Introduction
As AI adoption accelerates throughout industries, companies face an plain fact — 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 knowledge whereas making certain price effectivity, resilience, efficiency and operational agility.
At Cisco Assist Case Administration – IT, we confronted this problem head-on. Our staff 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 only case decision, leveraging best-in-class applied sciences and AI-driven automation. We obtain this whereas sustaining a platform that’s extremely scalable, extremely out there, and cost-efficient. To ship the very best buyer expertise, we should effectively retailer and course of large volumes of rising knowledge. This knowledge fuels and trains our AI fashions, which energy vital automation options to ship sooner and extra correct resolutions. Our largest problem was placing the precise steadiness between constructing a extremely scalable and dependable database cluster whereas making certain price and operational effectivity.
Conventional approaches to excessive availability typically depend on separate clusters per datacenter, resulting in important 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 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 large knowledge to assist AI and data-intensive workloads, all whereas retaining operational prices low? That’s precisely the problem our staff got down to remedy.
On this weblog, we’ll discover how we constructed an clever, scalable, and AI-ready knowledge infrastructure that allows 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 not nearly resolving circumstances, it’s about repeatedly studying and automating to make decision sooner and higher whereas effectively dealing with the fee 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 concept made good 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 making certain excessive efficiency, resilience, and minimal handbook intervention throughout outages required a wholly new method.
Moderately than following the standard 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 diminished 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 way in which for sooner, smarter, and less expensive assist options.
How we solved it – The expertise basis
Constructing a multi-data middle trendy index database cluster required a strong technological basis, able to dealing with high-scale knowledge processing, ultra-low latency for sooner knowledge 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 knowledge facilities is community efficiency. Conventional excessive availability architectures depend on separate clusters per knowledge middle, typically fighting knowledge replication, latency, and synchronization bottlenecks. To start with, we performed 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.
- Capability planning – We assessed our anticipated knowledge 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, making certain uninterrupted knowledge availability, even throughout outages.
How Cisco’s high-performance knowledge middle paved the way in which
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 offered by Cisco knowledge facilities exceeded our expectation to confidently transfer on to the subsequent step of designing a stretch cluster. Our implementation leveraged:
- Cisco Utility Centric Infrastructure (ACI) – Supplied a policy-driven, software-defined community, making certain optimized routing, low-latency communication, and workload-aware visitors administration between knowledge facilities.
- Cisco Utility Coverage 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 expertise made this potential. It empowered Cisco IT to take this concept ahead and enabled us to construct this profitable cluster which saves important prices and supplies excessive operational effectivity.
Our implementation – The multi-data middle stretch cluster leveraging Cisco knowledge middle and community energy
With the precise community infrastructure in place, we got down to construct a extremely out there, 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 – As a substitute of sustaining separate clusters per knowledge middle which doubles prices, will increase upkeep efforts, and calls for important 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.
- Clever 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 information, enhancing excessive availability and fault tolerance. Moreover, regionally connected storage disks on digital machines allow sooner knowledge synchronization, leveraging Cisco’s strong knowledge middle capabilities to realize minimal latency. This method optimizes each efficiency and cost-efficiency whereas making certain knowledge resilience for AI fashions and demanding workloads. This method helps in sooner 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 robotically because of the cluster’s inherent fault tolerance. Within the occasion of digital machine, node, or knowledge middle outages, visitors is seamlessly rerouted to out there nodes or knowledge facilities with minimal to no human intervention. That is made potential by the strong community capabilities of Cisco’s knowledge facilities, enabling knowledge 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, cut back decision time, and preserve resilience and effectivity. The outcomes have been realized shortly.
- Sooner case decision: Lowered decision time from hours/days to simply minutes by enabling real-time AI-powered automation.
- Value financial savings: Eradicated redundant clusters, slicing infrastructure prices whereas bettering useful resource utilization.
- Infrastructure price discount: 50% financial savings per quarter by limiting it to at least one 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: Supplied 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 knowledge scales, the infrastructure stays environment friendly and cost-effective.
By rethinking conventional excessive availability methods and leveraging Cisco’s cutting-edge knowledge middle expertise, we created a next-gen case administration platform—one which’s smarter, sooner, and AI-driven.
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