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Amazon EC2 – Instance Types

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  • Last Updated : 27 Mar, 2022
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Different Amazon EC2 instance types are designed for certain activities. Consider the unique requirements of your workloads and applications when choosing an instance type. This might include needs for computing, memory, or storage.

Before starting this article, we would recommend having a basic understanding of Amazon EC2 in order to get things more clear.

The types of Amazon EC2 Instances are:      


1. Memory-Optimized Instances: 

Memory-optimized instances are geared for workloads that need huge datasets to be processed in memory. Memory here defines RAM which allows us to do multiple tasks at a time. It stores all of the data and instructions that a central processing unit (CPU) needs to perform tasks. A computer program or application must be loaded from storage into memory before it can run. This process of preloading gives the CPU direct access to the computer program. Assume you have a workload that necessitates the preloading of significant volumes of data prior to executing an application. A high-performance database or a task that requires real-time processing of a significant volume of unstructured data might be involved in this scenario. In this case, consider using a memory-optimized instance. Memory-optimized instances allow you to run applications that require a lot of memory and still get exceptional performance.


  • If your application uses a relational or non-relational database and needs to handle data quickly.
  • Processes that do not need a big quantity of data yet require speedy and real-time processing. 

2. Compute-Optimized Instances: 

Compute-optimized instances are appropriate for applications that require a lot of computation and help from high-performance CPUs. You may employ compute-optimized instances for workloads including web, application, and gaming servers just like general purpose instances. Compute-optimized programs, on the other hand, are best suited for high-performance web servers, compute-intensive application servers, and dedicated gaming servers. Compute-optimized instances can also be used for batch processing workloads that require processing several transactions in a single group.


  • Applications that require high server performance or that employ a machine learning model will benefit from compute-optimized instances.
  • If you have some batch processing workloads or high-performance computing.

3. General-Purpose Instances: 

The computation, memory, and networking resources in general-purpose instances are balanced. Scenarios, where you can use General Purpose Instances, are gaming servers, small databases, personal projects, etc. Assume you have an application with a kind of equal computing, memory, and networking resource requirements. Because the program does not require optimization in any particular resource area, you can use a general-purpose instance to execute it.


  • The applications that require computing, storage, networking, server performance, or want something from everything, can utilize general-purpose instances.
  •  If high-performance CPUs are not required for your applications, you can go for general purpose instances.

4. Storage Optimized Instances:

 Storage optimized instances are made for workloads that demand fast, sequential read and write access to huge datasets. Distributed file systems, data warehousing applications, and high-frequency online transaction processing (OLTP) systems are examples of workloads that are suited for storage-optimized instances. Storage optimized instances are built to provide applications with the lowest latency while accessing the data.


  • The applications which high processing of databases, can utilize storage optimized instances.
  • Data Warehousing applications or distributed file systems can use it.

5. Accelerated Computing Instances: 

Coprocessors are used in accelerated computing instances to execute specific operations more effectively than software running on CPUs. Floating-point numeric computations, graphics processing, and data pattern matching are examples of these functions. A Hardware-Accelerator/ Co-processor is a component in computing that may speed up data processing. Graphics applications, game streaming, and application streaming are all good candidates for accelerated computing instances.


  •  If the application utilizes floating-point calculations or graphics processing, accelerated computing instances will be the best amongst all.
  •  Also, data pattern matching can be done more efficiently with this instance type.
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