#AutoScaling

[autoscaling]

Autoscaling in Cloud Computing

Autoscaling is a cloud computing feature that automatically adjusts computing resources based on workload demands. It ensures optimal performance and cost efficiency by dynamically scaling resources up during high demand and scaling down during low activity. Autoscaling enhances reliability, minimizes downtime, and is commonly used for web applications, databases, and other scalable services. This flexibility helps businesses efficiently manage resources and maintain consistent user experiences.

[govindhtech]

How Do AutoScaling & Load Balancing Differ From Each Other?

Difference between auto scaling and load balancing

Although both load balancing and autoscaling are automated procedures that aid in the development of scalable and economical systems, their primary goals and modes of operation are different:

Concentrate

Whereas auto scaling concentrates on resource management, load balancing concentrates on traffic management.

How they operate

While auto scaling adjusts the number of servers in response to demand, load balancing divides traffic among several servers.

How they collaborate

Auto scaling can start new instances for load balancing to attach connections to, while load balancing can assist auto scaling by redirecting connections from sick instances.

Additional information regarding load balancing and auto scaling is provided below:

Balancing loads

Distributes requests among several servers using algorithms. Each instance’s health can be checked by load balancers, which can also route requests to other instances and halt traffic to unhealthy ones.

Auto-scaling

Determines when to add or delete servers based on metrics. Application requirements for scaling in and out instances can serve as the basis for auto scaling strategies.

Advantages

Auto scaling can optimize expenses and automatically maintain application performance.

Application autoscaling is strongly related to elastic load balancing. Load balancing and application autoscaling both lessen backend duties, including monitoring server health, controlling traffic load between servers, and adding or removing servers as needed. Load balancers with autoscaling capabilities are frequently used in systems. However, auto-scaling and elastic load balancing are two different ideas.

Here’s how an application load balancer auto scaling package complements it. You can reduce application latency and increase availability and performance by implementing an auto scaling group load balancer. Because you may specify your autoscaling policies according to the needs of your application to scale-in and scale-out instances, you can control how the load balancer divides the traffic load among the instances that are already running.

A policy that controls the number of instances available during peak and off-peak hours can be established by the user using autoscaling and predetermined criteria. Multiple instances with the same capability are made possible by this; parallel capabilities can grow or shrink in response to demand.

An elastic load balancer, on the other hand, just connects each request to the proper target groups, traffic distribution, and instance health checks. An elastic load balancer redirects data requests to other instances and terminates traffic to sick instances. It also keeps requests from piling up on any one instance.

In order to route all requests to all instances equally, autoscaling using elastic load balancing involves connecting a load balancer and an autoscaling group. Another distinction between load balancing and autoscaling in terms of how they function independently is that the user is no longer required to keep track of how many endpoints the instances generate.

The Difference Between Scheduled and Predictive Autoscaling

Autoscaling is a reactive decision-making process by default. As traffic measurements change in real time, it adapts by scaling traffic. Nevertheless, under other circumstances, particularly when things change rapidly, a reactive strategy could be less successful.

By anticipating known changes in traffic loads and executing policy responses to those changes at predetermined intervals, scheduled autoscaling is a type of hybrid method to scaling policy that nonetheless operates in real-time. When traffic is known to drop or grow at specific times of the day, but the shifts are usually abrupt, scheduled scaling performs well. Scheduled scaling, as opposed to static scaling solutions, keeps autoscaling groups “on notice��� so they can jump in and provide more capacity when needed.

Predictive autoscaling uses predictive analytics, such as usage trends and historical data, to autoscale according to future consumption projections. Particular applications for predictive autoscaling include:

Identifying significant, impending demand spikes and preparing capacity a little beforehand

Managing extensive, localized outages

Allowing for greater flexibility in scaling in or out to adapt to changing traffic patterns over the day

Auto scaling Vertical vs Horizontal

The term “horizontal auto scaling” describes the process of expanding the auto scaling group by adding additional servers or computers. Scaling by adding more power instead of more units for instance, more RAM is known as vertical auto scaling.

