Edge Computing 101: Bringing Your Mobile App Closer to Aussie Users

Introduction

Imagine a user in Perth tapping your mobile app, only to wait while the request travels thousands of kilometers to a server in Sydney or even the US. This delay, caused by Australia's vast distances and remoteness from many global servers, can hurt user experience. Edge computing aims to solve this by running application logic on servers closer to the user, at the "edge" of the network according to Edge Computing Fundamentals. In this report, we'll explore what edge computing means for mobile and web apps, why it's especially relevant given Australia's unique geography and population distribution, and how major edge platforms (Cloudflare Workers, AWS Lambda@Edge, Fastly Compute@Edge) help reduce latency for Aussie users.

Australia presents a perfect case study for edge computing. The country's Internet users are spread across distant cities (Sydney, Melbourne, Brisbane, Perth, etc.) separated by vast land and sea. Meanwhile, much of the world's cloud infrastructure is overseas (North America, Europe, Asia), meaning Australian users often face high network latency to reach those servers. We'll examine how edge computing brings app servers into Australia's local networks to deliver faster, more resilient experiences. We'll also compare the leading edge providers on performance, pricing, coverage in Australia, and walk through deploying serverless functions on each. Real-world examples and benchmarks will illustrate the impact: from dramatic latency reductions to smoother streaming during big events. Let's dive in.

Australia's Geographic Challenges and Latency

Australia is geographically huge but sparsely populated outside a few coastal hubs. The majority of the population lives in cities like Sydney, Melbourne, Brisbane, Perth, and Adelaide – cities separated by thousands of kilometers. For example, a round-trip from Sydney to Perth is about 3,300 km and takes roughly 50–55 ms in network latency Australia Network Latency Analysis. If an app's server is only in Sydney, users in Perth inherently incur ~50 ms extra latency on every request. If that server is outside Australia (say in the United States or Europe), the latency explodes: a Sydney-to-US round trip is on the order of ~200 ms (e.g. Sydney to New York ~199 ms) and to Europe can be ~300 ms Global Network Latency Data. Such delays are perceptible – a blink of an eye is ~300 ms Human Perception Research – and they add up for interactive apps.

Australia's remoteness from other continents means that without local infrastructure, Aussie users often suffer high latency and slow performance on global applications. Historically, content delivery networks (CDNs) have alleviated this for static files by caching them on servers in Australia. But traditional app backends (APIs, dynamic content) still had to reach centralized servers, causing multi-second load times in worst cases medium.com. Moreover, with population spread across east and west coasts, even within Australia a centralized approach (e.g. only a Sydney data center) gives suboptimal latency to users in other regions.

Edge computing addresses these challenges by distributing computation to edge nodes in or near each region. In practice, this means running your code on servers located in Sydney, Melbourne, Perth, etc., so that users in those areas get responses from a nearby location rather than a far-off origin. The result is reduced round-trip time and faster content delivery. For example, when Cloudflare added an edge node in Perth, it shaved about 50 ms off response times for Perth users by eliminating the Sydney trip Edge Network Impact Study. Similarly, an Australian user who previously connected to a U.S. server (200+ ms away) can be served by a Sydney edge function in a few milliseconds – cutting latency by 90–95%. The table below illustrates some latency differences:

Table: Approximate round-trip latency for Australian users to distant vs. local edge servers. By serving content at a nearby edge node, apps eliminate long trans-Pacific or cross-continent hops, yielding significantly faster responses.

Beyond distance, Australia's network topology and intra-country links can introduce bottlenecks. Not all traffic between Aussie cities takes the shortest path, and international bandwidth can be limited. Edge computing mitigates these by handling requests within regional networks whenever possible. In summary, Australia's geography (large distances, isolated location) makes edge computing not just a nicety but often a necessity for low-latency mobile and web experiences.

What is Edge Computing for Mobile/Web Apps?

In the context of mobile and web applications, edge computing refers to running application logic on distributed infrastructure that is geographically closer to end-users, rather than in a central cloud data center. It's an extension of the CDN concept: where CDNs cache static files at edge locations, edge computing allows dynamic code execution at those locations Edge Computing vs CDN Comparison. This is usually achieved via serverless functions deployed to edge servers around the world. When a user makes a request (e.g. opens your app or hits your website), the request can be handled by a nearby edge function which can compute a response (personalize content, aggregate data, call other APIs, etc.) and send it back directly, without always reaching a distant origin server.

For mobile apps, this could mean API endpoints that run on edge nodes in-country, giving snappy responses (important for interactive apps and real-time features). For web apps, it might involve generating or modifying HTML on the fly at the edge, A/B testing, injecting localization, or caching API results. The key is that the logic – not just static cache – is executed near the user. Modern edge platforms run these functions in sandboxed environments with low overhead (often using V8 isolates or WebAssembly, rather than full containers) for high performance Edge Computing Performance Analysis.

Edge computing complements existing cloud infrastructure. Typically, you don't move your entire app to the edge; instead, you offload certain tasks: e.g. routing, authorization checks, personalization, or content assembly that benefits from being close to the user (or close to a data source like a local cache). The heavy lifting or database storage might remain in a central region, but edge functions can quickly serve requests that don't need a database hit every time. They can also act as intelligent proxies – consulting caches, applying business logic, and only querying the origin when necessary.

In summary, edge computing in apps means decentralizing parts of your backend to run on mini data centers around the world. This reduces the physical distance and often the time between your app and the user, leading to faster responses. It also distributes load and can improve resilience (if one region goes down, others can handle traffic). The next sections focus on three major platforms enabling edge functions and how they specifically help in Australia.

Edge Platforms: Cloudflare Workers, AWS Lambda@Edge, Fastly Compute@Edge

Several cloud and CDN providers offer edge-compute services. We will look at three popular ones and how they address latency and performance for Australian users:

• Cloudflare Workers – Cloudflare's serverless platform that runs JavaScript/WASM functions on its global CDN edge.
• AWS Lambda@Edge – Amazon's solution to run Lambda functions at CloudFront CDN edge locations worldwide.
• Fastly Compute@Edge – Fastly's edge computing service running custom code (via WebAssembly) on their CDN network.

