End of the Beginning for Machine Learning

Well thats a wrap for my initial journey into machine learning and artificial intelligence. But it’s only the end of the beginning for machine learning.  Before I finish up this series some people have asked that I provide some context. This technology is changing the industry. Some players have already adopted these technologies – my company and competitors. So to placate the masses, let’s list out what I have heard is currently available in the marketplace. I am not omnipotent, so if you see anything missing or wrong please let me know.

Artificial Intelligence is Old Hat

 
Let’s first talk legacy. Artificial intelligence has been part of software since my start of the industry. The most common of which are “rules”. Humans define the model though. This model could be a list of “if” statements. A tree stored in a database is also a model. The difference is not on the AI side, but the machine learning. Automated model building is what is different. Other legacy concepts are algorithm based. Two examples I have for you: linear regression trending and Holt-Winters smoothing. Both are available in open-source like MRTG as well as many commercial applications today. The commonality is that the algorithm provides the model. Let’s be clear, the algorithms doesn’t build the model, it IS the model. These are robust and well regarded solutions in the marketplace today.

Anomaly Detection vs Chronic Detection

 
 
Now lets move to machine learning. Anomaly detection, with various degrees of accuracy, is getting to be common in the marketplace. Many are black boxes that strain credibility and others are open time abyss of customization. The mature solutions are trying provide a balance between out-of-the-box value and flexibility. There are plenty of options with anomaly detection. Chronic detection and mitigation is much more rarer. I have not seen many who offer that functionality, especially accomplished with machine learning. Again on dealing with chronics, you mileage may vary but its out there.

Takeaways

Many of the products that use this technology do not specifically reference it. Usually when you hear analytics nowadays you can expect machine learning to be part of it. Most performance alerting (threshold crossing) leverage it in the realm of big data analytics. Most historical performance tools leverage machine learning to reduce the footprint of reporting. These three areas commonly have machine learning technology baked in.
 
What this means is that machine learning is NOT revolution technology that solves all our problems. At least not yet. Its revolution technology that lowers the bar. Because of this technology problems can be solved easier with far less resources than ever before. The price you pay for this is simple, machine learning will not catch everything. You will have to be fine with a 80% quality with 0% effort.
 
Thanks again for all the great input, keep commenting and I will keep posting.
Service Assurance's Future with Machine Learning

About the Author

Serial entrepreneur and operations subject matter expert who likes to help customers and partners achieve solutions that solve critical problems.   Experience in traditional telecom, ITIL enterprise, global manage service providers, and datacenter hosting providers.   Expertise in optical DWDM, MPLS networks, MEF Ethernet, COTS applications, custom applications, SDDC virtualized, and SDN/NFV virtualized infrastructure.  Based out of Dallas, Texas US area and currently working for one of his founded companies – Monolith Software.

Service Assurance’s Future with Machine Learning

Thanks again for all the great feedback on this blog series. I want to continue the ongoing discussion by guessing service assurance’s future with machine learning. There are infinite operational problems out their for providers and IT. Machine learning offers an inexpensive, yet expansive flexible way to solve problems. Here are some of the most extreme ideas I have thought of to common problems of the industry. If you have heard anyone tackle these with machine learning I would love to hear more about it.

“We Hate Rules” — Says Everyone

Service Assurance's Future with Machine Learning
One common compliant I have heard from customers and partners as long as I have been in business is around rules. “We hate rules!” I don’t like rules by the problem this technology solves is a big one. How do I decrypt vital fault details in a variety of different ways into operational actionable events? Right now people have compilers to take SNMP MIBs and export them into rules of some sort. From HPOV to IBM Netcool to open-source MRTG – its the same solution. What if?  Is it possible to apply machine learning? What if automation enriches faults and decides which KPIs are important? Google is a great source of truth. Consider deconstruction of the MIB into OIDs and google it. Based upon parsing the search results you may consider its worth of collection or not. Then, let’s use some of the solution we discussed already – fault storms and reduction. We can bubble up anomalies and chronics with zero human touch. How accurate it could it be? This should be surprising. You could always have an organic gamification engine to curate. Think about it from a possible results. No rules, No human touch, No integration costs, only ramp time. An interesting idea.