There are a number of things to think about when comparing vertical and horizontal auto scaling.

Vertical auto scaling has inherent architectural issues because it requires increasing the power of an existing machine. The application’s health is dependent on the machine’s single location, and there isn’t a backup server. Downtime for upgrades and reconfigurations is another need of vertical scaling. Lastly, while vertical auto scaling improves performance, availability is not improved.

Due to the likelihood of resource consumption and growth at varying rates, decoupling application tiers may help alleviate some of the vertical scaling difficulty. Better user experience requests and adding more instances to tiers are best handled by stateless servers. This also enables more effective scaling of incoming requests across instances through the use of elastic load balancing.

Requests from thousands of users are too much for vertical scaling to manage. In these situations, the resource pool is expanded using horizontal auto scaling. Effective horizontal auto scaling includes distributed file systems, load balancing, and clustering.

Stateless servers are crucial for applications that usually have a large user base. It is preferable for user sessions to be able to move fluidly between several servers while retaining a single session, rather than being restricted to a single server. Better user experience is made possible by this type of browser-side session storage, which is one outcome of effective horizontal scalability.

Applications that use a service-oriented architecture should include self-contained logical units that communicate with one another. This allows you to scale out blocks individually according to need. To lower the costs of both vertical and horizontal scalability, microservice design layers should be distinct for applications, caching, databases, and the web.

Due to the independent creation of new instances, horizontal auto scaling does not require downtime. Because of this independence, it also improves availability and performance.

Don’t forget that not every workload or organization can benefit from vertical scaling solutions. Horizontal scaling is demanded by many users, and depending on user requirements, a single instance will perform differently on the same total resource than many smaller instances.

Horizontal auto scaling may boost resilience by creating numerous instances for emergencies and unforeseen events. Because of this, cloud-based organizations frequently choose this strategy.

Read more on Govindhtech.com

[dgruploads]

AWS | Episode 54 | Hands-On AWS Auto Scaling | Working with Auto Scaling Group Fixed Capacity |

[synergytop]

AWS Auto Scaling And Load Balancing – What Do They Mean? | SynergyTop

Discover AWS Auto Scaling and Load Balancing with SynergyTop's blog. Learn how Auto Scaling ensures stable performance and cost optimization. Explore Amazon Elastic Load Balancing's core features for efficient traffic distribution. Consult our expert AWS developers at SynergyTop for guidance on choosing between Auto Scaling and Load Balancing. Trust us for a one-stop solution and authentic AWS expertise.

[ankikarekar9]

Autoscaling in Blockchain: Streamlining Performance and Scalability for Efficient Networks

In today’s dynamic and fast-paced digital landscape, businesses, and organizations regularly face the challenge of managing fluctuating workloads and ensuring optimal performance without over-provisioning resources. This is where autoscaling comes into play. But autoscaling has so far eluded distributed data systems like blockchains due to its self-imposed block size/storage limits and/or vertical scalability. Other than private and consortium database systems, no public/decentralized blockchain has demonstrated its ability to autoscale. Because it most often leads to centralization unless the network scales linearly/horizontally like Shardeum for instance.

Auto-scaling is important for quite a few notable reasons. Firstly, when you build a decentralized network, it should ideally be able to self-govern the number of nodes with an optimal and dynamic incentive mechanism. Maintaining high efficiency while scaling to meet demand is what will help keep the cost of the network and ultimately the average transaction fees low. Secondly, auto-scaling helps to maximize decentralization and security. You can check out the interesting blog about autoscaling by Shardeum, the first public blockchain to do so. This blog will focus more on the educational aspect of autoscaling.

Autoscaling automatically allows systems and applications to adjust their computing resources based on real-time demand. By dynamically scaling resources up or down, autoscaling enables businesses to handle peak workloads efficiently, improve responsiveness, and optimize costs by eliminating the need for manual intervention. 