Each of these platforms brings compute closer to users, but with different networks and approaches. Below is a high-level comparison of their presence, pricing, and developer experience for context, before we dive deeper:

How these platforms reduce latency: All three providers maintain infrastructure in Australia so that user requests can be served within-country rather than going overseas. For example, Cloudflare has edge servers in all major Australian cities, ensuring that 95% of Australian internet users are within ~50 ms of a Cloudflare node (and often much closer) according to Cloudflare's network data. AWS uses its CloudFront POPs in Sydney, Melbourne, etc., meaning a CloudFront+Lambda@Edge-delivered app will respond from the nearest Aussie POP (or NZ) instead of, say, an AWS Oregon region. Fastly similarly places servers on both east and west coasts of Australia, and by executing logic on those POPs, it avoids transcontinental hops.

All platforms also integrate with caching: e.g. your edge function can read from cache or KV storage at the edge to serve content immediately, only contacting origins as needed. This can dramatically improve Time to First Byte (TTFB). Cloudflare in particular has touted its ability to deliver sub-millisecond TTFB in many locations by eliminating cold starts and using their 12,000+ interconnects to local ISPs. In one benchmark, Cloudflare Workers' TTFB was measured as 196% faster than Fastly's Compute@Edge and 210% faster than AWS Lambda@Edge, based on a global test that simply returned a small response. (Fastly disputed some of these claims, but independent tests also show Cloudflare and CloudFront Functions with extremely low latency, and Lambda@Edge generally slower due to heavier runtime). The key takeaway is that running code on an edge node in Australia can cut hundreds of milliseconds of network time, and modern edge platforms are optimized to add minimal overhead on top of pure network transit.

Let's examine each provider in a bit more detail regarding capabilities and use in Australia:

Cloudflare Workers in Australia

Cloudflare's network has one of the broadest reaches. It operates data centers in over 270 cities worldwide, including at least Sydney, Melbourne, Brisbane, Perth, Adelaide, Canberra, and Hobart in Australia technologydecisions.com.au (as of 2022). In fact, Cloudflare expanded aggressively in Australia/NZ, adding four new Aussie cities in one year (Adelaide, Canberra, Hobart, plus Christchurch NZ) to bring its ANZ total to 9 cities technologydecisions.com.au. This means wherever your Australian users are, there's likely a Cloudflare edge server in the same city or a nearby one. All Cloudflare services – DNS, CDN, security, and Workers – run on every one of these edge nodes blog.cloudflare.com. When a mobile app or web request hits Cloudflare Workers, the closest Aussie data center handles it, drastically reducing latency and avoiding backhaul to a central server. For example, Cloudflare noted that before they had Perth/Brisbane edges, traffic from those areas went to Sydney (adding ~50ms); now, local edges handle it with "_no excuse" for ISPs not to peer locally blog.cloudflare.com.

From a developer perspective, Cloudflare Workers is very easy to use and developer-friendly. You write your code in JavaScript (or TypeScript) against a Service Worker API (e.g. fetch event handling) – essentially a familiar environment for web developers. You can also use languages like Rust, C, or others by compiling to WebAssembly and deploying that. Workers are event-driven and often used to intercept HTTP requests and responses (modify them, generate new responses, or proxy to origin). Cloudflare provides a CLI tool called Wrangler that makes deployment trivial: for instance, a single command wrangler deploy (or wrangler publish) will bundle your code and publish it to Cloudflare's network in seconds blog.ericcfdemo.net. There's no need to select regions – your Worker automatically lives in all Cloudflare edge locations globally. When users in Australia use your app (on a .workers.dev subdomain or your own domain with Cloudflare enabled), the Worker runs on the nearest Australian Cloudflare server. Cloudflare also offers Workers KV (a key-value store) and Durable Objects which can store data at the edge, as well as integrations like R2 (object storage) – allowing apps to be nearly entirely served from the edge.

Performance

Cloudflare Workers are known for low cold start times (often 5ms, effectively eliminating cold start in most cases blog.cloudflare.com) because they use V8 isolates rather than containers. Cloudflare boasts that as of 2021, "Workers is 210% faster than Lambda@Edge" at the 90th percentile response time blog.cloudflare.com, and importantly, no cold starts due to their approach blog.cloudflare.com. In practice, this means very fast first request handling, which is crucial for sporadic traffic patterns. Real-world Aussie example: Canva, a well-known Australian tech company, uses Cloudflare Workers to speed up their site. They cache certain pages and do SEO optimizations at the edge, ensuring even mobile users get instant responses. Canva reports that Workers has become "a critical part" of their software and saved them time and money — if they had to manage this globally themselves, "it would cost a lot of time and money," says their engineering lead cloudflare.com. Workers let Canva serve cached versions of pages based on device type and handle things like signed URL validation at the edge for security without slowing content delivery cloudflare.com. This improves page load times and SEO (Google penalizes slow mobile sites) cloudflare.com. Another case: Envato, another Australian company, leverages Cloudflare's global network (including Workers and CDN) to deliver content fast despite their origin servers being in the US. With Cloudflare's edge, Envato saw a ~50% decrease in response times on their flagship sites over 5 years cloudflare.com, translating to better user engagement. They also offload 20+ TB of data per month from origin thanks to edge caching, saving costs cloudflare.com.

Use cases for Aussie apps

You might use Cloudflare Workers to do things like A/B test a feature by redirecting a percentage of users to a variant at the edge (no slow round-trip to decide), or to localize content (detect user's region and modify the response accordingly right in Australia). If your mobile app pings an API endpoint for, say, configuration or updates, a Worker can serve that from Melbourne if the user is in Melbourne, possibly pulling from a cache or store to avoid hitting a distant database every time. The result is snappier app behavior that "feels" like it's running on a local server for the user.

