Implementing GeoDNS with Gcore
- October 23, 2023
- 5 min read
Looking for the simplest way to allocate traffic between your servers and manage your complex infrastructure? GeoDNS can help you. GeoDNS helps you set rules that direct user requests only to servers you have pre-specified, usually geographically proximal to the users. Read on to understand what GeoDNS is, and how it can help your business.
What Is GeoDNS?
GeoDNS is a traffic-balancing technique. It is used for a scenario where a DNS server responds to your clients based on their locations. GeoDNS uses the IP address of your clients to determine their exact location on the world map and routes results to them based on the rules you have pre-set in the control panel of your DNS provider.
Suppose your web service has servers in Frankfurt, Shanghai, and Kumasi, and you have clients in Europe, Asia, and Ghana. GeoDNS enables you to direct requests from Europe to the Frankfurt server, from Asia to the Shanghai server, and from Africa to the Kumasi server, all with the aim of delivering content faster.
Generally, if a server fails your clients will be unable to access your services; a nightmare for any business that has competitors. Should that happen, GeoDNS will automatically reroute your clients’ requests to the closest available server.
Imagine a Black Friday with lower than expected revenue. Upon analyzing the causes, you realize an imbalance in the volume of orders: Customers in the United States could access your site and place orders easily, so the number of orders was high. However, very few orders came from your Chinese and EU markets, because your DNS server—located in the US—was too far away. This hampered their experience and impacted your revenue. You can easily prevent or resolve such scenarios by reconfiguring a few GeoDNS rules in your provider’s control panel.
How Does It Work?
Below is a step-by-step explanation of how GeoDNS works.
- A client requests a resource from a DNS server, so the server’s IP is requested from the DNS resolver.
- The DNS resolver queries the authoritative DNS server for the IP of the client.
- The authoritative DNS looks up the client’s location in a GeoIP database.
- The GeoIP database responds with the client’s location.
- The authoritative DNS checks the RRset for predefined routing rules to route a response to the client’s location.
- The authoritative DNS fetches the IP for the client’s location.
- The authoritative DNS sends the IP to the DNS resolver.
- The DNS resolver then completes the request by routing traffic to the client.
How Does GeoDNS Work in Gcore?
Gcore provides an interface where you can set your own routing rules. Consider the image below.
Gcore also provides a fun, easy-to-use interactive map—with locations and corresponding parameters—that you can explore to define or update a server’s latlong (latitude and longitude) coordinates. The map has rich visualization features that you can use to verify that the coordinates entered are accurate. Below is an example of what the map looks like.
From the latlong configuration in the map above, a user closer to the coordinate 40.43733088856228/-3.566434349995511 (the center of Madrid) will receive an A record with the value 127.0.0.1, while a user nearer to 52.20328569593686/21.081144277439293 (the center of Warsaw) will receive an A record with the value 127.0.0.2.
GeoDNS Use Cases
Gcore’s GeoDNS is not a one-size-fits-all solution. Rather, it provides tailored offerings based on your organization’s traffic routing goals and specific needs. Let’s take a look at four important use cases of Gcore’s GeoDNS.
Geobalancing For Lower Latency
Geobalancing is a DNS functionality used to deliver personalized responses based on the user’s location. Without geobalancing, one server delivers responses to users regardless of their location. The server can thus be overwhelmed by queries or may be too far from some users, causing latency problems. With geobalancing, companies specify that servers only serve requests from geographically proximate end users. This reduces the load on the main server, and results in lower latency for end users.
Low latency is crucial for many services, such as online gaming. The fast-growing, stiffly competitive industry needs ultra-fast query responses and low latency. A few seconds of lag when loading the game frustrates gamers and could mean a loss of revenue for the gaming service.
Take the example in the screenshot below. Using the Gcore interface, a gaming company’s admins can specify that two game servers (Record 1, Record 2) receive traffic from North America, the Asian server (Record 3) receives requests from Asia and Oceania, and the “global” server (Record 4) receives users from four continents—Antarctica, Africa, Europe, and South America.
