Adaptive data collections may include various type of data arrays, sets, bags, maps, and other data structures. A simple interface for each adaptive collection may provide access via a unified API to adaptive implementations of the collection. A single adaptive data collection may include multiple, different adaptive implementations. A system configured to implement adaptive data collections may include the ability to adaptively select between various implementations, either manually or automatically, and to map a given workload to differing hardware configurations. Additionally, hardware resource needs of different configurations may be predicted from a small number of workload measurements. Adaptive data collections may provide language interoperability, such as by leveraging runtime compilation to build adaptive data collections and to compile and optimize implementation code and user code together. Adaptive data collections may also provide language-independent such that implementation code may be written once and subsequently used from multiple programming languages.

Upon receiving a request to hibernate a hypervisor of a virtualization system running on a first computer, acts are carried out to capture a state of the hypervisor, where the state of the hypervisor comprises hypervisor logical resource parameters and an execution state of the hypervisor. After hibernating the hypervisor by quiescing the hypervisor and storing the state of the hypervisor into a data structure, the data structure is moved to a different location. At a later moment in time, the data structure is loaded onto a second computing machine and restored. The restore operation restores the hypervisor and all of its state, including all of the virtual machines of the hypervisor as well as all of the virtual disks and other virtual devices of the virtual machines. Differences between the first computing machine and the second computing machine are reconciled before execution of the hypervisor on the second machine.

An approach for a hypervisor to throttle CPU utilization based on a CPU utilization throttling request received for a data flow is presented. A method comprises receiving a request for a CPU utilization throttling. The request is parsed to extract a CPU utilization level and a data flow identifier of the data flow. Upon receiving a data packet that belongs to the data flow identified by the data flow identifier, a packet size of the data packet is determined, and a rate limit table is accessed to determine, based on the CPU utilization level and the packet size, a rate limit for the data packet. If it is determined, based at least on the rate limit, that the CPU utilization level for the data flow would be exceeded if the data packet is transmitted toward its destination, then a recommendation is generated to drop the data packet.

Example implementations relate to application-specific policies for failing over from an edge site to a cloud. When an application becomes operational within an edge site, a discovery phase is performed by a local disaster recovery (DR) agent. I/O associated with a workload of the application is monitored. An I/O rate for data replication that satisfies latency characteristics of the application is predicted based on the incoming I/O. Based on results of tests against multiple clouds indicative of their respective RTO/RPO values, information regarding a selected cloud to serve as a secondary system is stored in an application-specific policy. The application-specific policy is transferred to a remote DR agent running in the selected cloud. Responsive to a failover event, infrastructure within a virtualized environment of the selected cloud is enabled to support a failover workload for the application based on the application-specific policy.

An information handling system may include at least one processor; and a non-transitory memory coupled to the at least one processor. The information handling system may be configured to: execute a plurality of virtual machines having workloads associated therewith; during selected times, apply a plurality of configuration settings relating to the at least one processor while executing the workloads of the plurality of virtual machines; track a plurality of performance metrics relating to the at least one processor during the selected times; and predictively determine a selected one of the plurality of configuration settings that is predicted to improve performance of the workloads.

A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.

Some embodiments provide a method for configuring a logical middlebox in a hosting system that includes a set of nodes. The logical middlebox is part of a logical network that includes a set of logical forwarding elements that connect a set of end machines. The method receives a set of configuration data for the logical middlebox. The method uses a stored set of tables describing physical locations of the end machines to identify a set of nodes at which to implement the logical middlebox. The method provides the logical middlebox configuration for distribution to the identified nodes.

Various systems and methods are provided in which a replication process is initiated between a primary site and a recovery site, each having plurality of gateway appliances. Replication loads are evaluated for each given gateway appliance of the plurality of gateway appliances. If a determination is made that at least one gateway appliance of the plurality of gateway appliances is not overloaded, the plurality of gateway appliances are sorted based on replication loads respectively associated with each gateway appliance, and a determination is made as to whether a relative difference in replication loads between a gateway appliance having a highest replication load and a gateway appliance having a lowest replication load exceeds a difference threshold to determine whether the replication workloads between the gateway appliances should be rebalanced.

Performing data synchronization is disclosed, including: receiving an indication to synchronize a container to a snapshot, wherein the container has a first data state and an identity, wherein the snapshot corresponds to a second data state; causing the container to have the second data state corresponding to the snapshot; and maintaining the identity of the container.

A network functions virtualization management and orchestration system with a VNF descriptor (VNFD) including a information element that allows an instance created based on the VNFD to be distinguished by name. The information element includes an information element of a VM name that describes a naming rule for a virtual machine (VM).