AWS Lambda@Edge in Australia

Amazon Web Services (AWS) operates a large Sydney region for core cloud services, but for edge computing, Lambda@Edge is the key offering. Lambda@Edge extends the AWS Lambda serverless functions to AWS's global CloudFront CDN network. CloudFront has multiple edge locations in Australia: currently Sydney, Melbourne, Brisbane, Perth (and one in Auckland, NZ) are listed as CloudFront edge cities AWS Global Infrastructure. This means when you serve content via CloudFront and attach a Lambda@Edge function, a user's request from, say, Perth will be processed by a CloudFront edge in Perth or the nearest available (rather than going to the Sydney AWS region or all the way to the US). In effect, it brings the compute closer to the user similar to Cloudflare.

How it works: Lambda@Edge is integrated with CloudFront distributions. You write a normal AWS Lambda function (supported languages include Node.js, Python, Java, C# .NET, and others). There are a few limitations: for example, Lambda@Edge functions for viewer request/response events have a max memory of 128 MB and must be deployed in the US East (N. Virginia) region in AWS so that AWS can replicate it globally Lambda@Edge Memory Limits. To deploy, you typically:

1. Write your function code and test it as a regular Lambda.
2. Use the AWS Console or CLI to create the Lambda in us-east-1 and select the option to replicate it to CloudFront (or you attach it to CloudFront in the next step).
3. Go to your CloudFront distribution and add an event trigger for the Lambda function (e.g. "Viewer Request" – runs when a request arrives at the edge; "Origin Response" – runs on the response back from origin, etc.) docs.aws.amazon.comjimmydqv.com.
4. CloudFront takes care of propagating that Lambda's code to all its edge POPs, including those in Australia. This propagation is automatic but can take some time when you first deploy or update (minutes to sync worldwide). After that, the function will execute on location.

Developer experience: Compared to Cloudflare or Fastly, deploying Lambda@Edge is a bit more involved – you're in the AWS ecosystem, dealing with IAM roles, Lambda deployment packages, CloudFront distribution IDs, etc. However, it's a familiar environment for AWS users and is well-documented in the AWS Developer Guide. Many use Infrastructure-as-Code (like CloudFormation, CDK, or Serverless Framework) to automate these steps. One thing to note is that because Lambda@Edge runs on the full Lambda service (just distributed), cold start times can be higher than Cloudflare/Fastly. Cold starts for Node/Python Lambdas can be hundreds of milliseconds or more if the function is large or in a cold state as documented in Serverless Talent's performance analysis. AWS did introduce CloudFront Functions in 2021 as a lighter-weight alternative (written in JavaScript, runs at edge with sub-ms startup, but with much more limited capabilities) according to performance analyses. CloudFront Functions are great for simple tasks (like header rewrite or URL redirects) and are even faster (AWS claimed ~20% faster than Cloudflare Workers in one test) according to independent benchmarks, but for more complex logic (e.g. calling other AWS services, doing authorization, etc.), Lambda@Edge is the go-to.

Latency and performance: With Lambda@Edge, Australian users' requests no longer need to go to, say, an AWS Sydney region (which might be fine for Sydney users but not for Perth), and certainly not to an overseas region. Instead, they are handled at the CloudFront edge node. For example, if you have an API Gateway or S3 website behind CloudFront, you can use Lambda@Edge to run code when a request hits the Sydney POP – potentially generating a response right there or deciding which origin to route to. This can cut down response time significantly. An AWS solution architect in Australia would highlight that Lambda@Edge can be used to "minimize the round trip to the origin server and significantly enhance performance" by running code closer to users, as noted in CloudFront developer guides. A common use case is personalization: e.g., detecting a user's country via CloudFront's viewer info and rewriting the request to a country-specific version of content. Without edge functions, such logic might require a trip to a server (adding latency); with Lambda@Edge, it's done at the edge in milliseconds.

AWS has many global customers using Lambda@Edge (examples include Amazon.com itself using it for some personalization, or others like Adobe, and even smaller companies like Aerobatic who used Lambda@Edge to reduce latency and costs according to AWS case studies). While specific Australian case studies are less public, we can extrapolate. If you're an Australian media site using AWS, you might use Lambda@Edge to insert regional ads or content based on user location instantly at the edge POP, rather than serving everyone from Sydney. Or if you run a multi-region app, you could use Lambda@Edge to route Australian users to an Australian origin versus others, with logic executing right as the request enters the AWS network in Australia.

Pricing and cost efficiency: It's worth noting that Lambda@Edge, while powerful, is a paid feature on top of CloudFront. At $0.60 per million requests according to Serverless Talent's comparison plus compute time, it can be more expensive than Cloudflare's flat $5/mo + cheap usage or Fastly's approach. However, for many apps the cost is offset by the reduction in origin load and the improved user experience. And the first 1M requests per month are free (part of AWS Lambda free tier). Developers should also remember that any CloudFront data transfer or request fees still apply. In practice, to serve Australian traffic, one might use CloudFront (with its edge locations in Australia) regardless, and Lambda@Edge is an add-on for dynamic logic.

In summary, AWS Lambda@Edge extends the reach of your AWS serverless code to be run from Sydney, Melbourne, Perth, etc. rather than a central region. It's a great option if you are already on AWS and want to inject logic at the edge (especially if integrating with other AWS services). It does a fantastic job improving latency for global users, Australians included, though developers must account for the more complex deployment and cost model compared to some competitors.

