Introduction
As AI adoption accelerates throughout industries, companies face an plain reality — AI is simply as highly effective as the information that fuels it. To actually harness AI’s potential, organizations should successfully handle, retailer, and course of high-scale information whereas making certain value effectivity, resilience, efficiency and operational agility.
At Cisco Help Case Administration – IT, we confronted this problem head-on. Our crew delivers a centralized IT platform that manages the complete lifecycle of Cisco product and repair circumstances. Our mission is to offer clients with the quickest and simplest 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 huge volumes of rising information. This information fuels and trains our AI fashions, which energy important automation options to ship quicker and extra correct resolutions. Our largest problem was putting the best steadiness between constructing a extremely scalable and dependable database cluster whereas making certain value and operational effectivity.
Conventional approaches to excessive availability usually depend on separate clusters per datacenter, resulting in vital prices, not only for the preliminary setup however to keep up and handle the information replication course of and excessive availability. Nonetheless, 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 huge information to assist AI and data-intensive workloads, all whereas maintaining operational prices low? That’s precisely the problem our crew got down to clear up.
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 as we speak’s AI-driven world, buyer assist is now not nearly resolving circumstances, it’s about repeatedly studying and automating to make decision quicker and higher whereas effectively dealing with the price and operational agility.
The identical wealthy dataset that powers case administration should additionally gas 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 thought made good sense, it launched two main considerations: value and scalability. As AI workloads develop, so does the quantity of information. Managing this ever-expanding dataset whereas making certain excessive efficiency, resilience, and minimal handbook intervention throughout outages required a completely new method.
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 lowered 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 quicker, smarter, and more cost effective assist options.
How we solved it – The expertise basis
Constructing a multi-data heart trendy index database cluster required a sturdy technological basis, able to dealing with high-scale information processing, ultra-low latency for quicker information replication, and cautious design method 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 performed 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 might scale effectively.
- Resiliency and failover readiness – The community wanted to deal with automated failovers, making certain 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 powerful basis for constructing the multi-data heart stretch single database cluster. The latency and bandwidth offered by Cisco information facilities exceeded our expectation to confidently transfer on to the following step of designing a stretch cluster. Our implementation leveraged:
- Cisco Utility Centric Infrastructure (ACI) – Provided a policy-driven, software-defined community, making certain optimized routing, low-latency communication, and workload-aware visitors administration between information facilities.
- Cisco Utility 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 expertise made this attainable. It empowered Cisco IT to take this concept ahead and enabled us to construct this profitable cluster which saves vital prices and supplies excessive operational effectivity.
Our implementation – The multi-data heart stretch cluster leveraging Cisco information heart and community energy
With the best 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 an alternative of sustaining separate clusters per information heart which doubles prices, will increase upkeep efforts, and calls for vital 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 novel copy of the information, enhancing excessive availability and fault tolerance. Moreover, regionally hooked up storage disks on digital machines allow quicker information synchronization, leveraging Cisco’s sturdy information heart capabilities to attain minimal latency. This method optimizes each efficiency and cost-efficiency whereas making certain information resilience for AI fashions and significant workloads. This method helps in quicker 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 mechanically 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 attainable by the sturdy 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 might energy automation at scale, cut back decision time, and keep resilience and effectivity. The outcomes have been realized shortly.
- Quicker case decision: Diminished decision time from hours/days to simply minutes by enabling real-time AI-powered automation.
- Value financial savings: Eradicated redundant clusters, reducing infrastructure prices whereas bettering useful resource utilization.
- Infrastructure value discount: 50% financial savings per quarter by limiting it to 1 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, making certain 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 expertise, we created a next-gen case administration platform—one which’s smarter, quicker, and AI-driven.
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