Optimizing business velocity with Cisco Full-Stack Observability

Fueling digital transformation success with cost and resource optimization over applications, workloads, and components

Digital transformation comes with an irony that is not lost on the IT teams. Applications and the digital experiences they enable require cloud-based resources for which costs can easily spiral out of control. Worse, lack of visibility means that utilization of these resources can be difficult to accurately assess.

This creates a conundrum. Fast, reliable application performance depends on sufficient allocation of cloud resources to support demand, even when usage spikes. Under-resourcing in this area can cause significant performance challenges that result in very user experience. With this in mind, teams responsible for migrating workloads to the cloud or spinning up resources for new applications can often over-provision cloud resources to be on the safe side.

The more complexity that is introduced by sprawling suites of tools, containers, application programming interfaces (APIs), and serverless components, the more ways there are to incur costs. And the more ways there are to fall short of efficiency goals  as cloud resources sit idle.

As a result, technologists are under pressure to find out where costs are out of alignment and whether resources have been allocated in ways that support the business.

Taking the guesswork out of optimization

Cisco Full-Stack Observability allows operational teams to gain a broad understanding of system behavior, performance, and security threats across the entire application estate. It also equips them to understand and optimize cloud resource utilization. This optimization helps organizations lower costs by properly modulating asset utilization across workloads, paying only for what they need through right-sizing resource allocation.

It offers optimization capabilities for resolving poorly aligned cloud spend with actionable insights into hybrid costs and application resources within their established monitoring practices. While over-provisioning to avoid downtime is wasteful from both a budgetary and sustainability perspective, under-allocation presents a serious risk.

When applications are constrained by insufficient resources, the resulting poor application performance or even downtime can damage organizational reputation and revenues. With Cisco Full-Stack Observability, teams can scale up or down to ensure resources sufficiently support workloads.

Moreover, Cisco Full-Stack Observability solutions provide visibility into application-level costs alongside performance metrics down to the pod level. It helps perform granular cost analysis of Kubernetes resources, allowing FinOps and CloudOps teams to understand the composition of their cloud spend as well as the cost of resources that are idle. Armed with granular cost insights, organizations can mitigate overspending on unused resources while ensuring that critical applications have adequate resources.

Driving optimization with AI and ML

Artificial intelligence (AI) is driving change in observability practices to improve both operational and business outcomes. Cisco Full-Stack Observability combines telemetry and business context so that AI and machine learning (ML) analytics can be uniformly applied. This allows IT Operations teams to extend their value and truly be strategic enablers for their business.

For example, application resource optimization with Cisco Full-Stack Observability takes aim at inefficiencies in Kubernetes workload resource utilization. By running continuous AI and ML experiments on workloads, it creates a utilization baseline, analyzing and identifying ways to optimize resource utilization. The resulting recommendations for improvement help to maximize resource usage and reduce excessive cloud spending.

Cisco Full-Stack Observability offers capabilities, moreover, to identify potential security vulnerabilities related to the application stack and optimize the stack against these threats. It continuously monitors for vulnerabilities within applications, business transactions, and libraries with the ability to find and block exploits automatically. The result is real-time optimization without constant manual intervention.

To understand and better manage the impact of risks on the business, Cisco security solutions use ML and data science to automate risk management at multiple layers. First, code dependencies, configuration-level security vulnerabilities, and leakage of sensitive data are continually assessed. Second, business priorities are established through a measurement of risk probability and business impact.

This comprehensive approach to optimization makes Cisco Full-Stack Observability a powerful solution for modern, digital-first organizations.

Source: cisco.com

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Secure Firewall & Multicloud Defense: Secure Connectivity With Simplified Policy Across Clouds

Most of our large customers today have datacenters and leverage multiple clouds to maximize flexibility and agility for meeting their business needs. Traditionally, the security for these environments has rested with different teams, each having their own tools and processes. But as our application and IT environments become more interwoven, the complexity of the environments and the challenge of securing them has massively increased. Siloed tools and teams are now part of the problem, generating new gaps and blind spots. Attackers are growing more sophisticated and taking advantage of these new challenges. In fact, last year, 39% of breaches spanned multiple environments and cost organizations an average of $4.75M per breach globally.

