Introduction
As AI adoption accelerates throughout industries, companies face an plain reality — AI is simply as highly effective as the info that fuels it. To really harness AI’s potential, organizations should successfully handle, retailer, and course of high-scale information whereas guaranteeing value effectivity, resilience, efficiency and operational agility.
At Cisco Assist Case Administration – IT, we confronted this problem head-on. Our crew delivers a centralized IT platform that manages your complete lifecycle of Cisco product and repair instances. Our mission is to offer clients with the quickest and handiest case decision, leveraging best-in-class applied sciences and AI-driven automation. We obtain this whereas sustaining a platform that’s extremely scalable, extremely accessible, and cost-efficient. To ship the absolute best buyer expertise, we should effectively retailer and course of large volumes of rising information. This information fuels and trains our AI fashions, which energy vital automation options to ship sooner and extra correct resolutions. Our greatest problem was hanging the proper steadiness between constructing a extremely scalable and dependable database cluster whereas guaranteeing value and operational effectivity.
Conventional approaches to excessive availability usually depend on separate clusters per datacenter, resulting in important prices, not only for the preliminary setup however to keep up and handle the info replication course of and excessive availability. Nevertheless, AI workloads demand real-time information entry, speedy processing, and uninterrupted availability, one thing legacy architectures wrestle to ship.
So, how do you architect a multi-datacenter infrastructure that may persist and course of large information to assist AI and data-intensive workloads, all whereas holding operational prices low? That’s precisely the problem our crew got down to remedy.
On this weblog, we’ll discover how we constructed an clever, scalable, and AI-ready information infrastructure that permits real-time decision-making, optimizes useful resource utilization, reduces prices and redefines operational effectivity.
Rethinking AI-ready case administration at scale
In right this moment’s AI-driven world, buyer assist is now not nearly resolving instances, it’s about constantly studying and automating to make decision sooner and higher whereas effectively dealing with the price and operational agility.
The identical wealthy dataset that powers case administration should additionally gasoline AI fashions and automation workflows, decreasing case decision time from hours or days to mere minutes, which helps in elevated buyer satisfaction.
This created a basic problem: decoupling the first database that serves mainstream case administration transactional system from an AI-ready, search-friendly database, a necessity for scaling automation with out overburdening the core platform. Whereas the concept made excellent sense, it launched two main issues: value and scalability. As AI workloads develop, so does the quantity of knowledge. Managing this ever-expanding dataset whereas guaranteeing excessive efficiency, resilience, and minimal handbook intervention throughout outages required a wholly new strategy.
Fairly than following the normal mannequin of deploying separate database clusters per information heart for top availability, we took a daring step towards constructing a single stretched database cluster spanning a number of information facilities. This structure not solely optimized useful resource utilization and diminished each preliminary and upkeep prices but in addition ensured seamless information availability.
By rethinking conventional index database infrastructure fashions, we redefined AI-powered automation for Cisco case administration, paving the way in which for sooner, smarter, and more cost effective assist options.
How we solved it – The know-how basis
Constructing a multi-data heart fashionable index database cluster required a strong technological basis, able to dealing with high-scale information processing, ultra-low latency for sooner information replication, and cautious design strategy to construct a fault-tolerance to assist excessive availability with out compromising efficiency, or cost-efficiency.
Community Necessities
A key problem in stretching an index database cluster throughout a number of information facilities is community efficiency. Conventional excessive availability architectures depend on separate clusters per information heart, usually combating information replication, latency, and synchronization bottlenecks. To start with, we carried out a detailed community evaluation throughout our Cisco information facilities specializing in:
- Latency and bandwidth necessities – Our AI-powered automation workloads demand real-time information entry. We analyzed latency and bandwidth between two separate information facilities to find out if a stretched cluster was viable.
- Capability planning – We assessed our anticipated information progress, AI question patterns, and indexing charges to make sure that the infrastructure may scale effectively.