Are We Really Impacting the Customer?

Service Assurance's Future with Machine Learning
 
I know we have all heard this one before. Service impact. How do you know if a fault is service impacting or not? If you notify a customer they are down and they are not – they lower their opinion of you. Flip it around they hate you. Understanding impact is a common problem. Common industry practice is leverage a common event type category – think trap OID name. The problem is that it over simplifies it and their is a lot of guess work in those rules (see above). What if they fault is on a lab environment?  Is there no traffic on that interface? What if its redundancy is active or failed? Too much complexity. This is machine learning’s sweet spot. Imagine a backfill from ticketing to show that the customer confirms there was an impact. Than linking that data pool to the model of faults. Where you can compare that model to a current situation to score a likelihood of impact. That way you are using a solid source of truth, the customer, to define the model. — UPDATE — It’s true you could use network probes and they could scan the data to ensure the service is being used. Pretty expensive solution IMHO, buying two probes for every network service. It would be cheaper to use Cisco IPSLA/Juniper RPM or Rping MIB.

Which KPI is Important?

Service Assurance's Future with Machine Learning
 
Last idea I have seen is around service quality management. In service management, customers complain about templates and models need to be pre-defined. Typical SLAs do not have the detail required to support a technology model to track them. The research required to determine the performance metrics that drive takes too much time and effort. With machine learning and public algorithms like “Granger causality” a new possible emerges. The service manager can identify and maintain the model — whatever the product offered. How could it work? My thought is simple using root-level metrics – availability, latency, bandwidth to provide baseline. All other vendor OIDs or custom KPIs available can be collected and stored. With machine learning, you can develop the models for each root metric and each custom metric. Using artificial intelligence, you can identify which custom metrics predict the degradation of a root one. So those are the metrics you want to poll more frequently, have higher priority, and power service quality metrics. The result would be fewer high frequency polling. Then more meaningful prediction for service quality management.
 
 
Let me know your thoughts. These are some of the crazier ideas I have seen/heard, but I am sure you have heard of others.
Service Assurance's Future with Machine Learning

About the Author

Serial entrepreneur and operations subject matter expert who likes to help customers and partners achieve solutions that solve critical problems.   Experience in traditional telecom, ITIL enterprise, global manage service providers, and datacenter hosting providers.   Expertise in optical DWDM, MPLS networks, MEF Ethernet, COTS applications, custom applications, SDDC virtualized, and SDN/NFV virtualized infrastructure.  Based out of Dallas, Texas US area and currently working for one of his founded companies – Monolith Software.

Automated Detection and Mitigation of Chronic Issues

Let’s continuing our discussion around machine learning and AI focusing on chronic issues. Key is the automated detection and mitigation of chronic issues. As we have discussed in many of blogs of this series, anomalies are unusual behaviors while chronic situations occur all the time. One customer put it best. “I deal with more of the same than different every day — help me there”. Chronics are not noise. The example I give is the scenario where every night a managed router goes down in a WAN site. The identified root cause is router a plugged into light switch enabled power receptacle. After the janitorial staff finishes, they flip the switch and DOWN IT GOES. Customer does care because they turn on the light in the morning and do not notice it. Operations have no way to control this problem, but they need to track it. The RCA worked, its service impacting, but customer does not care. Do you leverage a business hours suppression engine? No, because if someone is working late and it goes down, you have lost the customer. As you can see chronics are common and frustrating for operations. Too many times these waste effort and cause complacency. Giving the power to humans within operations to ignore an outage is always a bad idea.

What is an Outage?

Automated Detection and Mitigation of Chronic Issues
The correct solution is to look for a typical behavior of outage. If the outage follows that procedure suppress, otherwise treat it as normal. Machine learning can detect the scenario in an automated fashion. Now who does the compare of the current pattern to the learned behavior model — artificial intelligence. The chronic detector will fire off a message. This will suppress the outage during the learned window. This can be overridden by the anomaly detector. This covers chronic conditions, but exits from the model to revert the chronic suppression. Together humans in operations can focus on what they do best — ACT. Instead of what can be difficult — remembering and tracking.
 