It is time to rethink how organizations approach the hybrid-multicloud security strategy — converging the fabrics between on-premises and cloud network security to foster collaboration across teams and deliver a unified edge security strategy.

Today, we are we’re bringing on-prem and cloud security together into one unified platform through the Cisco Security Cloud to marry the power of Cisco Secure Firewall and Cisco Multicloud Defense. Combined, these solutions provide multi-environment customers with greater visibility and protection across environments, more consistent control to reduce risk, and simplified security policy creation to alleviate complex operations.

This year at RSA Conference 2024, customers can experience where security meets the network with new capabilities between these solutions — as part of our unified security platform.

Multicloud networking: Secure connectivity from ground to cloud

Imagine you have an application on-prem that needs to talk to an application in the cloud, how would you approach this challenge? Traditionally, organizations have had to rely on 3rd party native tools. However, these services can be costly — especially as you scale applications and environments. And as you scale, the complexity increases, reducing visibility and control of critical security functions. Now, by leveraging our unified platform with the Cisco Security Cloud, customers can build these connections in house with secure site-to-cloud and cloud-to-cloud connectivity between applications and environments. With this, organizations will be able to securely scale hybrid cloud operations while reducing cost and maintain visibility and control of their connections and data.

New network object sharing further simplifies policy creation across multi-environments

In many cases today, organizations are building, deploying, and managing policies in silos. This disparate method strains teams — creating laborious, redundant steps in the policy building process, leads to increased risk of human error and cues the dreaded swivel chair scenario — hopping between numerous tools and platforms to build policies.

At Cisco Live EMEA, we announced general availability of network object sharing for static objects. Today at RSA Conference, we’re reducing multi-environment complexity even further with the ability to now share dynamic objects using our unified management fabric. This gives organizations a single location to pool objects, simplifying policy building and management across environments. Baked into the Cisco Security Cloud platform, this capability empowers organizations to easily share objects between Secure Firewall and Multicloud Defense, reducing complexity, removing duplicative processes, and stopping the pain of maintaining yet another case of siloed operations across separate solutions.

As we continue to innovate across the Cisco Security Cloud, synergies across the network security portfolio will continue to grow. The launch of these shared capabilities between Cisco Secure Firewall and Cisco Multicloud Defense is a significant step towards converging the fabrics of best-in-class data center and cloud security to protect customers from ground to cloud.

Looking to get started? Understand your risk by signing up for our free Cloud Visibility and Risk Report. Powered by Cisco Defense Orchestrator and Cisco Multicloud Defense, our solutions run alongside your clouds to help you understand your risk with pervasive visibility into assets and connections — our experts then provide you with actionable security insights and recommendations to better protect your infrastructure.

Source: cisco.com

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INFRAM24: Measuring your IT strategy and capabilities to drive adoption and improve outcomes

Measuring your IT strategy and capabilities to drive adoption and improve outcomes

The world of healthcare is constantly shifting, and technology is at the spearhead of this continuous transformation. As organizations grapple with the complexities of digital adoption, cybersecurity, and sustainable operations, HIMSS’ Infrastructure Adoption Model (INFRAM) has been updated to meet the need, and lead with data-driven insights and strategic guidance. Cisco enables healthcare leaders to use INFRAM not just as an evaluation tool, but to drive change, mitigate risks, and ensure that technology investments translate into improved care delivery and clinical outcomes.

What’s New with INFRAM

Recently, HIMSS has revised INFRAM to not just look at infrastructure capabilities but to also look at IT strategy, assess technology adoption by internal stakeholders, and measure the direct impacts of infrastructure investments on clinical outcomes and operations. It now allows organizations to understand if their infrastructure capabilities are in line with their overall goals.  This update provides a closer look at the effectiveness of technology investments and identifies opportunities for improvements.

