DRYiCE partners with DagKnows to infuse AI-powered troubleshooting and NLP capabilities in its flagship product, DRYiCE NetBot

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November 08, 2021

Enterprise networks are on the path of perennial evolution and so is the complexity of running and managing those networks, what worked yesterday may be obsolete next month. The challenge is compounded by the rapid pace with which new devices are being added to the network, ranging from simple network elements and computing devices to sophisticated patient monitors, IoT sensors, smart home appliances, and more.

The ever-growing demand of cloud-based (public/private) applications is adding proportional stress on the scale and complexity of the underlying infrastructure. This has never been as pronounced as in last year when the pandemic created a sudden and massive shift towards digital transformation across organizations. Dealing with infrastructure issues at this scale isn’t feasible by just scaling the workforce.

The most recent case in point being the network outage experienced by Facebook on October 4th, 2021. The six-hour outage cost the company an estimated $60 MN in ad revenue losses not to mention the bad PR globally1. We need smarter tools that can effortlessly automate tasks and effectively use advanced technologies such as AI/ML/NLP to identify and resolve problems and prevent them from reoccurring.

For a ‘digital-first’ enterprise, it is imperative that its network infrastructure is always available and the issues that plague the network get resolved without causing adverse impact on business operations.

Automate network operations, what else?

DRYiCE NetBot, a network automation product empowers enterprises to manage the entire lifecycle of network devices from provisioning to change management, security, and compliance. With NetBot, DRYiCE provides autonomous network capabilities, such as network self-healing, auto remediation, automated change management, automated event management, automated configuration changes and provisioning.

NetBot, by virtue of its advanced cognitive automation capabilities, is a significant enabler for the NetOps teams. However, through our customer interactions, we realized that the next area where we can apply the benefits of AI and automation is the troubleshooting aspect which takes a large chunk of a network engineer’s time.

Today’s multi-vendor, multi-domain, and multi-cloud network environments are getting extremely complicated to troubleshoot as compared to the traditional on premise networks of the past. Lack of network visibility and control leads to longer network troubleshooting time for NetOps teams and prevents them from working on strategic business impacting projects.

 In tune with the market and customer requirements of an elaborate network troubleshooting offering, HCL has announced a partnership with DagKnows, a Silicon Valley based start-up, to strengthen NetBot with an underlying AI troubleshooting engine. The AI capabilities from DagKnows will help on board AI based troubleshooting, causal analysis and an NLP based vendor agnostic search interface in the NetBot solution stack.

The partnership synergy

Partnering with DagKnows gives NetBot an AI-enabled troubleshooting platform that helps troubleshoot even the most intricate network problems. Its AI-enabled automation engine helps administrators troubleshoot network problems by collaborating in real-time through a built-in conversational interface. This not only brings down the troubleshooting times (MTTR) considerably but also saves hundreds of hours of manual effort.

There are many tools out there that claim to troubleshoot and remediate issues quickly or to conduct operations effectively. Several products in this space under the AIOps umbrella ingest log files, alerts, events with the goal to discover patterns and reduce the alert fatigue. However, these tools have failed to deliver on the promise of automatic root-cause analysis. That’s because inference of causality from correlated events is a hard problem! AIOps tools need to be complemented with other tools that bring the ‘human’ expertise into play.

NetBot with DagKnows is focused on effectively harnessing human knowledge and expertise. When human knowledge is captured, organized, structured, and automated, most of the problems can be solved quickly and more efficiently. Structured knowledge not only paves the way for automation but also helps the organizations to elevate their team’s expertise. This gives NetBot a unique competitive advantage.

With the integration of DagKnows, NetBot now boasts of extensive levels of customization, competitive pricing and use cases driven by AI/ML constructs that very few competitors in the market can match.

Technical purview of the new offering

Human expertise in systematic troubleshooting is the key to solving complex infrastructure problems. DagKnows uses a construct called Directed Acyclic Graph (DAG) to model the cause and effect relationships in a problem space by leveraging human expertise. DagKnows helps users build these troubleshooting DAGs automatically in their act of troubleshooting. More importantly, it enables users to turn the knowledge DAGs into executable runbooks using a low-code automation framework.

DagKnows is integrated in NetBot as a troubleshooting module. NetBot’s native capabilities enable users to orchestrate various Ops activities, troubleshooting and remediation being one of them. When a ticket lands in ITSM systems such as ServiceNow or Jira, NetBot can extract the relevant contextual data from the ticket and invoke the automation runbooks (DAGs) available in DagKnows. These DAGs automatically troubleshoot the issue and in some cases remediate them as well.

Alternatively, the remediation process can wait for manual inputs. If the issue can’t be debugged all the way to the root cause, DagKnows will triage it and provide a wealth of contextual information saving valuable time to the users. Users can dig deeper into this data, potentially discovering a new cause of the problem which can be automatically inserted into the DAG knowledge base without switching the context. 

When issues need deeper expertise, DagKnows creates a workspace for experts and the front-line users to share the context and resolve it collaboratively. Thus, DagKnows combines three fundamental aspects of effective ops in one platform: real-time collaboration, intelligent knowledge base, and automation.

The partners

HCL Technologies is a next-generation global technology company that helps enterprises reimagine their businesses for the digital age. Through its worldwide network of R&D facilities and co-innovation labs, global delivery capabilities, and over 176,000+ ‘Ideapreneurs’ across 50 countries, HCL delivers holistic services across industry verticals to leading enterprises, including 250 of the Fortune 500 and 650 of the Global 2000. DRYiCE™ is a dedicated and rapidly growing organic software division of HCL that delivers best-in-class implementations of AI for enterprises.

DagKnows, Inc. is a Silicon Valley start-up bringing a unique approach to capture and utilize human expertise for solving automation challenges faced by DevOps, ITOps, and SRE teams. The DagKnows platform leverages cutting edge technologies like NLP, ML, and Graph Databases to transform institutional Ops knowledge into automation runbooks. These effortlessly and automatically curated runbooks enable users to build a more reliable DevOps process that minimizes repetitive work. Thus, DagKnows plays a pivotal role in accelerating the DevOps journey within organizations.

To know more about the offering drop us a line here.

 

Sources:

1. https://cnet.co/3womT4W

Mrinal Banerji

Mrinal Banerji, Product Manager, DRYiCE NetBot

Mrinal Banerji is a part of the DRYiCE Product Management Group. He is responsible for formulating product roll-out strategies, GTM plan, and new alliances. He has close to 10 years of experience into strategy consulting, counterparty due diligence, competitive and customer intelligence, sales enablement, and business intelligence for global technology clients. Prior to HCL, Mrinal has worked with Evalueserve, KPMG, Ericsson, and IBM in various capacities.