Abstract: A system can receive a changeset for an updated microservice and an identifier of a user account, wherein the updated microservice is relative to a current version of a microservice. The system can instantiate the updated microservice. The system can, in response to determining that a first instance of a dependent microservice depends on the current version of the microservice, instantiate a second instance of the dependent microservice. The system can update routing rules for the service mesh to indicate that any traffic that is associated with the user account and that is directed to the current version of the microservice is to be routed to the updated microservice, and any traffic directed from the updated microservice to the first instance of the dependent microservice is to be routed to the second instance of the dependent microservice.

Abstract: A system can identify that computer code that is executable to operate at least one microservice that is part of a group of microservices has been modified. The system can determine complexity data representative of a complexity of changes to the computer code. The system can determine conditions under which the changes to the computer code are invoked based on at least one of performing a static analysis of the computer code or instrumenting the computer code. The system can generate a progressive deployment plan for the at least one microservice based on the complexity of changes. The system can progressively direct traffic to the at least one microservice based on the progressive deployment plan, and the conditions under which the changes to the computer code are invoked.

Abstract: A system can determine complexity data representative of a complexity of changes to computer code that is executable to operate at least one microservice that is part of a group of microservices, wherein a portion of the changes corresponds to a library on which the computer code depends. The system can generate a progressive deployment plan for the at least one microservice based on the complexity of changes. The system can progressively direct traffic to the at least one microservice based on the progressive deployment plan.

Abstract: A system can determine complexity data representative of a complexity of changes to computer code that is executable to operate at least one updated microservice that is part of a group of microservices, wherein at least one current microservice is deployed, and wherein the at least one updated microservice corresponds to an update of the at least one current microservice. The system can determine a rate at which invocations of the at least one current microservice are made. The system can determine a threshold number of calls to be processed to proceed from a first stage of a progressive deployment plan to a second stage of the progressive deployment plan based on the complexity data and the rate. The system can progressively direct traffic to the at least one updated microservice based on the progressive deployment plan.

Abstract: Technology described herein relates to limiting microservice operation in response to security compromise of the microservice. A method can comprise facilitating, by a system operatively coupled to a processor, transmitting, to a container orchestrator controller that is part of a communication network, a network policy that, in response to deployment, operates to restrict, according to a restriction defined by the network policy, access between a first microservice and a second microservice of the communication network different from the first microservice, and instructing, by the system, the network policy to be deployed by the container orchestrator controller, to restrict, according to the restriction and in response to detection of a malfunction of the first microservice related to an intrusion to the first microservice, first connections employed during a flow between the first microservice and the second microservice by default and second connections that are not employed by default during the flow.

Abstract: The system can determine complexity data representative of a complexity of changes to computer code that is executable to operate at least one microservice that is part of a group of microservices. The system can generate a progressive deployment plan for the at least one microservice based on the complexity of changes. The system can progressively direct traffic to the at least one microservice based on the progressive deployment plan.

Abstract: One example method includes chunking a respective disk of each of a plurality of virtual machines (VM) to create a respective plurality of chunks associated with each of the VMs, creating, based on the chunking process, a cluster comprising one or more of the VMs, creating a VM template whose data and disk structure match respective data and disk structures of each of the VMs in the cluster, and in response to a file operation involving a first one of the VM disks, defragmenting the first VM disk so that a disk structure of the first VM disk is the same as a disk structure of the VM template.

Abstract: A system can determine respective health statuses for respective microservices of respective instances of a group of microservices. The system can monitor the requests to determine a correlation between respective requests of the requests and respective subgroups of microservices of the group of microservices that carry out the respective requests. The system can determine a subgroup of container clusters of container clusters that are available to serve a first request type, based on determining an intersection between the respective subgroups of microservices of the group of microservices that carry out the respective requests, and the respective health statuses for respective microservices of respective instances of the group of microservices. The system can, in response to receiving a first request of the first request type, assign, by a load balancer, the first request to be served by a first container cluster of the subgroup of container clusters.

Abstract: The selection of an optimal restore instance type based on a customer's speed/cost tradeoff resolution is disclosed. An automated restore activity may be performed on a baseline test VM of a predefined size using different restore instance types. The number of calibration runs or evaluations needed to identify an optimal restore instance type in terms of performance and price, with respect to bandwidth or other constraining factor, is performed on less than all of the restore instance types.