New INFRAM Domains

The updated version of INFRAM addresses 5 crucial aspect of healthcare infrastructure:

1. Cybersecurity: Data breaches are occurring more frequently and can have a larger effect on patient care and hospital operations than ever before, INFRAM emphasizes establishing robust cybersecurity practices, leveraging AI (Artificial Intelligence) for quick recovery, and aligning organizational efforts to minimize future risks. This domain allows organizations to understand their current capabilities in everything from network identity all the way to endpoint security.
2. IT Management & Performance: Systematic performance support and change management are critical for adhering to service-level agreements. INFRAM assists in leveraging technology to mitigate incidents and maintain high service standards. This helps the hospital understand their current IT capabilities across transport, wireless, collaboration, and data center for everything from location services all the way to their cloud usage strategy.
3. Adoption: Ensuring that innovative technologies are not just installed but integrated and optimized for maximum efficiency is vital. INFRAM ensures that patients, clinicians, staff, IT, and leadership reap the full benefits of technological advancements.
4. Outcomes: Perhaps most importantly, INFRAM provides a framework that aligns IT investments with clinical, financial, and operational objectives, ensuring measurable contributions to organizational goals.
5. Sustainability: Healthcare organizations are not exempt from the global call for environmental responsibility. INFRAM aids in developing strategies to reduce greenhouse gas emissions and carbon footprints, aligning healthcare with green initiatives.

Benefits to Healthcare Leaders

Using INFRAM, healthcare leaders can:

• Measure Value: Adopt an evidence-based approach for investments, gain critical buy-in, and achieve recognition for outcomes.
• Analyze Gaps: Pinpoint and address friction points in infrastructure development for tailored, strategic investments.
• Cybersecurity Risk Planning & Mitigation: Implement a proactive, best-practice approach to cybersecurity and establish a clear plan of action in the event of breaches.
• Build Governance and Drive Adoption: Ensure technology governance that maximizes value and supports care teams in delivering optimal outcomes.
• Drive Alignment- INFRAM provides a structured approach to evaluate a hospital’s infrastructure and Cisco can help ensure it aligns with the organization’s goals. This allows for cohesion between the Executive, Clinical, Operation and IT teams.

INFRAM24

INFRAM is more than an assessment tool; it is a strategic ally for healthcare organizations aiming to harness the power of technology to improve patient care and operational efficiency. As healthcare continues to evolve, INFRAM offers a structured path to navigate the complexities of digital transformation with confidence and clarity.

Source: cisco.com

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Empowering Cybersecurity with AI: The Future of Cisco XDR

In 2007, there was a study from the University of Maryland proving that internet-connected systems were attacked every 39 seconds on average. Today, that number has grown more than 60%. Cisco sees 64 attempts to connect to ransomware infrastructure every second. The world is becoming digitized, and hybrid, which creates an environment that criminals target with increasing sophistication. It’s too much for human-scale, and so a hybrid world requires a hybrid approach that sits between humans and machines.

Envision an AI Assistant that serves as a reliable partner for incident responders, offering precise, real-time guidance on the subsequent steps to take, tailored to the specific state of the incident at hand and allowing SOC (Security Operations Center) teams to respond faster and do more with less. I am pleased to announce the launch of the AI Assistant in XDR as a part of our Breach Protection Suite.

In our RSAC 2023 announcement, we introduced a vision of our Cisco SOC Assistant, designed to expedite threat detection and response. Today, this vision is realized and available in private preview. It enhances our Breach Protection Suite which is powered by Cisco XDR’s capabilities. It significantly speeds up investigations and responses, enabling security teams to safeguard their environments more efficiently and cost-effectively.

Assist with Information Discovery

In 2024, the global shortfall of 3.5 million security professionals, as reported by ISC2, underscores the importance of retaining and recruiting skilled personnel to counter increasingly sophisticated cyber threats and safeguard enterprises. Moreover, the lack of appropriate tools often leads to ineffective cyber risk management and professional burnout, adversely affecting staff retention and the SOC’s capacity to thwart attacks.

