International Business Machines Corporation

Vmware, Inc.

Sudipta Biswas

Pradipta Kumar Banerjee

Ashish Billore

Muthu Annamalai Muthiah

Monotosh Das

Hemant Kumar Shaw

Vijay K Sukthankar

Hemant K Shaw

Shubham Chauhan

Some embodiments provide a method that identifies a first number of requests received at a first application. Based on the first number of requests received at the first application, the method determines that a second application that processes requests after processing by the first application requires additional resources to handle a second number of requests that will be received at the second application. The method increases the amount of resources available to the second application prior to the second application receiving the second number of requests.

Predictive scaling of application based on traffic at another application

Dynamically scaling control plane for ingress services for large numbers of applications with minimal traffic disruption includes receiving an estimate of a number of applications to be executed by multiple clusters implemented by an orchestrator platform. Each cluster includes multiple containers. The multiple clusters implement a centralized controller that control execution of the applications by the multiple clusters. The centralized controller is sharded into a variable number of controllers that collectively control the estimated number of applications based on the estimate of the number of applications and a pre-determined number of applications that each controller can control. Each controller of the variable number of controllers controls an execution of a respective subset of the applications. In response to a change in the number of applications over time, the number of controllers is modified based on a number of applications to be executed by the multiple clusters at any given time.

Dynamically scaling control plane for ingress services for large numbers of applications with minimal traffic disruption

Technical solutions are described for assigning containers for executing functions in a software application using a server cluster that uses functions as a service architecture. An example method includes creating, by a global scheduler of the server cluster, a first container at a first host node in the server cluster for executing a first function from the software application. The method further includes intercepting, by an in-node scheduler of the first host node, a command from the first function for creating a second container for executing a second function. The method further includes assigning, by the in-node scheduler, an in-node container as the second container, the in-node container being a container executing on the first host node. The method further includes updating, by the in-node scheduler, a resource database that indicates that the second function is being executed in the in-node container on the first host node.

Computer server application execution scheduling latency reduction

An apparatus for implementing a user policy includes a policy module that issues a user policy for a plurality of computing devices. Two or more of the computing devices have different hardware configurations. The apparatus includes a soft metrics module that, in response to the user policy, sends soft metrics for a computing device of the plurality of computing devices, to determine if the computing device complies with the user policy, where the soft metrics are specific to a hardware configuration of the computing device. The apparatus includes an operational data module that receives, from the computing device, operational data responsive to the soft metrics, and a compliance module that analyzes the operational data to determine if the computing device complies with the user policy.

Sending hardware specific soft metrics for each of a plurality of computing devices based on a plain language user policy

A computer manages methods for determining workload placement in a computing environment. The computer receives a plurality of workloads with associated information, wherein the associated information for each workload contains at least: contract information, billing information, and resource availability information. The computer determines a profitability factor for each workload of the plurality of workloads, wherein the profitability factor is at least based on the billing information. The computer determines a penalty factor for each workload of the plurality of workloads, wherein the penalty factor is at least based on the contract information. The computer determines a preference factor for each workload of the plurality of workloads, wherein the preference factor is at least based on the resource availability information. The computer assigns a priority ordering for each of the workloads from the plurality of workloads.

Optimization of workload placement

Multi-site disaster recovery mechanism performed by the following steps: (i) providing a disaster recovery (DR) system that includes a plurality of sites where each site of the plurality of sites actively serves infrastructure-as-a-service to a set of tenant(s); (ii) for each site of the plurality of sites, determining the following characteristics of the site: workloads that require DR, workloads characteristics, tenants and capabilities; (iii) for each site of the plurality of sites, determining a plurality of associated sites; and (iv) on condition that a disaster occurs which impacts a first site of the plurality of sites, distributing a primary site workload of the first site across the associated sites of the first site. The determination of the plurality of associated sites associated with each site is based upon at least one of the following characteristics: capacity, workloads that require DR, workloads characteristics, tenants and/or capabilities.

Multi-site disaster recovery mechanism for distributed cloud orchestration software

Embodiments of the present invention provide systems, methods, and computer program products for optimizing a placement plan. In one embodiment, a method is disclosed in which a request for registration with an external advisor is received. A time to live is received from each external advisor and used to determine an overall timeout period value for a placement engine. After receiving a predictive failure alert, internal and external advisors are ranked according to criteria and advice is received from the qualified advisors. A placement plan is generated based on the advice received from the advisors.

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Bengaluru, India

Sudipta Biswas