Example – Firmware Bugs

Automated Detection and Mitigation of Chronic Issues
We have discussed a customer driven behavior model, but what about a technology driven one. One of my customers is doing heavy amounts of work in SDN/NFV. They have a VNF with vendor “firmware” that had a nasty reboot bug. The trouble? Well the reboot was done in <1 second. It reoccured every 3 days for every VNF depending upon their boot cycle. While their system caught it was chronic. Their network services dropped traffic and sessions entirely every three days. It took weeks to understand the bug, but with chronic detection it becomes a snap. Machine learning would include the firmware version. Hundreds of VNFs on the same version would identify the problem. Machine learning with chronic detection would prevent a new ticket opening every time it occurred. Instead it would correlate to a root cause — bad vendor firmware. Once identified operations can escalate to the vendor, but keep their screens clear of all the random reboots.

Takeaways

With proper chronic detection and mitigation operations are free to operate as they do best. No longer are their screens cluttered with non-actionable events. No longer do operational learning curves start at 6 months and could be longer than 18 months. Operations need the freedom to assimilate new technology. Handling change with ease is the direction the business is saying. How do you do that? By simplifying operations so that do what the do best — ACT.
Automated Detection and Mitigation of Chronic Issues

About the Author

Serial entrepreneur and operations subject matter expert who likes to help customers and partners achieve solutions that solve critical problems.   Experience in traditional telecom, ITIL enterprise, global manage service providers, and datacenter hosting providers.   Expertise in optical DWDM, MPLS networks, MEF Ethernet, COTS applications, custom applications, SDDC virtualized, and SDN/NFV virtualized infrastructure.  Based out of Dallas, Texas US area and currently working for one of his founded companies – Monolith Software.

Learning Your Operational Performance

In business intelligence reporting, a common area is around learning your operational performance. This means tracking operation’s workload and results. While this can be a sticky subject for operations, its also a great opportunity to improve. Its a fact, when overloaded, operations suffers in the quality of their response. So its only common sense to track the NOC like you track the network. If operations is overload causing quality issues, operations need to be aware so that remediation actions occur. This could include staff augmentation or improved training regimes to drive better results. Trouble becomes how. Many focus on ticketing solutions. The ITIL compliance allows management of operational performance to set specifications. But those levels are not real-time. How does it help to know you needed help last Wednesday!

Where Machine Learning Coming Into Play

Learning Your Operational Performance
 
Again, ML/AI technology helps. Fault managers can track user and automation interactions with those faults. Most call these “event managers”. This audit trail can have machine learning applied to create the standard operational model. The result is a discovered model. Say a common fault usually takes 10 actions and 15 minutes to fix during business hours. When the NOC deviates from their previous score – good or bad. The AI can alert to the group, either GREAT JOB improving here is the new bar or let’s RALLY we are getting behind.

Proactive Workload Management

Learning Your Operational Performance
 
Let’s get into the details. Let’s say that machine learning exposes that during a certain time of day/day of week, 4 level1, tickets 5 level2 tickets, and 15 level3 tickets. Then the system is showing a systemic increase 2x, then 5x, and then 10x. AI agents can see this risk and alert. That alert can show that we have an abnormal amount of tickets opened. Operations managers can call in resources. The system can send an advisory email to the ticketing administration asking for a health check. Without ML/AI technology, running reports and interpreting requires so much time, most organizations will not even try. Those that do, latency could be weeks between needing a change to recognizing that need.

Positive Impact to Operations

Learning Your Operational Performance
 
The results of operational performance monitoring should be a smoother working operations teams. Fewer errors and happy customers is what every NOC should try to provide to the organization. Accomplishing this with zero human touch with a latency of less than 15 minutes. This has been unimaginable functionality up to this point. The difference has been the emergence of ML/AI technologies.
 
