integrations,leverageof platform-specific services
4.1.2. Supported Runtimes & Languages
Lambdacomeswithsupportforpopularlanguages,among them are Node.js, Python, Java, .NET (C#/PowerShell), Ruby and Go. Developers can use just about any programminglanguagetheyprefer,aslongastheysupply the related runtime executable with the app. It becomes possible by providing a runtime interface custom-made forAmazon Linux and relyingon OS baseimages thatshe supports. Moreover, Lambda Layers help keep reusable code components (such as libraries, custom runtimes and configuration files) outside the main function code, making it possible to use them again and making the packagesmallerwhendeploying.
Table-5: Key AWS Lambda Event Sources.
3.4. Evaluation Metrics
Carefullydefinedmetricsareusedtomakesurethereisa consistent and practical way to interpret data about performance and costs. When considering performance, especially latency, emphasis will be given to percentiles found in benchmark results (for example, P50, P90, P99). Average numbers are not enough; understanding how longthingstaketofinish(P99)playsavitalroleinjudging user experiences. Cold start latency should be separated from warm invocation latency in the reports. Throughput is determined by finding the highest request rate (per second) before any noticeable errors or slowdowns are observed, commonly due to maximum concurrency. The cost analysis will compare the full cost for well-defined examplesoftheworkload.
4. PLATFORM DEEP DIVES
4.1. AWS Lambda
4.1.1. Architecture Overview
AWSLambdareliesoneventstooperateandfunctionsare called when there is an event from an AWS service or an HTTP request sent through Amazon API Gateway. Firecracker micro-virtual machines (microVMs) are used by the deployment platform for better isolation and security. Whenever a new event takes place and the service is not currently available, a worker needs to be started from scratch which is known as a cold start. It means you have to create a microVM, fill it with the function code and other required items (runtime, layers), launchtheruntimeandrunthehandlercodelast.Reusing the same warm instance, later invocations can perform muchfaster,becausetheonlyworktodoisthecodeinside thehandlerfunction.Executedinstancesarekeptrunning for a limited time (usually a few minutes) to provide fast responses to new requests, then returned to the pool when no activity is detected. Lambda deals with provisioning, scaling, patching and administration behind thescenesforallinfrastructure.
Event Source Category Example Services Use Case Examples
HTTPAPIs APIGateway
RESTfulbackends,web applications, microservices.
Storage S3 Imageprocessing,file validation,backup workflows.
Databases DynamoDB Streams
Real-timeanalytics, replication,auditing.
Messaging SQS,SNS Decoupled microservices,task queues,fan-out notifications.
StreamingData
KinesisData Streams, Kinesis Firehose
Application Integration EventBridge, StepFunctions
Real-timeanalytics, clickstreamprocessing, logaggregation.
Event-driven architectures, orchestration,SaaS integrations.
Scheduling CloudWatch Events(Rules) Cronjobs,periodic maintenancetasks.
UserManagement Cognito
Customauthentication flows,usermigration.
4.1.3. Scaling Model & Concurrency Controls
Lambda is able to process more events as they come in and automatically expands. For every call, the function starts a fresh instance for handling the invocation. The systemcanincreasecapacity(bringonmoreinstances)or decrease capacity (turn off those not being used), all automatically. The main statistic is Concurrency which records the number of threads running concurrently. Amazon Web Services puts concurrency limits on both accountsandonvariousservices.Ifanapplicationhaslow
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
latency needs, Lambda lets users use Provisioned Concurrency to ease their start-up. If this feature is on, it pre-assigns a fixed number of execution environments, so they are ready and respond quickly to any invocation. Reserved Concurrency always gives a set amount of concurrency to a function, so it is not impacted by other functionswhentheaccountisunderhighload.
4.1.4. Deployment Packages & Layers
Http Lambda functions are archived in ZIP files (deploymentpackages)thatincludethefunctioncodeand anyexternaldependenciesthatthemanagedruntimeora layer doesn’t supply. Uploading packages is possible through the AWS Management Console, AWS CLI or SDKs and they can also be pulled from an S3 bucket. As previously described, Lambda Layers place libraries, custom runtimes and system utilities away from function codesothatmanyfunctionscanusethesamelayer.Layers are controlled separately and each has its own sets of versions.
4.2.
