A computer implemented method using an artificial intelligence (A.I.) module to explain large scale scheduling solutions includes receiving an original instance of a resource constrained scheduling problem. The instance includes a set of tasks and a variety of resource requirements and a variety of constraints. An optimizer process determines a schedule for the set of tasks while minimizing a makespan of the schedule. A minimal set of resource links is generated based on resource dependencies between tasks. The resource links are added to the original instance of scheduling problem, as precedence constraints. All the resource constraints are removed from the original instance of the resource constrained scheduling problem. A set of critical tasks is computed using a non-resource constrained critical path. Schedules are provided with an explanation of an optimized order of the set of tasks based on the use of the non-resource constrained critical path.

The present disclosure relates to a non-transitory computer-readable recording medium storing an analysis program that causes a computer to execute a process. The process includes determining whether a resource usage of a machine executing a service exceeds a threshold when the machine processes a request for the service, notifying the machine of the request when it is determined that the resource usage does not exceed the threshold, and scaling out the machine when it is determined that the resource usage exceeds the threshold.

Aspects of the invention include receiving, by a controller, a workload comprising one or more tasks, generating a first pod comprising a first sidecar container, generating one or more ephemeral containers for the first pod based on the workload and one or more resource allocation metrics for the pod, executing the one or more tasks in the one or more ephemeral containers, monitoring the one or more resource allocation metrics for the pod, and generating at least one new ephemeral container in the first pod based on the one or more resource allocation metrics for the pod and the workload.

An approach for optimizing storage on a local storage device. The approach receives a cloud resource budget limit and a cloud budget time interval. The approach estimates future cloud resource requests expected to arrive before the end of the cloud budget time interval. The approach calculates definitive and estimated costs of cloud resource usage types. The approach calculates a total estimated resource budget consumption. The approach determines if the total estimated resource budget consumption exceeds the cloud resource budget limit. If the approach determines the cloud resource budget limit is not exceeded, then the approach outputs a set of existing unfulfilled cloud resource requests for fulfillment. If the approach determines the cloud resource budget limit is exceeded, then the approach outputs a subset of set of existing unfulfilled cloud resource requests that do not exceed the cloud resource budget limit for fulfillment.

In a hyper ledger-based blockchain network system, in order to adjust latency and throughput required by a specific hyper ledger-based network, by using a block size, an endorsement policy, the number of channels, and the number of vCPUs allocation, the latency and the throughput desired by a user are maintained.

Systems and techniques are provided for monitoring and managing the performance of services accessed by sites on a computing platform. When a performance issue is identified, a service is monitored to determine if calls to the service exceed a threshold completion time. If so, a resource available to call the service is adaptively throttled by the platform.

Systems and methods for throttling requests to web services are disclosed. A system is configured to receive, at a host, one or more requests during a first time period. Each request is for a web service hosted on a backend. The host is one of a plurality of hosts of an application programming interface (API) gateway to receive a plurality of requests for the web services. The system is further configured to: process at least a portion of the one or more requests for the one or more web services; count, by a local counter in a local cache of the host, the one or more requests received at the host during the first time period; compare a local count of the local counter to a local bucket size associated with the host; and provide an instruction to update a remote count of a remote counter based on the comparison.

Techniques for optimization-based pool protection for a cloud provider network are described. An exemplary method includes receiving historical usage data of virtual machine instances of a capacity pool of a cloud provider network for each account of a plurality of accounts of the cloud provider network, generating a linearly extrapolated usage, based at least in part on an extrapolating parameter, for each account based at least in part on respective usage percentiles of the virtual machine instances from the historical usage data, determining a usage of the virtual machine instances for each account based at least in part on the linearly extrapolated usage for a same extrapolating parameter value, receiving, by the cloud provider network, a request to launch a computing resource for an account, determining a usage limit for the account based at least in part on the usage for that account, and launching the computing resource when a requested usage for the computing resource is less than or equal to the usage limit and not launching the computing resource when the requested usage for the computing resource is greater than the usage limit.

In a service mesh, back-pressure is applied and relieved as needed by a control mechanism which is applied between pairs of services to control the rate at which service requests are made from one of service to the other via monitoring hardware and/or software metrics. A proxy of one service is monitored to observe the rate at which it receives service requests from the other service. If it is observed that the monitored metrics have breached allowable limits, back-pressure is applied to reduce the rate at which the other proxy transmits these service requests. Through continued monitoring of the proxy, the back-pressure can be later relieved when appropriate by increasing the permitted request rate.

The present disclosure relates to systems, methods, and computer readable media for predicting deployment growth on one or more node clusters and selectively permitting deployment requests on a per cluster basis. For example, systems disclosed herein may apply tenant growth prediction system trained to output a deployment growth classification indicative of a predicted growth of deployments on a node cluster. The system disclosed herein may further utilize the deployment growth classification to determine whether a deployment request may be permitted while maintaining a sufficiently sized capacity buffer to avoid deployment failures for existing deployments previously implemented on the node cluster. By selectively permitting or denying deployments based on a variety of factors, the systems described herein can more efficiently utilize cluster resources on a per-cluster basis without causing a significant increase in deployment failures for existing customers.