Optimizing for Critical Workloads
The true power of SASE and SD-WAN lies in creating and enforcing nuanced, multi-variable policies for different application types simultaneously. This section provides practical blueprints for optimizing three critical workload categories.
Blueprint for UCaaS
The performance of UCaaS platforms like Microsoft Teams is notoriously sensitive to network conditions, requiring low latency, minimal packet loss, and near-zero jitter to avoid choppy audio and frozen video. An effective SASE architecture transforms Teams into a reliable collaboration tool by identifying and prioritizing its traffic, optimizing the path to Microsoft's network, and actively remediating last-mile impairments.
Platform Spotlight: Delivering Flawless UCaaS
Not all platforms handle real-time traffic equally. The underlying architecture dictates performance.
Cato Networks: The key differentiator is its global private backbone. By moving traffic off the public internet and onto its SLA-backed network of 85+ PoPs, Cato provides a highly predictable, low-latency, and low-jitter path ideal for voice and video. This architecture minimizes the performance degradation common on long-haul internet connections.
Arista (VeloCloud): The strength of the VeloCloud platform lies in its Dynamic Multi-Path Optimization (DMPO) technology. DMPO excels at remediating issues on commodity internet links in real time. Using techniques like on-demand Forward Error Correction and packet duplication, it can repair packet loss and maintain high Mean Opinion Scores (MOS) even on unstable connections, making it a powerful solution for sites relying solely on broadband.
Legacy SD-WAN (e.g., Meraki, Prisma SD-WAN): Many first-generation SD-WAN solutions are designed for simplicity and are effective for basic data connectivity but often fall short for enterprise-grade UCaaS. Meraki, for instance, offers limited QoS controls, lacks advanced remediation features, and can experience slow failover times, making it less suitable for environments where call quality is paramount. Similarly, while Prisma SD-WAN is a capable solution, achieving a complete SASE posture for optimized remote access requires integration with a separate product (Prisma Access), adding complexity compared to a natively converged platform.
Blueprint for SaaS: Accelerating Salesforce Performance
Salesforce performance is primarily impacted by network latency. Traditional architectures that backhaul traffic through a corporate VPN can add crippling round-trip time (RTT), leading to slow page loads and frustrated users. A SASE architecture slashes this latency by enabling local internet breakout and routing traffic over an optimized "middle mile" to the SaaS provider's cloud infrastructure.
Platform Spotlight: Slashing SaaS Latency
For global organizations, the "middle mile", the path across the internet backbone, is the biggest source of SaaS latency.
Cato Networks: Again, the private backbone is the critical advantage. Once a user's Salesforce traffic hits the nearest Cato PoP, it travels across a fast, reliable, and optimized network directly to a PoP with high-speed peering to Salesforce's infrastructure.53 This bypass of the congested public internet can dramatically reduce RTT and is further enhanced by built-in TCP acceleration, which can boost throughput up to 40x.
Internet-Based SD-WAN (VeloCloud, Prisma): These platforms are highly effective at providing intelligent local breakout to get SaaS traffic to the internet quickly. However, they have no control over the middle mile. Traffic traverses the public internet, subject to its inherent unpredictability, congestion, and variable routing. While this is a major improvement over backhauling, it may not solve performance issues for users located far from the SaaS application's data centers.
Legacy SD-WAN (Meraki): Platforms like Meraki are primarily designed for simple branch connectivity. While they support basic traffic shaping, they lack the sophisticated application acceleration, TCP optimization, and global PoP architecture needed to reliably improve the performance of latency-sensitive SaaS applications for a distributed workforce.
Blueprint for AI: Securing and Prioritizing Inference Traffic
Real-time AI inference, where a user or application queries a trained model, creates latency-sensitive traffic that often carries proprietary data, making both performance and security critical. An SD-WAN and SASE fabric must be architected to provide high-throughput, low-latency transport with strict QoS, while the integrated security stack provides essential Zero Trust Network Access (ZTNA) and Data Loss Prevention (DLP) to protect these high-value workloads.
Platform Spotlight: Building the AI-Ready WAN
AI workloads introduce traffic patterns that legacy networks were not designed to handle. Forward-looking platforms are adapting to this new reality.
Arista Velocloud: With its deep roots in high-performance data center networking for AI clusters, Arista is uniquely positioned to address AI workloads. The recent acquisition of VeloCloud signals a strategic vision to extend this AI-ready fabric from the data center core to the campus and WAN edge, creating an end-to-end architecture capable of handling the intense, low-latency demands of both AI training and inference traffic.
Cato Networks: The predictable, low-latency performance of Cato's private backbone makes it an ideal transport for sensitive AI inference traffic flowing between edge locations, users, and centralized cloud or data center compute resources. The fully integrated security stack ensures these high-value data streams are protected by ZTNA and DLP policies without adding performance-degrading latency.
Legacy SD-WAN: Most traditional SD-WAN platforms were built to optimize well-known SaaS and data center applications. They lack the specific capabilities—such as support for lossless Ethernet fabrics or advanced load balancing for GPU clusters- required to efficiently handle the unique, high-throughput traffic patterns generated by large-scale AI workloads.
