Hyperscale Computing in Medicine

Hyperscale Computing in Medicine

Hyperscale Computing in Medicine

What is Hyperscale Computing?

Hyperscale computing describes a flexible data center architecture that can be rapidly scaled on demand using large horizontal server arrays and software defined networking (SDN). Specialized load balancing software directs traffic between clients and servers.

  • Hyperscale computing provides unprecedented levels of data throughput and hardware efficiency. Artificial intelligence (AI) is used to optimize the computing, networking, and big data storage processes, tailoring them to evolving service requirements.
  • Virtual machine (VM) or containerization processes allow software applications to be moved easily from one location to another. Container instances can also be replicated easily when demand increases.
  • The capacity and flexibility provided by hyperscale technology make it a popular option for cloud computing and big data storage. Both public and private cloud environments utilize hyperscale computing architectures.
  • The adoption of Machine Learning (ML) and the Internet of Things (IoT) will further accelerate the growth of hyperscale computing companies.

Hyperscale Use Case

The healthcare industry presents ready-made applications for ultra-reliable, low latency communication (URLLC), artificial intelligence (AI), big data storage, augmented reality, and the internet of things (IoT).

Unlike factories and ports, healthcare services are also closely linked to one-to-one human interaction. Creating a connected healthcare ecosystem where technology and personalized care seamlessly coexist is the goal of healthcare professionals, communications service providers (CSPs), engineers, and scientists.

Traditional healthcare IT infrastructures are being disrupted by the adoption of hyperscale cloud and edge computing, as well as colocation and hosting services. Healthcare institutions are shifting their IT infrastructure from on-premise to off-premise and rely on hybrid software-defined data centers for innovation.

Private data sharing between organizations can facilitate rapid innovation in healthcare, especially by enabling AI development using previously inaccessible data. The most significant barrier with data sharing in healthcare is the high cost and high effort level required to maintain compliance and patient outcomes.

Healthcare organizations are accountable for enormous quantities of sensitive data, including Protected Health Information. This data must be handled by strict federal, state, and association rules and regulations. New technologies and regulations challenge data management, security, and storage infrastructure.

For healthcare businesses that rely on their IT infrastructures to manage their operations, robust colocation data center infrastructure is crucial.

Defining Connected Health

The term “connected health” is a bit different from similar designations such as digital medicine and telehealth because it is based on a recognition that advanced technology is best used to strengthen, rather than replace, the connections between patients and healthcare providers. This includes ongoing efforts to reduce costs and improve patient awareness. Until recently, limitations on communication speed, latency, and capacity have prevented the full potential of connected health from being realized.

Why are 5G and Hyperscale So Important for Healthcare?

Providing widespread access to doctors, treatments, and diagnostics can be challenging, particularly in rural areas with less health centers and specialists.Practices including virtual consultations, remote patient monitoring, and robotic surgeries help free patients and healthcare providers from geographic constraints. These services also require the real-time video quality, bandwidth, and latency levels provided by 5G technology.

An aging population and epidemic levels of preventable diseases also remind us that proactive, rather than reactive, healthcare policies provide healthier, more cost-effective outcomes. Just as the IoT can be used to effectively monitor equipment and optimize maintenance intervals, predictive methods can be applied to medicine through a plethora of new IoT wearables.

The volume of data collected by these devices would be impossible for healthcare professionals to sort and analyze on their own. The computing power, artificial intelligence, and automation of hyperscale data centers help to convert this data into actionable patient intelligence.

Connected Health Applications

Medical Data Management: With patients frequently moving between providers and locations, managing patient data is an ongoing challenge. At the same time, the sensitive, personal nature of medical records emphasizes network security and confidentiality. Simply offloading data storage to the cloud is only a partial solution. This data must be easily accessible, transferrable, and protected from unauthorized access to ensure HIPAA compliance. While 3GPP 5G standards improve encryption and authentication practices to safeguard patient privacy, hyperscale computing and AI make the data more visible and actionable for healthcare professionals and patients.

Telemetry74: For decades, telemetry has been used to relay vital patient data to central monitoring stations. The IoT transforms telemetry from a hospital-centric practice dependent on continuous human monitoring into an untethered network that allows customized data to be analyzed and shared in real time. Advanced AI is the key to interpreting this information to determine if and when medical intervention is needed. 5G and hyperscale also improve transmission speeds and storage capacity for important diagnostic images like MRI and CAT scans with file sizes of up to 1 gigabyte.

Online Consultations: Online medical consultations expand on the convenience of traditional phone appointments by adding face-to-face communication and real time presentation of test results and scans to the mix. For this change to be fully accepted by patients, reliable high-quality video, low latency, and high bandwidth are essential. With the telehealth market projected to increase by more than 24% per year over the next 5 years, wired internet connections alone are not enough. 5G-enabled enhanced mobile broadband (eMBB) technology can deliver online consultation services to underserved areas without sacrificing connection quality, latency, or security.

