Keumars Afifi-Sabet

Bobby Hellard

Microsoft has added a host of features to its collaboration tool Teams, to celebrate the apps 2nd birthday.

The updates included customisable backgrounds, new security features and support for users who are hard of hearing or death. But, the most interesting addition is a camera-based feature that lets users share whiteboard content.

‘Infinite Whiteboards’ is a digital canvas for all participants to work on. It even allows for content to be transferred from a physical whiteboard by using a USB camera with Microsoft’s Intelligent Capture function. It allows one user to capture content from their whiteboard, re-focus or resize it, enhance the images and text so remote attendees can clearly see what is being brainstormed on the whiteboard. If changes are made to the board during the call, the content will still be seen by the remote attendees in real-time.

The feature will be available later in the year, but it’s one of a number of new functions for Teams that offer a different way to collaborate – making it more inclusive. Not everyone digests information in the same way and images may offer a better explanation to a complex project.

Similarly, Teams have also added support of those who are hard of hearing of death with live captions. These are essentially real-time subtitles for users to stay in sync with the conversation.

Also added to the service are secure private channels, where a user can restrict or enable participants to see certain channels without the need for creating separate teams. There is also ‘Information Barriers’ that limit who can see and share certain content, adding a layer of privacy and security to sensitive or private content.

“This week marks the second anniversary of the worldwide launch of Microsoft Teams,” said Lori Wright, GM of Microsoft 365. “Over the past two years, Teams has grown significantly in both new capabilities and customer usage, as the hub for teamwork that brings people together and fosters a culture of engagement and inclusion. Today, more than 500,000 organizations, including 91 of the Fortune 100, are using Teams to collaborate across locations, time zones and languages.

“Microsoft Teams is improving workplace collaboration by helping organizations move from an array of disparate apps to a single, secure hub that brings together what teams need including chat, meetings and calling, all with native integration to the Office 365 apps. Users can customize and extend their experience with third-party apps, processes and devices, giving them the tools they need to get work done.”

Clare Hopping

Clare Hopping

As you know, enterprise IT conversation over the past year have often centered upon the open-source Kubernetes container orchestration system. In fact, Kubernetes has emerged as the key technology — and even primary platform — of cloud migrations for a wide variety of organizations.

Kubernetes is critical to forward-looking enterprises that continue to push their IT infrastructures toward maximum functionality, scalability, and flexibility.

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Technology has changed tremendously in the last 20 years. From onion architectures to APIs to microservices to cloud and containers, the technology artifacts shipped by teams has changed. And that’s not all – roles have changed too. Functional silos have been replaced by cross-functional teams, the skill sets people need to have has been redefined and the tools and approaches for how software is developed and delivered has transformed. When we move from highly defined rigid roles and systems to more fluid ones, we gain agility at the cost of control. But where do we want to keep control? How do we take advantage of all these new changes without losing the ability to efficiently develop and ship great software? And how should program and project managers adapt?

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Amazon Web Services (AWS) will release its latest GPU-equipped instance with support for NVIDIA’s T4 Tensor Core GPUs with a particular focus on machine learning workloads.

The specs, while not fully there yet, promise so far AWS-custom Intel CPUs, up to 384 gibibytes (GiB) of memory, up to 1.8 TB of fast local NVMe storage, and up to 100 Gbps in networking capacity.

From NVIDIA’s side, T4 will be supported by Amazon Elastic Container Service for Kubernetes. “Because T4 GPUs are extremely efficient for AI inference, they are well-suited for companies that seek powerful, cost-efficient cloud solutions for deploying machine learning models into production,” Ian Buck, NVIDIA general manager and vice president of accelerated computing wrote in a blog post.

“NVIDIA and AWS have worked together for a long time to help customers run compute-intensive AI workloads in the cloud and create incredible new AI solutions,” added Matt Garman, AWS vice president of compute services in a statement. “With our new T4-based G4 instances, we’re making it even easier and more cost-effective for customers to accelerate their machine learning inference and graphics-intensive applications.”

The announcement came at NVIDIA’s GPU Technology Conference in San Jose alongside various other news. From the AWS stable it was also announced that the NVIDIA Jetson AI platform now supports robotics service AWS RoboMaker, while AI Playground, as reported by sister publication AI News, is an accessible online space for users to become familiar with deep learning experiences.

It’s worth noting that AWS is not the first company to score in this area. In November Google touted itself as the ‘first and only’ major cloud vendor to offer T4 compatibility. In January, the company announced its T4 GPU instances were available in beta across six countries.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

We are witnessing significant uplift in data transmission speeds offered by network connectivity providers. Service providers are now promising speeds in hundreds of MBs to GBs with which, for instance, we can see live streaming of Blu-ray movie prints without any buffering.

Such network speeds are set to trigger many new technology possibilities. Businesses cannot afford to stay behind, as they have to take into account new technologies which are widely adopted by the competitive market landscape. Therefore, the focus of businesses has become clear and narrow; to constantly satisfy customer demands with lucrative digital offerings and push businesses ahead for gaining competitive advantage.