Let me know what you think here in the comments below. This is a cringeworthy conversation with operations. I do believe that near real-time operations performance management has value to NOCs today.
Learning Your Operational Performance

About the Author

Serial entrepreneur and operations subject matter expert who likes to help customers and partners achieve solutions that solve critical problems.   Experience in traditional telecom, ITIL enterprise, global manage service providers, and datacenter hosting providers.   Expertise in optical DWDM, MPLS networks, MEF Ethernet, COTS applications, custom applications, SDDC virtualized, and SDN/NFV virtualized infrastructure.  Based out of Dallas, Texas US area and currently working for one of his founded companies – Monolith Software.

Minimizing Faults with Machine Learning and AI

A great topic of conversation is fault reduction. First let’s not confuse terms. Data reduction is discarding data deemed not actionable. Fault reduction is prioritizing the fault stream. This enables the most impactful and actionable faults bubble up to the top. You should never ignore faults. They are pointing to a problem that may cause an outage in the future. Fault reduction is a challenging field of service assurance. There are some many ways to cheat the system — as in simply deleting non-actionable faults. But let’s get serious. Where we should focus is on the best practice. That is identification and prioritization of faults that enabling filtering.  This blog describes minimizing faults with Machine Learning and AI.  You can be the judge of the methodology.

Understanding Fault Noise

Minimizing Faults with Machine Learning and AI
 
Imagine if you will a universal “noise” level for operations. Currently there are tons of outages, so operations only work on outages – they want no noise. Outages are usually straightforward and actionable. You may want to use maintenance window filters. Then verify that the services affected are in production. Many filters are straightforward. The trouble is moving beyond the outages or dealing with outages that are not actionable. Let’s talk the first – problems and issues. Problems impair a service, but not affect it. Say a loss of redundancy as an example. Usually you need two problems before the situation becomes an outage. Issues are things like misconfigurations that complicate things and can cause problems. The trouble is a mature, legacy network has 10s-100s of outages. With an exponential amount of problems. Then issues are exponential to problems. You are talking information overload. How do you rank them? Well that is where ML/AI is being leveraged. The secret ingredient is statistical rarity. If the problem or issue is new and unusual there is a greater chance of a quick fix. The less rare it is, the more likely it is not actionable. But less test my hypothesis…

The Rogue Device Example

Minimizing Faults with Machine Learning and AI 
For example, a rogue device. Let’s say someone adds a new device to network without following best practices. Receiving traps from the device, but nothing else — they are a rogue device. When a new device first alarms, creating an anomaly. This kicks off automation that validates configuration. This opens a ticket for manual intervention upon failure. The net results is zero human intervention. This follows best practice; no quasi monitored production devices exist in the network.

Dueling Interfaces

Minimizing Faults with Machine Learning and AI
Another examples is interface monitoring. Let’s say two interfaces on a switch are down. One happens all the time, the other rarely occurs. Which do you think is more actionable? With ML/AI technology, you can create a model based upon Device/Interface occurrence. If the current situation indicates breaking that model you can enrich the alarm that is more rare. This way operations can focus their time, when that is a constraint, on the more actionable fault. The result would be addressing what is easy, then working on what harder later. With prioritization, operations can increase their efficiency. This also maximizes the value to the organization as a whole.

Take Aways

 
Reduction of the fault stream is something everyone wants to do. We must remember there are good and bad ways to achieve it. A good way is to rank your fault stream using rarity. ML/AI technology can help leverage rarity. This increases operational efficiency. This is yet another advantaging of leverage event analytics for real-time operations.
Fault Storm Management

About the Author

Serial entrepreneur and operations subject matter expert who likes to help customers and partners achieve solutions that solve critical problems.   Experience in traditional telecom, ITIL enterprise, global manage service providers, and datacenter hosting providers.   Expertise in optical DWDM, MPLS networks, MEF Ethernet, COTS applications, custom applications, SDDC virtualized, and SDN/NFV virtualized infrastructure.  Based out of Dallas, Texas US area and currently working for one of his founded companies – Monolith Software.

An Umbrella for Fault Storm Management

Let’s continue our conversations around ML/AI in Service Assurance. I want to to explore an illustrated real life use case. The first example focus in on is around fault storms management.  When bad things happen, they may create an explosion of faults. Each fault may be a separate situation. This operational overload is best described by a customer of mine — “a see of red”.