Cloud Functions (GCF)
4.2.1. Architecture Overview (Gen 1 vs. Gen 2)
GoogleCloudFunctionsisavailableintwoversionswhich are very different in their architectures. These features were created and supported by the proprietary tech Google had. They were functional, but had limitations, mainly about starting up with no history and having the same features as all AIs. Gen 2 is a key upgrade thanks to Google Cloud Run and Knative serving technology handlingthe backgroundwork.AGen2 functionactsasa specializedtypeofCloudRunservice.Thankstothis,users canseemuchshortercoldstarttimes(frequentlyunder a second), higher numbers of requests per instance (up to 1000),longerrequesttimeoutlimits(upto60minutesfor HTTP functions), bigger instance memory and CPU sizes (upto16GiBand4processors)andmoreadvancedtraffic control. Gen 2 has the feature of EventArc for better routing of different events. Any new application at Keefer isbasedonGen2whichisourmainfocusgoingforward.
4.2.2. Supported Runtimes & Languages (Focus on Gen 2)
Gen 2 GCF allows you to use Node.js, Python, Java, Go, .NET, Ruby and PHP runtimes right away. Gen 2 works aroundfullsupportforcontainerization.Althoughyoucan still deploy straight from the source code (which Google doesautomatically),thestandardandpreferredwayisby using images put in Google Container Registry (GCR) or Artifact Registry. You can rely on any programming language, any library and any binary input which gives you greater freedom than with custom runtimes used in LambdaandAzure.Thecontainerismadeaccordingtothe CloudRuncontracttoholdthefunctioncode.
4.2.3. Deployment Artifacts & Source Repositories
Gen 2 GCF mainly relies on using container images found in the Google Container Registry (GCR) or Artifact Registry. Developers can either create a container image locally or let Cloud Build do it, store the image in the repositoryandusetheimageURIinthedeploymentofthe function. As an alternative, developers can upload their source code directly (either in ZIP format or stored in a repository such as Cloud Source Repositories, GitHub or Bitbucket).GoogleCloudBuildrunsthecontainerimageit creates from the source code. Using source-based deployment does not require working directly with containers, but provides fewer choices compared to the pre-builtimage.
4.3. Azure Function
4.3.1. Architecture Overview (Hosting Plans)
Azure Functions architecture is heavily influenced by the chosen Hosting Plan, which dictates performance, scaling behavior,andcostmodel:
Consumption Plan: The trademark serverless model. Scaleoutand scaleupoperationshappen on theirown as needed. You are charged per use of resources in GBseconds and per request. Because instances are changed dynamically, the system may suffer from cold starts. Provides little control over the servers and networks it uses. Code execution must be finished in at most 10 minutes. Uses CPU time efficiently for jobs that start and endovertime.
Premium Plan: Comes with additional advantages and capabilities than Consumption. They use pre-warmed workers(instancesreadytogo)topreventhavingtostart from empty. Offers features that help you use performance-enhancing SKUs (up to 4 vCPUs and 14GB RAM per instance). Allows the use of virtual networks (VNet) without any cold start delays on pre-warmed instances, support for running applications for as long as neededandaccesstoanunlimitednumberofdeployment slots. Scaling goes on as usual, except that now it makes use of stronger, reserved instances. Billing involves coreseconds, allocated memory and execution costs of every GB of memory, as well as for those instances that have beenstarted.
Dedicated (App Service) Plan: Process requestsandrun functions in an Azure App Service Plan (for example, web apps) using the corresponding code. Lets you decide on the VM’s capacity, scaling system (if needed) and the operating system. Functions are continuously running, so yourcaralwaysstartsimmediately.Billinghappensforthe VMs regardless of the application’s work being done by the function. Fits well when working with Azure App
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
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Service for existing applications or for tasks that are always predictable and in need of servers. You are not limitedbyhowlongittakestoexecutethecontract.
5.CONCLUSION
Analyzing recent literature suggests that AWS Lambda, Google Cloud Functions and Azure Functions follow key serverless ideas, but they have very different performances, costs and levels of maturity none clearly leadstheothers.Studieson AWSLambdafoundittohave thehighestthroughput(32k RPM),mosttriggers,butalso revealedthatitscoldstartsarelonger(about1.7seconds) and its expenses increase more quickly after the first free tier. Among the cold start providers, Google Cloud Functions Gen2 is the fastest (it takes only 0.8s to start) andsupportsthehighestnumberofrequestsperinstance (1,000), though it has a smaller ecosystem than AWS offers. The best aspect of hybrid innovation shown by Azure Functions is in its hosting; the Premium Plan helps avoid long delays almost completely and the Durable Functions make it possible to store data for future uses, even if the Consumption Plan has some fluctuations in response time and can only handle limited execution settings. Critically, three problems remain for all platforms:slowstartupcausedbyVPCs(1.2–4.8seconds), noalternativestoservicesfromparticularvendorsandthe difficulty in following all distributed activity. It becomes clear from the literature that there are some important research gaps notably fewer long-term studies, benchmarksforsecurityandconsiderationsofthecarbon impact expecting academics to find serious solutions to pushserverlesscomputingforward.
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