Auto-scaling cloud-based memory-intensive applications

A method for reactively scaling resources for memory-intensive applications and for automatically adapting to workload changes with no pre-specified thresholds, said method comprising:

• generating a miss ratio curve (MRC) of an application based on application runtime statistics that are extracted from the application;
• modeling the application's MRC as a hyperbola;
• identifying an area on the hyperbola where the MRC is determined to satisfy a flatten threshold;
• establishing a resource allocation threshold based on the identified area on the hyperbola, the resource allocation threshold indicating how many resources are to be provisioned for the application;
• and
• scaling the resources that are to be provisioned for the application using a resource scaling policy that is based on the resource allocation threshold, wherein scaling the resources that are to be provisioned for the application includes:
• determining a new virtual machine (VM) is to be launched to increase total system memory in response to an increased workload of the application;
• initiating launch of the VM;
• and
• launching a determined number of cloud functions (CF) to temporarily absorb the increased workload while the VM is being launched,
• wherein the number of CFs that are launched provide an aggregate amount of memory equal to the VM,
• wherein requests are routed to the CFs to begin immediate processing while the VM is being launched, and
• wherein, when the VM is online, the CFs are terminated and new requests are shifted to the VM.

A method for reactively scaling resources for memory-intensive applications and for automatically adapting to workload changes with no pre-specified thresholds, said method comprising:

• generating a miss ratio curve (MRC) of an application based on application runtime statistics that are extracted from the application;
• modeling the application's MRC as a hyperbola;
• identifying an area on the hyperbola where the MRC is determined to satisfy a flatten threshold;
• establishing a resource allocation threshold based on the identified area on the hyperbola, the resource allocation threshold indicating how many resources are to be provisioned for the application;
• and
• scaling the resources that are to be provisioned for the application using a resource scaling policy that is based on the resource allocation threshold,
• wherein the resource scaling policy, when implemented, causes an initial pool of cloud functions (CFs) to be initiated, and
• wherein the CFs in the pool are in a suspended state until such time as an increased memory requirement of the application is detected.