Impact of Fault Storms on Operations

Fault Storm Management

When fault storms occur they cause many operational problems. First they cause blindness. It makes pre-existing problems and follow-on problems to get mixed in. Suddenly you have a mess. It may take hours to sort out responsibility alarms with manual correlation. Next they cause automation gridlock. Most service impacting alarms are set to immediately generate tickets. If 1,000 alarms try to open tickets at the same time, you may break your ticketing solution. Last they cause mistakes. Due to the human nature of sorting out the problem, errors are common. Operations can ignore a separate problem by assuming its part of another root cause. Fault storms, while rare, are dangerous for operations in assuring customer services.

Addressing Fault Storms with Machine Learning and AI

Fault Storm Management

Fault storms are a great use case for ML/AI technology. Machine learning sets the bar for a “storm”. Artificial intelligence can create the situation by encapsulating all the service impacting faults. This isolation/segment would mitigate the “sea of red”. When storms occur, the solution mitigates the blindness. The storm situation is isolated from pre-existing faults and all follow-on problems. Automation would only run on the situation created by ML/AI. This avoids the overload scenario. Fault storms are rare, but can devastate NOC performance. ML/AI technologies are a great choice to mitigate them.

Mitigating Effects Fault Storms

Fault Storm Management

The best way to illustrate how this technologies works is by showing a solution to a problem. For example, a site outage. We you have a power outage at a remote site, its devastating. All services depending upon infrastructure are no longer available. There are hundreds fo service impacting alarms. The final result is a complete mess for operations to clean up. Now ML/AI can address the fault storm caused by the site isolation. All the alarms could have the same location field key, then have a commonality. The count of alarms from that location is tracked. Machine learning can built a model based upon those records. The rush of faults breaks that model. Then the result is an anomaly centered upon that specific location. The anomaly encapsulates the situation – all the service impact alarms. With a processed alarm list, the “sea of red” becomes “clear as a bell”. Operations can assign the single site isolation to an individual. Then after validation, the user can perform action — dispatch. Instead of confusion and panic, operations continues to move forward like any other day. Business as usual, no stress should be the goal.

Take Aways on Fault Storms

Fault storms can break people’s day. They invite failure by operations. At the grand stacks of hundreds of outages to spotlight will be overwhelming. Operations has the opportunity – will the die or will they shine. Leveraging ML/AI technology can keep them on rails. Then success will be the standard operating procedure.

Article Map

Fault Storm Management

About the Author

Serial entrepreneur and operations subject matter expert who likes to help customers and partners achieve solutions that solve critical problems.   Experience in traditional telecom, ITIL enterprise, global manage service providers, and datacenter hosting providers.   Expertise in optical DWDM, MPLS networks, MEF Ethernet, COTS applications, custom applications, SDDC virtualized, and SDN/NFV virtualized infrastructure.  Based out of Dallas, Texas US area and currently working for one of his founded companies – Monolith Software.

Challenges Addressed by Machine Learning with AI

Now let us discuss the challenges addressed by machine learning with AI. As we learned in the previous blog, machine learning is excellent in model building. These operational models enable leveraging historical data. Artificial Intelligence is great at doing comparisons to produce results. Identifying anomalies and chronic with ease lowers effort requirements. So how does this help us? Lost plot by many, operations have discrete problems. Technology is only valuable if applied to problem areas. Here are some the common discussed areas I have seen in the marketplace — names withheld.

Normalizing Faults

Challenges Addressed by Machine Learning with AI
One common problem is the normalization of fault data. For example, SNMP traps are a very common fault format and protocol. Its binary format of enterprise and integer indicating a trap number. This requires human beings create database lookups (called MIBs) to provide descriptive detail of the fault. This discounts operational configurations like Up/Down correlation or aging configuration settings. Learning these configurations is a possible area for using machine learning technology. AI can compare common worded, complete configured trap types and guess what they should be. Human beings can right-click, update where applicable. The result would be a build-as-you-go rules engine curated on-the-fly. Many Managed Service Providers (MSP) find this interesting. Any organization with diverse and changing data set would find it valuable.