Fastly Compute@Edge in Australia

Fastly is a modern CDN known for its fast network and is popular with high-traffic sites (e.g. many global news and streaming platforms). Fastly's edge cloud includes Compute@Edge, a capability to run custom code on their edge POPs worldwide. In Australia, Fastly has POPs in Sydney, Melbourne, Brisbane, Perth, and Adelaide as detailed in their network overview. Fastly specifically expanded capacity in Sydney and Melbourne in recent years to meet growing demand as announced in their press releases. This means Fastly can serve both east and west coast of Australia with low latency. In fact, Fastly highlights working with Aussie customers like Network 10, Nine Publishing, Kogan.com, and the NRL to deliver "fast, low-latency, highly personalized online experiences" via their edge network as mentioned in their customer success stories. A showcase example was the 2020 Melbourne Cup: Network 10's streaming of the horse race was delivered through Fastly's edge, which handled a 1400% increase in traffic during the event's peak, yet streamed smoothly to users according to their case study. This demonstrates both the scalability and the locality of Fastly's edge – they were able to cache and compute at the edge so effectively that even huge spikes in Aussie traffic were served without a hitch.

Technology: Compute@Edge runs your code in a WebAssembly VM, which is extremely fast and safe. Fastly originally offered Varnish Configuration (VCL) for custom logic, but Compute@Edge is a more flexible developer-centric model. You typically write your code in Rust (their primary supported language with SDK), though Fastly also supports other languages via WASM (recently JavaScript/TypeScript support via AssemblyScript, and Go, etc.). The code is compiled to a WebAssembly module. When deployed, this module can initialize in microseconds at the edge – Fastly touts that they can handle 100k+ concurrent cold starts per second across their network due to WASM's lightweight nature in their performance benchmarks. The absence of a runtime like Node.js means you have to work with their SDK for requests/responses, but it also yields great performance and very low memory overhead.

Deploying is done via the Fastly CLI or API. For example, you can use fastly compute init to create a starter project (with options for Rust, JS, etc.), then fastly compute build and fastly compute publish to deploy it as described in the Fastly Compute Documentation. This packages your WASM, uploads it to Fastly, and activates it on their edge cloud. Fastly operates a bit differently in that your code is tied to a Fastly service configuration. You might have a config that routes certain requests to origins or serves static, and you can attach the Compute@Edge module to run on requests, or even run as a full application (Fastly can now serve some apps entirely from Compute@Edge, with no origin at all if not needed).

One thing to be aware of: Fastly's free tier is only a time-limited trial; beyond that it's usage billed and can be relatively pricey at scale (as shown, $0.50 per 1M requests) according to their pricing documentation. They do offer packages (including a Starter package with 500M requests/month included according to Vertice's vendor analysis). For serious Australian user bases, companies often go on a committed plan with Fastly that covers a bundle of usage.

Performance: Fastly is known for very fast content delivery. In independent CDN latency tests, Fastly often ranks near the top for regions like Oceania. With Compute@Edge, Fastly has claimed that their approach can even beat Cloudflare in some scenarios, especially when using Rust (since Cloudflare's comparisons were with JS, Fastly argued that a Rust-based Compute@Edge could be just as fast or faster for raw CPU speed) in their performance response. Cloudflare's own data put Fastly Compute slightly behind Workers for JS code, but it's a close race and likely not noticeable to end-users in most cases according to their performance comparison.

Developer considerations: One technical detail – writing and debugging Rust (or WASM) might be a bit more challenging than writing JavaScript for some developers. Fastly does provide good SDKs and local testing tools (you can run fastly compute serve to test locally). They also recently added JavaScript support to appeal to a broader audience, though that still compiles to WASM behind the scenes. The benefit is that Rust/WASM gives very deterministic performance (no GC pauses, etc.). If your mobile app requires, say, cryptographic processing or image manipulation at the edge, Fastly's platform (with native code via WASM) could be very fast at that. Fastly supports edge data storage like their KV store (similar to Workers KV) and has other features like Edge Dictionaries and Object Store in beta, which can be useful for storing config or content at the edge.

Use cases in practice: Many streaming services and websites use Fastly in Australia to customize content. For instance, an e-commerce mobile app might use Fastly Compute@Edge to do custom routing – when a user opens the app, the edge code checks a cookie or JWT and routes the request to a variant (A/B test or personalized backend) without having to call the origin first. Or consider API aggregation: a Compute@Edge function could aggregate responses from several microservices and return one payload to the app, reducing round trips for the client. Doing this at the edge (maybe even within Australia if those services have endpoints nearby or cached data) can save a lot of time versus the mobile app having to make multiple calls around the world.

To summarize, Fastly Compute@Edge offers powerful, low-latency edge computing with a focus on performance. It shines for applications that demand speed at scale (like streaming events or high traffic APIs) and for teams comfortable with a more low-level, WASM-centric development. In Australia, Fastly's network has proven capable in big events (like the Melbourne Cup streaming) and daily delivery for major sites – a testament to the edge computing model working for real-world needs.

Deploying Serverless Functions on Each Platform (Developer Guide)

Now, let's get a bit more concrete about how to deploy serverless functions to these edge platforms. If you're a developer onboarding to edge computing, these are the typical steps and technical details for each provider:

• Cloudflare Workers – Deployment Steps: First, sign up for a Cloudflare account (if you have a website on Cloudflare, you already have access to Workers, even on the free plan). You can write your Worker code in the Cloudflare dashboard Workers section or use the Wrangler CLI for a better developer experience. For example, using Wrangler:

  1. Install Wrangler (npm install -g @cloudflare/wrangler or use npx wrangler). Login with wrangler login.
  2. Run wrangler init to create a new Worker project (it can template a JavaScript Worker project).
  3. Write your code in index.js (Cloudflare provides a template "hello world" that simply returns a response).
  4. Test it locally with wrangler dev (it runs a local simulator).
  5. When ready, run wrangler publish to deploy. In literally a few seconds, Cloudflare will upload and activate your Worker on its global edge Cloudflare Workers Documentation. You'll get a *.workers.dev URL for testing, or you can bind it to your custom domain.