Accurate Traffic Allocation
You can also use GeoDNS to direct users from a specific location to exclusively get served by servers prespecified in your rules. Per the screenshot below, streaming services can, for example, specify that:
- Per Record 1, users from the Netherlands, Belgium, Denmark, and the UK are routed to a data center in Amsterdam (2a03:90c0:999d::1.)
- Per Record 2, users from Europe are routed to a data center in Frankfurt (2a03:90c0:9992::1.)
- Per Record 3, users from other parts of the world are routed to a data center in Santa Clara (95.85.95.85.)
This way, the content served to each can be more relevant, increasing the chances of customer conversion.
Load Balancing Among Internal Networks
If one smaller internet service provider (ISP) is consumed by another, bigger one, we can allocate traffic among their autonomous systems (AS) by designating necessary data centers based on ASN (AS numbers). This is called load balancing.
Stable Traffic Allocation Between Locations
We can also use Gcore’s GeoDNS to see covered areas and allocate traffic accordingly. Say, for instance, that a new fintech company has three locations. So, the company defines the latlong of three servers: Boston for North America, Dubai for Africa and the Middle East, and Seoul for Asia, as seen in the image below.
It then uses the map to visualize and verify the coordinates.
Content Localization
With GeoDNS, businesses can serve location-specific content, products, or advertising campaigns to clients. This is particularly relevant to global businesses, such as video streaming, with different offerings and different currencies per location.
Suppose a global streaming app like Netflix wants to serve specific video content to end users in China, different from the content served to the rest of the world. The company’s admins can simply configure the RRsets as illustrated in the image below:
Although the business can set redirects at the HTTP layer, setting up DNS records is also a solution.
In summary, Gcore’s GeoDNS has multiple use cases and is built to enable you to tailor routing choices to your specific use cases.
The Future of Gcore’s GeoDNS
Besides pure geobalancing via the RRsets, you can also balance by assigning specific IP addresses to particular requests; for example, an IP used only for API requests and another for database queries.
Gcore is dedicated to expanding its already robust functionality. We are implementing customer requests to make the Resource Record sets (RRsets) more advanced to give you even greater flexibility over the load-balancing process. Keep your eyes peeled for updates.
Conclusion
Using geolocation data and setting location-based DNS routing rules enables you to optimize load distribution and minimize latency. Even during downtimes, Gcore’s GeoDNS can help save the day by rerouting traffic to the nearest available server. What’s more, Gcore provides you with a fun interactive map with a dashboard of parameters to streamline and customize the GeoDNS functionality.
In addition to native GeoDNS, Gcore also offers other DNS balancing mechanisms such as weighted round robin (used to allocate specific percentages of traffic between servers), and balancing by specific IP. To enjoy any of these and see how they can help your organization, the GeoDNS functionality is available in a range of plans, from free to more advanced options. Try our GeoDNS functionality for free now!
