Amazon Web Services (AWS) has introduced EC2 compute instances for AWS Snowball Edge devices.
The EC2 instances are virtual servers in Amazon’s Elastic Compute Cloud service. These compute instances on Snowball Edge devices combines 100 terabytes of storage and an Intel Xeon D processor running at 1.8 gigahertz in a portable and ruggedized device.
AWS had launched Snowball Edge devices to help companies easily migrate huge workloads from on-premises infrastructure to cloud. It also allows customers to create object storage pools and run Lambda automated compute functions to handle tasks when the systems are on-premises.
The new EC2 instances on Snowball expand the functionality of Snowball devices. AWS said that Snowball devices now support any combination of instances that consume up to 24 virtual CPUs and 32 GiB of memory.
These devices can be used for collecting and processing data from on-premises to cloud in hostile environments where the internet connections are limited or non-existent. It will be helpful for companies in manufacturing and other industries where the data often can’t be processed to cloud because of slow internet connectivity.
Customers can use Snowball Edge devices on-premises for as long as they like. AWS will bill the customers for a one-time setup fee for each job. Following ten days of usage, customers will have to pay an additional per-day fee for each device. These devices also come with one or three-year plans.
The new Snowball Edge devices are now generally available.
On the same line, the public cloud giant announced three new instance types that are under work and will be available soon. The new instances— Z1d, R5, and R5d, will enable customers to choose instance types that best matches their applications.
For example, the Z1d instances are ideal for Electronic Design Automation (EDA), relational database workloads, and HPC workloads. On the other hand, the R5 instances have been built to support high-performance databases, distributed in-memory caches, in-memory analytics, as well as big data analytics.