The AI Assistant in XDR acts as a potent enhancer, empowering SOC teams to maximize their efficiency and effectively close the personnel and skill gap. When an incident occurs, the assistant will contextualize events across email, the web, endpoints, and the network to tell the SOC analyst exactly what happened and its impact on their environment. It presents a short description of the incident that quickly answers what, when and how an incident happened. It also provides a long description of the incident which explains the timeline of events that have happened in this active incident.

Figure 1: Short Description of Incident Details generated by the AI Assistant

Figure 2: Long Description of Incident Details and Events Timeline

Moreover, our AI Assistant utilizes XDR’s patented ability to prioritize critical incidents, reducing alert fatigue for the SOC team and enhancing their efficiency in handling active incidents.

Figure 3: Targeted Prioritization of Incidents by AI Assistant that Need Immediate Attention

Augment and Elevate SOC Teams with Best Practice Recommendations

Today’s SOCs often struggle with a fragmented technology stack, making it difficult to respond effectively to cyber threats. Alert fatigue is a major hurdle for modern SOC teams, hindering proactive threat hunting and leading to overlooked alerts and burnout. The Cisco AI Assistant comes to the rescue and jumpstarts the incident response process for a modern SOC team.

Our AI Assistant, powered by Cisco XDR the platform for Cisco’s Breach Protection Suite, synthesizes data from email, web, processes, endpoints, cloud, and network domains, offering precise action recommendations to effectively contain ongoing cyber-attacks. It works at machine scale to identify patterns and potential attacks that humans might miss because of alert fatigue, if a defender is only looking at one domain in isolation, or while trying to manually correlate data. The AI Assistant is context aware, meaning it tracks the state of the incident in real-time and generates tailored recommendations specific to that incident.

Figure 4: Tailored Recommendations for an Incident by the AI Assistant

Mean Time to Detection (MTTD) and Mean Time to Respond (MTTR) are two primary metrics that SOC teams want to optimize for. Cisco XDR with our AI Assistant enables security teams to reduce these metrics by jumpstarting investigations and incident response by providing tailored recommendations for that specific incident.

Enable Seamless Collaboration Across Security Teams

The Cisco AI Assistant, embedded within XDR, facilitates team collaboration using Webex, Teams, or Slack. This empowers security teams to swiftly assemble the right experts for an active incident, thereby speeding up the MTTR. The AI Assistant unifies the team by setting up WAR rooms, summarizing messages, and logging them in XDR for instant audit-readiness.

Figure 5: AI Assistant creates a Webex WAR Room and brings the right experts together for Incident Response

Automate Workflows to Neutralize Threats Across the Enterprise

Today’s SOCs often lack a cohesive technology stack to respond to cyber threats efficiently and consistently. As the IT environment grows beyond the on-premises data center to cloud, hybrid-cloud and multi-cloud country specific data centers, organizations accumulate point solutions to monitor and protect pieces of the environment. As a result, SOC analysts must do a lot of the heavy lifting required to detect and respond to an attack. This includes logging into different tools to execute workflows that contain an attack.

Our AI Assistant taps into advanced workflows and atomics with Cisco XDR’s 90+ integrations. Our AI assistant enables the execution of workflows at a single click, guided by the AI Assistant’s personalized recommendations that consider the incident’s playbook and current state in real-time.

Figure 6: Execution of Automated Workflows by the AI Assistant to Contain an Incident

Gone are the days when security teams had to juggle multiple isolated products and execute workflows in each to mitigate an attack. With Cisco Breach Protection Suite, billions of security events can be correlated and recommended actions can be generated and executed all in one place. This is the transformative power of the Cisco XDR combined with Cisco’s AI Assistant revolutionizing enterprise security.

Source: cisco.com

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Synergizing Advanced Identity Threat Detection & Response Solutions

In an ever-evolving digital landscape, cybersecurity has become the cornerstone of organizational success. With the proliferation of sophisticated cyber threats, businesses must adopt a multi-layered approach to ensure the integrity of their digital assets and safeguard their sensitive information. Two leading players in this space, Cisco’s Duo Security and Cisco Identity Intelligence, have emerged as champions in Identity Threat Detection & Response. In this blog post, we will explore how Cisco Identity Intelligence seamlessly complements Cisco’s Duo Security to provide a comprehensive and robust cybersecurity strategy.