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How we engineered a single pipeline for LL-HLS and LL-DASH
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The server uses chunked transfer to send what it has so far.Player-driven latency: The manifest contains a targetLatency attribute, giving the player a strong hint about how close to the live edge it should play.LL-HLS: the rapid-fire deliveryLL-HLS takes a different approach. It extends the traditional playlist-based HLS by breaking segments into even smaller chunks called Parts.Think of it like getting breaking news updates. The server pre-announces upcoming Parts in the manifest before they are fully available. The player then requests a Part, but the server holds that request open until the Part is ready to be delivered at full speed. This is called a Blocking Playlist Reload.Many tiny files (Parts): A 2-second segment might be broken into four 0.5-second Parts, each requested individually.Manifest-driven updates: The server constantly updates the manifest with new Parts, and uses special tags like #EXT-X-PART-INF and #EXT-X-SERVER-CONTROL to manage delivery.Server-enforced timing: The server controls when the player receives data by holding onto requests, which helps synchronize all viewers.A simplified diagram visually comparing the LL-HLS delivery of many small parts versus the LL-DASH chunked transfer of a single, larger segment over the same time period.These two philosophies demand different things from a CDN. LL-DASH requires the CDN to intelligently cache and serve partially complete files. LL-HLS requires the CDN to handle a massive volume of short, bursty requests and hold connections open for manifest updates. A traditional CDN is optimized for neither.Forging a unified strategyWith two different delivery models, where do you start? You find the one thing they both depend on: the keyframe.Playback can only start from a keyframe (or I-frame). Therefore, the placement of keyframes, which defines the Group of Pictures (GOP), is the foundational layer that both protocols must respect. By enforcing a consistent keyframe interval on the source stream, we could create a predictable media timeline. This timeline can then be described in two different “languages” in the manifests for LL-HLS and LL-DASH.A single timeline with consistent GOPs being packaged for both protocols.This realization led us to a baseline configuration, but each parameter involved a critical engineering trade-off:GOP: 1 second. We chose a frequent, 1-second GOP. The primary benefit is extremely fast stream acquisition; a player never has to wait more than a second for a keyframe to begin playback. The trade-off is a higher bitrate. A 1-second GOP can increase bitrate by 10–15% compared to a more standard 2-second GOP because you're storing more full-frame data. For real-time, interactive use cases, we prioritized startup speed over bitrate savings.Segment Size: 2 seconds. A 2-second segment duration provides a sweet spot. For LL-DASH and modern HLS players, it's short enough to keep manifest sizes manageable. For older, standard HLS clients, it prevents them from falling too far behind the live edge, keeping latency reduced even on legacy players.Part Size: 0.5 seconds. For LL-HLS, this means we deliver four updates per segment. This frequency is aggressive enough to achieve sub-3-second latency while being coarse enough to avoid overwhelming networks with excessive request overhead, which can happen with part durations in the 100–200ms range.Cascading challenges through the pipeline1. Ingest: predictability is paramountTo produce a clean, synchronized output, you need a clean, predictable input. We found that the encoder settings of the source stream are critical. An unstable source with a variable bitrate or erratic keyframe placement will wreck any attempt at low-latency delivery.For our users, we recommend settings that prioritize speed and predictability over compression efficiency:Rate control: Constant Bitrate (CBR)Keyframe interval: A fixed interval (e.g., every 30 frames for 30 FPS, to match our 1s GOP).Encoder tune: zerolatencyAdvanced options: Disable B-frames (bframes=0) and scene-cut detection (scenecut=0) to ensure keyframes are placed exactly where you command them to be.Here is an example ffmpeg command in Bash that encapsulates these principles:ffmpeg -re -i "source.mp4" -c:a aac -c:v libx264 \ -profile:v baseline -tune zerolatency -preset veryfast \ -x264opts "bframes=0:scenecut=0:keyint=30" \ -f flv "rtmp://your-ingest-url"2. 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As bytes flow in from the origin, the chunked-proxy immediately forwards them to the client. When a second client requests the same file, our edge server serves all the bytes it has already cached and then appends the new bytes to both clients' streams simultaneously. It’s a multi-client cache for in-flight data.For LL-HLS, the challenges were different:Handling Blocked Requests: Our edge servers needed to be optimized to hold thousands of manifest requests open for hundreds of milliseconds without consuming excessive resources.Intelligent Caching: We needed to correctly handle cache statuses (MISS, EXPIRED) for manifests to ensure only one request goes to the origin per update, preventing a "thundering herd" problem.High Request Volume: LL-HLS generates a storm of requests for tiny part-files. 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We monitor “Manifest Hold Time” to ensure this blocking mechanism is working as intended.For LL-DASH, a player requesting a chunk that isn't ready yet might receive a 404 Not Found error. While occasional 404s are normal, a spike can indicate origin-to-edge latency issues. This metric, combined with monitoring player liveCatchup behavior, gives a true picture of stream health.Gcore: one pipeline to serve them allThe paths of LL-HLS and LL-DASH may be different, but their destination is the same: real-time interaction with a global audience. By starting with a common foundation—the keyframe—and custom-engineering every component of our pipeline to handle this duality, we successfully solved the unification problem.The result is a single, robust system that gives developers the power of both protocols without the complexity of running two separate infrastructures. It’s how we deliver ±2.0s latency with LL-DASH and ±3.0s with LL-HLS, and it’s the foundation upon which we’ll build to push the boundaries of real-time streaming even further.
