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Introduction
As AI adoption accelerates throughout industries, companies face an plain fact — 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 knowledge whereas guaranteeing price effectivity, resilience, efficiency and operational agility.
At Cisco Support Case Management – IT, we confronted this problem head-on. Our workforce delivers a centralized IT platform that manages your entire lifecycle of Cisco product and repair circumstances. Our mission is to supply prospects with the quickest and best case decision, leveraging best-in-class applied sciences and AI-driven automation. We obtain this whereas sustaining a platform that’s extremely scalable, extremely obtainable, and cost-efficient. To ship the very best buyer expertise, we should effectively retailer and course of huge volumes of rising knowledge. This knowledge fuels and trains our AI fashions, which energy important automation options to ship quicker and extra correct resolutions. Our largest problem was placing the correct stability between constructing a extremely scalable and dependable database cluster whereas guaranteeing price and operational effectivity.
Traditional 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 info replication course of and excessive availability. However, AI workloads demand real-time knowledge entry, fast processing, and uninterrupted availability, one thing legacy architectures battle to ship.
So, how do you architect a multi-datacenter infrastructure that may persist and course of huge knowledge to assist AI and data-intensive workloads, all whereas preserving operational prices low? That’s precisely the problem our workforce got down to resolve.
In this weblog, we’ll discover how we constructed an clever, scalable, and AI-ready knowledge 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 fee and operational agility.
The similar 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. While the thought made excellent sense, it launched two main considerations: price 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 method.
Rather than following the normal mannequin of deploying separate database clusters per knowledge middle for top availability, we took a daring step towards constructing a single stretched database cluster spanning a number of knowledge facilities. This structure not solely optimized useful resource utilization and decreased each preliminary and upkeep prices but in addition ensured seamless knowledge availability.
By rethinking conventional index database infrastructure fashions, we redefined AI-powered automation for Cisco case administration, paving the best way for quicker, smarter, and cheaper assist options.
How we solved it – The know-how basis
Building a multi-data middle trendy index database cluster required a sturdy technological basis, able to dealing with high-scale knowledge processing, ultra-low latency for quicker knowledge replication, and cautious design method to construct a fault-tolerance to assist excessive availability with out compromising efficiency, or cost-efficiency.
Network Requirements
A key problem in stretching an index database cluster throughout a number of knowledge facilities is community efficiency. Traditional excessive availability architectures depend on separate clusters per knowledge middle, usually fighting knowledge replication, latency, and synchronization bottlenecks. To start with, we carried out a detailed community evaluation throughout our Cisco knowledge facilities specializing in:
- Latency and bandwidth necessities – Our AI-powered automation workloads demand real-time knowledge entry. We analyzed latency and bandwidth between two separate knowledge facilities to find out if a stretched cluster was viable.
- Capacity planning – We assessed our anticipated knowledge 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, guaranteeing uninterrupted knowledge availability, even throughout outages.
How Cisco’s high-performance knowledge middle paved the best way
Cisco’s high-performance knowledge middle networking laid a powerful basis for constructing the multi-data middle stretch single database cluster. The latency and bandwidth supplied by Cisco knowledge facilities exceeded our expectation to confidently transfer on to the subsequent step of designing a stretch cluster. Our implementation leveraged:
- Cisco Application Centric Infrastructure (ACI) – Offered a policy-driven, software-defined community, guaranteeing optimized routing, low-latency communication, and workload-aware site visitors administration between knowledge facilities.
- Cisco Application Policy Infrastructure Controller (APIC) and Nexus 9000 Switches – Enabled high-throughput, resilient, and dynamically scalable interconnectivity, essential for fast knowledge synchronization throughout knowledge facilities.
The Cisco knowledge middle and networking know-how 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 middle stretch cluster leveraging Cisco knowledge middle and community energy
With the correct community infrastructure in place, we got down to construct a extremely obtainable, scalable, and AI-optimized database cluster spanning a number of knowledge facilities.
Cisco multi-data middle stretch Index database cluster
Key design selections
- Single logical, multi-data middle cluster for real-time AI-driven automation – Instead of sustaining separate clusters per knowledge middle which doubles prices, will increase upkeep efforts, and calls for vital handbook intervention, we constructed a stretched cluster throughout a number of knowledge facilities. This design leverages Cisco’s exceptionally highly effective knowledge middle community, enabling seamless knowledge synchronization and supporting real-time AI-driven automation with improved effectivity and scalability.
- Intelligent knowledge placement and synchronization – We strategically place knowledge nodes throughout a number of knowledge facilities utilizing customized knowledge allocation insurance policies to make sure every knowledge middle maintains a singular copy of the info, enhancing excessive availability and fault tolerance. Additionally, regionally connected storage disks on digital machines allow quicker knowledge synchronization, leveraging Cisco’s sturdy knowledge middle capabilities to realize minimal latency. This method optimizes each efficiency and cost-efficiency whereas guaranteeing knowledge resilience for AI fashions and significant workloads. This method helps in quicker AI-driven queries, decreasing knowledge retrieval latencies for automation workflows.
- Automated failover and excessive availability – With a single cluster stretched throughout a number of knowledge facilities, failover happens mechanically because of the cluster’s inherent fault tolerance. In the occasion of digital machine, node, or knowledge middle outages, site visitors is seamlessly rerouted to obtainable nodes or knowledge facilities with minimal to no human intervention. This is made attainable by the sturdy community capabilities of Cisco’s knowledge facilities, enabling knowledge synchronization in lower than 5 milliseconds for minimal disruption and most uptime.
Results
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 had been realized rapidly.
- Faster case decision: Reduced decision time from hours/days to simply minutes by enabling real-time AI-powered automation.
- Cost financial savings: Eliminated redundant clusters, chopping infrastructure prices whereas bettering useful resource utilization.
- Infrastructure price discount: 50% financial savings per quarter by limiting it to 1 single-stretch cluster, by finishing eliminating a separate backup cluster.
- License price discount: 50% financial savings per quarter because the licensing is required only for one cluster.
- Seamless AI mannequin coaching and automation workflows: Provided scalable, high-performance indexing for steady AI studying and automation enhancements.
- High 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 knowledge scales, the infrastructure stays environment friendly and cost-effective.
By rethinking conventional excessive availability methods and leveraging Cisco’s cutting-edge knowledge middle know-how, we created a next-gen case administration platform—one which’s smarter, quicker, and AI-driven.
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