The Power of Identity Threat Detection & Response

Identity Threat Detection & Response (ITDR) has become a vital aspect of modern cybersecurity. It focuses on identifying anomalies in user behavior, detecting potential unauthorized access, and responding to security incidents promptly. Cisco’s Duo Security has established itself as a prominent solution in this domain, offering a range of features such as multi-factor authentication (MFA) and access controls that protect against unauthorized access.

Cisco Identity Intelligence: Elevating Cybersecurity Preparedness

Cisco Identity Intelligence brings an additional layer of protection to the table with its advanced capabilities in anomaly detection and behavioral analytics. This innovative technology analyzes user behavior patterns, device interactions, and network activities to identify even the subtlest deviations from normal behavior. This is particularly crucial in today’s threat landscape, where attackers are becoming increasingly adept at mimicking legitimate user actions.

How Cisco Identity Intelligence Complements Cisco’s Duo Security

1. Enhanced Anomaly Detection: While Cisco’s Duo Security provides robust MFA and access controls, Cisco Identity Intelligence takes it a step further by analyzing user activities in real time. By establishing a baseline of normal behavior, Cisco Identity Intelligence can swiftly identify any unusual actions, potentially preventing unauthorized access even after initial authentication.

2. Behavioral Analytics: Cisco Identity Intelligence’s AI-driven behavioral analytics can identify complex attack patterns that may go unnoticed by traditional security measures. By correlating data across multiple dimensions, Cisco Identity Intelligence helps security teams detect identity-related threats that could lead to data breaches or system compromise.

3. Holistic Threat Response: When integrated with Cisco’s Duo Security, Cisco Identity Intelligence enables a comprehensive threat response strategy. The combined capabilities of these two solutions allow organizations to not only prevent unauthorized access but also respond proactively to emerging threats, minimizing potential damage.

4. Adaptive Security: Cisco Identity Intelligence’s adaptive security approach means that it continuously learns from new data and adjusts its understanding of what constitutes normal behavior. This adaptability ensures that evolving attack techniques are promptly recognized and mitigated.

5. Reduced False Positives: Cisco Identity Intelligence’s sophisticated engine minimizes false positives by understanding context and user intent. This helps security teams focus their efforts on genuine threats, reducing alert fatigue and streamlining incident response.

Conclusion

As the cyber threat landscape continues to evolve, the collaboration between leading cybersecurity solutions becomes imperative. Cisco’s Duo Security and Cisco Identity Intelligence collectively fortify an organization’s defense by combining multi-factor authentication, access controls, AI-driven anomaly detection, and behavioral analytics. This synergy creates a robust shield against identity-related threats and provides a holistic approach to cybersecurity.

Remember, in today’s digital age, a comprehensive cybersecurity strategy is not a luxury but a necessity. By embracing the combined power of Cisco’s Duo Security and Cisco Identity Intelligence, organizations can confidently navigate the complex realm of cyber threats and safeguard their digital assets with unwavering resolve.

Source: cisco.com

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Computing that’s purpose-built for a more energy-efficient, AI-driven future

Just as humans use patterns as mental shortcuts for solving complex problems, AI is about recognizing patterns to distill actionable insights. Now think about how this applies to the data center, where patterns have developed over decades. You have cycles where we use software to solve problems, then hardware innovations enable new software to focus on the next problem. The pendulum swings back and forth repeatedly, with each swing representing a disruptive technology that changes and redefines how we get work done with our developers and with data center infrastructure and operations teams.

AI is clearly the latest pendulum swing and disruptive technology that requires advancements in both hardware and software. GPUs are all the rage today due to the public debut of ChatGPT – but GPUs have been around for a long time. I was a GPU user back in the 1990s because these powerful chips enabled me to play 3D games that required fast processing to calculate things like where all those polygons should be in space, updating visuals fast with each frame.

