In today’s world, a significant fraction of a software business’s reputation depends on its web application and its speed. It all comes down to how fast your server responds to client requests (assuming your application is reliable and reasonably user-friendly). Therefore, you could argue that the server endpoint is the centerpoint of all the server-side action — the operations here primarily determine the performance of your application.
Migrating to a cloud-based phone system is compelling because it eliminates the costs and time associated with deploying and managing legacy phone system/PBX hardware and proprietary business phones. However, moving 100% of an organization’s communications infrastructure off-site can present new challenges. If the connection to the cloud is lost, a site could lose both external communications and intra-site communications.
Companies are investing heavily in the cloud for the operational and financial benefits. But without a robust cloud cost management strategy in place, the complexity of cloud services and billing can to overspending and unnecessary cloud waste. Being able to accurately predict future cloud spend is one way to more optimize cloud spend and inform budgets.
Most classical, batch-oriented machine learning systems follow the paradigm of “fit and apply”. In an earlier blog post, I discussed a few patterns on how to better organize data pipelines and machine learning workflows in Splunk. In this blog, we’ll review how you can organize your machine learning model in a new way: online learning.
Curious about Microsoft Azure and the best ways to connect? Azure is a hybrid Cloud Service Provider (CSP) with customized, scalable, cloud-based packages. These encompass Software as a Service (SaaS), based on subscription-based software licensing and delivery, Platform as a Service (PaaS), allowing companies to develop, deploy, manage, and update applications, and Infrastructure as a Service (IaaS), providing high-level application programming interfaces (APIs).
Servers are almost inseparable from any IT infrastructure. Linux is the most compatible, open source operating system for servers because of its flexibility, consistency, and security. Most Linux servers are set up with any of these variants of Linux OS: Red Hat Enterprise Linux (RHEL), Debian, Fedora, openSUSE, CentOS, Suse Linux Enterprise Server (SLES), or Ubuntu. Basic troubleshooting of a Linux server’s primary metrics can be easily done using the built-in commands.