Correlating Faults

Challenges Addressed by Machine Learning with AI
Correlation is another concern. Operations need efficient ways to identify, isolate, and act on situations. They do not have time to discern association. As discussed, machine learning can identify a model of what is normal. The same model enabling the anomaly enables a set of context, which allows for reverse correlation. The allows anomalies to drive encapsulation of the situation.
 
Now looking forward, machine learning with AI can identify if this has happened before – or chronic detection. This opens the door to closing out chronic situations. Imagine a burst outage. Operations sees a failed service that clears – down, then back up. Most people end up ignoring these errors. The reason is that a final resolution is impossible to achieve. Unfortunately its uncommon to track them. If they re-occur, tools need to identify that they are not “blips” they are chronic. Long-term, the goal is to forecast them. That way preparations can occur to capture key data. The goal will be to engage to get final resolution of the chronic and prevent their repetition. With machine learning with AI, chronic detect and mitigation becomes possible — which we will discuss in more detail later.
 

Prioritizing Faults

Challenges Addressed by Machine Learning with AI
Another problem for operations is data overload. While a problem may be a root cause and an individualized “situation”, operations may not CARE. If a customer takes down their service, operations must make the logical choice to IGNORE a situation. Leaving this up to humans introduces human error. With machine learning you can understand that this problem common and should be identified as a chronic situation. This enrichment allows operations to re-prioritize new situations over chronic situations. This allows a more accurate report of what is going on with an operational priority assigned.
 
Operations also have a problem with reporting. Post-mortem analysis can encumber operations available to learn from failures. In a matter of minutes, machine learning with AI technology can scan years of raw data to see a the particular pattern. That pattern can segment what affect a situation had on the network. The bottom line is operations can report on the what, why, where, and how using machine learning with AI technologies.
Challenges Addressed by Machine Learning with AI

About the Author

Serial entrepreneur and operations subject matter expert who likes to help customers and partners achieve solutions that solve critical problems.   Experience in traditional telecom, ITIL enterprise, global manage service providers, and datacenter hosting providers.   Expertise in optical DWDM, MPLS networks, MEF Ethernet, COTS applications, custom applications, SDDC virtualized, and SDN/NFV virtualized infrastructure.  Based out of Dallas, Texas US area and currently working for one of his founded companies – Monolith Software.

The Technology of Machine Learning with AI

The technology of machine learning with AI should be our first focus. As with all new technology it has new terms and new concepts. Several of these are heretical to the status quo. Its important to set a proper context so we can have serious discussions.
 
Good to my word, here is the first blog in the series on machine learning with AI in service assurance. To explain some of the solutions in the marketplace, first we need to talk about the technology. The terms and concepts are new. The goals are the same for operations: increasing automation, increasing quality.

Defining Machine Learning

Technology of Machine Learning 
Let’s start on machine learning. First check out the wikipedia article. Below is the definition:
 
“Machine learning is a field of computer science that often uses statistical techniques to give computers the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.”
 
I summarize it like an ant farm. The farm, made by worker ants, creates the tunnels. Once done the model is complete. In this case, ants are the machine learning. As days go on and if necessary the ant farm will change. Then, say the farm falls over (oops). So the ants have to re-build and new pathways in the model, updating them. The fact is the ant farm is a always changing and learning from the environment. Like the ants, machine learning builds and maintain the patterns, or as I call it models. These models compare to the current situation in real-time. Artificial intelligence can see if they align (chronics) or diverge (anomalies).
 

Defining AI

Technology of Machine Learning
Now AI as defined by wikipedia as “the study of intelligent agents: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
 
The complexity of AI is a spectrum. On one side some AI is cognitive, virtual reasoning. On the other, AI is nothing more than rules processing. When in the context of machine learning, AI is usually applying or comparing models. This is what machine learning has learned with a separate set of data. In the context of service assurance, doing the comparison is the value. Comparing the past with present or projecting the future using the past. This provides analytics with insights.