- Resiliency and failover readiness – The community wanted to deal with automated failovers, guaranteeing uninterrupted information availability, even throughout outages.
How Cisco’s high-performance information heart paved the way in which
Cisco’s high-performance information heart networking laid a robust basis for constructing the multi-data heart stretch single database cluster. The latency and bandwidth supplied by Cisco information facilities exceeded our expectation to confidently transfer on to the following step of designing a stretch cluster. Our implementation leveraged:
- Cisco Software Centric Infrastructure (ACI) – Supplied a policy-driven, software-defined community, guaranteeing optimized routing, low-latency communication, and workload-aware visitors administration between information facilities.
- Cisco Software Coverage Infrastructure Controller (APIC) and Nexus 9000 Switches – Enabled high-throughput, resilient, and dynamically scalable interconnectivity, essential for fast information synchronization throughout information facilities.
The Cisco information heart and networking know-how made this doable. It empowered Cisco IT to take this concept ahead and enabled us to construct this profitable cluster which saves important prices and offers excessive operational effectivity.
Our implementation – The multi-data heart stretch cluster leveraging Cisco information heart and community energy
With the proper community infrastructure in place, we got down to construct a extremely accessible, scalable, and AI-optimized database cluster spanning a number of information facilities.
Cisco multi-data heart stretch Index database cluster
Key design choices
- Single logical, multi-data heart cluster for real-time AI-driven automation – As a substitute of sustaining separate clusters per information heart which doubles prices, will increase upkeep efforts, and calls for important handbook intervention, we constructed a stretched cluster throughout a number of information facilities. This design leverages Cisco’s exceptionally highly effective information heart community, enabling seamless information synchronization and supporting real-time AI-driven automation with improved effectivity and scalability.
- Clever information placement and synchronization – We strategically place information nodes throughout a number of information facilities utilizing customized information allocation insurance policies to make sure every information heart maintains a singular copy of the info, enhancing excessive availability and fault tolerance. Moreover, regionally hooked up storage disks on digital machines allow sooner information synchronization, leveraging Cisco’s strong information heart capabilities to attain minimal latency. This strategy optimizes each efficiency and cost-efficiency whereas guaranteeing information resilience for AI fashions and important workloads. This strategy helps in sooner AI-driven queries, decreasing information retrieval latencies for automation workflows.
- Automated failover and excessive availability – With a single cluster stretched throughout a number of information facilities, failover happens robotically because of the cluster’s inherent fault tolerance. Within the occasion of digital machine, node, or information heart outages, visitors is seamlessly rerouted to accessible nodes or information facilities with minimal to no human intervention. That is made doable by the strong community capabilities of Cisco’s information facilities, enabling information synchronization in lower than 5 milliseconds for minimal disruption and most uptime.
Outcomes
By implementing these AI-focused optimizations, we ensured that the case administration system may energy automation at scale, scale back decision time, and keep resilience and effectivity. The outcomes had been realized shortly.
- Quicker case decision: Lowered decision time from hours/days to only minutes by enabling real-time AI-powered automation.
- Value financial savings: Eradicated redundant clusters, slicing infrastructure prices whereas enhancing useful resource utilization.
- Infrastructure value discount: 50% financial savings per quarter by limiting it to at least one single-stretch cluster, by finishing eliminating a separate backup cluster.
- License value discount: 50% financial savings per quarter because the licensing is required only for one cluster.
- Seamless AI mannequin coaching and automation workflows: Offered scalable, high-performance indexing for steady AI studying and automation enhancements.
- Excessive resilience and minimal downtime: Automated failovers ensured 99.99% availability, even throughout upkeep or community disruptions.
- Future-ready scalability: Designed to deal with rising AI workloads, guaranteeing that as information scales, the infrastructure stays environment friendly and cost-effective.
By rethinking conventional excessive availability methods and leveraging Cisco’s cutting-edge information heart know-how, we created a next-gen case administration platform—one which’s smarter, sooner, and AI-driven.
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