  Workers can be deployed as single scripts or as modules (and you can bundle NPM packages, use frameworks like Cloudflare's Miniflare for testing, etc.). Cloudflare also supports deploying via Terraform or API if needed. For static sites or full applications, Cloudflare offers Pages Functions which use Workers under the hood – useful for deploying an entire app (frontend + backend logic) to the edge in one go.

  Technical details: Cloudflare Workers have a default 50ms CPU time limit per request (on free plan) and can be set higher on paid plans (up to 30s for some tasks, but that's rare for typical usage) Workers CPU Limits. They run on V8 isolates, which means you don't get a full Node.js environment – some Node APIs aren't available, but the platform provides Web APIs (fetch, URL, Request, Response, etc.) and many Cloudflare-specific APIs (for KV, Durable Objects, etc.). Workers can also make sub-requests (HTTP calls) to other services, including your origin servers or third-party APIs, and you won't be billed for the time spent waiting on those requests in the new pricing model (you pay primarily for actual CPU time used) Workers Pricing Model. This is great for latency: even if a Worker in Sydney has to fetch something from an origin in Sydney, that fetch is likely fast (few ms) and doesn't count against much CPU time. If it had to fetch from the US, at least it's doing so in parallel while the user's connection is local.

• AWS Lambda@Edge – Deployment Steps: Prerequisites: an AWS account, and usually an existing CloudFront distribution (for your app or site content). If you don't have one, you'd create a CloudFront distribution that points to your origin (e.g. S3, ALB, custom server) – this is what users will connect to, and where you attach the Lambda@Edge. Then:

  1. Write your Lambda function code. Let's say in Node.js for simplicity. The function should follow the Lambda handler signature specific to CloudFront events – e.g., for a viewer request event, your handler gets an event object with CloudFront request data, and you return a modified request or a response. (AWS provides blueprints for common use cases in their docs).
  2. Go to the AWS Lambda console in US East (N. Virginia) region. Create a new Lambda function. Use Node.js 14/16 or Python etc., and paste your code. For Lambda@Edge, you do not check the VPC option or anything – keep it lightweight. After creation, you must publish a version (Lambda@Edge only works with versioned Lambdas, not $LATEST).
  3. Now, head to the CloudFront console. Select your distribution, and in the behaviors settings, you'll see options to add a Lambda function on triggers: Viewer Request, Origin Request, Origin Response, Viewer Response. Choose the appropriate one and select your Lambda function version (the console will list the ARNs of versions you published).
  4. Save changes. CloudFront will now start replicating that function to all edges. It can take a few minutes to deploy globally. Once done, any new requests flowing through CloudFront in, say, Sydney or Melbourne will invoke your Lambda@Edge there.

  You can test by making requests (CloudFront has a testing tool, or just use the app). Logs from Lambda@Edge go to CloudWatch Logs in the same region (US-East-1), which can be a bit inconvenient (since Aussie edge execution still logs back to Virginia). But it works transparently.

  Technical details: Lambda@Edge functions have some limits: for viewer-facing events (Viewer Request/Response) the max execution time is 5 seconds, memory up to 128 MB, and they cannot use some AWS services that might call back to a specific region. For origin-facing events, you can use more memory (up to 3008 MB) and longer time (up to 30 sec) Lambda@Edge Memory Limits, since those run less frequently. This matters: e.g., heavy computation should be done in origin-response trigger if possible. Also, you cannot write to disk, and environment variables must be set at deploy time. But you can call other AWS services (with some latency penalty since it might call a regional endpoint – e.g., DynamoDB query from an edge in Sydney likely hits the nearest regional endpoint or goes to the service's region). Many AWS services now have global or edge integration (for example, AWS has an Edge Optimized API Gateway that could integrate with Lambda@Edge).

  For deployment automation, AWS SAM or CloudFormation can streamline the creation and association of Lambda@Edge. Additionally, AWS's new CloudFront Functions simplifies certain cases: it's even easier to deploy (through CloudFront console, you paste a JS snippet) and propagates almost instantly, but again it's only for simple JavaScript logic (no external calls, strict CPU time limit ~1ms). CloudFront Functions might handle tasks like redirect HTTP to HTTPS or minor header rewrites entirely within the edge, complementing Lambda@Edge for bigger jobs.

• Fastly Compute@Edge – Deployment Steps: To deploy on Fastly's edge, you'll need a Fastly account (you can start with a free developer trial). Also, have the Fastly CLI installed (fastly command). The typical workflow:

  1. Run fastly compute init. This will prompt for a project name, language, and provide starter templates (for example, a default Rust starter that simply returns "Hello from Compute@Edge" on a request). Choose a language (Rust is default; if you want JavaScript, there's an option since they have a JS starter that uses AssemblyScript).
  2. This creates a project with a Fastly manifest (fastly.toml) and some starter code. Edit the code to implement your logic. In Rust, you'd use the fastly crate to handle incoming requests (Request objects) and send responses. For example, you can fetch from an origin or return a synthetic response.
  3. Test locally if possible. Fastly has a local testing utility (using fastly compute serve which runs your Wasm module locally, or you can use cargo test for Rust logic).
  4. When ready, run fastly compute publish. This will build the project (compiling Rust to WASM, for instance), upload the package to Fastly, and deploy it. On first deploy, it will start a trial if you're not already on a plan Fastly Compute Documentation. Within seconds, Fastly will activate the service globally.
  5. You will get a Fastly service ID and a domain (usually something like <project>.edgecompute.app for testing). You can also tie it to your custom domain via DNS settings on Fastly.

  Fastly's deployment ties into their configuration API. Essentially, each deployment creates a new version of your service configuration with the WASM attached. If you already use Fastly for CDN, you may integrate the Compute@Edge logic into an existing config (or call out to it). For example, you might have certain endpoints of your site handled by Compute@Edge, and others just cached normally.

