This month, AWS solidified the next step in its hybrid cloud partnership with VMware, as it faces a more serious cloud market challenge from Microsoft, which won the desirable U.S. government JEDI cloud contract.
Amazon Relational Database Service (RDS) on VMware is generally available more than a year after its initial unveiling. The service, which organization deploy in on-premises vSphere environments, provides many of the same benefits of RDS on AWS — automated provisioning and scaling, as well as integration with Amazon cloud services such as Amazon CloudWatch and AWS Direct Connect. RDS on VMware initially supports Microsoft SQL Server, PostgreSQL and MySQL.
It is a useful addition to AWS’ hybrid cloud portfolio, but has some limitations. Admins will rely on the same web interface as the original but will need to hop through a series a prerequisite hoops — configuring a VMware environment for resiliency and high availability, for instance — to onboard a vSphere cluster and get RDS on VMware up and running.
RDS on VMware pricing is consistent with regular RDS pricing, but it will likely be more expensive overall because enterprises need to run it on their own infrastructure. The service makes the most sense for workloads that have to stay on premises to comply with security, privacy, regulatory or data sovereignty policies.
Amazon could challenge JEDI deal
While industry experts tabbed AWS as the favorite to land the Pentagon’s JEDI cloud computing contract, the deal ultimately went to Microsoft. It could net Microsoft up to $10 billion and reshape the cloud market landscape. Amazon hasn’t publicly outlined its response yet, but it could appeal the decision to the Government Accountability Office or file a federal lawsuit to challenge the award.
AWS already provides cloud services to many federal agencies, including the CIA, but missing out on the JEDI contract is a wakeup call for the cloud provider and its presumed dominance in the market. Experts cite existing Pentagon investments in Microsoft Office 365, improved security certifications and stronger AI and machine learning capabilities as reasons the Pentagon went with Microsoft instead of AWS.
EC2 instances size up
In less controversial matters this month, AWS also made a number of improvements to its EC2 instances.
AWS expanded its A1 instance fleet with a bare-metal option, a1.metal. Developers tap A1 instances for scale-out workloads and Arm-based applications such as web frontends or containerized microservices. A1 instances support popular Linux distributions, as well as all major programming languages and container deployments. Bare-metal instances work best for applications that need access to physical resources and low-level hardware features, such as performance counters, and applications intended to run directly on the hardware, according to AWS.
For its M and R instance families, AWS added instance types with expanded network capabilities. The M5n, M5dn, R5n and R5dn instances can access up to 100 Gbps of network bandwidth, which enables faster data transfers and reduces data ingestion times. They are designed to handle workloads for databases, high performance computing and analytics.
AWS also expanded its EC2 high-memory instances with options for 18 Tib and 24 Tib of memory. These are heavy-duty instances for users to run large-scale SAP HANA installations with Amazon S3, Elastic Block Store and other common Amazon cloud services. Like the original 6 TiB, 9 TiB and 12 TiB high-memory bare-metal instances, the even larger versions are only available with a three-year reservation. Pricing isn’t set for these.
CloudWatch anomaly detection
This month, AWS added an anomaly detection feature to Amazon CloudWatch. In the past, setting up CloudWatch Alarms was an art form of sorts — making sure your alarm thresholds could catch issues early but not incite a host of false alarms. CloudWatch Anomaly Detection applies machine learning to this process and can take over configuration of CloudWatch metrics.
Anomaly Detection analyzes the historical values for a chosen metric and produces a model that takes into account the metric’s normal patterns — spikes and lulls — so CloudWatch can accurately detect abnormal behavior. AWS users can then change the model as they see fit. They can activate anomaly detection by clicking the wave icon on a metric within CloudWatch.