Introduction
Fans of science fiction books or movies have all experienced a sequence when someone who has not yet committed a crime is arrested because AI predicts they will in the coming hours or days. Can this impressive outcome be experienced today for networks?
Continue reading “Preventing failures with Machine Learning – is that possible? Anatomy of a predictive optical anomaly “Mark Seery
Augtera Networks
mark@augtera-com
website: www.augtera.com
Augtera Networks Accelerates Adoption of Multi-Cloud Solutions with Partners
Palo Alto, CA (Business Wire): Augtera Networks, the industry leader in AI/ML-powered Network Operations Solutions, today announced that Allan Rickhi has joined as Vice President of Channels and Alliances. Allan’s responsibilities will include accelerating Augtera adoption through existing partners and developing new alliances.
Allan brings over twenty years of Sales Leadership experience to Augtera Networks selling to both the Private and Public Sectors having held key roles at Dell Technologies, Cumulus, Plexxi, Crossbeam, Unisphere and Bay Networks. Most recently Allan was the North American Sales Leader responsible for Dell Technologies Select where he led double digit growth for four quarters in a row across the DT Select and Large Enterprise segments including: Hyperscalers, Retail, Financials, Manufacturing, and Telcos.
“Enterprises increasingly want Augtera Network AI integrated with their broader data center and multi-cloud compute, storage and network ecosystems.” said Rahul Aggarwal, Founder and CEO of Augtera Networks. “We are seeing growing production adoption of our joint solutions with strategic partners like Dell and AMD and are thrilled to have Allan join us to accelerate this adoption.”
As IT complexity increases, solution partners that understand and support complex ecosystems are essential. Augtera’s strategic partners have massive market adoption and expertise in servers, storage, networking, composable architectures, distributed network services, virtual overlay networks, high performance computing, and hyperconverged architectures. For IT teams looking for AIOps enabled compute, networking, and storage in one solution, Augtera’s partners are best positioned to meet their needs.
“I am honored to join the Augtera Networks team and enable our strategic partners in using best in breed AI/ML to automate network observability and deliver on the full promise of DevOps.” said Allan Rickhi, VP of Channels and Alliances for Augtera Networks. “Enterprises can no longer compete in an era defined by AI using Network Performance Monitoring tools designed 30 years ago – this will be a game changer for Network Operations teams, radically reducing their workload and improving KPIs”.
About Augtera Networks
Augtera Networks eliminates noise, enables proactive operations, and prevents incidents, for Enterprise and Service Provider networks. The first AI/ML-powered network operations platform, Augtera is being used by hyperscale cloud platforms, financial institutions, communications service providers, managed service providers, and enterprises in multiple verticals. Additional information can be found at www.augtera.com
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I met Rahul Aggarwal in June of 2022 and took an immediate liking to the team. Early in a joint customer engagement, it became very clear that there was something special here – not just special but BIG. The customer knew it too. Little did I know it would lead to a dramatic personal change as well.
The BIG is AI, the special is that it will dramatically disrupt the networking industry that Cisco has dominated for 30 years in ways that white box, disaggregation, and software defined will never match. Augtera is an AI platform purpose-built for proactively identifying network disruptions and dramatically reducing downtime. Soon, Augtera will use its advanced AI/ML to provide suggested mitigations for some common network problems. Over time, we will instantly and automatically solve them with little to no human intervention.
Our large-scale enterprise and service provider customers are already seeing 90%+ reduction in time to detect, 55%+ reduction in mitigation times, and 4x increase in time between revenue impacting incidents and we are only just getting started.
I’ve been fortunate to have worked with some great leaders and visionaries, helping to bring disruption to the world of networking, supporting our war fighters in the most critical of missions, and building lifelong relationships along the way. Although I had just met him, I knew Rahul was going to be another one of those leaders.
SONiC and Dell’s work there will be transformative to the networking world, but it won’t solve the reality that networks have surpassed Human Scale. Augtera already does. At Dell, I built the best team I have ever worked with and was well positioned for continued success – but after six months of sleepless nights it was obvious – the time to move was now.
