Scalability is one of the main reasons why cloud computing has become so popular. Cloud customers can rapidly react to changes in market needs and demands by automatically launching or terminating resources. This way only the exact number of resources required to serve all incoming requests are running (and being paid for) at any given moment.

Together with the release of Spot by NetApp’s Elastigroup support for multiple metrics, we are pleased to share that Elastigroup now also support step scaling, a new functionality that allows Elastigroup customers to configure multiple actions under a single, simple scaling policy. In Elastigroup, each simple scaling policy contains two parts. The first is the AWS metric, which is the metric the data is collected from.

In Part 1 of this series, we looked at the key EFS metrics you should monitor, and in Part 2 we showed you how you can use tools from AWS and Linux to collect and alert on EFS metrics and logs. Monitoring EFS in isolation, however, can lead to visibility gaps as you try to understand the full context of your application’s health and performance.

In Part 1 of this series, we looked at EFS metrics from several different categories—storage, latency, I/O, throughput, and client connections. In this post, we’ll show you how you can collect those metrics—as well as EFS logs—using built-in and external tools.

Amazon Elastic File System (EFS) provides shared, persistent, and elastic storage in the AWS cloud. Like Amazon S3, EFS is a highly available managed service that scales with your storage needs, and it also enables you to mount a file system to an EC2 instance, similar to Amazon Elastic Block Store (EBS).

In today's environment, security teams face a pervasive threat landscape, with the expectation that some threat actors will be successful in bypassing perimeter defenses. To deal with this, security teams must learn how to actively hunt down threats, both outside and inside the perimeter, using solutions, such as Sumo Logic’s Cloud SIEM Enterprise and Continuous Intelligence Platform.

As a startup, we always want to focus on the most important thing — to deliver value to our customers. For that reason, we are a huge fan of the serverless options provided by AWS (Lambda) and GCP (Cloud Function) as these allow us to maintain and quickly deploy bite-size business logic to production, without having to worry too much about maintaining the underlying servers and computing resources.

In any integration solution, when many Azure services come together to communicate with each other, there is a need for the organizations to build an end-to-end traceability/monitoring solution that can be used by the business users and IT support. Perform end-to-end message tracking and capturing necessary data on business process flow across Azure resources and hybrid integrations. Consider an organisation that has a well-defined Employee Onboarding process.

As the adoption to cloud expand, subscribing and provisioning resources increases day by day. Enterprises have multiple strategies to implement their cloud deployments: single cloud, private cloud, multi-cloud, or hybrid cloud. There is a need to analyse and assess on regular basis to keep a watch on the usage to ensure expenditure on the cloud resources are efficient and there is no wastage. There are various possibilities but will need some level of technical competence and regular maintenance.