  Technical details: Fastly imposes limits like 50ms CPU time per request (which is quite a bit, given how fast WASM is – you can do plenty in 50ms), and the 128 MB memory allocation per request (WASM instances use this memory) Fastly Compute Limits. If you exceed, it throws an error. Also, if your code crashes or traps, Fastly will return an error. Logging from Compute@Edge can be done via their logging endpoints (you can print to stdout which goes to a configured log drain). Fastly supports multiple data stores: Edge Dictionaries (small key-value pairs, good for config), KV Store/Object Store (beta for larger data), and Fanout (for WebSocket handling at edge). These can enhance what your edge code can do (e.g. store user preferences at edge, etc.).

  One thing to keep in mind is that because it's WASM, you don't have native libraries unless they're compiled to WASM. Rust covers a lot, but if you need, say, an AI library or image library, you'd use a WASM-compatible one. There's no Node.js or Python environment – it's a different paradigm.

To summarize the deployment differences: Cloudflare is arguably the simplest (no build step needed if using JS, one CLI command to go live). AWS requires coordinating with CloudFront and dealing with AWS's tooling, which is a steeper learning curve initially. Fastly requires a compile step and knowledge of WASM/Rust, but their CLI is straightforward and the edge performance is excellent. All three providers allow you to update your code rapidly as well – typically within seconds to minutes, you can roll out new code worldwide. This is fantastic for agile development and iterative improvements to your edge logic.

Benefits of Edge Computing for Developers and Users

Adopting edge computing via these platforms yields tangible benefits for both the developers building the apps and the end users consuming them:

• Ultra-Low Latency = Better User Experience: This is the most obvious benefit. By serving content and handling requests within Australia (or within the user's region generally), you avoid long network delays. Users get faster API responses, quicker page load times, and more responsive interactions. On mobile apps, this can be the difference between an action feeling instantaneous versus laggy. It's well documented that faster response times improve user engagement (e.g. Amazon famously found every 100ms of latency cost them 1% in sales). For Australian users who previously might have endured 200ms+ just in transit to servers abroad, edge computing can cut that down to a few milliseconds – making apps feel "local" and snappy even if the core service is global. A faster UX also means mobile devices use less battery and radio time waiting for data, which users appreciate.
• Geographic Reach without Multiple Data Centers: From a developer/architect's viewpoint, edge platforms let you deploy globally without managing infrastructure in each location. Without edge computing, if you wanted good performance across Australia, you might spin up servers in multiple Australian cities or at least multiple availability zones and maybe an extra regional deployment in Perth or so. And for global reach, you'd consider regions in Asia, US, Europe… That's a lot of overhead – managing consistency, deployments, scaling in each. Edge platforms abstract all that. You deploy your code once, and it's automatically running everywhere (e.g. Cloudflare's 300 locations, AWS's edge network, etc.). This is a huge win for developers – you get global performance out-of-the-box. As Cloudflare puts it, "deploying to the tubes of the Internet" so your code is always within ~50ms of users worldwide Cloudflare's Global Network.
• Resilience and Redundancy: Edge computing can improve reliability. Because code runs in many locations, your app can withstand the failure of one location by simply serving from the next closest. For example, if Sydney's edge goes down or is unreachable (say a fiber cut), Cloudflare or Fastly will automatically route users to Melbourne or another nearest site. Users might not even notice beyond a small increase in latency. This multi-edge redundancy is baked into these networks. It's like having a failover data center for every major region without doing anything. Additionally, edges can serve cached content when origins are down: Cloudflare has an "Always Online" feature that can serve cached pages if your server is offline. Fastly and AWS can similarly be configured to handle certain errors by delivering stale content from cache. This means even if your central server or one cloud region fails, your users might still get some content or graceful degradation via the edge. The offline resilience extends to scenarios like spotty mobile connectivity: while not a direct fix for a user going offline, having edge servers nearby means less dependence on long-haul networks (which can be more prone to issues). And if a user drops connection mid-request, the retry only goes as far as the edge, not all the way to origin, which can succeed faster when connectivity resumes.
• Reduced Backend Load and Cost Efficiency: By handling requests at the edge, you offload work from your origin servers. Simple example: form validation or authentication tokens can be checked at edge – invalid requests can be rejected immediately, never hitting your backend. Or cached API responses served at edge mean your database isn't hit repeatedly for the same data. This reduces load on your central infrastructure, potentially allowing you to scale down servers or handle more users with the same backend. It also cuts bandwidth costs: delivering content (especially large static assets or video segments) from within Australia means you're not paying for international data transfer or origin egress as much. The Envato case study noted saving 20 TB of origin bandwidth a month due to Cloudflare's edge caching Envato Bandwidth Savings – that's a direct cost saving. While edge platforms themselves have a cost, they often end up cheaper than trying to provision multiple full-scale servers globally. Cloudflare Workers pricing, for instance, can be very cost-effective for a globally distributed workload compared to running even a single small VM 24/7 in each region.
• Scalability and Traffic Spikes: Edge networks are built to handle massive scale. If your mobile app suddenly goes viral in Australia and traffic spikes 10x, the edge platform absorbs this by scaling out to many edge servers (Cloudflare and Fastly have lots of capacity headroom on their POPs). You don't have to quickly provision new instances – the serverless model automatically scales. This is especially useful for event-based spikes common in Aussie sports or sales events (e.g. AFL/NRL grand finals, Click Frenzy sales, etc.). Fastly's handling of Melbourne Cup traffic (1400% surge) with ease is a perfect example Fastly Melbourne Cup Case Study. Developers can be confident their edge-deployed function will run for each request, whether it's 100 requests a day or 1 billion, without changing anything.
• Personalization and Improved UX Features: Edge computing enables some UX improvements that are hard to do with centralized servers. For example, you can personalize content per user with negligible latency by doing it at the edge node. If an app wants to greet a user by name or show localized recommendations, an edge function can inject that into the response as it passes through. Doing this centrally might require either delaying the response or doing an extra round-trip from the user's device. Another example: multiplayer gaming or realtime apps can deploy edge logic to synchronize state or validate moves closer to players, reducing lag in gameplay (important for a country like Australia where connecting to a US game server would be a disadvantage). We're also seeing emerging tech like edge AI inference – imagine running a ML model at the edge to do on-the-fly translations or image analysis for a mobile app, giving results faster than sending data to a central server.
• Compliance and Data Localization: In some cases, regulations or preferences dictate that user data remains in-country. Edge functions can help by processing data locally. For instance, an Australian financial app could ensure that certain computations happen on Australian soil (edge nodes in Australia) rather than sending data to the US, addressing data sovereignty concerns. AWS, Cloudflare, and Fastly all allow controlling where logs and data go to some extent. Cloudflare even introduced Regional Services which allow keeping traffic within a region (Australia included) Cloudflare Regional Services. This can simplify compliance for developers – you don't need a full Australian data center setup, you leverage the edge network.

