Edge to Cloud
What is Edge to Cloud?
Edge to cloud refers to the fact that enterprise data is no longer confined to the data center; It is being generated at the edge in ever-growing amounts, processed and stored in the cloud, and used by an increasingly distributed global workforce.
The modern edge-to-cloud environment
As organizations generate data from Internet of Things (IoT) devices, smart sensors, and other devices on the edge of their networks, this data must be collected, stored, and processed. In order to extract business insight from this data, it must flow seamlessly between edges, clouds, data centers, and users in a wide variety of work locations and environments.
Why is edge to cloud necessary?
One driver for today’s edge-to-cloud approach is the growing need for real-time data-driven decision-making, especially at the edge. For example, autonomous driving technologies depend on artificial intelligence (AI) and machine learning (ML) systems that can determine, in a fraction of a second, if an object in the street is another vehicle, a person, or a piece of road debris.
What is an edge-to-cloud platform?
An edge-to-cloud platform is designed to bring the cloud experience to all of an organization’s apps and data, regardless of where they may reside. It offers a consistent user experience with security as a critical component of its design. And it allows organizations to pursue new business opportunities with point-and-click simplicity to provision new services, as well as easy scalability to meet changing business demands.
How does an edge-to-cloud platform work?
To deliver a cloud experience everywhere, a platform needs to incorporate several defining characteristics :
Self-service: Organizations need the ability to easily and quickly spin up resources for new projects, such as new virtual machines (VMs) or services for containers or MLOps. Point-and-click simplicity enables users to select and deploy the cloud services they need. A true edge-to-cloud platform should also provide a view into costs, usage, and forecast capacity across the entire hybrid IT estate, including assets in the public cloud.
Rapidly scalable: To deliver on the cloud’s promise of agility, a platform needs to include built-in buffer capacity, so when more capacity is needed, it is already installed and ready to go. Scaling down capacity should also be simplified, making it possible to closely align infrastructure usage with business needs.
Pay-as-you-go: Billing should be based on the actual capacity used, so businesses can get new projects up and running without heavy upfront costs and procurement delays. This eliminates both wasteful over-provisioning and the risk of disruptions caused by under-provisioning.
Managed for you: An edge-to-cloud platform should lift the operational burden of managing and updating the infrastructure, so IT can focus on building the business and generating revenue. To meet organizations’ security needs, an edge-to-cloud platform must also deliver production-ready, enterprise-class security, together with the reassurance of knowing that data and apps remain in an organization’s own facilities (or those of a colocation provider) and under their control. This approach also supports sustainability goals by deploying modern, energy-efficient equipment and eliminating underutilized infrastructure.
Why is an edge-to-cloud approach important?
Organizations around the world are embracing digital transformation, but in many cases, they must reexamine the ability of their existing technology infrastructure to meet the demands of data growth, edge expansion, IoT, and distributed workforces. With data being generated and consumed across clouds, edges, data centers, and colocations, there is a significant risk of information silos forming across the enterprise, limiting an organization’s ability to make effective, data-driven decisions.
While most data still resides on-premises, other types of data that are collected, processed, and managed at the edge—outside of traditional data centers or public clouds—are expected to grow significantly in the near future. Managing workstreams across these remote sites, in addition to ones on-premises, to ensure always-on connectivity, compliance, and security in the most cost-effective way, is not an easy task. It requires platforms, capabilities, and advisory and consulting services that enable organizations to manage, protect, and capitalize on all their data, from edge to cloud.
An edge-to-cloud approach offers a unified experience with the same agility, simplicity, and pay-per-use flexibility across an organization’s entire hybrid IT estate. This means that organizations no longer have to make compromises to run their mission-critical apps, and crucial enterprise data services can leverage both on-premises resources and the public cloud.
Edge computing vs. Cloud computing
Both edge computing and cloud computing take place outside the traditional data center; the exact location of that computing is the difference between these two concepts.
Cloud computing is a delivery model in which storage, servers, apps, and more are delivered remotely via the Internet. In this model, users access virtual compute, network, and storage resources made available online by a remote provider. Rather than having to buy and maintain extensive computing, storage, and other IT infrastructure, much of this responsibility is instead assumed by the cloud services provider.