In technical terms, GPUs can process many parallel floating-point operations faster than standard CPUs and in large part that is their superpower. It’s worth noting that many AI workloads can be optimized to run on a high-performance CPU.  But unlike the CPU, GPUs are free from the responsibility of making all the other subsystems within compute work with each other. Software developers and data scientists can leverage software like CUDA and its development tools to harness the power of GPUs and use all that parallel processing capability to solve some of the world’s most complex problems.

A new way to look at your AI needs

Unlike single, heterogenous infrastructure use cases like virtualization, there are multiple patterns within AI that come with different infrastructure needs in the data center. Organizations can think about AI use cases in terms of three main buckets:

1. Build the model, for large foundational training.

2. Optimize the model, for fine-tuning a pre-trained model with specific data sets.

3. Use the model, for inferencing insights from new data.

The least demanding workloads are optimize and use the model because most of the work can be done in a single box with multiple GPUs. The most intensive, disruptive, and expensive workload is build the model. In general, if you’re looking to train these models at scale you need an environment that can support many GPUs across many servers, networking together for individual GPUs that behave as a single processing unit to solve highly complex problems, faster.

This makes the network critical for training use cases and introduces all kinds of challenges to data center infrastructure and operations, especially if the underlying facility was not built for AI from inception. And most organizations today are not looking to build new data centers.

Therefore, organizations building out their AI data center strategies will have to answer important questions like:

• What AI use cases do you need to support, and based on the business outcomes you need to deliver, where do they fall into the build the model, optimize the model, and use the model buckets?
• Where is the data you need, and where is the best location to enable these use cases to optimize outcomes and minimize the costs?
• Do you need to deliver more power? Are your facilities able to cool these types of workloads with existing methods or do you require new methods like water cooling?
• Finally, what is the impact on your organization’s sustainability goals?

The power of Cisco Compute solutions for AI

As the general manager and senior vice president for Cisco’s compute business, I’m happy to say that Cisco UCS servers are designed for demanding use cases like AI fine-tuning and inferencing, VDI, and many others. With its future-ready, highly modular architecture, Cisco UCS empowers our customers with a blend of high-performance CPUs, optional GPU acceleration, and software-defined automation. This translates to efficient resource allocation for diverse workloads and streamlined management through Cisco Intersight. You can say that with UCS, you get the muscle to power your creativity and the brains to optimize its use for groundbreaking AI use cases.

But Cisco is one player in a wide ecosystem. Technology and solution partners have long been a key to our success, and this is certainly no different in our strategy for AI. This strategy revolves around driving maximum customer value to harness the full long-term potential behind each partnership, which enables us to combine the best of compute and networking with the best tools in AI.

This is the case in our strategic partnerships with NVIDIA, Intel, AMD, Red Hat, and others. One key deliverable has been the steady stream of Cisco Validated Designs (CVDs) that provide pre-configured solution blueprints that simplify integrating AI workloads into existing IT infrastructure. CVDs eliminate the need for our customers to build their AI infrastructure from scratch. This translates to faster deployment times and reduced risks associated with complex infrastructure configurations and deployments.

Another key pillar of our AI computing strategy is offering customers a diversity of solution options that include standalone blade and rack-based servers, converged infrastructure, and hyperconverged infrastructure (HCI). These options enable customers to address a variety of use cases and deployment domains throughout their hybrid multicloud environments – from centralized data centers to edge end points. Here are just a couple of examples:

• Converged infrastructures with partners like NetApp and Pure Storage offer a strong foundation for the full lifecycle of AI development from training AI models to day-to-day operations of AI workloads in production environments. For highly demanding AI use cases like scientific research or complex financial simulations, our converged infrastructures can be customized and upgraded to provide the scalability and flexibility needed to handle these computationally intensive workloads efficiently.
• We also offer an HCI option through our strategic partnership with Nutanix that is well-suited for hybrid and multi-cloud environments through the cloud-native designs of Nutanix solutions. This allows our customers to seamlessly extend their AI workloads across on-premises infrastructure and public cloud resources, for optimal performance and cost efficiency. This solution is also ideal for edge deployments, where real-time data processing is crucial.