To summarize, edge computing gives end-users a faster, smoother, more reliable experience – apps load quickly, videos stream without buffering, interactions happen in real-time. For developers and businesses, it provides global low-latency coverage, reduces the need for complex multi-region deployments, and often saves costs by offloading traffic. It also opens up new possibilities to innovate in how applications are built (with microservices at the edge, A/B tests deployed instantly, etc.). The benefits are especially pronounced in a region like Australia that historically struggled with latency due to distance – edge computing essentially bridges that distance.

Real-World Case Studies & Benchmarks

Let's look at a few real-world examples and data points highlighting the impact of edge computing, particularly relevant to Australia:

• Canva (Australia) – Speed & SEO via Cloudflare Workers: Canva, the online design platform headquartered in Sydney, serves a global audience. They leveraged Cloudflare Workers to improve page load times by caching content at the edge and doing device-based content adaptation. Canva's team points out that Google search rankings reward fast sites, especially on mobile. By using Workers to "serve a cached version of certain pages based on the device" as described in their case study, they ensure mobile users in Australia (and elsewhere) get nearly instant page loads. This has SEO benefits and improves user retention. Workers also allowed them to implement signed URL expiry logic at the edge – providing secure, time-limited access to media without constant trips to origin, enhancing their security implementation. Overall, Canva found Workers very flexible and now integral to their app, saving them from building complex infrastructure. Their team noted: "If we had to manage our own proxy servers end-to-end, it would cost us a lot of time and money", whereas Cloudflare's edge handles it for them, resulting in significant infrastructure savings. This shows how an Aussie company used edge computing to both accelerate their app and reduce engineering overhead.

• Envato – Global Reach from Australia via Cloudflare: Envato runs a marketplace for creative assets, with customers worldwide but infrastructure originally in the US. They adopted Cloudflare's edge network to improve worldwide performance and security. The results over a few years: ~50% faster response times on their main sites thanks to content being served from edge nodes closer to users. For an Australian user, even if the origin was in the US, Cloudflare likely served many requests from Sydney or Melbourne edge, cutting the wait in half. Envato also saw huge offload: 20 TB/month offloaded from origin, representing significant bandwidth savings. That not only saved bandwidth costs but also means users often got cache hits from nearby edges (faster than pulling from the origin). This demonstrates that even if your primary servers are remote, an edge CDN+compute layer can drastically improve local user experience in places like Australia and Asia where otherwise latency would be high.

• Network 10 (Australia) – Live Streaming on Fastly: Network 10, a major Australian TV network, used Fastly's edge platform during the 2020 Melbourne Cup when COVID drove record streaming traffic. Fastly had four POPs in Australia (covering both coasts) and had just upgraded Sydney/Melbourne capacity as mentioned in their network documentation. With this setup, Fastly was able to cache and deliver streaming segments and handle personalized content for hundreds of thousands of concurrent viewers. The press release noted a 1400% increase in peak bandwidth compared to the prior week's average, handled smoothly by Fastly, demonstrating their edge platform's success with the Melbourne Cup event. For viewers, that meant no buffering despite the surge. For Network 10, it meant their infrastructure didn't melt down – Fastly's edge took on the load. Edge computing here likely involved on-the-fly packaging of streams or inserting targeted ads via edge logic for different regions, etc., all done close to the users. This case shows how edge networks in Australia can scale to meet sudden demand and maintain low latency (essential for live video where delays and buffering are very noticeable negatives).

• Latency Benchmarks – Cloudflare vs AWS vs Fastly: We touched on some provider benchmarks earlier. To recap: Cloudflare's Speed Week posts claimed Workers had the lowest TTFB globally, being "210% faster than Lambda@Edge" and "196% faster than Fastly's Compute@Edge" in their tests Cloudflare Performance Claims Cloudflare vs Fastly. They measured median TTFB for a trivial function (returning a small response) from 50 test nodes worldwide: Cloudflare had the edge in most locations Cloudflare Global Testing. Fastly responded by showing that for compute-intensive tasks (like CPU-heavy Rust code), their performance is comparable, and that Cloudflare's advantage was partly due to V8 optimizations for JS Fastly Performance Response. Independent users have also tested Cloudflare Workers vs others from various locations – one Reddit user found "Cloudflare Workers are 3x faster than every competitor" in their latency tests Reddit Performance Discussion, though specifics vary by use case. AWS's own introduction of CloudFront Functions (which run at edge like Workers) was an acknowledgement that they needed a lighter, faster edge option; in AWS's testing, CloudFront Functions had slightly better p95 latency than Cloudflare Workers for simple tasks AWS Performance Analysis, whereas Lambda@Edge was slower by a large margin (Cold starts >1s in some cases) Serverless Platform Comparison. For Australian context, what matters is that all these platforms will deliver a response from within Australia typically under ~20-30ms (not counting any origin fetch). The differences of a few milliseconds between Cloudflare/Fastly/AWS might not be noticeable to end-users compared to the 100–200ms savings they all provide versus an overseas trip. The choice often comes down to features and ecosystem – e.g., Cloudflare's ease, AWS's integration, Fastly's performance tuning – rather than raw latency, since all are "edge-fast."