Understanding Anomalies

 When performing a compare, either they align or diverge within some set degree. If the current situation is a repeat of the past, you have detected a chronic situation. When the current situation is new and unusual its called an anomaly. Datascience.com defines three types of anomalies:
 
  • Point anomalies: A single instance of data is anomalous if it’s too far off from the rest. Business use case: Detecting credit card fraud based on “amount spent.”
  • Contextual anomalies: The abnormality is context specific. This type of anomaly is common in time-series data. Imagine spending $100 on food every day during the holiday season is normal, but may be odd otherwise.
  • Collective anomalies: A set of data instances collectively helps in detecting anomalies. Imagine someone is trying to copy data form a remote machine to a local host. If this odd, an anomaly that would flag this as a potential cyber attack.
Consider chronics as a “negative” anomalies. Many customers I have talked to, see chronic detection being the most common AI tool to have. Both anomalies and chronics are important AI tools operations can use to better monitor their estate.
 

Focusing on Service Assurance

Technology of Machine Learning

This blog focuses on technology as it applies to service assurance (my major focus). Service assurance, as defined by wikipedia, is:
 
“The application of policies and processes by a Communications Service Provider to ensure that services offered over networks meet a pre-defined service quality level for an optimal subscriber experience.”
 
Otherwise defined as assuring the quality of the service. Two ways to increase the quality of your services. One, automate resolutions, thus reducing how much issues impact customers. Two, proactively addressing issues before they become problems and problems before outages.

Increasing Automation with Machine Learning

Increasing automation, reducing downtime, is always a goal for operations. Machine learning with AI tools focus on correlation. The ability to segment faults into new terms like “situations”. Addressing each situation is key, but they must be actionable. Any correlation would leverage a machine learning built model. Then compare that model against the current fault inventory to produce the segmentation. This is the current focus of the industry today with mixed results as we will discuss later.

Being Proactive with Machine Learning

 Being proactive is another buzzword from the late 90s — that was a decade ago right? The reality is its hard, you need to provide educated guesses. You can make quality guesses if you don’t have the data. Data reduction, prevalent in the industry, has caused operations to discard 99% (or more) of their data. Without this data your guesses will be poor. A machine learning model with AI can leverage low level information that may predict a future outage. That prediction will give operations lead time to fix the issue before an outage occurs. This is the hype focus on the industry today.
 
It is important to provide context when discussing machine learning with AI. This helps the us all understand the technology to enable the deeper discussion. The concepts and terms are new, but the industry hasn’t changed. Next up will be applying this technology to problems experienced by the industry.
Journey into Machine Learning AI

About the Author

Serial entrepreneur and operations subject matter expert who likes to help customers and partners achieve solutions that solve critical problems.   Experience in traditional telecom, ITIL enterprise, global manage service providers, and datacenter hosting providers.   Expertise in optical DWDM, MPLS networks, MEF Ethernet, COTS applications, custom applications, SDDC virtualized, and SDN/NFV virtualized infrastructure.  Based out of Dallas, Texas US area and currently working for one of his founded companies – Monolith Software.

My Journey into Machine Learning with AI

I’m alive! Sorry for the lengthy lapse in updates, but things have been so busy. With the release of some of my work as of late, I now have some time to share what I have been working on. As the title suggests, its all around machine learning and artificial intelligence (AI). Despite being hot buzzwords, there are tons of success stories using the technology today. To explain what I have learned, I have written a blog series. My hope is to cover my journey in machine learning with artificial intelligence.

Blog Focus: Machine Learning

Journey into Machine Learning AI
 
First focus is around the technology. As with all new technology it has new terms and new concepts. Several of these are heretical to the status quo, so its important to set a proper level set.  Then we want to discuss where it applies. What problems does it solve? How well do they solve them? How do the new solutions compare to legacy ones?

Solving Problems with Machine Learning

Journey into Machine Learning AI 
Now we have a firm introduction, lets solve some problems. First with the use case of fault storm management. How are storms detected and mitigated? What are the rewards of using ML/AI when applied?
 
