Edge Data Center Deployment
Another argument for choosing a multi-location data center design may have to do, for example, with the global trend around edge computing, where applications need to run in a distributed manner close to its users.
Data center resilience and business continuity are also some of the main advantages of edge computing, but there’s more. Sure, with edge computing, sites at the edge can continue to function independently in the case of an outage – it’s more redundant than a setup in a centralized data center since the infrastructure is local, but that’s just a particle of the benefits it may bring. Across all sectors and use cases, edge computing may help improve user experiences by enabling quicker, more consistent, and more reliable services.
Edge computing through a multi-location data center design allows for decision making in fast paced environments based on real-time data. At the edge is where real-time decision making can excel. If the amount of time needed for decision making and data processing is so small that one cannot afford delay by transmitting the data across the Internet, it can probably best be done at the network edge. Here, decisions can be made decentralized based on a combination of data from multiple local sensors.
Data processing at the edge provides great benefits for example for Internet of Things (IoT) applications, as the processing capabilities are brought as near as feasible to IoT devices. IoT computing tasks at the edge can save time and resources compared to transferring data to be processed at central data centers. Processed data will be quicker available at its target IoT destinations. So, edge computing keeps the processing of IoT data near to the source.
For businesses deploying IoT-based products and services, this might result in significant IT infrastructure advantages in terms of performance, latency, security, and cost.
Another development for which edge computing through a multi-location data center setup can be interesting, is connected to the rapid increase in data production and processing in general – reinforced by the use of AI and ML powered applications. With the increase in data creation, the ‘data pipelines’ between the various Internet Exchanges across the globe may grow too big and too complex. If all the expanding volumes of data will first be sent across the Internet before it’s getting processed, in the coming years, the worldwide web will simply not be equipped to handle all data that has to be moved and processed.
Here, however, we must make a small side note – as Zumiv low-latency global network backbone with only 45% utilization has more than 10 Tbit/s of bandwidth available thus ensuring future network growth for our clients. More generally speaking though, it would be wise to move some computing tasks to the edge, in the interest of the Internet infrastructure overall. Future applications will increasingly rely on machine learning (ML) and artificial intelligence (AI), further increasing the burden on Internet traffic and placing even more focus on the need for network speed and capacity as well as data processing at the edge.
Enhancing Security, Latency, Cost Efficiencies
One of the prevalent misconceptions regarding the edge is that cloud computing would somehow be replaced by it. In practice, edge and cloud should cooperate. The intelligent approach presupposes that a decentralized edge and a centralized cloud are in sync. Whether it be public, hybrid, or private cloud, a cloud environment may provide for a platform to centralize all data and use it as and where it is required throughout an organization.
Deploying IT infrastructure at the edge through a multi-location data center setup can be beneficial for an enhanced security architecture as well. Sure, edge computing may increase the potential attack surface for those with malicious intent, but the potential impact on the company in its entirety may significantly be reduced by it. The fact that less data may be intercepted when less data is being transferred over the Internet is another reality that may be beneficial in terms of a company’s security. In addition, edge computing aids organization in resolving challenges with data sovereignty, local compliance, and privacy legislation.
When it comes to network latency, with an autonomous car as an example, the relevance of the data being processed may decrease with processing time. So, transferring and processing data quickly is important since much of the data an autonomous vehicle gathers ‘at the edge’ can be worthless after a few seconds. Particularly on a crowded road, milliseconds count for autonomous driving, enhancing the need for the lowest latencies when it comes to transferring data.
Another example where milliseconds in processing time delay may count, in other words where the lowest network latencies can play a key role is within Industry 4.0 settings. Here, AI-based technologies continuously need to monitor all parts of a production process to maintain data consistency. With Industry 4.0 setups, there is frequently insufficient time to send data back and forth between a manufacturing location on the one side and a centralized data center or clouds on the other. In circumstances such as equipment failures and potentially fatal accidents, instant data analysis might be indispensable. Reaction times are boosted by eliminating latency and pushing data processing to the edge, at the spot where the data is created.
When it comes to the cost of transferring, maintaining, and safeguarding data for edge usage, spending the same amount of money on all data might not seem the smartest choice – as not all data is the same and not all data will hold the same value for an organization. Some data can be vital to business operations, while other data can probably be of less value or even useless. As a company, you might save money by keeping as much data at edge locations as possible rather than having to use expensive bandwidth for your data to travel back and forth to the edge. Again, a multi-location data center design may benefit edge use cases, allowing for cost savings as well.
Other applications for which a multi-location data center design can be interesting concerns latency-sensitive operations such as for media players and, for example, also Voice-over-IP (VoIP). A media player will most probably drop out if there is excessive delay in the procedure of handling a media playing event for users at the edge. The same accounts for VoIP. With latency too high, the quality of conversations between users at edge locations will falter, which can eventually lead to the drop out of telephone calls, something that is utterly unacceptable if you are a provider offering such a service to customers. As a provider, you will probably also want to offer service level agreements (SLAs) with your VoIP services. With a multi-location data center setup, controlling latency values will become easier while increasing the likelihood that VoIP-based phone calls will run flawlessly thus to customer satisfaction.
A provider that has numerous data centers spread out across a geographical region, being a country, a continent, or the world, will be able to offer a more reliable experience. A VoIP based call will be of higher quality and reliability the closer the data centers are to its users.
With VoIP telephony, every call being made generates data packets. The amount of data a data center must manage rises in direct proportion to the number of telephone calls. One VoIP service provider might need to handle VoIP telephone calls for thousands of businesses. This implies that millions of data packets might be compressed, sent, and received globally.
As a VoIP service provider, you want to be certain that your infrastructural setup can accommodate client demand, even when temporary traffic spikes are concerned, or because of necessary scale expansion due to customer growth. The amount of data that a centralized data center can store and handle has its limits though. Performance of a VoIP application might suffer when trying to centralize everything into a single data center. See it as a funnel. If too much data is sent to a centralized data center, conversations by phone may experience jitter and latency, leading to infrastructural inaccuracies and a drop of conversation quality.
With a multi-location data center setup, these issues are not that likely, because the processing of data packets is handled close to its users. As a VoiP provider, you are then assured that the quality of the VoIP phone calls remains at a very high level while you can also arrange for better SLA agreements with your customers.