System downtime is one of the most pressing concerns for today’s organizations. With so many businesses shifting to cloud-based IT deployments and utilizing a wide range of online productivity applications and tools, there are few industries remaining that don’t have to worry about servers going down. From lost revenues and diminished productivity to brand damage and missed opportunities, the consequences of system downtime on today’s organizations can be equal parts extensive and expensive.
Given the stakes involved, smart companies are hard at work optimizing network performance to ensure better uptime reliability. Regardless of their IT infrastructure, these organizations can implement a number of strategies that will put their servers and networks in the best position possible.
Relying on a purely public cloud solution comes with its hazards. Uptime SLA reliability for even well-established providers like Amazon Web Services is often much lower than businesses would prefer. Although a 99.99% uptime SLA might sound impressive, it’s really more of a minimum standard that simply isn’t reliable enough for many companies.
One strategy for reinforcing a network architecture is to shift to a hybrid deployment that combines a private cloud server with a public cloud service. Vital, mission-critical processes can be located in the private cloud while additional services run through the public portion of the network. This has the effect of distributing the infrastructure workload, ensuring that the private side isn’t being overburdened while the public cloud isn’t forced to manage data it isn’t actively processing.
Knowledge is power, as the old cliche goes, but with today’s predictive analytics programs actively optimizing network performance, these words are truer than ever. Whether an organization needs to manage server power consumption and cooling needs in a data center environment or monitor website performance to ensure everything is running as efficiently as possible, analytics software can make ongoing adjustments based on real-time data and account for anticipated usage spikes and other fluctuations.
Analytics can also anticipate problems before they occur, identifying hardware components that are likely to fail and inefficiencies that could cause unnecessary strain on the network. This is especially important for companies that maintain an on-site data solution or colocate assets with a data center because they retain so much control over their IT deployment. Armed with predictive analytics, they can optimize their networks to deliver fast and efficient services while minimizing the possibility of unexpected downtime.
Organizations can’t optimize their networks if they don’t have adequate visibility into their IT operations. This requires real-time monitoring with business intelligence software which provides insight into every aspect of performance. Combined with predictive analytics, the data gathered by these platforms can reveal how IT deployments could be organized more efficiently to optimize network performance. They also provide the first warning signs when problems emerge, allowing organizations and remote hands services to respond to threats that might compromise uptime before the system actually goes down.
For companies that deliver services over their networks, monitoring performance provides a variety of key indicators that can be targeted for improvement. From memory utilization to connection time, networks can be analyzed based on gathered data to identify inefficiencies and ensure that applications are running as smoothly as possible.
Relying on a single ISP to access services effectively introduces a bottleneck into a network. Not only does that single connection make the network more vulnerable to failure, but it also introduces bandwidth constraints that can prevent systems from operating at peak efficiency. For organizations that connect to their services through a robust data center environment, their networks can be optimized with multiple backups and redundancies to ensure that services are not disrupted even if one connection goes down or is overburdened with heavy traffic.
As more companies implement internet of things (IoT) devices into their networks, edge computing deployments offer significant advantages to minimize downtime. By pushing processing functions and data management to the edge of the network, services can remain available even if the core of the network goes down. Critical services can continue to operate through an edge network of IoT devices and edge data centers that are capable of operating independently of a centralized cloud architecture. While this deployment may not be suitable for every organization, it can be invaluable for any company that makes use of IoT devices or delivers services to remote end users by way of regional data centers.
Considering the high costs of server downtime, organizations need to be as creative and resourceful as possible when considering how to optimize their networks. By taking advantage of options available through a data center environment and combining cutting-edge technology like real-time analytics with traditional performance monitoring and redundancy solutions, businesses can minimize downtime and provide the products and services their customers demand.
As the Marketing Manager for vXchnge, Kaylie handles the coordination and logistics of tradeshows and events. She is responsible for social media marketing and brand promotion through various outlets. She enjoys developing new ways and events to capture the attention of the vXchnge audience.