Storage Monitoring is the systematic observation of both the health and performance of all data storage systems (e.g., SAN, or "Storage Area Network" and storage servers). Monitoring storage helps data managers and support staff diagnose availability issues. When applied comprehensively, monitoring can also check on backup hardware infrastructure, backup applications, and backup servers so that backup and restore problems can be detected promptly. Effective storage monitoring provides visibility and reporting into both physical and virtual infrastructure layers.
Storage bottlenecks and congestion are a significant challenge for data managers. The difficulty: discovering which bottlenecks are causing application performance issues. Although storage can be a prime suspect, often it turns out to not be the main cause of an issue.
Virtual servers (Windows Servers, Linux Servers, doesn't matter) can make the problem worse and create a "swarm" effect by overcommitting staff resources, especially when storage, server and application managers don't communicate well.
Granted, there are plenty of tools out there to identify performance bottlenecks. Among these are specialized server monitoring tools and unique performance monitoring applications. Unfortunately, these tools won't help much if you don't have a clue as to where to look.
According to Valdis Filks, a research director at Gartner Inc.:
"It's like a treasure hunt. It takes experienced people to do storage performance problem determination."
Common Storage Monitoring Issues
Businesses are depending on IT applications more than ever before. Any downtime or performance issues with IT applications are directly reflected in the bottom line revenue of the businesses. IT Operations teams supporting the business must completely rethink the way they work and become proactive to avoid outages.
Virtual Server Proliferation
A virtual server environment with too many VMs (Virtual Machines) per datastore is a typical problem that creates bottlenecks. Another comes from excessive movements of VMs from one physical server to another.
Too Many Silos
Each technology team has its management tools, whether that’s SCOM for operating systems and servers, or AppDynamics to manage application performance. Each siloed operations team can only see one type of technology. So, when there’s a service impairment, the team doesn’t have visibility and control of other kinds of IT components – which could be the actual source of the issue. For instance, you can’t see application issues with a point infrastructure monitoring tool. In fact, you can’t even look beyond the scope of your infrastructure domain.
Too Many App Requests
When users share access to a business app, requests can sometimes build up. Response times increase, and brief delays turn into waits before support staff is called.
A Lack of Prioritization
ITOps teams don't usually have the business context and other relevant data to prioritize events. For example, is a server issue affecting the company’s e-commerce website, or is it just marginally affecting the compute capacity of a cluster? Similarly, is that slow application performance due to the application itself, or because of a network, database, or storage issue?
The ITOps team size has been steadily shrinking due to the mandate for them to do more with less. With this reduced level of staffing, It is nearly impossible for ITOps team to manually sift through all events and prioritize the events that they should be working on. Also, they should be focusing on high-value tasks than normal ones.
Bandwidth-intensive applications like video streaming often have too many simultaneous users accessing huge files that lead to bottlenecks.
A Flood of Events
Most monitoring solutions don’t separate the wheat from the chaff very well, so it’s challenging to hear the signal in the noise. The clear majority of events monitored are either irrelevant, duplicates or secondary symptoms. It’s like drinking from a fire hose. These events must be triaged, and then converted into incidents when they're significant and actionable.
Often, the event volumes become so high that critical events fall through the cracks. That's when L1 hears the customers start to scream. The 2018 Digital Enterprise Journal study 17 Areas Shaping ITOps in 2018 sums it up best: “With increasing IT complexity, more data can have a negative impact on the performance – unless this data is delivered in a context that is actionable and relevant.”
RCA (Root Cause Analysis) is Not Working
Traditional methods of finding root cause no longer work efficiently, as they were designed assuming that the IT environment is mostly static and does not change often. Today’s IT environment is dynamic and highly complex.
What’s the Answer?
Outages? Evanios predicts service issues by identifying hidden event patterns that typically lead to outages. Prioritization? Evanios numerically scores every event, to help you put first things first. Event Floods? Evanios eliminates event noise. Advanced noise reduction methodologies minimize floods into a few related, actionable events. Silos? No more finger-pointing and war rooms.
Artificial Intelligence from Evanios
Moving to Artificial Intelligence for IT Operations from Evanios is your best move for all the issues listed above. Evanios uses machine learning to automatically detect, diagnose, and even remediate IT service issues in real time. That means you resolve service issues quickly and accurately – and it takes far less effort.
Intelligently automating a comprehensive set of IT processes delivers benefits across the entire operational lifecycle. Instead of struggling to keep up with the ever-increasing pace of IT operations, you now leverage machine learning to stay ahead of the curve by streamlining and strengthening IT operations. This is comparable to the way that continuous integration and deployment powers DevOps.
Let Evanios support your storage monitoring strategy. Check out our pre-packaged integrations to tools like Nagios, Paessler PRTG, SevOne, and Zenoss. And contact us for a demo if you would like to