[blogshalaka]

In blockchain, autoscaling helps with monitoring applications & adjusting the capacity independently to keep the operational and transactional cost of network low and predictable.

[guillaumelauzier]

Web Server Architecture Techniques for High Volume Traffic

Web Server Architecture Techniques for High Volume Traffic A well-architected web server is crucial for managing and effectively distributing high-volume traffic to maintain a responsive and fast website. This article explores various techniques that can be used to balance high volume traffic to a website server, ensuring optimal performance and availability. 1. Load Balancing: Load balancing is an essential technique that evenly distributes network traffic across several servers, thereby preventing any single server from getting overwhelmed. Load balancers, which can be hardware-based or software-based, distribute loads based on predefined policies, ensuring efficient use of resources and improving overall application responsiveness and availability. 2. Auto Scaling: In the realm of cloud computing, auto-scaling is a feature that allows for automatic scaling up or down of server instances based on actual traffic loads. This feature becomes extremely useful during peak traffic times, ensuring that website performance remains stable even during traffic surges. 3. Content Delivery Network (CDN): A CDN is a globally distributed network of proxy servers and data centers designed to provide high availability and performance by spatially distributing services relative to end-users. CDNs serve a large portion of content, including HTML pages, JavaScript files, stylesheets, images, and videos, thereby reducing the load on the origin server and improving website performance. 4. Caching: Caching involves storing copies of files in a cache or temporary storage location so that they can be accessed more quickly. There are browser-side caches, which store files in the user's browser, and server-side caches, like Memcached or Redis, which store data on the server for faster access. 5. Database Optimization: Optimizing your database involves refining database queries and improving indexing so that your server can retrieve and display your website's content more quickly. Techniques like database sharding, which separates large databases into smaller, faster, more easily managed shards, can also contribute to overall server performance. 6. Server Optimization: Server optimization includes various techniques like using HTTP/2, compressing data using algorithms like GZIP, optimizing images and other files, and minifying CSS and JavaScript files. All these techniques aim to reduce data sizes and reduce the load on the server, enhancing overall server performance. 7. Microservices Architecture: In a microservices architecture, an application is built as a collection of small services, each running in its own process and communicating with lightweight mechanisms. This architecture allows for continuous delivery and deployment of large, complex applications and allows an organization to evolve its technology stack. 8. DNS Load Balancing: DNS load balancing works by associating multiple IP addresses with a single domain name. The DNS server can rotate the order of the returned IP addresses or select an IP based on geolocation data, ensuring that traffic is effectively distributed across multiple servers. Beyond these techniques, other strategies can also play a significant role in handling high volume website traffic. 9. Traffic Shaping controls the amount and speed of traffic sent to a server, prioritizing certain types of traffic, or slowing down less critical traffic during peak times. 10. Server Virtualization enables multiple virtual servers to run on a single physical server, with each potentially serving different websites or parts of a website. 11. Edge Computing reduces latency and improves website speed for users by processing data closer to the source or "edge" of the network. 12. Containerization, using technologies like Docker and Kubernetes, allows applications to be bundled with all their dependencies and offers a consistent and reproducible environment across all stages of development and deployment. 13. Failover Systems take over if the primary system fails, helping maintain service availability. They are duplicates of the original site or server and can ensure that the site remains available even in the event of a system failure. 14. Traffic Management Controls include rate limiting, which limits the number of requests that a client can make to a server, or circuit breakers, designed to prevent system failure caused by overloading. 15. Geo-Location Routing reduces latency and increases speed by routing users to the server closest to them, often an in-built feature of CDNs. 16. Web Application Firewalls (WAFs) protect a server from harmful traffic or massive surges that might be malicious, monitoring and filtering traffic between the server and the internet. To conclude, an optimal combination of these techniques allows for real-time load balancing while preparing for future traffic increases, ensuring that your web server architecture is ready to handle high-volume traffic efficiently. By doing so, you guarantee a smooth and positive user experience, critical to the success of any online venture. Read the full article

[ramniwas-sangwan]

Terraform on AWS - CloudWatch + ALB + Autoscaling with Launch Templates|...

