Name: The Edge of Innovation and Security is Built on SD-WAN
Start: 2022-08-31T20:00:00Z
End: 2022-08-31T20:00:31.000Z
Location: BrightTALK
Rating: 0

The Enterprise Architecture channel  presents the next generation of Enterprise IT: recognizing the strategic importance of digital transformation and the data center to make companies more nimble and competitive. These presentations will help demanding IT and BizOps professionals achieve flexibility, scalability and performance with reduced risk and complexity.

As breakthrough technologies like AI mature, they inevitably evolve from centralized to decentralized architectures. With global edge computing spending projected to reach $378 billion by 2028, organizations that continue relying solely on centralized AI risk falling behind in both efficiency and performance. The pervasive waste and expense of centralized generative AI models have underscored the urgent need for intelligence at the edge.

While edge computing concepts have existed since the 1990s with content delivery networks, today's businesses face the complex challenge of transforming their architectures to process data closer to its source. This session explores how leading organizations are navigating this transition, reducing latency and costs while competing with decades of established centralized cloud infrastructure.

Join Alan Morrison (Advanced Data Technologies Consultant and Writer) to discover how decentralized AI and edge computing are revolutionizing data processing across industries.

Key Takeaways:

- Understand why centralized AI approaches are becoming unsustainable and how edge computing addresses these limitations
- Learn practical strategies for implementing edge intelligence from organizations already succeeding with this approach
- Discover the economic benefits of processing data at the edge versus transmitting everything to centralized systems
- Gain insights into market growth projections and how to position your infrastructure investments accordingly
- Identify the architectural transformation challenges of edge computing and how to overcome them

Decentralized AI: The Future of Edge Computing

Quite aside from the quest to spread out the work of new AI-based services, there are other issues centered on data and the network that are increasing interest in and need for edge data centers. 

We’ll explore these drivers for a new generation of distributed data centers, and how they will fit into enterprise networks.

Expanding the Network Edge

Information systems vary in their primary roles and objectives. Real-time information systems, in particular, impose strict deadlines for generating control responses, as delays in data generation, processing, or transfer may lead to critical failures. For such systems, ensuring proper transactions depends not only on their logical correctness but also on their ability to operate within the required time constraints—as excessive latency renders them ineffective.

Timely data processing is essential for data intelligence, influencing the methods and tools used for classifying and integrating real-time information. Achieving low-latency solutions requires leveraging local resources instead of relying solely on centralized servers or cloud infrastructure. This shift brings computing closer to the network's physical endpoints, introducing the edge computing paradigm. Expanding the network edge extends computational and data processing capabilities to the point of data generation, reducing cloud dependency while enhancing real-time analytics, security, and efficiency.

Discover the key technologies driving edge expansion, including edge computing, cloud-edge advanced communication protocols, and AI-driven edge networks, and gain expert insights into the future of edge computing.

Discover Technologies Driving the New Network Edge

This webinar will provide a foundational understanding of Artificial Intelligence (AI) and Machine Learning (ML). We will explore the basic concepts, history, and applications of AI/ML, setting the stage for more advanced topics in subsequent webinars. Attendees will gain insights into how AI/ML technologies are transforming industries and everyday life and have the opportunity to see one of the earliest AI experiments: Training a neural network to recognize written letters.
 
Key Concepts:
• Definition and History of AI and ML
• Differences Between AI, ML, and Deep Learning
• Real-World Applications of AI/ML
• Basic Terminology and Concepts

Introduction to AI and Machine Learning

In this webinar, we’ll introduce the PCI Express (PCIe) interface which is an essential component of AI systems due to its ability to provide high bandwidth and low latency over short distances. We’ll introduce the basics, then discuss the evolution, requirements and benefits. We’ll cover features in PCIe Gen6 including bandwidth, AER (Advanced Error Reporting), DPC (Downstream Port Containment), different modes and signaling techniques. We will also cover PCIe switching and its applications for AI. Join us for an insightful discussion on:
- What is PCIe and why it matters? 
- Discovery/Enumeration and Hot-plugging 
- NTB (Non-Transparent Bridging) applications and uses
- Shared credit/received buffer
- Selective NAK vs. Standard NAK for Flits replay
- PCIe Gen6 Enhancements
- PCIe Switch and applications for AI

Everything You Wanted to Know About PCIe But Were Too Proud to Ask

In an era where application environments are distributed, the network often directly determines the cost, the efficiency and the experience of nearly every application. With the era of AI on the immediate horizon, how will organizations evolve their network and infrastructure planning and operations to adapt? Which deployment strategies are being considered for AI workloads, and what role must the network play in the various scenarios? What are the biggest challenges facing organizations building networks for AI? And, what are the most important features and capabilities required to ensure the network can be an enabler, and not a barrier, to AI deployments? Join Jim Frey as he helps answer these questions in the latest research on Networking for AI.