AR/VR Training: Augmented reality (AR) and virtual reality (VR) are often associated with gaming and entertainment applications, but their impact on medical training could be equally important. As the technology improves and 5G latency drops, AR and VR will transport medical students into virtual operating rooms with delicate procedures witnessed or performed without risking human or animal subjects.

Augmented reality also has tremendous potential for in-person or remote surgical procedures. 3D markers overlayed onto real time images will help surgeons to navigate between organs, blood vessels, and other anatomical features more effectively.

Remote Surgery: Remotely performed surgery is an eagerly anticipated connected health application where latency, reliability, and bandwidth requirements have potentially life or death implications. The current version of robotic surgery includes a nearby physician carefully guiding a robot to operate in lieu of human hands.

With 5G improving display resolution and reducing latency to the 1 millisecond range, remote robotic surgery or “telesurgery” will enable specialists to share their expertise globally. URLLC demands will fall squarely on edge computing locations with hyperscale computing providing the requisite horsepower behind AI rules, machine learning (ML), and big data storage.

The Future of Connected Health and Hyperscale

5G arrives at an opportune time to alleviate the performance constraints preventing breakthrough AR/VR, robotics, and wearables from fully redefining the healthcare industry. As these applications expand and evolve, the delicate balance between technology and human interaction will continue. Fully robotic surgeries or online consultations guided by advanced AI potentially improve patient outcomes, but not without the assistance of healthcare professionals assuaging the hesitancy of their patients. Hyperscale data centers will play a pivotal role by providing the intelligence to meld medical data and technology with human intuition and empathy.

Advantages of Hyperscale Computing

Hyperscale computing companies utilize the latest hardware and software technology to ensure a high level of reliability and responsiveness to customer demand.

Unlimited Scalability: The scalable architecture of hyperscale computing can accommodate peak demand levels. When data centers do approach their capacity limits, distributed computing, made possible by high-speed data center interconnects (DCIs), seamlessly extends the network geographically to tap into available resources.

Efficiency: Automation, software defined networking (SDN), and UPS power distribution methods help to reduce overall energy consumption. Custom airflow handling and balanced workload distribution across servers optimize cooling efficiency. Industrial IoT (IIoT) temperature and power sensors add additional layers of intelligence and efficiency to the feedback loop.

Redundancy: By utilizing containerized workloads that can easily be migrated between servers, hyperscale computing companies maintain redundancy without significantly increasing their power output. Important applications and data are preserved in the event of an outage or breach.

Disadvantages of Hyperscale Computing

The scale associated with hyperscale cloud computing provides benefits and performance levels that cannot be attained by a conventional data center. At the same time, high traffic volumes and complex flows can make real-time hyperscale monitoring difficult. Visibility into external traffic can also be complicated by the speed and quantity of fiber and connections.

  • Security issues are magnified by the size of the hyperscale data center. Although proactive security systems are an essential part of cloud computing, a single breach can expose huge amounts of sensitive customer data.
  • Construction schedules are compressed by the increased demand for internet content, big data storage, and telecom applications. These pressures can lead to minimized or omitted pre-deployment fiber and performance testing. Automated fiber certification and network traffic emulation tools minimize schedule impact while significantly reducing post-deployment service degradation.
  • Hyperscale data center proportions will continue to expand, even as the available talent pool continues to shrink, especially in remote or undeveloped regions. More automation, machine learning, and virtualization is needed to prevent the demand for resources from overwhelming the ecosystem.
  • Greenhouse emissions are a growing concern for hyperscale computing companies. Data centers already consume approximately 3% of the world’s electricity. This has prompted many leading cloud computing companies and data center owners, including Google and Amazon, to pledge carbon neutrality by 2030.

The Future of Hyperscale Computing

Although it is impossible to predict the future shape and direction of hyperscale computing, it is certain that the unprecedented demand for computing services and big data storage will continue unabated. Fueled by the advent of 5G, the IoT, and artificial intelligence, this data center market size is expected to multiply in the coming decades.

These market factors are also moving hyperscale cloud computing into a more distributed model. Edge computing will accelerate continually, slowly shifting intelligence and storage closer to the expanding universe of IoT sensors and devices. Complex fiber networks connecting these locations will heighten the importance of automated pre-deployment MPO-based fiber testing and high-speed transport testing.

The reality of unmanned hyperscale data centers will contribute to a sharply reduced carbon footprint. Many leading hyperscale computing companies have already committed to 100% renewable energy sources. Innovative projects like Microsoft’s Project Natick subsea data center and Green Data ground and rooftop solar panels prove that hyperscale computing can be re-imagined to sustainably coexist with the environment.