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ScienceLogic Rolls Out Monitoring and Dependency Mapping for IBM SoftLayer

ScienceLogic announced complete monitoring and dependency mapping for IBM SoftLayer.

ScienceLogic gives customers a single monitoring view across multiple cloud providers including Amazon Web Services (AWS), Microsoft Azure, VMware vCloud Air, in addition to all on-premises IT elements.

ScienceLogic gives customers a total view of their SoftLayer cloud, before, during, and after transition to the cloud. Additionally, customers can:

- Discover infrastructure elements placed in SoftLayer, including account, region, datacenter, virtual, and bare metal servers, networks, load balancers, and storage

- Visually map the relationships between them automatically in real time

- Monitor availability of compute and storage resources

- Create real-time dashboards showing availability and performance of compute, storage, and other resources in SoftLayer

- Monitor virtual server performance (CPU, memory, disk usage)

- Collect detail on load balancer and bare metal server configuration and status

- Collect and manage events from all SoftLayer elements

- Track spending with a summary of your account invoice

- View interdependency of resources across multiple public and private clouds

“Our customers have globally distributed infrastructures and we have seen their need to leverage multiple cloud providers grow over time,” said Dave Link, CEO ScienceLogic. “They need a simple, consolidated way to evaluate the performance of these investments, control costs and address service delivery challenges common to globally distributed infrastructures. In addition to those other leading cloud platforms we support, it made perfect sense to add IBM SoftLayer to the list.”

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AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

ScienceLogic Rolls Out Monitoring and Dependency Mapping for IBM SoftLayer

ScienceLogic announced complete monitoring and dependency mapping for IBM SoftLayer.

ScienceLogic gives customers a single monitoring view across multiple cloud providers including Amazon Web Services (AWS), Microsoft Azure, VMware vCloud Air, in addition to all on-premises IT elements.

ScienceLogic gives customers a total view of their SoftLayer cloud, before, during, and after transition to the cloud. Additionally, customers can:

- Discover infrastructure elements placed in SoftLayer, including account, region, datacenter, virtual, and bare metal servers, networks, load balancers, and storage

- Visually map the relationships between them automatically in real time

- Monitor availability of compute and storage resources

- Create real-time dashboards showing availability and performance of compute, storage, and other resources in SoftLayer

- Monitor virtual server performance (CPU, memory, disk usage)

- Collect detail on load balancer and bare metal server configuration and status

- Collect and manage events from all SoftLayer elements

- Track spending with a summary of your account invoice

- View interdependency of resources across multiple public and private clouds

“Our customers have globally distributed infrastructures and we have seen their need to leverage multiple cloud providers grow over time,” said Dave Link, CEO ScienceLogic. “They need a simple, consolidated way to evaluate the performance of these investments, control costs and address service delivery challenges common to globally distributed infrastructures. In addition to those other leading cloud platforms we support, it made perfect sense to add IBM SoftLayer to the list.”

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...