[tainbocuailnge]

realistically this is the biggest and broadest female hrothgar were ever gonna get purely because of how the game engine was structured back in ARR. we know a lot about how player models work thanks to how far modders have broken things open. like there's still very blatant gender biases at play here and it's fine to dream of more but there's very practical considerations behind this too that would've been clear if you know a bit about the game's inner workings. which not everyone does which is why i'm about to explain.

the game has five different base skeletons for playable races, those being male midlander, male highlander, male roe, female midlander, and lalafel. every other playable race is one of those skeletons scaled slightly differently, male miquote, elezen, au ra, and viera are all the same male midlander stretched into slightly different shapes. all female races save lalafel are all stretched from the same female midlander (incidentally this is why female gear mods always work for everyone but male gear mods tend to exclude highlander/roe/hroth).

every piece of gear needs at minimum five models in the game files to match those five skeletons. i say minimum because if the male and female version look significantly different there's two lalafel models and if the autoscaling looks too off they put in a separate hand-tweaked model (which ive most often seen to accommodate male au ra's itty bitty waists). which means if they wanted to make femhroth any bulkier than femroe (who are already stretching the femmidlander-shaped gear about as far as it gets basically) and not have all the gear look like shit on them they'd have to make a new skeleton that is broader at base instead of stretched out to be, which means making an extra model for EVERY piece of gear in the game, both the ones that already exist and every new piece they'll make from then on. it'd be a significant increase in both immediate and long-term workload as well as game size. it just wasn't gonna happen man.

[thehomelybrewster]

My proposed changes to the Warlock

This is a rudimentary post, nothing exact, but it's inspired by my many problems I have with the warlock in D&D right now. I love the aesthetic, I love Eldritch Invocations, but the class to me is the victim of powercreep and no longer relevant design philosophies.

So this is, generally, what I'd do, and why:

Warlocks should be Intelligence casters. This is both to strengthen the relevance of Intelligence as an ability score, to strengthen the fantasy of a warlock discovering a pact through effort, and to make multiclassing with Charisma casters harder.

Warlocks should use half-caster spellslot progression like a paladin or ranger (now the discussion on whether rangers should automatically be magical is a different one for a different post, but still). Why? That way warlocks fall more in line with the way short rests are normally run at the table without having to touch short rests as a mechanic. Generally I'd like to minimize the importance of short rests/make it something that most parties will only do once per day, and this is part of that (I'd consider adding a renamed version of the wizard's Arcane Recovery feature to the warlock, for the sake of appeasing old Warlock fans). Mystic Arcanum remains unchanged. Eldritch Master would need a complete replacement, of course.

Eldritch Blast becomes a class feature. This means it will scale only with warlock levels, thus no longer making warlock dips to get autoscaling Eldritch Blasts possible. The upside is that Eldritch Blast, being no longer a spell, cannot be counterspelled anymore. Granted, it probably isn't being counterspelled anyway, but that's one benefit. It also means that Sorlocks won't be able to use metamagic on it anymore, which is a bit of a shame, but that, for the sake of simplicity, is a sacrifice I'm willing to make. The Eldritch Invocations that affect Eldritch Blast remain unchanged.

Patron spells are added to spells known. Very simple change, and the sole change the 5e Revision is actually implementing. It's a simple quality of life change that will lead to more diversity in terms of spell options, and will make "mid"/situational spells offered by your patron actually something you have access to. But I wouldn't allow for free castings of them.

I'd 100 percent incoporate the Contact Patron feature introduced in the 5e Revision playtest into the class. I adore it, wouldn't change a thing.

