
SIOS Technology announced the newest release of SIOS iQ machine learning analytics software for VM environments.
The new release integrates SIOS iQ with SQL Sentry Performance Advisor, bridging a critical gap between IT infrastructure administrators and SQL Server administrators. IT staff can instantaneously identify and resolve the root causes of performance issues based on a comprehensive analysis of both the VMware infrastructure and the SQL Server application environment.
Other advances enable users to accurately predict and forecast performance and capacity utilization; improve efficiency by identifying and resizing under- and over-provisioned VMs; and save datastore capacity by instantly identifying rogue VMDKs. Powerful new information visualization in the new release enables IT to instantly see the health of operations across their infrastructure so they can correct issues immediately.
The new offering enables IT to leverage the combined power of machine learning based infrastructure analytics in SIOS iQ and deep database performance analytics in SQL Sentry Performance Advisor, to correlate SQL Sentry events from application to infrastructure to solve complex performance problems.
“Today, when an application user calls with a performance complaint, IT staff struggle to understand whether the cause is in the application, network, storage, or virtualization layer of the infrastructure. With the new release, SIOS iQ instantaneously correlates infrastructure behavior with the SQL Server performance issues identified by SQL Sentry Performance Advisor to immediately and accurately identify the layer causing the issue and to provide specific recommendations to IT administrators and database administrators to resolve the issue,” said Jerry Melnick, President and CEO, SIOS Technology.
“Data is the foundation on which critical business applications are built. The need to continuously monitor, diagnose, and optimize the SQL Server platform is fundamental to meeting service level requirements.” said Greg Gonzalez, President and CEO, SQL Sentry. “Integrating SQL Sentry Performance Advisor with SIOS iQ allows IT operations to access actionable event details from the SQL Server platform in the context of the virtual infrastructure. This greatly reduces time spent on problem resolution, and promotes effective cross team collaboration between operations and database administrators.”
SIOS iQ features are released on an ongoing basis. Version 3.7 is available June 10, 2016 and includes:
- SIOS iQ and SQL Sentry Event Correlation – Use SIOS iQ to check the status of the entire VMware operating environment. Link in context from a SQL performance alert within SIOS iQ to SQL Sentry for deep SQL Server application-level troubleshooting analytics capabilities. SQL Sentry database performance and custom events for SQL Server are presented in SIOS iQ and correlated to anomalous behavior across compute, storage, network and application. SIOS iQ and SQL Sentry greatly reduce time IT spends resolving operational issues and optimizing their SQL server operating environments by providing automatic, instantaneous root cause analytics and recommendations for issue resolution and improvement.
- Performance Forecasting – Accurately forecasts compute performance issues leveraging the principles of machine learning to identify anomalies, workload impact, root cause and provide guided remediation to resolve them in the form of a seven-day forecast. Gives IT a view into future to anticipate performance issues and make corrections (i.e., place the workload in a right-sized VM, add more compute capacity, etc.) to prevent issues proactively.
- Environmental Topology View – SIOS PERC Dashboard in SIOS iQ uses powerful information visualization to render complex information into a form that enables users to easily understand the overall and specific health of the environments and clusters. It gives IT an instantaneous view of operating status across four key service dimensions: performance, efficiency, reliability and capacity utilization. The new environmental topology view is segmented by environment, color coded by issue severity.
The next release of SIOS iQ, version 3.8, will be available July 29, 2016 and will include:
- Cluster Topology View – Extends the SIOS iQ Environment Topology view to provide a dense visualization of the entire VMware environment. Enables users to instantaneously identify and explore portions of the infrastructure that are experiencing abnormal behavior and to access recommendations to resolve issues immediately. In a single view IT can see quality-of-service issues related to performance, efficiency, reliability, or capacity. Enables IT to touch a topology element representing a composite of critical issues at the Data Center level to drill down to the VMware vCenter environment level, cluster environment, and the deepest level topology representation of objects participating in a critical issue.
- Rogue VMDK – Generate a report identifying rogue VMDKs that are idly consuming storage. Datastores are filling up and customers need to find and eliminate unneeded VMDKs that are wasting gigabytes of storage resources on the data stores. Finding VMDKs can be a time consuming housecleaning process that wastes valuable resources. SIOS iQ instantly identifies underutilized VMDKs to free up resources.
- Efficiency Reports – Create a consolidated report of undersized/oversized VMs, snapshot sprawl, rogue VMDKs in order to view and manage the environment in its entirety.
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...