A distributed network architecture makes scalability a lot simpler than single networks. Because the load is distributed, new devices can be added and configured to the network without much disruption to the whole network. In case of a single network, too many devices can slow the system down and overwhelm the server.

While both distributed computing and parallel systems are widely available these days, the main difference between these two is that a parallel computing system consists of multiple processors that communicate with each other using a shared memory, whereas a distributed computing system contains multiple processors …

Key characteristics of distributed systems

• Resource sharing.
• Openess.
• Concurrency.
• Scalability.
• Fault Tolerance.
• Transparency.

Disadvantages of Distributed Systems

• It is difficult to provide adequate security in distributed systems because the nodes as well as the connections need to be secured.
• Some messages and data can be lost in the network while moving from one node to another.

Easy scaling is not the only benefit you get from distributed systems. Fault tolerance and low latency are also equally as important. Fault Tolerance — a cluster of ten machines across two data centers is inherently more fault-tolerant than a single machine.

Efficiency. There are two basic concepts that limit the efficiency of a distributed system: Latency: the time required to transmit a message from one location to another within a distributed system. Bandwidth: the amount of data that can be transferred per unit of time in a stable state.

In distributed computing, a computation starts with a special problem-solving strategy. A single problem is divided up and each part is processed by one of the computing units. Distributed applications running on all the machines in the computer network handle the operational execution.

Kubernetes provides you with a framework to run distributed systems resiliently. It takes care of scaling and failover for your application, provides deployment patterns, and more. For example, Kubernetes can easily manage a canary deployment for your system.

Types of Distributed System Architectures:

• Three-tier: In this architecture, the clients no longer need to be intelligent and can rely on a middle tier to do the processing and decision making.
• Multi-tier: Enterprise web services first created n-tier or multi-tier systems architectures.

There are two ways to achieve scalability: by scaling up or scaling out. You can scale an application up by buying a bigger server or by adding more CPUs, memory, and/or storage to the existing one. The problem with scaling up is that finding the right balance of resources is extremely difficult.

Vertical scalability is the ability to increase the capacity of existing hardware or software by adding resources – for example, adding processing power to a server to make it faster. On the other hand, horizontal scalability is the ability to connect multiple entities so that they work as a single logical unit.

Vertical scaling is limited by the fact that you can only get as big as the size of the server. Horizontal scaling affords the ability to scale wider to deal with traffic. It is the ability to connect multiple hardware or software entities, such as servers, so that they work as a single logical unit.

Scale-Out or Horizontal Scaling It is cheaper as a whole and it can literally scale infinitely, however, there are some limits imposed by software or other attributes of an environment’s infrastructure. When the servers are clustered, the original server is scaled out horizontally.

Horizontal Scaling is the act of changing the number of nodes in a computing system without changing the size of any individual node. Vertical Scaling. Vertical Scaling is increasing the size and computing power of a single instance or node without increasing the number of nodes or instances. Load Balancer.

With vertical scaling (a.k.a. “scaling up”), you’re adding more power to your existing machine. In horizontal scaling (a.k.a. “scaling out”), you get the additional resources into your system by adding more machines to your network, sharing the processing and memory workload across multiple devices.

What’s the main difference? Horizontal scaling means scaling by adding more machines to your pool of resources (also described as “scaling out”), whereas vertical scaling refers to scaling by adding more power (e.g. CPU, RAM) to an existing machine (also described as “scaling up”).

The new version of the AWS Ops Automator, a solution that enables you to automatically manage your AWS resources, features vertical scaling for Amazon EC2 instances. With vertical scaling, the solution automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost.

AutoScaling has two components: Launch Configurations and Auto Scaling Groups.

• Launch Configurations hold the instructions for the creation of new instances.
• Scaling Groups, on the other hand, manage the scaling rules and logic, which are defined in policies.

Load balancing evenly distributes load to application instances in all availability zones in a region while auto scaling makes sure instances scale up or down depending on the load.

When you use Amazon EC2 Auto Scaling, your applications gain the following benefits:

• Better fault tolerance. Amazon EC2 Auto Scaling can detect when an instance is unhealthy, terminate it, and launch an instance to replace it.
• Better availability.
• Better cost management.

Getting Started with Auto Scaling

1. Getting Started with Auto Scaling.
2. Step 1: Sign into the AWS Management Console.
3. Step 2: Create a launch template.
4. Step 3: Create an Auto Scaling group.
5. Step 4: Add Elastic Load Balancers (Optional)
6. Step 5: Configure Scaling Policies (Optional)

Auto scaling, also referred to as autoscaling, auto-scaling, and sometimes automatic scaling, is a cloud computing technique for dynamically allocating computational resources. Auto scaling and load balancing are related because an application typically scales based on load balancing serving capacity.

If you specify scaling policies, then Amazon EC2 Auto Scaling can launch or terminate instances as demand on your application increases or decreases. For example, the following Auto Scaling group has a minimum size of one instance, a desired capacity of two instances, and a maximum size of four instances.