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Chapter: Distributed and Cloud Computing: From Parallel Processing to the Internet of Things : Computer Clusters for Scalable Parallel Computing

Fault-Tolerant Cluster Configurations

The cluster solution was targeted to provide availability support for two server nodes with three ascending levels of availability: hot standby, active takeover, and fault-tolerant.

Fault-Tolerant Cluster Configurations


The cluster solution was targeted to provide availability support for two server nodes with three ascending levels of availability: hot standbyactive takeover, and fault-tolerant. In this section, we will consider the recovery timefailback feature, and node activeness. The level of availability increases from standby to active and fault-tolerant cluster configurations. The shorter is the recovery time, the higher is the cluster availability. Failback refers to the ability of a recovered node return-ing to normal operation after repair or maintenance. Activeness refers to whether the node is used in active work during normal operation.


    Hot standby server clusters In a hot standby cluster, only the primary node is actively doing all the useful work normally. The standby node is powered on (hot) and running some monitoring programs to communicate heartbeat signals to check the status of the primary node, but is not actively running other useful workloads. The primary node must mirror any data to shared disk storage, which is accessible by the standby node. The standby node requires a second copy of data.


    Active-takeover clusters In this case, the architecture is symmetric among multiple server nodes. Both servers are primary, doing useful work normally. Both failover and failback are often supported on both server nodes. When a node fails, the user applications fail over to the available node in the cluster. Depending on the time required to implement the failover, users may experience some delays or may lose some data that was not saved in the last checkpoint.


      Failover cluster This is probably the most important feature demanded in current clusters for commercial applications. When a component fails, this technique allows the remaining system to take over the services originally provided by the failed component. A failover mechanism must provide several functions, such as failure diagnosisfailure notification, and failure recovery. Failure diagnosis refers to the detection of a failure and the location of the failed component that caused the failure. A commonly used technique is heartbeat, whereby the cluster nodes send out a stream of heartbeat messages to one another. If the system does not receive the stream of heartbeat messages from a node, it can conclude that either the node or the network connection has failed.



Example 2.8 Failure Diagnosis and Recovery in a Dual-Network Cluster

A cluster uses two networks to connect its nodes. One node is designated as the master node. Each node has a heartbeat daemon that periodically (every 10 seconds) sends a heartbeat message to the master node through both networks. The master node will detect a failure if it does not receive messages for a beat (10 seconds) from a node and will make the following diagnoses:


    A node’s connection to one of the two networks failed if the master receives a heartbeat from the node through one network but not the other.


    The node failed if the master does not receive a heartbeat through either network. It is assumed that the chance of both networks failing at the same time is negligible.


The failure diagnosis in this example is simple, but it has several pitfalls. What if the master node fails? Is the 10-second heartbeat period too long or too short? What if the heartbeat messages are dropped by the net-work (e.g., due to network congestion)? Can this scheme accommodate hundreds of nodes? Practical HA sys-tems must address these issues. A popular trick is to use the heartbeat messages to carry load information so that when the master receives the heartbeat from a node, it knows not only that the node is alive, but also the resource utilization status of the node. Such load information is useful for load balancing and job management.


Once a failure is diagnosed, the system notifies the components that need to know the failure event. Failure notification is needed because the master node is not the only one that needs to have this informa-tion. For instance, in case of the failure of a node, the DNS needs to be told so that it will not connect more users to that node. The resource manager needs to reassign the workload and to take over the remaining workload on that node. The system administrator needs to be alerted so that she can initiate proper actions to repair the node.



3.1 Recovery Schemes


Failure recovery refers to the actions needed to take over the workload of a failed component. There are two types of recovery techniques. In backward recovery, the processes running on a cluster per-iodically save a consistent state (called a checkpoint) to a stable storage. After a failure, the system is reconfigured to isolate the failed component, restores the previous checkpoint, and resumes nor-mal operation. This is called rollback.


Backward recovery is relatively easy to implement in an application-independent, portable fashion, and has been widely used. However, rollback implies wasted execution. If execution time is crucial, such as in real-time systems where the rollback time cannot be tolerated, a forward recovery scheme should be used. With such a scheme, the system is not rolled back to the previous checkpoint upon a failure. Instead, the system utilizes the failure diagnosis information to reconstruct a valid system state and continues execution. Forward recovery is application-dependent and may need extra hardware.


Example 2.9 MTTF, MTTR, and Failure Cost Analysis


Consider a cluster that has little availability support. Upon a node failure, the following sequence of events takes place:


1.   The entire system is shut down and powered off.


2.   The faulty node is replaced if the failure is in hardware.


3.   The system is powered on and rebooted.


4.   The user application is reloaded and rerun from the start.


Assume one of the cluster nodes fails every 100 hours. Other parts of the cluster never fail. Steps 1 through 3 take two hours. On average, the mean time for step 4 is two hours. What is the availability of the cluster? What is the yearly failure cost if each one-hour downtime costs $82,500?


Solution: The cluster’s MTTF is 100 hours; the MTTR is 2 + 2 = 4 hours. According to Table 2.5, the availability is 100/104 = 96.15 percent. This corresponds to 337 hours of downtime in a year, and the failure cost is $82500 × 337, that is, more than $27 million.


Example 2.10 Availability and Cost Analysis of a Cluster of Computers


Repeat Example 2.9, but assume that the cluster now has much increased availability support. Upon a node failure, its workload automatically fails over to other nodes. The failover time is only six minutes. Meanwhile, the cluster has hot swap capability: The faulty node is taken off the cluster, repaired, replugged, and rebooted, and it rejoins the cluster, all without impacting the rest of the cluster. What is the availability of this ideal cluster, and what is the yearly failure cost?


Solution: The cluster’s MTTF is still 100 hours, but the MTTR is reduced to 0.1 hours, as the cluster is available while the failed node is being repaired. From to Table 2.5, the availability is 100/100.5 = 99.9 percent. This corresponds to 8.75 hours of downtime per year, and the failure cost is $82,500, a 27M/722K = 38 times reduction in failure cost from the design in Example 3.8.


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