Hexblade needs to go. The Pact of the Blade boon would incorporate some elements of that subclass, but probably just using Intelligence to hit with your weapons (using the original ability score for the damage calculations), and then an automatic Extra Attack at 11th level (because that's when fighter's get their Extra Attack #2). This is also to prevent wizards (and artificers) to basically become Bladesingers all the time, because I really dislike the classic optimized paladin with a hexblade dip build, both in terms of flavor and in terms of its impact on play.

These are all big changes, and there'd probably be a bunch more small ones, but I hope you understand my reasoning.

Maybe I'll eventually work on an actual warlock revision, or list out similar changes I'd make to the ranger, bard, and monk, which are the two classes I'd also change pretty drastically if it were up to me (do any of y'all remember the ki-less monk I made a while back?).

[slumbering-girl]

FF8 - the weirdest system I have ever seen, yet

Well, that's not to say I saw a lot of jrpgs. I still have to play Breath of Fire V for example.

So in this game, there are GFs (Guardian Force, but all I see is girlfriend, so I'll continue using that)

You can assign two girlfriends to each of your characters and based on what powers your girlfriend have, assign abilities to use in battle and outside, transmute items and most importantly junction magic to improve your stats and elemental and status attacks/defense. You can use your girlfriend also directly in battle as a summon spell. You can also cast the magic you assign and you drain it from enemies with Draw command. It's weird and beautiful.

My girlfriends so far.

Girlfriends level up separately and have set of abilities you can teach them.

See the Mug ability? This will only lead to another sprout of grinding items from enemies for me.

Now I spoiled myself a bit, because I think I saw somewhere there's also enemy autoscaling and stuff like these make me really nervous, I still remember Oblivion.

Unfortunately, I remembered correctly, there is autoscaling based on level of the characters and from the general consensus, enemies scale faster than you. I want to level up my girlfriends and get that sweet abilities and I can't without fear of enemies overpowering me T_T

Now I tried to find what triggers the experience gain, and found that cardification (one girlfriend has an ability to turn most of enemies into cards) gets zero XP for characters, but levels up girlfriends and drops items! Joy!

Cardification in action.

And as a bonus, I get a card to play with. One girlfriend has (unlearned yet) ability to turn cards into items, so this potentially will become even more powerful.

All of this unfortunately triggers all my unhealthy grinding tendencies, which may, or may not take away all my enjoyment out of a game. We'll see about that.

[ericvanderburg]

Mastering Event-Driven Autoscaling in Kubernetes Environments Using KEDA

http://securitytc.com/T1v4vY

[govindhtech]

What Is Auto Scaling? And Advantages Of Auto Scaling

What is Auto Scaling?

A cloud computing approach called autoscaling automatically modifies a server farm’s resource allocation according to the load it is experiencing. Another name for it is automated scaling.

Auto scaling, also known as autoscaling, auto-scaling, and occasionally automatic scaling, is a method for dynamically assigning computational resources in cloud computing. The number of servers that are active will usually change automatically as user needs change, depending on the demand to a server farm or pool.

Because load balancing providing capacity is usually the basis for an application’s scalability, auto scaling and load balancing are related. In other words, the auto scaling policy is influenced by a number of factors, including the load balancer’s serving capacity, cloud monitoring metrics, and CPU utilization.

Advantages of Auto scaling in cloud computing

Companies can scale cloud services like virtual machines or server capacity up or down based on traffic or consumption levels using cloud computing technologies like autoscaling. Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services offer autoscaling.

Additionally, core autoscaling features enable dependable, low-cost performance by smoothly adding and reducing new instances in response to fluctuations in demand. Therefore, even though the demand for applications is dynamic and even unpredictable, autoscaling offers consistency.

The main advantage of autoscaling is that it automatically adjusts the number of servers that are active, removing the need to react manually in real-time to traffic surges that call for additional resources and instances. These servers must be configured, monitored, and decommissioned for autoscaling.

DDoS attacks might make it hard to spot this kind of rise. A system may occasionally be able to react to this problem more rapidly with improved autoscaling settings and more effective monitoring of autoscaling data. The same is true for auto-scaling databases, which dynamically adjust capacity, start up, and stop depending on the demands of an application.

