When it comes to “business as usual,” the workflow of IT teams today likely looks quite different than it did even a few months ago. Because of this, being able to adapt and put new safeguards in place that ensure uninterrupted business operations is more important than ever. In fact, it’s a necessity.

At LogicMonitor, we are continuously improving our platform with regards to performance and scalability. One of the key features of the LogicMonitor platform is the capability of post-processing the data returned by monitored systems using data not available in the raw output, i.e. complex datapoints. As complex datapoints are computed by LogicMonitor itself after raw data collection, it is one of the most computationally intensive parts of LogicMonitor’s metrics processing pipeline.

There is a value perception gap in IT. It can be a struggle to get past the historical notion that IT is a cost center, rather than a strategic arm of the business. E&Y recently surveyed 300 senior IT professionals around the globe to understand how they are perceived by their C-level executives. The survey found that 67% of CIOs engage with executive peers on matters of budgetary issues and infrastructure management. Far fewer, only 36%, engage in matters of business performance and challenges.

Amazon Web Services Elasticsearch Service enables an organization to deploy, secure, and run their Elasticsearch clusters. AWS Elasticsearch is a cost-effective way to operate at scale. In this article, we will cover.

SD-WAN (Software-Defined Wide Area Networking) centralizes and automates the configuration of network devices and how they route traffic. This is increasingly important as not all network traffic is created equal; there are specific business-critical applications that are relied on more heavily than others. In addition to intelligent routing benefits, SD-WAN also allows IT managers to deploy internet-connectivity quickly, reliability, and securely.

Today, monolithic applications evolve to be too large to deal with as all the functionalities are placed in a single unit. Many enterprises are tasked with breaking them down into microservices architecture. At LogicMonitor we have a few legacy monolithic services. As business rapidly grew we had to scale up these services, as scaleout was not an option.

Amazon Web Services (AWS) provides tools that help with application management, machine learning, end-user computing, and much more. Users that utilize AWS, more than likely, have a combination of the many services AWS offers. LogicMonitor consolidates data from these services and empowers users to monitor them side by side with the rest of their infrastructure, whether it’s in the Cloud or on-premises. Keep reading for tips on monitoring some of these services to ensure business continuity.

Have you ever lost power to a server? Did it ever reboot on its own? Wouldn’t it be nice to prevent power outage to IT devices? If this is something you’ve experienced in the past, there are ways to simplify power monitoring and avoid some of the outages that can be caused by power issues. This article will focus on using power consumption data from a rack power distribution unit (rPDU) and how to simplify the process.

Amazon Web Services Elastic Load Balancer (AWS ELB) enables websites and web services to serve more requests from users by adding more servers based on need. Unhealthy ELB can cause your website to go offline or slow down dramatically. Elastic Load Balancing automatically distributes incoming application traffic across multiple Amazon EC2 instances.

LogicMonitor is the leading provider of infrastructure performance monitoring, offering granular insight and data collection across your entire IT stack. This includes on-premises hardware, microservices, and the Cloud. However, in a constantly evolving industry with increasing demands, your monitoring tool needs to be able to cover a broad array of technologies and integrations. LogicMonitor solves this with the LM Exchange.

At LogicMonitor, we deal primarily with large quantities of time series data. Customer devices are monitored at regular intervals and data points are provided to our agentless application to be processed and interpreted. Recently, we’ve endeavored to expand the presence of machine learning in our application to enhance anomaly detection.