Architecting the Network for an AI-Powered World: Customer Challenges, Plans, and Priorities

Consistent excellence in application experience must be a priority for any digital business, whether that experience is for its customers or its employees. Observability and AIOps tools play a critical role in runtime post-deployment (Day 2) operations across distributed cloud environments. What are the current pain points, best practices, and investment priorities for developers and IT operators related to observability, visibility, and monitoring? How much time do application development teams spend on troubleshooting production environments, maintaining or modernizing existing application code, and productively writing new business code? How can modern AI-enabled tools and technologies help? And what barriers persist? Join Torsten Volk as he helps answer these questions in the latest research on How the AI Revolution will Transform AIOps and Observability.

How the AI Revolution Will Transform AIOps and Observability

The rapid growth of AI is driving unprecedented demand for high-bandwidth, high-capacity memory solutions. Compute Express Link (CXL) is emerging as a key enabler in 2025, unlocking new levels of memory scalability and efficiency. Join experts as they discuss how CXL-based solutions are addressing memory bottlenecks and capacity constraints in modern data centers.
 
Attendees will:
Learn about real-world applications and customer platforms using CXL
Gain insights into benchmarks for CXL performance and latency
Understand how OEM customers are leveraging the CXL solution

Accelerating AI with Real-World CXL Platforms

Businesses enjoy a wealth of application deployment choices between cloud providers, data centers, colocation, as well as edge locations. As cloud adoption matures organizations become more distributed with investments increasing both on and off premises. How do businesses make application deployment and migration decisions between locations? Why do “cloud-first “organizations keep workload on premises? When do organizations switch primary cloud providers? What rationale, considerations, and processes matter? How have the adoption of cost-analysis tools impacted organizations investment decisions. Answering these questions is the key to understanding the future of hybrid, and multi-cloud investment. Join Scott Sinclair as he discusses recent research on Understanding Workload, App, and Data Deployment and Migration Decision Making across Distributed cloud environments.

Understanding Workload, App, and Data Deployment and Migration Decision Making

With AI and data-intensive workloads transform IT priorities, data storage infrastructure plays a crucial role in the success of digital operations. Data drives modern business success, yet data environments are more distributed and silo’d than ever before. What are the top challenges of distributed data environments? How is AI shifting storage investments and considerations? What is fueling a resurgence of on-premises storage investment? What role does the hypervisor and hyperconverged infrastructure play in the modernization of hybrid and multi-cloud environments? Answering these questions is the key to optimizing data storage and access in the modern digital age of business. Join Simon Robinson as he discusses recent research on the State of Enterprise Storage and HCI.

Navigating the Cloud and AI Revolution: The State of Enterprise Storage and HCI

As enterprises scale and digital complexity grows, traditional IT operations struggle to keep up with the demands of speed, reliability, and efficiency. This session explores how AIOps—Artificial Intelligence for IT Operations—enables organisations to transform their IT environments into agile, intelligent, and self-healing systems. By combining machine learning, real-time analytics, and automated remediation, AIOps reduces incident response times, prevents outages, and empowers teams to shift from reactive firefighting to proactive innovation.



Attendees will learn how leading enterprises are leveraging AIOps to modernise their operations, enhance service delivery, and build resilience in an increasingly dynamic digital landscape.

Transforming IT Ops with AIOps for Agile, Smart, and Resilient Enterprises

As digital ecosystems grow increasingly intricate and interdependent, IT operations teams find themselves navigating a labyrinth of complexity while stakeholders demand flawless performance. Traditional reactive approaches to IT operations are proving insufficient, with technical issues costing organisations an average of $5,600 per minute in lost productivity and revenue.

The integration of artificial intelligence, generative AI, and autonomous agents represents a major shift in operational resilience. By implementing intelligent frameworks that can identify issues, understand their origins, and autonomously resolve problems across IT and business systems, organizations can transform from reactive firefighting to proactive management. This evolution requires fundamental changes in how enterprises approach automation, cross-system data integration, and operational culture—but delivers dramatic improvements in system reliability, cost efficiency, and business alignment.

Join Guru Rajaram and Rajib Deb from Deloitte's AI & Data practice to explore how reimagined IT operations can drive measurable business value through proactive, resilient systems.