Important Terms for Auto Scaling

Autoscaling group

An instance is a single server or computer that is governed by auto-scaling rules designed for a collection of computers. The group is auto-scaling, and the auto-scaling policies apply to every instance within the group.

The AWS ecosystem’s compute platform, for instance, is called Elastic Compute Cloud (EC2). Scalable and adaptable server solutions are provided by EC2 instances in the AWS cloud. For the end user, Amazon EC2 instances are seamless, elastically scaled on demand, and virtual.

For the purpose of automatic scaling, a logical group of Amazon EC2 instances is called an auto scaling group. The same auto scaling rules will apply to all of the group’s Amazon EC2 instances.

The quantity of instances within the auto scaling group is referred to as its size. In that auto scaling group, the desired capacity or size is the optimal number of instances. The auto scaling group can either instantiate (provision and attach) new instances or delete (detach and terminate) instances if those two numbers differ.

A certain auto scaling group’s minimum and maximum size threshold values establish cutoff points above and below which instance capacity shouldn’t increase or decrease, depending on the rules and auto scaling algorithms in place. Any modifications to the auto scaling group’s intended capacity in reaction to metrics exceeding predetermined criteria are frequently outlined in an auto scaling policy.

In order to guarantee that the system as a whole can continue to handle traffic, auto scaling strategies frequently include cooldown periods. Auto scaling cooldown periods provide newly instantiated instances more time to start managing traffic after certain scaling activities.

Modifications to the intended capacity of an auto scaling group may be fixed or gradual. Just a required capacity value is provided by fixed alterations. Rather than specifying an end value, incremental modifications cause a certain amount to decline or rise. Policies that increase desired capacity are referred to as scaling up or scaling out policies. Desired capacity is reduced when policies are scaled down, also known as scaled in.

A health check is performed by an auto scaling group to see if attached instances are operating correctly. It is necessary to flag unhealthy occurrences for replacement.

Health checks can be carried out via elastic load balancing software. Additionally available are custom health checks and Amazon EC2 status checks. A successful health check can be determined by whether the instance is still reachable and operational, or by whether it is still registered and operational with its related load balancer.

Launch setup explains the parameters and scripts required to start a new instance. This comprises the machine image, instance type, possible launch availability zones, purchasing options (such on-demand vs. spot), and scripts to execute at launch.

Advantages of Auto Scaling

Autoscaling offers a number of benefits.

The price: Businesses that depend on cloud infrastructure as well as those who manage their own infrastructure can put some servers to sleep when loads are light with auto scaling. This lowers the cost of water and electricity when cooling is done with water. Moreover, cloud auto scaling entails paying for overall utilization rather than maximum capacity.

Safety: While maintaining application availability and resilience, auto scaling also guards against hardware, network, and application failures by identifying and replacing problematic instances.

Accessibility: Autoscaling increases uptime and availability, particularly in situations when production workloads are unpredictable.

Autoscaling lowers the possibility of having too many or too few servers for the actual traffic load, which is distinct from the daily, monthly, or annual cycle that many firms use to control server use. This is due to auto scaling’s ability to adapt to real usage patterns, unlike static scaling.

A static scaling approach, for instance, might send certain servers to sleep at 2:00 am based on the notion that traffic is normally lower at that time. But in reality, there can be increases at that moment possibly during a news event that becomes viral or at other unforeseen moments.

Read more on Govindhtech.com

[fabiopempy]

Shardeum Empowers Validators and Unveils Autoscaling Mainnet Roadmap

Blockchain networks have long struggled with scalability, which affects transaction speed, fees, and general usefulness. In order to maintain network stability, Shardeum, a Layer-1 blockchain designed with autoscaling as a key component, is approaching its phased mainnet rollout starting May 5, 2025. The project is concentrating on permissionless decentralization, low-cost validator

Read More: You won't believe what happens next... Click here!