Edge computing can be considered an evolution of cloud computing, born out of the rise of 5G networks and IoT. It allows organizations to perform comprehensive analysis of data collected at the edge without the IT infrastructure of a traditional data center. Edge computing has many possible applications, including security and medical monitoring, self-driving vehicles, video conferencing, and enhanced customer experiences.
At a base level, edge computing streamlines how much data businesses and organizations can process at any given time, and as a result, they are learning more and uncovering insights at an incredible rate. With more detailed data from a variety of multi-access edge computing locations, businesses are better equipped to predict, manage, prepare, and adapt for future demands using historical and near-real-time data and scalable and flexible processing without the costs and constraints of older IT options.
Organizations do not need to make a binary choice between edge computing and cloud computing. The two models are complimentary, and each is suited to different use cases. An edge-to-cloud platform enables organizations to employ each model where it makes the most business sense for them to do so while keeping information flowing throughout the enterprise network.
Benefits of edge-to-cloud technology
The edge-to-cloud experience delivers a number of benefits to organizations:
- Achieve greater agility: Edge-to-cloud platforms give organizations the flexibility to respond quickly to requests from the business, capitalize on market opportunities when they arise, and accelerate time-to-market for new products.·
- Modernize applications: Even mission-critical workloads that may not be suited to move to public cloud can run efficiently on today’s as-a-service platforms. Plus, cloud services can help organizations achieve the benefits of container technology, including savings in compute and memory usage, accelerated application development, and run-anywhere portability.
- Leverage the power of hybrid cloud environments without the complexities: The edge-to-cloud platform delivers the advantages of hybrid cloud adoption without the management challenges that come with it. Applications running on an as-a-service platform retain their familiar user experience. They can maintain their existing app associations, and users do not need to learn new skills or processes.
- Develop hybrid cloud strategies with confidence: With edge-to-cloud technologies, organizations can easily create the optimal combination of on- and off-premises assets and quickly switch between them as business and market conditions change. An edge-to-cloud platform can even enable the monitoring and management of public cloud resources.
- Realize the transformational value of apps and data: Some data sets are simply too large or business-critical to make the move to the cloud. An edge-to-cloud platform offers maximum availability and minimum latency for data assets. It provides a powerful way to create and clean data lakes and extract vital information through analytics and AI
Why use edge-to-cloud?
Edge-to-cloud computing enables organizations to avail the benefits of edge computing and cloud infrastructure, facilitating real-time decision-making and boosting system performance.
- Reduced latency: The edge-to-cloud architecture processes data nearer to the source instead of transferring data to the cloud for processing. This closeness reduces the distance needed to send data to the processing infrastructure. Data is processed locally to get insights and make decisions, lowering latency associated with transferring data to the cloud and processing it further.
- Increased security: The data accumulated at the edge is stored in the same location, enabling enterprises to adhere to privacy, compliance, and governance standards. It is easy to implement security policies to minimize risks. Since the data is stored locally, it is less susceptible to security breaches.
- Improved scalability: The distributed architecture enables enterprises to scale their edge computing resources as needed to facilitate parallel processing and improve scalability.
- Enhanced reliability: Multiple edge nodes reduce the risk of failure of single points. If an edge device stops working, the workload is shifted to other devices to reduce downtime and ensure continuity of operations.
- Reduced costs: Processing data locally instead of in the cloud lowers the IT investments of an organization by cutting down the transmission and storage costs.
What is an edge-to-cloud architecture?
The edge-to-cloud computing architecture distributes processing across multiple edges in the network for data collection, processing, storage, and analysis.
Different components of an edge-to-cloud architecture
- Cloud: A cloud is a storehouse for workloads such as machine learning models and applications. It can be private or public; it hosts and executes the applications to control edge nodes. The workloads on the edge interact with workloads on the cloud.
- Edge device: An edge device refers to equipment such as sensors, gateways, actuators, and IoT devices that are located on the edge of the network. It collects information and transfers it to the cloud for further processing.
- Edge node: It refers to edge devices, edge gateways, and edge servers where computing occurs.
- Edge gateways: An edge gateway is an edge server that works as a medium between the devices and cloud technologies. It hosts applications, workloads, and services and manages network processes to transfer data to the cloud for processing.
