Search results for openstack

glusterfs-swift-object-3.4.0-0.5.lbn13.noarch

OpenStack Object Storage (swift) aggregates commodity servers to work together in clusters for reliable, redundant, and large-scale storage of static objects. This package contains the glusterfs-swift object server.

glusterfs-swift-proxy-3.4.0-0.5.lbn13.noarch

OpenStack Object Storage (swift) aggregates commodity servers to work together in clusters for reliable, redundant, and large-scale storage of static objects. This package contains the glusterfs-swift proxy server.

golang-github-terraform-providers-openstack-1.24.0-0.1.gita89c8c9.lbn25.x86_64

Terraform OpenStack provider

golang-github-terraform-provider-openstack-1.31.0-0.1.git1835cbe.lbn36.x86_64

Terraform OpenStack provider

grafana-mimir-2.7.2-0.1.git76a1021.lbn36.x86_64

Grafana Mimir is an open source software project that provides a scalable long-term storage for Prometheus. Some of the core strengths of Grafana Mimir include: Easy to install and maintain: Grafana Mimir’s extensive documentation, tutorials, and deployment tooling make it quick to get started. Using its monolithic mode, you can get Grafana Mimir up and running with just one binary and no additional dependencies. Once deployed, the best-practice dashboards, alerts, and playbooks packaged with Grafana Mimir make it easy to monitor the health of the system. Massive scalability: You can run Grafana Mimir's horizontally-scalable architecture across multiple machines, resulting in the ability to process orders of magnitude more time series than a single Prometheus instance. Internal testing shows that Grafana Mimir handles up to 1 billion active time series. Global view of metrics: Grafana Mimir enables you to run queries that aggregate series from multiple Prometheus instances, giving you a global view of your systems. Its query engine extensively parallelizes query execution, so that even the highest-cardinality queries complete with blazing speed. Cheap, durable metric storage: Grafana Mimir uses object storage for long-term data storage, allowing it to take advantage of this ubiquitous, cost-effective, high-durability technology. It is compatible with multiple object store implementations, including AWS S3, Google Cloud Storage, Azure Blob Storage, OpenStack Swift, as well as any S3-compatible object storage. High availability: Grafana Mimir replicates incoming metrics, ensuring that no data is lost in the event of machine failure. Its horizontally scalable architecture also means that it can be restarted, upgraded, or downgraded with zero downtime, which means no interruptions to metrics ingestion or querying. Natively multi-tenant: Grafana Mimir’s multi-tenant architecture enables you to isolate data and queries from independent teams or business units, making it possible for these groups to share the same cluster. Advanced limits and quality-of-service controls ensure that capacity is shared fairly among tenants.

grafana-mimir-2.7.2-0.1.git76a1021.lbn36.x86_64

Grafana Mimir is an open source software project that provides a scalable long-term storage for Prometheus. Some of the core strengths of Grafana Mimir include: Easy to install and maintain: Grafana Mimir’s extensive documentation, tutorials, and deployment tooling make it quick to get started. Using its monolithic mode, you can get Grafana Mimir up and running with just one binary and no additional dependencies. Once deployed, the best-practice dashboards, alerts, and playbooks packaged with Grafana Mimir make it easy to monitor the health of the system. Massive scalability: You can run Grafana Mimir's horizontally-scalable architecture across multiple machines, resulting in the ability to process orders of magnitude more time series than a single Prometheus instance. Internal testing shows that Grafana Mimir handles up to 1 billion active time series. Global view of metrics: Grafana Mimir enables you to run queries that aggregate series from multiple Prometheus instances, giving you a global view of your systems. Its query engine extensively parallelizes query execution, so that even the highest-cardinality queries complete with blazing speed. Cheap, durable metric storage: Grafana Mimir uses object storage for long-term data storage, allowing it to take advantage of this ubiquitous, cost-effective, high-durability technology. It is compatible with multiple object store implementations, including AWS S3, Google Cloud Storage, Azure Blob Storage, OpenStack Swift, as well as any S3-compatible object storage. High availability: Grafana Mimir replicates incoming metrics, ensuring that no data is lost in the event of machine failure. Its horizontally scalable architecture also means that it can be restarted, upgraded, or downgraded with zero downtime, which means no interruptions to metrics ingestion or querying. Natively multi-tenant: Grafana Mimir’s multi-tenant architecture enables you to isolate data and queries from independent teams or business units, making it possible for these groups to share the same cluster. Advanced limits and quality-of-service controls ensure that capacity is shared fairly among tenants.