Many Network Performance Monitoring (NPM) tools are out there; some of which have been the standard of IT organizations since Bill Clinton’s first term – Many are proprietary, victimizing customers with vendor lock; while some network vendors even have multiple platforms that are incompatible with each other. From the perspective of being able to look backward whether it is visualizing the network, monitor SNMP and syslog and even analyze flows; these legacy tools are static, failed to evolve and certainly are incapable of surviving, much less thriving in the era of AI/ML. No one will ever confuse legacy NPM with “Best of Breed” anything, especially when it comes to protecting the security of their customers’ most trusted assets.
Most enterprises have focused on the front end. Tools like Ansible and other Infrastructure such as Code Platforms focus on provisioning, configuration management and deployment. Once they are up and running, users are on their own setting thresholds and watching reruns of networking in the 90’s.
These days, every network and infrastructure vendor have some fancy GUI or widget that tells the user the “health” of the environment. It may even tell you when something breaks – but what it does not do is determine mean time to innocence or root cause analysis across silos. There is nothing agile or orchestrated about waiting for a dashboard to update after the phone calls and escalations have already started pouring in from end users.
The elephant in the room is that there is no “automated” way to understand what is going on in these “automated” environments. Understanding the IT infrastructure of any modern enterprise has moved way beyond Human Scale.
There are billions of logs and metrics being created daily in a Fortune 500 environment and this is without even considering SD-WAN and Multi-Cloud connectivity. During a network outage or a security breach – teams do not have the time to sift through data manually one machine at a time only to deliver a root cause analysis 6 weeks later. Shareholders and customers are NOT that patient.
Augtera transforms IT operations by automating the observability of the network. We do that by using best in breed AI/ML – learning the network automatically from all dimensions and normalizing the data in a single model that predicts outages and downtime regardless of where it’s located in the network. Automating the observability of the network is the next step in any digital transformation, shifting the value of network operations from reactive to valuing prevention and preparation when and if the unfortunate happens.
According to a recent survey done by Enterprise Management Associates, 81% of Network Practitioners do not associate AIOps / Machine Learning with Observability.
AIOps that is purpose-built for Networking, is a generational step forward, going well beyond monitoring and observability to achieve new outcomes: dramatic reduction in alert / trouble ticket fatigue, multi-layer incident root identification, cloud-native geo-diverse scaling & HA, automation playbooks, and more.
There are some metrics and signatures for which thresholds / rules work well. These are static definitions that are always “true”. For example, a Network Operations team may have a policy that it wants to be alerted whenever packet loss goes above 1%. On the other hand, setting and maintaining latency thresholds for links with varying distances and loads, is time consuming, prone to error, and often produces many false positives, or false negatives, depending on how they are set. AIOps machine learning algorithms learn patterns and report meaningful / relevant deviations from patterns that humans have difficulty spotting. Learn more about thresholds and ML algorithms in our previous blog: Machine Learning Anomaly Detection – Beyond Thresholds.
It’s no secret that one of the biggest problems plaguing network operations teams is the number of generated alerts / trouble tickets from an average of 4-10 networking tools per team. Network AIOps is a game changer. One source of false positive reduced truth, across all data sources, of what is an anomaly, what is a redundant alert / trouble ticket, and what is the lowest layer incident root object. ML algorithms that produce less false positives. Multi-layer autocorrelation that reduces redundant alarms / tickets, and better models’ relationships across all layers, to identify incident root. Learn more about Noise elimination in our previous blog: Noise Elimination in Network AI and the case study: Fortune 500 Data Center Solution.