Abstract: One example method includes receiving input concerning a boot order sequence, where the input includes VM metadata, entering a training phase which includes generating a boot sequence rule based on the input, using the boot sequence rule to generate a proposed boot sequence, performing the proposed boot sequence, and gathering information concerning performance of the proposed boot sequence. The gathered information can be used as a basis to generate a modified boot sequence.

Abstract: One example method includes receiving data segments that are not already part of a full disk image of a backup, storing the data segments in storage, determining whether or not an aggregate total of data segments in the storage, that are not already part of a full disk image of a backup, equals or exceeds a threshold, when the aggregate total of data segments in the storage equals or exceeds the threshold, creating a full disk image of a backup that includes the data segments in storage, and storing the created full disk image of the backup to a recovery disk.

Abstract: Techniques described herein relate to a method for diagnosing and remediating service failures. The method includes identifying, by a diagnostic and remediation manager, a diagnostic event associated with a service of services; generating a dependency directed acyclic graph (DAG) associated with the service; generating health vectors associated with each node of the dependency DAG; updating the dependency DAG using the health vectors to generate an unhealthy subgraph; and remediating the service based on the unhealthy subgraph.

Abstract: The selection of an optimal restore instance type based on a customer's speed/cost tradeoff resolution is disclosed. Aspects of the anticipated completion time to complete the recovery and completion cost to perform the recovery may be extrapolated based on a baseline or test recovery and/or actual recovery times and costs. An automated restore activity may be performed on a baseline test VM of a predefined size using different restore instance types. An optimal restore instance type is used to form worker VMs that perform the recovery operations.

Abstract: Described is a system (and method) that provides the ability to create an endpoint to allow cloud-based components to access services directly using network infrastructure of a cloud system. To provide such an ability, access connections from components of a production system to the cloud system may be monitored to derive a storage service and a datastore based on portions of a domain name. The derived storage service and datastore are then used to determine configuration settings required to automatically create a service endpoint. The service endpoint may then be deployed within the cloud system allowing the cloud-based component to access the storage service directly. Accordingly, the system provides the ability to automatically configure and deploy service endpoints by leveraging information derived from monitoring network access connections between a production environment and a cloud environment.

Abstract: Methods, systems, and computer program products for cloud to cloud disaster recovery are disclosed. Exemplary embodiments may: discover one or more virtual machines associated with a first auto scaling group; and generate a plan to backup the one or more virtual machines to a second cloud.

Abstract: One example method includes receiving input concerning a boot order sequence, where the input includes VM metadata, entering a training phase which includes generating a boot sequence rule based on the input, using the boot sequence rule to generate a proposed boot sequence, performing the proposed boot sequence, and gathering information concerning performance of the proposed boot sequence. The gathered information can be used as a basis to generate a modified boot sequence.

Abstract: Embodiments for systems and methods of providing a boot order for containers in a cloud native application environment by collecting container environment data from a first container site; determining dependencies and connections between the containers and applications executed within the containers based on a number of system parameters; calculating a recommended order for booting or rebooting the containers during a disaster recovery process; and communicating the recommended order to a system administrator through a graphical user interface (GUI) for acceptance or modification by the system administrator.

Abstract: Automatic network configuration for the recovery of virtual machines. A network configuration can be automatically provisioned by discovering the network topology of a source site and then matching the virtual machines to their locations or subnets in the network topology. The network is then implemented on a target site such that the required connectivity is present in the recovered target site even when the infrastructure of the source site and the target site are different.

Abstract: One example method includes chunking a respective disk of each of a plurality of virtual machines (VM) to create a respective plurality of chunks associated with each of the VMs, creating, based on the chunking process, a cluster comprising one or more of the VMs, creating a VM template whose data and disk structure match respective data and disk structures of each of the VMs in the cluster, and in response to a file operation involving a first one of the VM disks, defragmenting the first VM disk so that a disk structure of the first VM disk is the same as a disk structure of the VM template.

Abstract: Systems and methods for automatically configuring security groups during data protection operations including disaster recovery operations. In preparation for recovering a source site to a target site, security information at the source site is collected and classified. The classified security information is stored as a disaster recovery plan at least for security aspects of the disaster recovery process. The disaster recovery plan can be implemented at the target site such that security risks are minimized and connectivity errors are minimized during the recovery process.