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Tag Archives: hybrid cloud

Protecting workloads in the cloud with minimal effort through VMware vCloud Availability

Among the many challenges an organization and its IT department confront on a daily basis, availability of services is particularly critical for the survival of the businesses that entrust and rely on the technologies on which their services have been built. At the same time, several legislations across different countries are creating continuous pressure on each and every organization to maintain an appropriate plan to protect and secure their data and their services.

Historically, every large enterprise has planned and built its own approach to face a disaster of small or large proportions in the most suitable way for their businesses: backups, hardware redundancy, host clustering, data mirroring, replication, geographically distributed sites, and so on, are just few identifiers for technologies and strategies to build a solution trying to address the problem.

Over the years, some of these technologies have been commoditized. Still for some of them, the financial burden to allow their implementation has been an overwhelming capital expense for many medium and small organizations. In addition, expertise is required to manage and organize the software, hardware, and storage components involved.

In this context, a great opportunity for cloud service providers has materialized. The market has increased its confidence in using cloud-based services offering a more cost-effective (subscription based) access to resources. Disaster recovery as a service (DRaaS) is a highly desirable service to offer to all organizations, but particularly for the ones that might have concerns or financial exposures caused by planning and building their own secondary data center site to make their services more robust and resilient to local disasters. Continue reading

VMware vCloud Director Virtual Machine Metric Database

Hybrid Cloud PoweredThis article is a preview of a section from the Hybrid Cloud Powered Automation and Orchestration document that is part of the VMware vCloud® Architecture Toolkit – Service Providers (vCAT-SP) document set. The document focuses on architectural design considerations to obtain the VMware vCloud Powered service badge, which guarantees true hybrid cloud experience for VMware vSphere® customers. The service provider requires validation from VMware that their public cloud fulfills hybridity requirements:

  • Cloud is built on vSphere and VMware vCloud Director®
  • vCloud user API is exposed to cloud tenants
  • Cloud supports Open Virtualization Format (OVF) for bidirectional workload movement

This particular section focuses on a new feature of vCloud Director—virtual machine performance and resource consumption metric collection, which requires deployment of an additional scalable database to persist and make available a large amount of data to cloud consumers.

Virtual Machine Metric Database

As of version 5.6, vCloud Director collects virtual machine performance metrics and provides historical data for up to two weeks.

Table 1. Virtual Machine Performance and Resource Consumption Metrics

Table 1. Virtual Machine Performance and Resource Consumption Metrics

Retrieval of both current and historical metrics is available through the vCloud API. The current metrics are directly retrieved from the VMware vCenter Server™ database with the Performance Manager API. The historical metrics are collected every 5 minutes (with 20 seconds granularity) by a StatsFeeder process running on the cells and are pushed to persistent storage—Cassandra NoSQL database cluster with KairosDB database schema and API. The following figure depicts the recommended VM metric database design. Multiple Cassandra nodes are deployed in the same network. On each node, the KairosDB database is running, which also provides an API endpoint for vCloud cells to store and retrieve data. For high availability load balancing, all KairosDB instances are behind a single virtual IP address which is configured by the cell management tool as the VM metric endpoint.

Figure 1. Virtual Machine Metric Database Design

Figure 1. Virtual Machine Metric Database Design

Design Considerations

  • Currently only KairosDB 0.9.1 and Cassandra 1.2.x/2.0.x are supported.
  • Minimum cluster size is three nodes (must be equal or larger than the replication factor). Use scale out rather than scale up approach because Cassandra performance scales linearly with number of nodes.
  • Estimate I/O requirements based on the expected number of VMs, and correctly size the Cassandra cluster and its storage.

n … expected number of VMs
m … number of metrics per VM (currently 8)
t … retention (days)
r … replication factor

Write I/O per second = n × m × r / 10
Storage = n × m × t × r × 114 kB

For 30,000 VMs, the I/O estimate is 72,000 write IOPS and 3288 GB of storage (worst-case scenario if data retention is 6 weeks and replication factor is 3).

  • Enable Leveled Compaction Strategy (LCS) on the Cassandra cluster to improve read performance.
  • Install JNA (Java Native Access) version 3.2.7 or later on each node because it can improve Cassandra memory usage (no JVM swapping).
  • For heavy read utilization (many tenants collecting performance statistics) and availability, VMware recommends increasing the replication factor to 3.
  • Recommended size of 1 Cassandra node: 8 vCPUs (more CPU improves write performance), 16 GB RAM (more memory improves read performance), and 2 TB storage (each backed by separate LUNs/disks with high IOPS performance).
  • KairosDB does not enforce a data retention policy, so old metric data must be regularly cleared with a script. The following example deletes one month’s worth of data:


if [ "$#" -ne 4 ]; then
    echo "$0  port month year"

let DAYS=$(( ( $(date -ud 'now' +'%s') - $(date -ud "${4}-${3}-01 00:00:00" +'%s')  )/60/60/24 ))
if [[ $DAYS -lt "42" ]]; then
 echo "Date to delete is in not before 6 weeks"

METRICS=( `curl -s -k http://$1:$2/api/v1/metricnames -X GET|sed -e 's/[{}]/''/g' | awk -v k="results" '{n=split($0,a,","); for (i=1; i<=n; i++) print a[i]}'|tr -d '[":]'|sed 's/results//g'|grep -w "cpu\|mem\|disk\|net\|sys"` ) echo $METRICS for var in "${METRICS[@]}" do for date in `seq 1 30`;   do     STARTDAY=$(($(date -d $3/$date/$4 +%s%N)/1000000))     end=$((date + 1))     date -d $3/$end/$4 > /dev/null 2>&1
    if [ $? -eq 0 ]; then
       ENDDAY=$(($(date -d $3/$end/$4 +%s%N)/1000000))
       echo "Deleting $var from " $3/$date/$4 " to " $3/$end/$4
       echo '
          "metrics": [
            "tags": {},
            "name": "'${var}'"
          "cache_time": 0,
          "start_absolute": "'${STARTDAY}'",
          "end_absolute": "'${ENDDAY}'"
       }' > /tmp/metricsquery
       curl http://$1:$2/api/v1/datapoints/delete -X POST -d @/tmp/metricsquery

rm -f /tmp/metricsquery > /dev/null 2>&1

Note: The space gains will not be seen until data compaction occurs and the delete marker column (tombstone) expires. This is 10 days by default, but you can change it by editing gc_grace_seconds in the cassandra.yaml configuration file.

  • KairosDB v0.9.1 uses QUORUM consistency level both for reads and writes. Quorum is calculated as rounded down (replication factor + 1) / 2, and for both reads and writes quorum number of replica nodes must be available. Data is assigned to nodes through a hash algorithm and every replica is of equal importance. The following table provides guidance on replication factor and cluster size configurations.
Table 2. Cassandra Configuration Guidance

Table 2. Cassandra Configuration Guidance