by Balaji Parthasarathy, Director, Enterprise Architecture and Experience Design, Business IT
VMware IT has been experiencing an increasing demand to scale and support a complex footprint of business applications and technologies. In the past three years, the company accelerated its delivery from 80+ to 120+ features/week due to continuous integration/continuous development (CI/CD) and DevOps best practices.
Scaling the L2 application support operations was key to managing this growth. Analysts spent 80% of their time in reactive support: system and application monitoring, standard operating procedure (SOP)-based issue resolution, and log and alert triaging. IT needed a new, more scalable way to increase analyst productivity and responsiveness.
IT developed a Next-Generation (Next-Gen) Support Framework to deliver preemptive support, including issue avoidance or instant, no-touch issue resolution to streamline the response process. The framework enabled VMware’s Auto-Support application for L2 customer support to change service delivery in three areas: reduced time to response and improved quality of response and customer satisfaction.
Architecture and Implementation
Auto-Support uses the VMware vRealize Suite to help automate the application ticket and service request resolution. The application Is comprised of four main components:
- vRealize Automation to govern the approval policies and approval routing in the integrated developer experience area of the central application plane
- vRealize Orchestrator to orchestrate actions and remediation across apps
- vRealize Log Insight to capture the real-time logs and auditing of the application
- An underlying communication and a collaboration platform and ticketing system for auto ticket logging and resolution
The Auto-Support process works like this. Issues are generated through ticketing systems, issue logs, social media channels, and chat bots. vRealize Automation analyzes the issue, then, based on the patterns, determines if the issue can be auto-resolved or requires a manual resolution.
- Auto-resolved. Auto-Support uses a rule-based triaging and resolution framework that supports 30+ issues for automated resolution in domains ranging from order management to finance and entitlement management. If needed, Auto-Support can also route issues for additional approvals before the resolution is implemented using a threshold of confidence levels.
- Analyst intervention. Auto-Support takes advantage of automated case routing, which routes the ticket to the right support analyst based on pattern-learning using a Naïve Bayes algorithm. Auto-Support also features a recommendation engine that uses term frequency–inverse document frequency (TFIDF) and Cosine Similarity to provide analysts with an importance rating a well as insights from past related cases, reducing response time.
A vital feature of the framework is its incorporation of emerging technologies, such as artificial intelligence (AI), machine learning, natural language processing (NLP), and chat bots to streamline the ticket routing process. For example, the chat bot uses rule-based resolution to understand and interpret the issue and find information to speed resolution. Pattern and machine learning enable intelligence improvements over time.
Since the implementation of Auto-Support based on this framework, IT has benefitted in several ways:
- An integrated experience across various issue inflow sources, including systems, logs, and chat bots
- Extensibility of Auto-Support across multiple applications and end-points
- Greater agility from streamlined processing and analysis
- Support for a high volume of feature requests in a 24×7 operation
- Scalability, unlike traditional models
The Next-Gen Support Framework has radically transformed VMware’s system and application support operations. Since its adoption three years ago, we achieved a ticket avoidance of ~20% and stabilized at a rate of 15% Year over Year. The results are even more amazing considering that more than 125 new features were introduced each week last year. The platform has proven that it is capable of handling continued growth and scalability while maintaining our high commitment to responsiveness, quality, and customer satisfaction.
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