Software platforms / services built in the last-few years are fundamentally different than those built a decade or more ago, and/or cobbled together from tens of acquisitions. Today’s leading-edge Network AIOps platforms have cloud-native and cloud-independent architectures enabling horizontal scaling, of geo-diverse active-active high availability architectures, as SaaS, Hybrid Cloud, or On-prem deployments. SaaS reduces many startup and management costs for teams that value those characteristics, while on-prem meets the needs of those companies that are not comfortable with their data being in the “cloud”. Flexible deployment, geo-diverse high availability, and scalability are just some of the attributes of next generation software architectures.
Automation is the end goal of Network Operations today. Agility, productivity, and error-reduction. However, every team is taking this journey at their own pace. Leading Network AIOps tools will have a “plug-in” architecture that allows customers to consume events, alerts, and other operations data, so their own automation playbooks can decide next steps.
There are many log solutions that retain every log message, for long periods of time, with the ability to query archives. Great for compliance, deep-dive analysis of difficult to understand issues, and more. However, this is not leveraging logs for real-time anomaly detection, metric extraction, burst/rate-change detection, rare message detection, signature matching, and multi-layer, multi-data type auto correlation, at the speed of streaming data. That requires a totally different architecture from a logic, performance, and efficiency perspective. Learn more about how the Augtera Network AI approach to log analysis is different than existing log solutions in our previous blog: LogAI vs Existing Log Solutions.
Network AIOps has some overlap with IT AIOps, however it is significantly different, purpose-built for the Network Operations mission.
To give some examples of a difference this makes to auto-discover the physical, optical, Ethernet, and IP topologies, it is important to have a robust SNMP collection capability. There is often no equivalent dynamic REST API capability. Similarly, IT AIOps solutions do not support network-specific interfaces like gRPC / gNMI / OpenConfig. Another good example is to connect the dots between Application Experience problems and Network issues, requires a robust collection and analysis capability for flow data – a difficult to develop capability that requires a network focus. Even log analysis requires a network focus.
Network AIOps focuses on Network use cases. That focus drives development of the necessary interfaces, constructs, equipment types, suppliers, partnerships, algorithms, models, and types of anomalies.
Network Operations teams have multiple significant Network Tool Complaints. Addressing these complaints requires a clean page AI/ML, cloud-native approach that delivers significant improvements well beyond monitoring and observability. This is the vision of Augtera’s Network AI platform/service, to go beyond reactive Network Operations, and create a new era of preventative capabilities, so incidents can be remediated before they even occur, where Network Operations responses are automated, and where simplicity can be returned to Network Operations by integrating all Network Operations data into one anomaly detection and incident root identification platform. This is made possible, today, because of the general progress made by all of IT in AI/ML, and by purpose-built solutions for Network Operations.
Augtera’s Network AI is a monitoring, observability, and AIOps platform with production proven benefits:
Network Operations tools need to understand multi-layer topology, network equipment, and network data sources. This is distinct from generic IT AIOps tools. Augtera’s Network AI has all this in addition to 9+ AI/ML algorithms purpose-built for Network distributions and anomalies.
While Network AI is designed as a Network Management and Automation solution, it can be used for monitoring as well. The powerful UI / visualizations can also be used to triage complex incidents.
Augtera’s Network AI has recognized growing anomalies three days before outages have happened, enabling preventative action. Machine learning recognizes operationally relevant deviation from normal patterns that can often not be recognized by looking at a visualizaiton. This is particularly important for metrics such as latency, environmental degradation, optical levels, and TCP errors.
Network AI collects all network operations data, detects anomalies, correlates to reduce redundancy and identify incident root, with multiple notification options including Slack, Teams, ServiceNow, and customer-written automation. This enables Network Operations teams to dramatically reduce the number of outstanding Trouble Tickets, focus on operationally relevant incidents, prevent future incidents, and improve overall relaibility.
Regardless of where Network Operations teams are on their monitoring, observability, and AIOPs journey, Network AI is the leading platform, available on-Prem or as SaaS. It is being used by Service Providers such as Orange, Colt, and Vyve Broadand. It is also being used by Fortune 500 eCommerce, Financial, Retail, and Transport Enterprises.
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