Key Takeaways:

- Understand the architectural components of AI-powered operations frameworks that enable autonomous issue detection and resolution
- Learn practical strategies for implementing intelligent automation that scales across enterprise systems
- Discover approaches for integrating generative and agentic AI capabilities into existing operational workflows
- Explore methods for measuring business impact and ROI of advanced IT operations
- Gain insights into the organizational and cultural changes needed to successfully adopt AI-driven operations

Intelligent ITOps: Harnessing AI and Automation for Resilient Enterprise Systems

In a world where minutes of downtime translate to millions in losses, the elusive promise of self-healing IT systems remains the holy grail for operations teams everywhere. The promise of AIOps to transform IT operations through intelligent automation has created significant market buzz, yet many implementations fail to deliver expected outcomes because they overlook the critical foundation: high-quality data and mature ITSM processes.

Today's IT leaders face mounting pressure to demonstrate value through automation while navigating a landscape filled with vendor hype and unrealistic expectations. This session cuts through the noise to reveal how successful AIOps implementations depend on addressing fundamental data quality challenges before automated remediation can become reality. By understanding the interdependencies between your ITSM framework and AIOps capabilities, you'll learn practical strategies to build a sustainable foundation for truly proactive operations that deliver measurable business value.

Discover how to transform your IT operations by building the right data foundation for effective AIOps-driven remediation.

Key Takeaways:

- Understand the critical data quality dimensions (completeness, accuracy, governance) that determine AIOps success or failure in automated remediation scenarios
- Learn practical methods to assess your organization's AIOps readiness and identify specific data improvements needed before implementation
- Discover strategies to align ITSM processes with AIOps capabilities to create a seamless remediation workflow
- Walk away with a practical framework for building organizational alignment around data quality as the foundation for successful automated remediation

Proactive Remediation: The ITSM-AIOps Integration Advantage

The tsunami of data overwhelming IT operations has exposed a critical flaw in our approach: analyzing more data faster doesn't deliver better business outcomes. Forward-thinking enterprises are pivoting from data-centric to outcomes-driven AIOps strategies—where success is measured not by the insights generated, but by the tangible business results achieved through anticipatory operations.

Implementing post-AI Quantum technologies presents a number of significant challenges, from integration with legacy systems to establishing new governance frameworks for ethical use. However, organizations that successfully navigate this transition gain unprecedented capabilities to anticipate disruptions before they occur, dynamically adapt to emerging threats, and transform operational resilience into competitive advantage. This paradigm shift requires rethinking how we evaluate, deploy, and measure the impact of advanced AI technologies within enterprise environments.

In this session, Dr.-Prof.-Eng. Yogesh Malhotra, Founder, Chairman & CEO of Global Risk Management Network LLC and AWS Quantum Valley™ US Air Force-Pentagon MVP, explores how outcomes-driven AIOps transforms enterprise agility in the post-Quantum-GenAI era.

Key Takeaways:
- Master the transition from data-driven to outcomes-driven AIOps.
- Overcome key implementation challenges for post-AI Quantum technologies.
- Develop more trustworthy AI models through human-centered, meaning-aware approaches.
- Enhance operational resilience through anticipatory rather than merely predictive systems.
- Create a strategic roadmap for measuring outcomes-based success in your AIOps initiatives.

Beyond Prediction: Outcomes-Driven AIOps for Enterprise Agility

In the labyrinth of modern enterprise technology, AIOps often feels like a mystical black box -- powerful, yet frustratingly opaque. Over the last two decades we have all watched countless promising technologies get sidelined not because they lack potential, but because leadership can't see inside the mechanism.

This session from industry thought leader Gael Decoudu isn't another technical deep-dive. It's a pragmatic roadmap for transforming AIOps from an intimidating technological black box into a strategic business asset that speaks the language of value, risk mitigation, and organizational efficiency.

Key Takeaways
- Learn practical translation frameworks that convert technical AIOps metrics into business value language that resonates with leadership. 
- Discover the four critical layers of AIOps explainability and how to implement them within your organization's unique culture. 
- See examples of how successful organizations transformed "black box" AI technologies into transparent business assets. 
- Get a phased implementation roadmap for building an explainability practice that scales across your enterprise. 
- Walk away with actionable communication strategies that help technical teams articulate AIOps value in terms of performance impact, risk mitigation, and operational efficiency.

From Black Box to Business Buy-In: Making AIOps Explainable for Enterprise-Wide Adoption

During SNIA's Cloud Object Storage (COS) multi-vendor compatibility plugfests, it became apparent to the community that a lack of commonly accepted open-source COS test tools is one of the more obvious pain points for the industry. The consensus from the plugfest participants is that we need to collaborate on test tools because often tools are proprietary and biased, and given the number of tools, it is nearly impossible to test them all. Participants found that working together, using other people's test code and results, was highly beneficial, providing broader coverage vs. testing solely with their own tools.