To align with this trend, businesses have already started to optimise and redesign their data centres to handle a vast amount of data generated by a growing number of consumer devices. It is obvious for businesses to transform the data centre for addressing the need for upgrading. A transition would involve the use of:

• Virtual network functions (VNFS), which replaces server hardware with software-based packages to specific work – network function virtualisation (NFV)
• Software-defined networking to gain a central control of the network using a core framework which will allow admins to define network operations and security policies
• Seamless orchestration among several network components using ONAP, ETSI OSM, Cloudify among others
• Workloads (VM and containers) and data centre management by implementing OpenStack, Azure Stack, Amazon S3, CloudStack, and Kubernetes. Containers are getting widely adopted due to features like faster instantiation, integration, scaling, security, and ease in management

The next thing which will disrupt the data centre is the adoption of edge architecture. Edge computing will bring a mini data centre closer to where data is going generated by devices like smartphones, industrial instruments, and other IoT devices. This will add more endpoints before data is gathered by the central data centre. But the advantage is that maximum computing will be done at the edge that will help to reduce the load on network transmission resources. Adding to this, hyperconvergence can be used at edge nodes to bring simplification in the required mini data centre.

Mobile edge computing (MEC), a core project maintained by ETSI, is emerged at an edge computing model to be followed by telecom operators. ETSI is maintaining and working on innovations to improve the delivery of core network functionalities using MEC, as well as guiding vendors and service providers.

Aside from edge computing, network slicing is a new architecture introduced in 5G that will have an impact on how data centres are designed for particular premises, and dedicated for specific cases such as Industrial IoT, transportation, and sports stadia.

Data centre performance for high speed networks

In this transforming age, a large amount of data will transfer between devices and the data centre as well as between data centres. As low latency and high bandwidth is required by new use cases, it is important to obtain a higher level of performance from the data centre. It is not possible to achieve such paramount performance with legacy techniques and by adding more capacity to data centres.

With the ‘data tsunami’ of recent years, data centre technology vendors came up with new inventions and communities formed to address performance issues raised by different types of workloads. One of the techniques which has been significantly utilised in new age data centres is to offload some of the CPU tasks to network or server interconnecting switches and routers. Let’s take an example of the network interface card (NIC) which, when used to connect servers to network components of the data centre, has become a SmartNIC, offloading processing tasks that the system CPU would normally handle. SmartNICs can perform network-intensive functions such as encryption/decryption, firewall, TCP/IP, and HTTP processing.

Analyst firm Futorium conducted a Data Centre Network Efficiency survey targeted to IT professionals about their perceptions and views on data centres and networks. Apart from virtualising network resources and workloads, for efficient processing of data for high-speed networks, SmartNIC usage and process offload techniques have emerged as the top interest for IT professionals. This reveals how businesses are relying more on smart techniques which can save costs, along with notable data centre performance improvements for faster networks.

Workload accelerators, like GPUs, FPGAs, and SmartNICs are widely used in current enterprise and hyperscale data centres to improve data processing performance. These accelerators interconnect with CPUs for generating faster processing of data and require much lower latency for transmitting data back and forth from the CPU server.

Most recently, to address the high speed and lower latency requirements between workload accelerators and CPUs, Intel, along with companies including Alibaba, Dell EMC, Cisco, Facebook, Google, HPE and Huawei, have formed an interconnect technology called Compute Express Link (CXL) that will aim to improve performance and remove the bottlenecks in computation-intensive workloads for CPUs and purpose-built accelerators. CXL is focused to create high speed, low latency interconnect between the CPU and workload accelerators, as well as maintain memory coherency between the CPU memory space and memory on attached devices. This allows for resource sharing for higher performance, reduced software stack complexity, and lower overall system cost.

NVMe is another interface introduced by the NVM Express community. It is a storage interface protocol used to boost access to SSDs in a server. NVMe can minimise CPU cycles from applications and handle enormous workloads with lesser infrastructure footprints. NVMe has emerged as a key storage technology and has had a great impact on businesses, which are dealing with vast amounts of fast data particularly generated by real-time analytics and emerging applications.

Automation and AI

Agile 5G networks will result in the growth of edge compute nodes in network architecture to process data closer to endpoints. These edge nodes, or mini data centres, will sync up with a central data centre as well as be interconnected to each other.

For operators, it will be a task ahead to manually set up several edge nodes. The edge nodes will regularly need initial deployment, configuration, software maintenance and upgrades. In the case of network slicing, there could be a need to install, or update VNFs for particular tasks for devices in the slice. It is not possible to do this manually. At this point, automation comes into the picture where operators need to get the central dashboard at the data centre to design and deploy configuration for edge nodes.

Technology businesses are demonstrating or implementing AI and machine learning at the application level for enabling auto-responsiveness – for instance, using chatbots on a website. Much of the AI is applied for a data lake, to generate insights from self-learning AI-based systems. These types of autonomous capabilities will be required by the data centre.

AI systems will be used for monitoring server operations for tracking activities meant for self-scaling for a sudden demand in compute or storage capacity, as well as self-healing from breakdowns, and end-to-end testing of operations. Already, tech businesses have started offering solutions for each of these use cases; for example, a joint AI-based integrated infrastructure offering by Dell EMC Isilon and NVIDIA DGX-1 for self-scaling at the data centre level.

Conclusion

New architectures and technologies are being introduced with the revolution in the network. Most of this infrastructure has turned software-centric as a response to the growing number of devices and higher bandwidth. Providing lower latency – up to 10 microseconds – is a new challenge for operators to enable new technologies in the market. For this to happen, data centres need to complement the higher broadband network. It will form the base for further digital innovation to occur.

Editor’s note: Download the eBook ‘5G Architecture: Convergence of NFV & SDN Networking Technologies’ to learn more about the technologies behind 5G and the status of adoption, along with key insights into the market

The post Analysis: How are Faster Networks Advancing the New-Age Datacenters appeared first on Calsoft Inc. Blog.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

Clare Hopping