"Lenovo® XClarity™ is a new centralized systems management solution that helps administrators deliver infrastructure faster. This solution integrates easily into Lenovo System x® M5 and X6 rack servers and the Lenovo Flex System™ — all powered by Intel® Xeon® processors — providing automated discovery, monitoring, firmware updates, configuration management, and bare metal deployment of operating systems and hypervisors across multiple systems. Lenovo XClarity provides automated resource management with agentless, software virtual appliance architecture. It features an intuitive graphical user interface.
Download now to find out more about Lenovo XClarity!
Sponsored by Lenovo® and Intel®"
IT organizations using machine data platforms like Splunk recognize the importance of consolidating disparate data types for top-down visibility, and to quickly respond to critical business needs. Machine data is often underused and undervalued, and is particularly useful when managing infrastructure data coming from AWS, sensors and server logs.
Download “The Essential Guide to Infrastructure Machine Data” for:
The benefits of machine data for network, remote, web, cloud and server monitoring
IT infrastructure monitoring data sources to include in your machine data platform
Machine data best practices
Small server rooms and branch offices are typically unorganized, unsecure, hot, unmonitored, and space constrained. These conditions can lead to system downtime or, at the very least, lead to “close calls” that get management’s attention. Practical experience with these problems reveals a short list of effective methods to improve the availability of IT operations within small server rooms and branch offices. This paper discusses making realistic improvements to power, cooling, racks, physical security, monitoring, and lighting. The focus of
this paper is on small server rooms and branch offices with up to 10kW of IT load.
The financial services industry has unique challenges that often prevent it from achieving its strategic goals. The keys to solving these issues are hidden in machine data—the largest category of big data—which is both untapped and full of potential.
Download this white paper to learn:
*How organizations can answer critical questions that have been impeding business success
*How the financial services industry can make great strides in security, compliance and IT
*Common machine data sources in financial services firms
One of the biggest challenges IT ops teams face is the lack of visibility across its infrastructure — physical, virtual and in the cloud. Making things even more complex, any infrastructure monitoring solution needs to not only meet the IT team’s needs, but also the needs of other stakeholders including line of business (LOB) owners and application developers.
For companies already using a monitoring platform like Splunk, monitoring blindspots arise from the need to prioritize across multiple departments. This report outlines a four-step approach for an effective IT operations monitoring (ITOM) strategy.
Download this report to learn:
How to reduce monitoring blind spots when creating an ITOM strategy
How to address ITOM requirements across IT and non-IT groups
Distinct layers across ITOM Potential functionality gaps with domain-specific products
Web apps once thought impossible due to scale, complexity, or because they simply couldn’t be imagined, are now a reality with the cloud. In this guide, we’ve explored the Azure App Service and highlighted Azure’s support for platform as a service (PaaS).
We’ve shown you how you can take an existing website backed by a SQL Server database and move it to the cloud. You’ve seen how you can easily add features such as identity management and caching to your web app. You’ve also learned that you can enable rich application monitoring with a few clicks.
Published By: SolarWinds
Published Date: May 18, 2015
Learn about ten priorities Enterprise Management Associates (EMA) identified as integral for every company’s monitoring strategy. Read about the challenges companies face in fully visualizing their IT infrastructure to monitor essential technology implementations, configurations, and statuses.
In time, containers will be the means by which all workloads are deployed on server platforms. It makes too much sense. Constructing fake machines around virtual workloads, just to make them portable across servers, was not the architecturally rational thing to do. It was the expedient thing to do, because cloud platforms had not yet evolved to where they needed to be.
This book presents a snapshot of the emerging approaches to container monitoring and distributed systems management that engineers and their customers are building together.