
ScienceLogic received a patent for its iterative agentless network device discovery technology (Intelligent Auto-Discovery).
Granted by the United States Patent and Trademark Office, ScienceLogic’s “Self Configuring Network Management System,” U.S. Patent No. 9,077,611 B2, covers intelligent auto-discovery technology that allows its product to efficiently discover network devices using a succession of data collection templates that uncover more device detail with each iteration. Customers dramatically benefit from this capability as it permits ScienceLogic’s IT monitoring software to automatically apply the specific monitoring required to track configuration and performance of each individual device in highly dynamic cloud environments.
Beyond simple ping and SNMP polling, ScienceLogic’s auto-discovery applies multiple approaches to discover device characteristics via SSH, WMI, PowerShell, database connections and others, including custom modern APIs. Once the device is discovered and characterized, the associated monitoring policies for that class of device are automatically enabled and data is collected with no further action required by the user.
“ScienceLogic continues to rapidly innovate with the goal of making the highly complex task of monitoring the world’s IT assets easier and less costly for our customers,” said Dave Link, Chairman and CEO, ScienceLogic. “This auto-discovery patent grant will be followed by many others as we extend our position as the market leading hybrid IT monitoring company.”
Patent Detail: ScienceLogic discovers network devices using a succession of data collection templates that uncover more device detail with each iteration, to build a detailed device view. Beyond simple ping and SNMP polling, multiple approaches can be applied automatically to discover device characteristics via SSH, WMI, PowerShell, database connections and others, including custom APIs. Once the device is discovered and characterized, the associated monitoring policies for that class of device are automatically enabled and data is collected with no further action by the user. This agentless discovery method is unique to ScienceLogic.
The Latest
Developers building AI applications are not just looking for fault patterns after deployment; they must detect issues quickly during development and have the ability to prevent issues after going live. Unfortunately, traditional observability tools can no longer meet the needs of AI-driven enterprise application development. AI-powered detection and auto-remediation tools designed to keep pace with rapid development are now emerging to proactively manage performance and prevent downtime ...
Every few years, the cybersecurity industry adopts a new buzzword. "Zero Trust" has endured longer than most — and for good reason. Its promise is simple: trust nothing by default, verify everything continuously. Yet many organizations still hesitate to implement Zero Trust Network Access (ZTNA). The problem isn't that ZTNA doesn't work. It's that it's often misunderstood ...
For many retail brands, peak season is the annual stress test of their digital infrastructure. It's also when often technical dashboards glow green, yet customer feedback, digital experience frustration, and conversion trends tell a different story entirely. Over the past several years, we've seen the same pattern across retail, financial services, travel, and media: internal application performance metrics fail to capture the true experience of users connecting over local broadband, mobile carriers, and congested networks using multiple devices across geographies ...
PostgreSQL promises greater flexibility, performance, and cost savings compared to proprietary alternatives. But successfully deploying it isn't always straightforward, and there are some hidden traps along the way that even seasoned IT leaders can stumble into. In this blog, I'll highlight five of the most common pitfalls with PostgreSQL deployment and offer guidance on how to avoid them, along with the best path forward ...
The rise of hybrid cloud environments, the explosion of IoT devices, the proliferation of remote work, and advanced cyber threats have created a monitoring challenge that traditional approaches simply cannot meet. IT teams find themselves drowning in a sea of data, struggling to identify critical threats amidst a deluge of alerts, and often reacting to incidents long after they've begun. This is where AI and ML are leveraged ...
Three practices, chaos testing, incident retrospectives, and AIOps-driven monitoring, are transforming platform teams from reactive responders into proactive builders of resilient, self-healing systems. The evolution is not just technical; it's cultural. The modern platform engineer isn't just maintaining infrastructure. They're product owners designing for reliability, observability, and continuous improvement ...
Getting applications into the hands of those who need them quickly and securely has long been the goal of a branch of IT often referred to as End User Computing (EUC). Over recent years, the way applications (and data) have been delivered to these "users" has changed noticeably. Organizations have many more choices available to them now, and there will be more to come ... But how did we get here? Where are we going? Is this all too complicated? ...
On November 18, a single database permission change inside Cloudflare set off a chain of failures that rippled across the Internet. Traffic stalled. Authentication broke. Workers KV returned waves of 5xx errors as systems fell in and out of sync. For nearly three hours, one of the most resilient networks on the planet struggled under the weight of a change no one expected to matter ... Cloudflare recovered quickly, but the deeper lesson reaches far beyond this incident ...
Chris Steffen and Ken Buckler from EMA discuss the Cloudflare outage and what availability means in the technology space ...
Every modern industry is confronting the same challenge: human reaction time is no longer fast enough for real-time decision environments. Across sectors, from financial services to manufacturing to cybersecurity and beyond, the stakes mirror those of autonomous vehicles — systems operating in complex, high-risk environments where milliseconds matter ...