With this ongoing COS Plugfest effort, SNIA member companies aim to create and publish COS interoperability test software suites that help automate protocol compatibility testing for popular industry cloud object storage APIs, wire protocols, and services. If you’re curious about cross-vendor S3 compatibility, join us to learn about the work within our community. 

In this webinar, experts engaged in proprietary and/or open-source software development will discuss:
· The real-world need for COS interoperability test tools
· Examples of compatibility gaps
· Highlight open-source test suites (e.g., Ceph, Versity and others) 
· Progress to date from ongoing testing
· How to get involved

SNIA Cloud Object Storage Test Tools (Open-Source Industry Interoperability Project)

Agentic AI is an emergent and disruptive paradigm beyond generative AI that carries great promise and great risk. With generative AI, a large language model (LLM) generates content as the result of a prompt. With agentic AI, the language model now generates actions to execute on behalf of a user using tools at its disposal. It has agency to act on your behalf and even run autonomously.  But how can this be done safely and effectively for enterprises?
 
This webinar will introduce Agentic AI and cover:
• What is agentic AI?
• What makes it different from generative AI?
• Adoption challenges and what to consider when evaluating solutions for enterprise use
• Sample use case
 
Join us to understand both the risks and benefits of agentic AI for enterprises use cases.

Agentic AI – Use Cases, Benefits, Risks

Explore cutting-edge innovations in solid-state drive (SSD) power efficiency and liquid cooling, designed to mitigate AI workload bottlenecks and reduce Total Cost of Ownership (TCO) in data centers. By focusing on optimizing SSD performance per watt, we can significantly enhance data center sustainability, operational efficiency, and economic viability.
Key topics include:
1. NVMe® Power Tuning Techniques: Maximizing performance per watt through advanced power management strategies.
2. SSD Controller Architecture Breakthroughs: Leveraging hardware offloads and computational storage innovations to improve energy efficiency.
3. Liquid-Cooled SSD Technologies: Pioneering advancements in liquid cooling that enable significant reductions in power consumption and facilitate the transition to fan-less server designs.
By examining the latest trends in immersion and liquid cooling technologies, this presentation will demonstrate their critical role in driving data center performance, sustainability, and cost-effectiveness forward. Join us to discover how these advancements can help overcome challenges in high-density AI environments.

Unlocking Sustainable Data Centers: Optimizing SSD Power Efficiency and Liquid Cooling for AI Workloads

In this fireside chat, ESG senior analyst Paul Nashawaty and OVHcloud’s GM, Jeffrey Gregor, explore:
 
* The rise of the alternative cloud providers and why one size no longer fits all
* The following reasons to look for alternatives: complexity of migration, talent to support proprietary applications, cost of re-engineering applications, cloud operations, compliance, and data sovereignty
* What a hybrid and multi-cloud future looks like
* How OVHcloud leverages the market-leading capabilities of VMware in a unified architecture using familiar tools to address use cases such as on-demand capacity, disaster recovery and back-up
* New product/service announcements

The Future is Hybrid and Multi-Cloud: How Does that Look?

Today, 75% of companies use 2 or more public clouds including sovereign clouds in Europe and Asia pacific to support their modern applications as well as their cloud migration. This creates exponential complexity for developers, operators and security professionals. In this fireside chat, ESG senior analyst Mark Bowker and VMware’s VP of Cross Cloud Solutions, Vittorio Viarengo, explore:

* Multi-Cloud Complexity
* VMware Cross-Cloud Services strategy and latest announcements
* Sovereign clouds and regulatory and data privacy compliance
* Multi-Cloud Customer Examples
* VMware ESG Commitment

Cross-Cloud Services and Taming the Complexity of Multi-Cloud

BrightTALK @ VMware EXPLORE 2022

Join ESG Principal Analyst Bob Laliberte and Abe Ankumah, VP of VMware’s SASE Business, as they discuss future trends in networking. The rise of cloud services, remote work, and operational efficiency has driven organizations to adopt SASE and SD-WAN architectures. SD-WAN is essential to provide application delivery from disparate clouds to dispersed users today. But the right SD-WAN architecture is also critical to accelerate future digital transformation projects including connectivity over private 5G networks and edge compute applications which use IoT sensors and AI to convert local intelligence into smarter business decisions.

The Edge of Innovation and Security is Built on SD-WAN

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The Edge of Innovation and Security is Built on SD-WAN

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