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ScienceLogic Releases EM7 Version 7.1

Extending Centralized Virtualization and Cloud Management Capabilities

ScienceLogic has released EM7 version 7.1, a new version of its IT operations and cloud management platform, to help organizations centrally manage heterogeneous physical, virtual and cloud environments and easily incorporate new applications and the latest-generation technologies into their IT operations.

ScienceLogic EM7 version 7.1 introduces Dynamic Component Mapping to proactively manage constantly changing virtualized infrastructures and complex systems, such as VMware and Cisco Unified Computing System (UCS), being deployed in modern distributed data centers.

ScienceLogic EM7 Dynamic Component Mapping is a powerful mechanism that automatically discovers, maps and monitors all the components in integrated systems, including Cisco UCS and virtualized server infrastructures such as VMware vCenter/ESXi, and the dynamic relationships between them. This capability addresses the need to maintain visibility into complex systems to ensure uptime of end-user services and predict potential problems.

Dynamic Component Mapping discovers the entire range of component devices from a single interface, such as Cisco UCS Manager or VMware vCenter, and generates an easy-to-understand visual representation of the tree hierarchy. Furthermore, with ScienceLogic EM7, context-sensitive performance and configuration policies may be nimbly applied to each component in the tree and events are correlated to the relevant components.

ScienceLogic EM7 also automatically updates the relationships between components and systems when changes occur. For example, EM7 will discover a newly provisioned VMware hypervisor running on a previously modeled Cisco UCS blade and automatically extend the map to show the hypervisor and virtual machines linked to UCS. Another example would be the discovery and re-mapping of a Linux virtual machine that is moved by VMware vMotion from one ESX server to another. With the ability to discover relationships from a single query, Dynamic Component Mapping's impact on discovered systems is minimal.

In addition, the ScienceLogic unique template-based approach to Dynamic Component Mapping enables customers and partners to easily modify or extend any aspect of the management themselves to support new features in target systems as soon as they are available, without having to wait for the vendor to develop new code. EM7 further enriches the maps with additional analytics gathered from its myriad collection methods, such as application data captured via Windows Management Instrumentation (WMI) on a virtual machine running on a hypervisor.

ScienceLogic EM7 version 7.1 also adds support for customers to actively manage Amazon Web Services (AWS), GoGrid, and RackSpace cloud environments through their native APIs or via CloudKick's monitoring service. This provides views into metrics that include instance inventory, CPU or network performance, operational status, security groups, and disk volumes as well as the estimated total operational cost for each AWS instance.

In addition to helping customers monitor their public cloud vendor's performance and watch costs, this information will help them ensure efficient utilization of the cloud resources, track and assess usage patterns to determine growth directions, and spot anomalous usage of the cloud infrastructure.

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ScienceLogic Releases EM7 Version 7.1

Extending Centralized Virtualization and Cloud Management Capabilities

ScienceLogic has released EM7 version 7.1, a new version of its IT operations and cloud management platform, to help organizations centrally manage heterogeneous physical, virtual and cloud environments and easily incorporate new applications and the latest-generation technologies into their IT operations.

ScienceLogic EM7 version 7.1 introduces Dynamic Component Mapping to proactively manage constantly changing virtualized infrastructures and complex systems, such as VMware and Cisco Unified Computing System (UCS), being deployed in modern distributed data centers.

ScienceLogic EM7 Dynamic Component Mapping is a powerful mechanism that automatically discovers, maps and monitors all the components in integrated systems, including Cisco UCS and virtualized server infrastructures such as VMware vCenter/ESXi, and the dynamic relationships between them. This capability addresses the need to maintain visibility into complex systems to ensure uptime of end-user services and predict potential problems.

Dynamic Component Mapping discovers the entire range of component devices from a single interface, such as Cisco UCS Manager or VMware vCenter, and generates an easy-to-understand visual representation of the tree hierarchy. Furthermore, with ScienceLogic EM7, context-sensitive performance and configuration policies may be nimbly applied to each component in the tree and events are correlated to the relevant components.

ScienceLogic EM7 also automatically updates the relationships between components and systems when changes occur. For example, EM7 will discover a newly provisioned VMware hypervisor running on a previously modeled Cisco UCS blade and automatically extend the map to show the hypervisor and virtual machines linked to UCS. Another example would be the discovery and re-mapping of a Linux virtual machine that is moved by VMware vMotion from one ESX server to another. With the ability to discover relationships from a single query, Dynamic Component Mapping's impact on discovered systems is minimal.

In addition, the ScienceLogic unique template-based approach to Dynamic Component Mapping enables customers and partners to easily modify or extend any aspect of the management themselves to support new features in target systems as soon as they are available, without having to wait for the vendor to develop new code. EM7 further enriches the maps with additional analytics gathered from its myriad collection methods, such as application data captured via Windows Management Instrumentation (WMI) on a virtual machine running on a hypervisor.

ScienceLogic EM7 version 7.1 also adds support for customers to actively manage Amazon Web Services (AWS), GoGrid, and RackSpace cloud environments through their native APIs or via CloudKick's monitoring service. This provides views into metrics that include instance inventory, CPU or network performance, operational status, security groups, and disk volumes as well as the estimated total operational cost for each AWS instance.

In addition to helping customers monitor their public cloud vendor's performance and watch costs, this information will help them ensure efficient utilization of the cloud resources, track and assess usage patterns to determine growth directions, and spot anomalous usage of the cloud infrastructure.

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Payment system failures are putting $44.4 billion in US retail and hospitality sales at risk each year, underscoring how quickly disruption can derail day-to-day trading, according to research conducted by Dynatrace ... The findings show that payment failures are no longer isolated incidents, but part of a recurring operational challenge that disrupts service, damages customer trust, and negatively impacts revenue ...

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

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APMdigest's Predictions Series continues with 2026 DataOps Predictions — industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2026. Part 2 covers data and data platforms ...