AI Infrastructure with sustainability in mind 

Cisco’s engineering teams are focused on embedding energy management, software and hardware sustainability, and business model transformation into everything we do. Together with energy optimization, these new innovations will have the potential to help more customers accelerate their sustainability goals.

Working in tandem with engineering teams across Cisco, Denise Lee leads Cisco’s Engineering Sustainability Office with a mission to deliver more sustainable products and solutions to our customers and partners. With electricity usage from data centers, AI, and the cryptocurrency sector potentially doubling by 2026, according to a recent International Energy Agency report, we are at a pivotal moment where AI, data centers, and energy efficiency must come together. AI data center ecosystems must be designed with sustainability in mind. Denise outlined the systems design thinking that highlights the opportunities for data center energy efficiency across performance, cooling, and power in her recent blog, Reimagine Your Data Center for Responsible AI Deployments.

Recognition for Cisco’s efforts have already begun. Cisco’s UCS X-series has received the Sustainable Product of the Year by SEAL Awards and an Energy Star rating from the U.S. Environmental Protection Agency. And Cisco continues to focus on critical features in our portfolio through agreement on product sustainability requirements to address the demands on data centers in the years ahead.

Look ahead to Cisco Live

We are just a couple of months away from Cisco Live US, our premier customer event and showcase for the many different and exciting innovations from Cisco and our technology and solution partners. We will be sharing many exciting Cisco Compute solutions for AI and other uses cases. Our Sustainability Zone will feature a virtual tour through a modernized Cisco data center where you can learn about Cisco compute technologies and their sustainability benefits. I’ll share more details in my next blog closer to the event.

Source: cisco.com

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Bridging the Digital Divide with Subscriber Edge

Bridging the digital divide has been a longstanding top priority for countries globally. According to Broadband Research, in 2023 approximately five billion people (64% of the world’s population) were connected to the internet. That means roughly three billion people do not have the basic digital necessities such as access to data, sharing information, or communicating. In addition, they do not have the same access to educational, employment, and economic opportunities that could improve the quality of their lives through digital connections.

The World Bank has estimated that increasing the percentage of people with internet access to 75% would “boost the developing world’s collective GDP by $2 trillion and create 140 million new jobs.”

The good news is that the public and private sectors have been partnering to help close the digital divide, but as Broadband Research states: “Factors like increased affordability of devices, improved infrastructure and innovative services drive this growth.”

Role of subscriber edge

Accessing the internet requires a subscription to a broadband service from communications service providers (CSPs), using either cable, fiber, DSL, fixed wireless access (FWA), satellite or 4G/5G infrastructure and devices. Subscriber edge is the access point function for subscribers in a service provider network through which they connect to the broadband network for high-speed connectivity, such as accessing the internet.

Subscriber edge can be deployed with other services on the same platform by converging residential and enterprise services using multiservice nodes. Subscriber edge solutions involve managing subscriber sessions, and include functions like IP address management, policy and quality of service (QoS) enforcement, and secure access to the network through authentication and billing.

Shifting application and traffic demands

Traditional approaches for offering broadband services can be revenue-impacting and could affect the quality of experience (QoE) for a broadband user (see Figure 1). For example, with the advancement of applications and evolving transport protocols—such as Quick UDP Internet Connections (QUIC) and Transmission Control Protocol/ Transport Layer Security (TCPLS)—traffic patterns within broadband networks are experiencing a shift away from traditional transport protocols. These new protocols offer greater control to end-user applications, which reduces the dependency on the underlying broadband network and requires relatively simpler QoS models.

This shift is a pivotal opportunity for CSPs to simplify and modernize their complex traditional broadband networks to address higher bandwidth demands, growing user base, increasing video traffic and rising costs. As a result, there is a need to relook at subscriber edge with the overall subscriber services network design, and address important areas such as:

• Subscriber anchor point in the network
• Selection of subscriber edge devices and architecture
• Improve end-user experience and offer new services
• Address rising network costs

Figure 1. Traditional broadband centralized architecture

Source: cisco.com

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