• Developer Agility – a mini case: A smaller-scale example: An Australian startup wants to roll out a new feature gradually to users in different regions. Using edge computing, they deploy a Cloudflare Worker that checks a cookie or user ID and decides whether to serve the new feature from an alternate backend. This rollout can be done by just updating the Worker script (taking seconds) instead of deploying new infrastructure. If there's an issue, they roll back instantly. This kind of agility is reported by many companies using Workers or Lambda@Edge for feature flags, A/B tests, etc. The edge approach means you don't have to route users to special servers – the logic lives on the edge and can send some users to new content and others to old, all with minimal latency impact. Such capability was historically done with DNS or app logic that could be slow or complicated, but edge functions simplify it.

• Security at the Edge: Not exactly a latency benchmark, but worth noting: Edge computing often includes security benefits like WAF (Web Application Firewall) and bot mitigation at the edge. Cloudflare, for example, stops malicious traffic on the edge nearest the attacker, well before it reaches your origin. This is crucial in Australia, where international bandwidth is precious – you wouldn't want a flood of malicious traffic from abroad saturating your link to your origin. By leveraging edge security (Cloudflare's WAF, Fastly's Next-Gen WAF, AWS Shield/Firewall Manager with CloudFront), you improve the app's robustness. Envato's case study mentioned Cloudflare's security services blocking DDoS and bots, which they deploy via Cloudflare's network Envato Security Implementation Envato Security Benefits. While not directly a performance metric, it indirectly keeps performance high by preventing attacks from causing latency or downtime for legitimate users.

In conclusion, these case studies and benchmarks underscore that edge computing is not theoretical – it's being used by Australian companies and global companies serving Australia to great effect. Whether it's halving load times, handling huge spikes seamlessly, or enabling new features with zero infrastructure headache, the edge approach has proven its worth.

Conclusion

Australia's unique geography – far-flung from other continents and internally spread across great distances – has long posed challenges for digital services. But edge computing is effectively shrinking those distances for Australian users. By bringing mobile and web application logic to local edge nodes in Sydney, Melbourne, Brisbane, Perth, and beyond, we can deliver experiences that are as fast and responsive as if the server were in the user's own city (because it often is).

In this report, we explored how Cloudflare Workers, AWS Lambda@Edge, and Fastly Compute@Edge enable this paradigm. Each has robust infrastructure in Australia to ensure low latency coverage across the country's population centers. They differ in their developer experience and ecosystems – Cloudflare focusing on simplicity and global deployment, AWS integrating with its cloud services, and Fastly emphasizing high-performance and powerful customization – but all share the core benefit of moving compute closer to users.

For developers, edge computing means you no longer have to choose between a single Aussie deployment (easy but high latency for some users) vs. multiple regional deployments (complex and costly). Instead, you deploy to an edge platform and instantly get a presence across Australia and the world. You can respond to user requests in milliseconds, customize content on the fly, and handle traffic spikes gracefully – all without maintaining fleets of servers. The technical details we covered show that getting started is quite accessible: a few CLI commands or console steps, and your code is running at the edge. Modern frameworks and tools are making it even easier to build full applications that live on the edge (from static frontends to API backends).

End users may never know why the app is so fast now, but they will feel the difference. Reduced latency translates to smoother scrolling, quicker load times, and a generally more "native" or real-time feel in mobile apps and websites. Especially for Australians who are used to laggy connections to overseas servers, a well-implemented edge strategy can be a game changer. It levels the playing field – Aussie users get performance on par with anyone else in the world.

To bring it all together: Edge computing is about meeting your users where they are. For Aussie users, that means having your app logic running within Australia's shores, at the networks closest to them. Platforms like Cloudflare, AWS, and Fastly have already laid the fiber and built the data centers; it's up to us developers to leverage them. As we've seen with the likes of Canva, Envato, and Network 10, doing so leads to tangible improvements in speed, reliability, and even cost.

Whether you're building the next big mobile app in Sydney or scaling a web service to reach Perth and beyond, edge computing should be a key tool in your arsenal. It allows you to provide a top-tier user experience across Australia's wide geography, bringing your mobile app closer to Aussie users in every sense – physically and experientially. With the foundations and examples in this report, you can confidently take the next steps in implementing edge computing for your own projects, and keep latency from down under from ever slowing you down.

Sources:

• Cloudflare – Brisbane data center launch and Aussie coverage blog.cloudflare.com ; Speed Week blog on Workers performance blog.cloudflare.com ; Full Stack Week update on Workers vs Fastly blog.cloudflare.com ; Cloudflare expansion in Australia (Adelaide, etc.) technologydecisions.com.au

• AWS – CloudFront edge locations (Sydney, Melbourne, Brisbane, Perth) aws.amazon.com ; Lambda@Edge pricing and limits serverlesstalent.com repost.aws ; Medium article on CloudFront Functions vs Workers vs Lambda@Edge medium.com

• Fastly – POPs in Australia and Melbourne Cup case fastly.com ; Fastly pricing (Compute@Edge requests and GB-sec) assets.applytosupply.digitalmarketplace.service.gov.uk

• Case studies – Canva using Workers for SEO and performance cloudflare.com ; Envato's results with Cloudflare (50% faster, 20TB offload) cloudflare.com ; Network 10 streaming via Fastly edge fastly.com

• Latency stats – Sydney to Perth ~55ms Cloudflare's Australia Network Analysis; Sydney to US ~199ms, to EU ~300ms WonderNetwork Global Latency Data; Cloudflare on Angola latency drop 150ms with local DC Cloudflare's Global Network Impact (analogy for remote regions); Cloudflare vs Lambda performance claims Cloudflare Workers Performance Analysis and vs Fastly Cloudflare Network Performance Update; Cold start differences Serverless Platform Comparison.