Workload Optimizer addresses the inability of many legacy applications (e.g., legacy databases and stateful streaming processors) running in the cloud to automatically scale with the workload.
Workload Optimizer packs idle containers onto a small number of VMs during the idle period, minimizing the number of active VMs and thus reducing the cost of keeping services online. When the workload increases, Workload Optimizer relocates containers onto different VMs, without any service interruption.
Workload Optimizer solves the problem of over-provisioning in the cloud and the waste of idle resources.
Many legacy applications running in the cloud cannot automatically scale up and down with the workload. Examples of such applications include legacy databases and stateful streaming processors. To serve dynamic workload with minimum impact on service latency, people often over-provision resources according to a peak workload that happens rarely, leading to a huge amount of wasted resources in the cloud when the applications are idle. Using Cornell patented live-migration technology, Workload Optimizer packs idle containers onto a small number of VMs during idle periods.