Mechanisms and techniques are employed for managing the allocation and load balancing of storage system resources for the containerized, distributed execution of applications on a storage system. A control component executing on a processing component of the storage system may control reserving the necessary resources on one or more processing components to implement an application, and control a container management module to create, deploy and/or modify one or more containers on one or more processing components of the storage system. The one or more containers then may be executed to implement the application. Multiple processing components of the storage system may have a resource management module executing thereon. The control component may exchange communications with the one or more resource management modules of each processing component to determine the resources available within the processing component; e.g., to determine whether the processing component can satisfy the resource requirements of the application.

A method and system of deployment of a virtualized service on a cloud infrastructure while taking into consideration variability/elasticity of the cloud resources are described. A first service function specification is selected. Candidate computing systems and candidate links are determined. The determination of the candidates is performed based on the availability and characteristics of the computing systems and the network resources in the cloud infrastructure which includes varying characteristics. A first computing system and one or more links are determined from the candidates for placing the first service function in the cloud infrastructure.

A method for scheduling services in a computing environment includes receiving a service scheduling request corresponding to the computing environment and identifying a resource pool and a set of compliance requirements corresponding to the computing environment. The method continues by identifying target resources within the resource pool, wherein target resources are resources which meet the set of compliance requirements, and subsequently identifying a set of available target resources, wherein available target resources are target resources with scheduling availability. The method further includes analyzing the set of available target resources to determine a risk score for each available target resource and selecting one or more of the set of available target resources according to the determined risk scores. The method continues by scheduling a service corresponding to the service scheduling request on the selected one or more available target resources.

The disclosure herein describes management of distribution of resources between a global pool and an associated plurality of local pools using a flush threshold. A request for resources is received at the global pool from a local pool, the request indicating a requested quantity of resources. Based on the received request, it is determined that available resources in the global pool are below a flush threshold of the global pool. Based on this determination, flush instructions are sent to the local pools, wherein the flush instructions instruct each local pool to release unused resources (e.g., available to be released) to the global pool. Based on the available resources of the global pool then exceeding the requested quantity of resources and/or the flush threshold, resources of the global pool are allocated to the requesting local pool, whereby the local pool is enabled to use the allocated resources.

Methods and systems are provided for assigning nodes to execute functions in a serverless computing environment. In one embodiment, a method is provided that includes receiving a function for execution in a serverless computing environment and identifying a storage pool needed during execution of the function. The serverless computing environment may include nodes for executing functions and a first set of nodes may be identified that implement the storage pool. Colocation measures may be determined between the first set of nodes and a second set of nodes. Available computing resources may be determined for the second set of nodes, such as available processing cores and available memory. The second set of nodes may be ranked according to the colocation measures and the available computing resources and a first node may be selected based on the ranking. The first node may be assigned to execute the function.

Embodiments of this disclosure disclose a method and system for optimizing resources for cloud-based scalable distributed search data analytics service. The method may include generating a computing capacity rightsizing recommendation on the computing capacity based on the computing utilization metrics and generating a memory capacity rightsizing recommendation on the memory capacity based on the memory utilization metrics. The method may further include determining a recommended instance type for the service resource unit based on the computing capacity rightsizing recommendation and the memory capacity rightsizing recommendation. The method may further include performing a storage volume check on the service resource unit to obtain a storage volume check result. The method may further include determining whether to accept the recommended instance type as a final optimization recommendation based on the storage volume check result, the computing capacity rightsizing recommendation, and the memory capacity rightsizing recommendation.

Zero copy message reception for devices is disclosed. For example, a host has a memory, a processor, a supervisor, and a device with access to device memory addresses mapped in a device page table via an IOMMU. An application has access to application memory addresses and is configured to identify a first page of memory addressed by an application memory address to share with the device as a receiving buffer to store data received by the device for the application, where the first page is mapped to a first device memory address in a first device page table entry (PTE). A supervisor is configured to detect that the first application has disconnected from the device, and in response to detecting the application disconnecting, to update the first device PTE to address a second page instead of the first page.

According to a compute instance scheduling method, usage of a physical resource included in each computer, and usage of a physical resource occupied by a compute instance running on each computer in a compute instance provisioning system are collected. Then, a compute instance in the compute instance provisioning system is scheduled based on the usage of the physical resource in each computer and the usage of the physical resource occupied by the compute instance running on each computer.

A method and system for intelligently provisioning resources in storage systems. Specifically, the method and system disclosed herein entail throttling the allocation of resources aiding in the performance of background service tasks on a backup storage system. That is, throughout a predicted span of a background service task, resources may be dynamically allocated towards the performance of the background service task at discrete time intervals within the predicted span, thereby improving overall system utilization.

Mechanisms and techniques are employed for managing the allocation and load balancing of storage system resources for the containerized, distributed execution of applications on a storage system. A control component executing on a processing component of the storage system may control reserving the necessary resources on one or more processing components to implement an application, and control a container management module to create, deploy and/or modify one or more containers on one or more processing components of the storage system. The one or more containers then may be executed to implement the application. Multiple processing components of the storage system may have a resource management module executing thereon. The control component may exchange communications with the one or more resource management modules of each processing component to determine the resources available within the processing component; e.g., to determine whether the processing component can satisfy the resource requirements of the application.