My next favorite use case is around fault stream reduction. Fewer faults mean less effort for operations. Can ML/AI help? How well does it work? How hard is it to use?
 
Operational performance management is a touchy subject, but a worthwhile exercise. Why should you monitoring your NOC? How can help operations without being Orwellian about it?
 
Chronic Detection & Mitigation is a common use case for operations. How does operations iron out the wrinkles of their network?  Can operations know when to jump on a chronic to fix them for good. Getting to 99.999% is hard without addressing chronic problems.
 

What is the Future of Machine Learning

Journey into Machine Learning AI
As part of this series, we should address the future of this technology. With ML/AI being so popular, where should this technology be applied? How can it help with service assurance to make an impact.
 
The plan is to wrap up the series with a review of what is currently available in the marketplace. So we are all are aware of what is current versus what is possible.
 
Stay tuned, the plan is to release the blogs weekly. Don’t be afraid to drop comments or questions, as would love to do a AMA or blog on the questions.

Article Map

Journey into Machine Learning AI

About the Author

Serial entrepreneur and operations subject matter expert who likes to help customers and partners achieve solutions that solve critical problems.   Experience in traditional telecom, ITIL enterprise, global manage service providers, and datacenter hosting providers.   Expertise in optical DWDM, MPLS networks, MEF Ethernet, COTS applications, custom applications, SDDC virtualized, and SDN/NFV virtualized infrastructure.  Based out of Dallas, Texas US area and currently working for one of his founded companies – Monolith Software.

A Tale of Two Sales

Its been a long quarter, but to celebrate some of the successes I have had, I wanted to share.   Service assurance sales strategies is something everybody is looking for and most do not know how to achieve.   I get asked all the time, why does this sell and this do not.    To summate my response I decided to write a blog post — A Tale of Two Sales.

Sale A: Relationship Sale

Service assurance sales strategies

When customers or partner need help, they go to people they trust. If they have no one with answers they trust, they do RFI/RFPs. They do proof of concepts until they obtain trust. So many sales engagements is about trust – obtaining and fostering it. Once trust is there, the deal will only need to be palatable. Until trust is there, you must be patient and listen. This sales is also called a “consultive sale”. Customers must share their pain, their vendor must share their solutions. If there is a fit and proved, you have a match. This can take months or day, it all depends upon how open the engagement. Once the trust is there, its a matter of business case creation. Project approval and budget follow — sale done and dusted…

 

Sale B: Package Sale

Service assurance sales strategies

Same situation – a customer or partner in need. They have no trusted advisor relationship. Hello RFI/RFP! They get back a complete, documented, sourced, and independently verified business case. This “known” quantity details provides all the tangibles. This includes ROI projections, feature/benefit, comparison studies, and case studies. The entire business case with minor adjustments required. I call this the package sale. Sales makes the pitch and leaves the bank with the customer. References provide the trust. The customer can go to their management assured in their support. The quality of the deliverable is everything. Management agrees, project approval and budget follow. Sale is complete.

 

For you buyers out there…

Sales is hard, be nice do your sales rep.    Tell them what you want and why you want it.   They can get it for you if you ask.   If they every tell you false information, stop talking to them.    Its not worth putting your career at risk.   Even for a slightly superior product.

 

For you sellers out there…

Its all about the benefits.   Completely documented and third party certified benefits.    Features are for demos, benefits are for closers.   Customer and partners buy solutions to solve headaches or get promotions.    Its you job to help on both accounts.

I personally believe it does not matter which strategy is used.   Service assurance sales strategies are not complex, you just have to listen to your customer.    The customer will guide you and if you do it right, you will have both deliverables: the trust and business case.

Service assurance sales strategies

About the Author

Serial entrepreneur and operations subject matter expert who likes to help customers and partners achieve solutions that solve critical problems.   Experience in traditional telecom, ITIL enterprise, global manage service providers, and datacenter hosting providers.   Expertise in optical DWDM, MPLS networks, MEF Ethernet, COTS applications, custom applications, SDDC virtualized, and SDN/NFV virtualized infrastructure.  Based out of Dallas, Texas US area and currently working for one of his founded companies – Monolith Software.