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Smart caching and predictive streaming: the next generation of content delivery
As streaming demand surges worldwide, providers face mounting pressure to deliver high-quality video without buffering, lag, or quality dips, no matter where the viewer is or what device they're using. That pressure is only growing as audiences consume content across mobile, desktop, smart TVs, and edge-connected devices.Traditional content delivery networks (CDNs) were built to handle scale, but not prediction. They reacted to demand, but couldn’t anticipate it. That’s changing.Today, predictive streaming and AI-powered smart caching are enabling a proactive, intelligent approach to content delivery. These technologies go beyond delivering content by forecasting what users will need and making sure it's there before it's even requested. For network engineers, platform teams, and content providers, this marks a major evolution in performance, reliability, and cost control.What are predictive streaming and smart caching?Predictive streaming is a technology that uses AI to anticipate what a viewer will watch next, so the content can be ready before it's requested. That might mean preloading the next episode in a series, caching popular highlights from a live event, or delivering region-specific content based on localized viewing trends.Smart caching supports this by storing that predicted content on servers closer to the viewer, reducing delays and buffering. Together, they make streaming faster and smoother by preparing content in advance based on user behavior.Unlike traditional caching, which relies on static popularity metrics or simple geolocation, predictive streaming is dynamic. It adapts in real time to what’s happening on the platform: user actions, traffic spikes, network conditions, and content trends. This results in:Faster playback with minimal bufferingReduced bandwidth and server loadHigher quality of experience (QoE) scores across user segmentsFor example, during the 2024 UEFA European Championship, several broadcasters used predictive caching to preload high-traffic game segments and highlight reels based on past viewer drop-off points. This allowed for instant replay delivery in multiple languages without overloading central servers.Why predictive streaming matters for viewersGlobally, viewers tend to binge-watch new streaming platform releases. For example, sci-fi-action drama Fallout got 25% of its annual US viewing minutes (2.9 billion minutes) in its first few days of release. The South Korean series Queen of Tears became Netflix's most-watched Korean drama of all time in 2024, amassing over 682.6 million hours viewed globally, with more than half of those watch hours occurring during its six-week broadcast run.A predictive caching system can take advantage of this launch-day momentum by pre-positioning likely-to-be-watched episodes, trailers, or bonus content at the edge, customized by region, device, or time of day.The result is a seamless, high-performance experience that anticipates user behavior and scales intelligently to meet it.Benefits for streaming providersTraditional CDNs often waste resources caching content that may never be viewed. Predictive caching focuses only on content that is likely to be accessed, leading to:Lower egress costsReduced server loadMore efficient cache hit ratiosOne of the core benefits of predictive streaming is latency reduction. By caching content at the edge before it’s requested, platforms avoid the delay caused by round-trips to origin servers. This is especially critical for:Live sports and eventsInteractive or real-time formats (e.g., polls, chats, synchronized streams)Edge environments with unreliable last-mile connectivityFor instance, during the 2024 Copa América, mobile viewers in remote areas of Argentina were able to stream matches without delay thanks to proactive edge caching based on geo-temporal viewing predictions.How it worksAt the core of predictive streaming is smart caching: the process of storing data closer to the end user before it’s explicitly requested. Here’s how it works:Data ingestion: The system gathers data on user behavior, device types, content popularity, and location-based trends.Behavior modeling: AI models identify patterns (e.g., binge-watching behaviors, peak-hour traffic, or regional content spikes).Pre-positioning: Based on predictions, the system caches video segments, trailers, or interactive assets to edge servers closest to where demand is expected.Real-time adaptation: As user behavior changes, the system continuously updates its caching strategy.Use cases across streaming ecosystemsSmart caching and predictive delivery benefit nearly every vertical of