hashicorp-packer-0.10.2-2.0.1.lbn19.x86_64

Packer is a tool for building identical machine images for multiple platforms from a single source configuration. Packer is lightweight, runs on every major operating system, and is highly performant, creating machine images for multiple platforms in parallel. Packer comes out of the box with support for the following platforms: Amazon EC2 (AMI). Both EBS-backed and instance-store AMIs DigitalOcean Docker Google Compute Engine OpenStack Parallels QEMU. Both KVM and Xen images. VirtualBox VMware Support for other platforms can be added via plugins. After Packer is installed, create your first template, which tells Packer what platforms to build images for and how you want to build them. In our case, we'll create a simple AMI that has Redis pre-installed. Save this file as quick-start.json. Be sure to replace any credentials with your own. { "builders": [{ "type": "amazon-ebs", "access_key": "YOUR KEY HERE", "secret_key": "YOUR SECRET KEY HERE", "region": "us-east-1", "source_ami": "ami-de0d9eb7", "instance_type": "t1.micro", "ssh_username": "ubuntu", "ami_name": "packer-example {{timestamp}}" }] } Next, tell Packer to build the image: $ packer build quick-start.json ... Packer will build an AMI according to the "quick-start" template. The AMI will be available in your AWS account. To delete the AMI, you must manually delete it using the AWS console. Packer builds your images, it does not manage their lifecycle. Where they go, how they're run, etc. is up to you.

hashicorp-packer-1.4.1-1.lbn25.x86_64

Packer is a tool for building identical machine images for multiple platforms from a single source configuration. Packer is lightweight, runs on every major operating system, and is highly performant, creating machine images for multiple platforms in parallel. Packer comes out of the box with support for the following platforms: Amazon EC2 (AMI). Both EBS-backed and instance-store AMIs DigitalOcean Docker Google Compute Engine OpenStack Parallels QEMU. Both KVM and Xen images. VirtualBox VMware Support for other platforms can be added via plugins. After Packer is installed, create your first template, which tells Packer what platforms to build images for and how you want to build them. In our case, we'll create a simple AMI that has Redis pre-installed. Save this file as quick-start.json. Be sure to replace any credentials with your own. { "builders": [{ "type": "amazon-ebs", "access_key": "YOUR KEY HERE", "secret_key": "YOUR SECRET KEY HERE", "region": "us-east-1", "source_ami": "ami-de0d9eb7", "instance_type": "t1.micro", "ssh_username": "ubuntu", "ami_name": "packer-example {{timestamp}}" }] } Next, tell Packer to build the image: $ packer build quick-start.json ... Packer will build an AMI according to the "quick-start" template. The AMI will be available in your AWS account. To delete the AMI, you must manually delete it using the AWS console. Packer builds your images, it does not manage their lifecycle. Where they go, how they're run, etc. is up to you.

hashicorp-packer-1.9.5-1.lbn36.x86_64

Packer is a tool for building identical machine images for multiple platforms from a single source configuration. Packer is lightweight, runs on every major operating system, and is highly performant, creating machine images for multiple platforms in parallel. Packer comes out of the box with support for the following platforms: Amazon EC2 (AMI). Both EBS-backed and instance-store AMIs DigitalOcean Docker Google Compute Engine OpenStack Parallels QEMU. Both KVM and Xen images. VirtualBox VMware Support for other platforms can be added via plugins. After Packer is installed, create your first template, which tells Packer what platforms to build images for and how you want to build them. In our case, we'll create a simple AMI that has Redis pre-installed. Save this file as quick-start.json. Be sure to replace any credentials with your own. { "builders": [{ "type": "amazon-ebs", "access_key": "YOUR KEY HERE", "secret_key": "YOUR SECRET KEY HERE", "region": "us-east-1", "source_ami": "ami-de0d9eb7", "instance_type": "t1.micro", "ssh_username": "ubuntu", "ami_name": "packer-example {{timestamp}}" }] } Next, tell Packer to build the image: $ packer build quick-start.json ... Packer will build an AMI according to the "quick-start" template. The AMI will be available in your AWS account. To delete the AMI, you must manually delete it using the AWS console. Packer builds your images, it does not manage their lifecycle. Where they go, how they're run, etc. is up to you.

hashicorp-packer-1.9.5-1.lbn36.x86_64

Packer is a tool for building identical machine images for multiple platforms from a single source configuration. Packer is lightweight, runs on every major operating system, and is highly performant, creating machine images for multiple platforms in parallel. Packer comes out of the box with support for the following platforms: Amazon EC2 (AMI). Both EBS-backed and instance-store AMIs DigitalOcean Docker Google Compute Engine OpenStack Parallels QEMU. Both KVM and Xen images. VirtualBox VMware Support for other platforms can be added via plugins. After Packer is installed, create your first template, which tells Packer what platforms to build images for and how you want to build them. In our case, we'll create a simple AMI that has Redis pre-installed. Save this file as quick-start.json. Be sure to replace any credentials with your own. { "builders": [{ "type": "amazon-ebs", "access_key": "YOUR KEY HERE", "secret_key": "YOUR SECRET KEY HERE", "region": "us-east-1", "source_ami": "ami-de0d9eb7", "instance_type": "t1.micro", "ssh_username": "ubuntu", "ami_name": "packer-example {{timestamp}}" }] } Next, tell Packer to build the image: $ packer build quick-start.json ... Packer will build an AMI according to the "quick-start" template. The AMI will be available in your AWS account. To delete the AMI, you must manually delete it using the AWS console. Packer builds your images, it does not manage their lifecycle. Where they go, how they're run, etc. is up to you.