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Are you trying to decide whether a move to the cloud would work for your organisation? Take these factors into consideration when you are weighing up your on-premise or cloud options.
Load balancing enables cloud customers to distribute their workload across several computers to avoid overusing one single resource.
This process is transparent to the user who sees only one point of access to the resource. Servers can be assigned to take on more workload when scale issues arise and to distribute capacity.
These resources scale automatically to match the demands made by the application at peak load times.
Load balancing uses virtual LANs to ensure that a balance request across nodes made by one customer does not affect the service on another customer’s implementation.
Cloud bursting enables resources to “burst” into the cloud during peaks of extremely high resource requirements such as Thanksgiving Black Friday, and other seasonal events.
This enables corporate applications to be redirected over a set of load balanced cloud based resources.This could happen when internal resources struggle to fulfil demands, or when capacity is reached on the corporate network.
Azure. Microsoft officials loosely define machine learning as "a way of applying historical data to a problem by creating a model and using it to successfully predict future behavior or trends."
This means customers do not have to develop their own machine learning solution and instead use insights from big data to predict outcomes such as which products your customers might buy next from them.
Content caching improves the performance of a web site by temporarily storing frequently used data that was recently accessed. Cached data eliminates requests for that data from the web server, instead the request will be served by the load balancer.
Request response times are improved and there is less load requirements at the web server.
Applications can determine which data blocks are most popular and should remain in cache. The cache can be configured to be distributed across all running instances to minimise data redundancy in each virtual machine instance.
Cloud services such as Microsoft Azure, Google and Amazon EC2 enables users to change capacity requirements quickly and seamlessly. The web service can invoke several instances at the same time as demand requires as it adapts to changes in the workload.
It can automate the provision and de-provision of resources to allow demand to be met without user intervention. Resources can be paid for on a pay per use basis or per virtual machine, datacentre or using the platform as a service option (PaaS).
With cloud self-service or self-provisioning, the customer purchases resources from the cloud provider which are made available for use within minutes.
Private clouds allow you to securely allocate resources to your network, dedicated to your organization with a cluster of dedicated customers. You can deploy applications, pool resources and configure self-service in your private cloud and manage them in the public cloud if you wish.
You can manage and deploy applications and services and control your own virtual networking environment and scale up quickly and easily. A range of licensing and pricing options is offered by cloud vendors.
Private clouds are suited for secured confidential information and core systems.