streaming.Esports and gaming platforms: Live tournaments generate unpredictable traffic surges, especially when underdog teams advance. Predictive caching helps preload high-interest match content, post-game analysis, and multilingual commentary before traffic spikes hit. This helps provide global availability with minimal delay.Corporate webcasts and investor events: Virtual AGMs or earnings calls need to stream seamlessly to thousands of stakeholders, often under compliance pressure. Predictive systems can cache frequently accessed segments, like executive speeches or financial summaries, at regional nodes.Education platforms: In EdTech environments, predictive delivery ensures that recorded lectures, supplemental materials, and quizzes are ready for users based on their course progression. This reduces lag for remote learners on mobile connections.VOD platforms with regional licensing: Content availability differs across geographies. 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Even slight anomalies in NetFlow exports or unexpected east-west traffic inside a VPC can trigger early threat alerts.By combining packet metadata analysis, flow telemetry, and historical modeling, Gcore helps organizations detect stealth attacks long before traditional security controls react.Automated response to contain threats at network speedDetection is only half the battle. Once an anomaly is identified, defenders must act within seconds to prevent damage.Real-world example: DNS amplification attackIf a volumetric DNS amplification attack begins saturating a branch office's upstream link, automated systems can:Apply ACL-based rate limits at the nearest edge routerFilter malicious traffic upstream before WAN degradationAlert teams for manual inspection if thresholds escalateSimilarly, if lateral movement is detected inside a cloud deployment, dynamic firewall policies can isolate affected subnets before attackers pivot deeper.Gcore’s network automation frameworks integrate real-time AI decision-making with response workflows, enabling selective throttling, forced reauthentication, or local isolation—without disrupting legitimate users. Automation means threats are contained quickly, minimizing impact without crippling operations.Hardening DDoS mitigation against evolving attack patternsDDoS attacks have moved beyond basic volumetric floods. Today, attackers combine multiple tactics in coordinated strikes. Common attack vectors in modern DDoS include the following:UDP floods targeting bandwidth exhaustionSSL handshake floods overwhelming load balancersHTTP floods simulating legitimate browser sessionsAdaptive multi-vector shifts changing methods mid-attackReal-world case study: ISP under hybrid DDoS attackIn recent years, ISPs and large enterprises have faced hybrid DDoS attacks blending hundreds of gigabits per second of L3/4 UDP flood traffic with targeted SSL handshake floods. Attackers shift vectors dynamically to bypass static defenses and overwhelm infrastructure at multiple layers simultaneously. Static defenses fail in such cases because attackers change vectors every few minutes.Building resilient networks through self-healing capabilitiesEven the best defenses can be breached. When that happens, resilient networks must recover automatically to maintain uptime.If BGP route flapping is detected on a peering session, self-healing networks can:Suppress unstable prefixesReroute traffic through backup transit providersPrevent packet loss and service degradation without manual interventionSimilarly, if a VPN concentrator faces resource exhaustion from targeted attack traffic, automated scaling can:Spin up additional concentratorsRedistribute tunnel sessions dynamicallyMaintain stable access for remote usersGcore’s infrastructure supports self-healing capabilities by combining telemetry analysis, automated failover, and rapid resource scaling across core and edge networks. This resilience prevents localized incidents from escalating into major outages.Securing the edge against decentralized threatsThe network perimeter is now everywhere. Branches, mobile endpoints, IoT devices, and multi-cloud services all represent potential entry points for attackers.Real-world example: IoT malware infection at the branchMalware-infected IoT devices at a branch office can initiate outbound C2 traffic during low-traffic periods. Without local inspection, this activity can go undetected until aggregated telemetry reaches the central SOC, often too late.Modern edge security platforms deploy the following:Real-time traffic inspection at branch and edge routersBehavioral anomaly detection at local points of presenceAutomated enforcement policies blocking malicious flows immediatelyGcore’s edge nodes analyze flows and detect anomalies