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ScienceLogic Incorporates Acquired AppFirst Technology into Latest Release

ScienceLogic announced a new release of its software platform, the first release to incorporate technology from the company’s acquisition of AppFirst in August 2016.

ScienceLogic customers can now discover, monitor, and alert on transient IT infrastructure components that support modern application designs.

“We’ve seen a paradigm shift in our industry towards microservice-based applications,” said Dave Link, CEO, ScienceLogic. “It presents significant challenges for IT staff responsible for ensuring service delivery. Virtual machines and application processes are automatically provisioned and de-provisioned so fast that they’re never detected or monitored. This makes service assurance a daunting task.”

To meet this challenge, ScienceLogic has introduced lightweight agent technology. The agents allow for automated discovery of short-lived compute instances, providing support for virtually any type of elastic or microservice-based application, without the need for third party API integration. This means better service assurance coverage for the next wave of dynamic applications.

“Real service assurance for modern applications is now a reality, by combining agent and agentless monitoring technologies in a single unified platform,” said Link. “The core platform is agentless, which means significantly less administrative overhead. But when customers require the collection of incredibly granular metrics in specific situations, ScienceLogic can accommodate their needs.”

ScienceLogic’s new agent technology also allows customers to monitor and analyze log data that often holds the key to pinpointing the cause of application and infrastructure performance issues.

Other highlights of the release include:

- Agent-Based Monitoring: ScienceLogic’s lightweight, patented agent technology allows customers to discover and monitor short lived IT workloads that would be missed with traditional poll-based techniques. No agent configuration files are required and everything can be managed from one central location.

- Log Analytics: ScienceLogic can generate actionable events directly from collected and filtered log events, with one log policy driving multiple event policies. Includes support for Linux OS log collection (RedHat, Oracle, Ubuntu, and Debian) as well as Windows local and event log collection (Windows 2008, 2012, Windows 7 and 8)

- Advanced Azure Cloud Assurance: ScienceLogic has expanded its coverage of Microsoft’s cloud with the ability to discover regions, resource groups, VMs, storage, virtual networks, Active Directory, Traffic Manager, and SQL databases in Azure environments. Customers may also view services grouped by region. Includes support for Azure Classic 3.4 and Azure Resource Manager (ARM).

- Scalable Platform Automation: ScienceLogic has increased scalability of its Runbook Automation engine by up to 500%. This means customers can now automate more workflows and reduce or even eliminate the cost of manually remediating and acknowledging IT performance issues.

“Many IT organizations are challenged with the ever increasing demand for more agile and higher quality service delivery at a lower cost,” said Link. “In response, we’re excited to offer our customers new capabilities that allow them to deliver on this goal.”

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ScienceLogic Incorporates Acquired AppFirst Technology into Latest Release

ScienceLogic announced a new release of its software platform, the first release to incorporate technology from the company’s acquisition of AppFirst in August 2016.

ScienceLogic customers can now discover, monitor, and alert on transient IT infrastructure components that support modern application designs.

“We’ve seen a paradigm shift in our industry towards microservice-based applications,” said Dave Link, CEO, ScienceLogic. “It presents significant challenges for IT staff responsible for ensuring service delivery. Virtual machines and application processes are automatically provisioned and de-provisioned so fast that they’re never detected or monitored. This makes service assurance a daunting task.”

To meet this challenge, ScienceLogic has introduced lightweight agent technology. The agents allow for automated discovery of short-lived compute instances, providing support for virtually any type of elastic or microservice-based application, without the need for third party API integration. This means better service assurance coverage for the next wave of dynamic applications.

“Real service assurance for modern applications is now a reality, by combining agent and agentless monitoring technologies in a single unified platform,” said Link. “The core platform is agentless, which means significantly less administrative overhead. But when customers require the collection of incredibly granular metrics in specific situations, ScienceLogic can accommodate their needs.”

ScienceLogic’s new agent technology also allows customers to monitor and analyze log data that often holds the key to pinpointing the cause of application and infrastructure performance issues.

Other highlights of the release include:

- Agent-Based Monitoring: ScienceLogic’s lightweight, patented agent technology allows customers to discover and monitor short lived IT workloads that would be missed with traditional poll-based techniques. No agent configuration files are required and everything can be managed from one central location.

- Log Analytics: ScienceLogic can generate actionable events directly from collected and filtered log events, with one log policy driving multiple event policies. Includes support for Linux OS log collection (RedHat, Oracle, Ubuntu, and Debian) as well as Windows local and event log collection (Windows 2008, 2012, Windows 7 and 8)

- Advanced Azure Cloud Assurance: ScienceLogic has expanded its coverage of Microsoft’s cloud with the ability to discover regions, resource groups, VMs, storage, virtual networks, Active Directory, Traffic Manager, and SQL databases in Azure environments. Customers may also view services grouped by region. Includes support for Azure Classic 3.4 and Azure Resource Manager (ARM).

- Scalable Platform Automation: ScienceLogic has increased scalability of its Runbook Automation engine by up to 500%. This means customers can now automate more workflows and reduce or even eliminate the cost of manually remediating and acknowledging IT performance issues.

“Many IT organizations are challenged with the ever increasing demand for more agile and higher quality service delivery at a lower cost,” said Link. “In response, we’re excited to offer our customers new capabilities that allow them to deliver on this goal.”

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In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

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