- Edge cluster: An edge cluster refers to the edge cloud computing devices used to run application workloads and shared services across the network.
- IoT sensors: The IoT sensors are equipment that gathers and transfers data to the edge for processing and analysis. IoT sensors and edge clusters enable organizations to get real-time analytics.
How does data flow through an edge-to-cloud architecture?
The data is created at the network's edge by sensors, edge servers, and IoT devices.
Further, it is processed on the edge servers locally with the help of data filtering and aggregation to get insights. The edge devices filter and select data depending on specific criteria, which is sent to the cloud through network connections, ensuring the safety and integrity of data.
Next, the databases store data, process, and analyze it with the help of edge cloud computing resources. Use analytics techniques to get results and make data-driven decisions.
What are the challenges of edge-to-cloud?
Enterprises must be careful about the following challenges before implementing edge-to-cloud technologies.
- Security: Data transfer from edge-to-cloud technologies may result in unauthorized access or intervention. Therefore, it is vital to ensure data privacy by following robust protocols and authentication mechanisms to minimize risks.
- Networking: Generally, edge devices are present in distributed premises with restricted network connectivity. Therefore, setting up high bandwidth between the edge devices and the cloud is complex. It is critical to address network congestion, latency, and bandwidth constraints.
- Cost: The edge-to-cloud environment requires multiple edge devices, which need regular maintenance and upgrades resulting in downtime and high maintenance costs.
What is the future of edge-to-cloud?
The growing demand for edge cloud computing solutions
Edge-to-cloud has a bright future with technologies such as robotics, extended reality, heterogeneous hardware, and AI. It is a powerful solution to lower latency, process data, and ensure persistence during a network failure. Enterprises must opt for edge computing to benefit from distributed cloud technologies. Businesses must embrace partnerships and ecosystems instead of depending on a single-vendor approach.
The challenges that need to be addressed
Managing data consistency and interoperability across different edge devices and platforms and overcoming latency and bandwidth limitations are potential challenges of edge-to-cloud computing. Overcoming them will ensure high operational efficiency and digital transformation across industries.
How can HPE help you adapt to an edge-to-cloud world?
HPE makes the modern cloud experience possible everywhere—across edges, colocation facilities, data centers, and multiple clouds—with the HPE GreenLake edge-to-cloud platform. This platform enables customers to transform and modernize their workloads to a cloud operating model, optimize and secure applications from edge to cloud, and achieve a future-ready position capable of addressing and leveraging all forms of data, regardless of location. It features a broad portfolio of cloud services such as machine learning operations (ML Ops), containers, storage, compute, VMs, data protection, and more, delivered to your facility in as little as 14 days with no upfront cost. Sophisticated metering enables accurate and transparent pay-per-use billing that scales up and down with usage. And with 24x7 monitoring and management, HPE takes on the heavy lifting associated with managing infrastructure.
Tying everything together is the unifying platform, HPE GreenLake Central, which centralizes operations and insights across the hybrid estate. In this self-service platform, users simply point and click to get up-to-date information about their costs and capacity, or easy access to management capabilities for containers, virtual machines, and other services to which they’re subscribed.
Further streamlining the edge-to-cloud experience is HPE GreenLake Lighthouse, which provides a seamless, intelligent operational experience to easily run and manage different workload-optimized solutions. It is a secure, cloud-native infrastructure that removes the entire process of having to order and wait for a new configuration by allowing customers to add new cloud services in just a few clicks in HPE GreenLake Central and run them simultaneously in just minutes. Customers can use HPE GreenLake Lighthouse to run a variety of cloud services in any location, whether it is in their data center, with a colocation provider of their choice, or at the edge.
Cloud-native and intelligent, HPE GreenLake Lighthouse is built with HPE Ezmeral software to autonomously optimize different cloud services and workloads by composing resources to deliver the best performance, lowest cost, or a balance of both, depending on business priorities.
And for organizations seeking to take a data-driven approach to achieve the optimal cloud operating model across all environments, the HPE Edge-to-Cloud Adoption Framework can help. It leverages HPE’s expertise in delivering solutions on-premises to meet a broad spectrum of business needs for customers across the globe. HPE has identified several critical areas that enterprises should evaluate and measure to execute an effective cloud operating model.