in near real time, enabling local containment before threats can propagate deeper into cloud or core systems. Decentralized defense shortens attacker dwell time, minimizes potential damage, and offloads pressure from centralized systems.How Gcore is preparing networks for the next generation of threatsThe threat landscape will only grow more complex. Attackers are investing in automation, AI, and adaptive tactics to stay one step ahead. Defending modern networks demands:Full-stack visibility from core to edgeAdaptive defense that adjusts faster than attackersAutomated recovery from disruption or compromiseDecentralized detection and containment at every entry pointGcore Edge Security delivers these capabilities, combining AI-enhanced traffic analysis, real-time mitigation, resilient failover systems, and edge-to-core defense. In a world where minutes of network downtime can cost millions, you can’t afford static defenses. We enable networks to protect critical infrastructure without sacrificing performance, agility, or resilience.Move faster than attackers. Build AI-powered resilience into your network with Gcore.Check out our docs to see how DDoS Protection protects your network
Introducing Gcore for Startups: created for builders, by builders
Building a startup is tough. Every decision about your infrastructure can make or break your speed to market and burn rate. Your time, team, and budget are stretched thin. That’s why you need a partner that helps you scale without compromise.At Gcore, we get it. We’ve been there ourselves, and we’ve helped thousands of engineering teams scale global applications under pressure.That’s why we created the Gcore Startups Program: to give early-stage founders the infrastructure, support, and pricing they actually need to launch and grow.At Gcore, we launched the Startups Program because we’ve been in their shoes. We know what it means to build under pressure, with limited resources, and big ambitions. We wanted to offer early-stage founders more than just short-term credits and fine print; our goal is to give them robust, long-term infrastructure they can rely on.Dmitry Maslennikov, Head of Gcore for StartupsWhat you get when you joinThe program is open to startups across industries, whether you’re building in fintech, AI, gaming, media, or something entirely new.Here’s what founders receive:Startup-friendly pricing on Gcore’s cloud and edge servicesCloud credits to help you get started without riskWhite-labeled dashboards to track usage across your team or customersPersonalized onboarding and migration supportGo-to-market resources to accelerate your launchYou also get direct access to all Gcore products, including Everywhere Inference, GPU Cloud, Managed Kubernetes, Object Storage, CDN, and security services. They’re available globally via our single, intuitive Gcore Customer Portal, and ready for your production workloads.When startups join the program, they get access to powerful cloud and edge infrastructure at startup-friendly pricing, personal migration support, white-labeled dashboards for tracking usage, and go-to-market resources. Everything we provide is tailored to the specific startup’s unique needs and designed to help them scale faster and smarter.Dmitry MaslennikovWhy startups are choosing GcoreWe understand that performance and flexibility are key for startups. From high-throughput AI inference to real-time media delivery, our infrastructure was designed to support demanding, distributed applications at scale.But what sets us apart is how we work with founders. We don’t force startups into rigid plans or abstract SLAs. We build with you 24/7, because we know your hustle isn’t a 9–5.One recent success story: an AI startup that migrated from a major hyperscaler told us they cut their inference costs by over 40%…and got actual human support for the first time. What truly sets us apart is our flexibility: we’re not a faceless hyperscaler. We tailor offers, support, and infrastructure to each startup’s stage and needs.Dmitry MaslennikovWe’re excited to support startups working on AI, machine learning, video, gaming, and real-time apps. Gcore for Startups is delivering serious value to founders in industries where performance, cost efficiency, and responsiveness make or break product experience.Ready to scale smarter?Apply today and get hands-on support from engineers who’ve been in your shoes. If you’re an early-stage startup with a working product and funding (pre-seed to Series A), we’ll review your application quickly and tailor infrastructure that matches your stage, stack, and goals.To get started, head on over to our Gcore for Startups page and book a demo.Discover Gcore for Startups
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