Economical Fault-Tolerant Networks
In the past decade or so, we have seen the evolution of computer networks as a merger of the communication and computing worlds. This revolution has made the dream of global connectivity possible, and with it has brought an explosion in the number of people utilizing computer networks for numerous vital tasks. The sheer volume and importance of tasks being ported to network-based applications and services has made reliability an undeniable need. On the other hand, networks fail all the time for a variety of reasons, including but not limited to power failures, communication link breakdowns, hardware failures and software crashes. Overcoming these failures and maintaining network services to users is addressed by implementing fault tolerance.
One common method for implementing fault tolerance is redundant data replication. Employment of an exact copy in place of a failed network component ensures availability of services on the network. The process of fault detection and replacement must be quick and automatic in order to make the fault invisible at the client end.
Our solution bases itself on a cluster of identical Linux servers, providing various network services. We did not employ any additional links other than the standard network connectivity. Various components within the solution include an algorithm to select a successor to a failed component, and both clock and data synchronization procedures among the processes. The services and data are replicated on all computers within the cluster. In the event of failure of a server, it is to be replaced by a replica, which carries a redundant copy of all data and service configurations offered by the crashed machine. After the detection of a failure, an election process is enacted among the identical servers and a replacement for the crashed server comes forward. Under normal working conditions of the network, the current server regularly pushes clock time, data and key configuration files to the rest of the cluster in order to maintain them as peers.
The standard techniques for improving the reliability of the network can be divided into two broad groups.
Dedicated hardware: this is expensive, and needs special maintenance. Examples of such a method include RAID (redundant array of inexpensive disks) and non-IP solutions requiring secondary communication links between machines.
Secondary backup machines: these are machines identical to the primary server and are maintained as mirrors of the primary. Their major drawback is not their high cost, but that under conditions of network failure, the switchover to the backup machine is not transparent to the clients. In fact, the clients need to be configured in advance with secondary (or slave) server parameters. The clients for most of the services will usually try the primary server first; then, on timeout, attempt to use the secondary server. This introduces unnecessary overhead and delay. Another important thing to note is that the backup machine is 100 percent redundant, used only when the primary server is under fault.
The problem is in achieving a software solution to ensure reliability, without the need of additional hardware and to keep the switchover transparent to the clients. Furthermore, we would not like to waste resources by completely dedicating one or more machines to a backup role.
Our solution finds its roots in the already-established backup system of primary and secondary servers. Instead of using two computers, we employ a set of computers. One of these computers is selected to act as a master machine to coordinate the network services, while the rest assume the role of slave machines: general-purpose server-class machines that take part in the election. The master server sets up a virtual IP address and starts all services required for normal operation of the network. The slaves monitor the status of the master for failures. If a failure occurs, a new master will be chosen and services to the network restored; the change is transparent to the clients. The slaves are not intended to be dedicated solely to this purpose; rather, they may be employed to perform other tasks (compute servers, workstations, etc.). Only on being chosen master does a machine also take on the task of establishing the virtual server.
The most crucial task in the event of failure is the selection of a new master machine. It is not possible to predict which machines in the chain would be unavailable due to earlier failures or being switched off. Therefore, a simple pre-established hierarchy of switchover, like the one in primary/secondary servers, is not practical. Moreover, a single coordinator for selecting a master cannot be used, because failure of this coordinator would result in total failure of the network.
With these facts in mind, we used a distributed election algorithm to select the appropriate successor to the dead master. Election algorithms are widely used to select a coordinator process in parallel and distributed computing. Initially, we tried the “Bully Election” algorithm. (See Resources.) This suffers from a handicap in that when a failed master is fixed and brought back into the network, it bullies the already-established master into handing over services. This creates an unnecessary switchover and results in loss of newer and updated data, besides causing network delay. The data loss occurs because updates may have been made in the new master machine, and when the original crashed master revives, it does not have the updated data.
- Smoothwall Express
- Machine Learning Everywhere
- Bash Shell Script: Building a Better March Madness Bracket
- Own Your DNS Data
- Simple Server Hardening
- Understanding OpenStack's Success
- From vs. to + for Microsoft and Linux
- The Weather Outside Is Frightful (Or Is It?)
- Understanding Firewalld in Multi-Zone Configurations
- Ensono M.O.
Pick up any e-commerce web or mobile app today, and you’ll be holding a mashup of interconnected applications and services from a variety of different providers. For instance, when you connect to Amazon’s e-commerce app, cookies, tags and pixels that are monitored by solutions like Exact Target, BazaarVoice, Bing, Shopzilla, Liveramp and Google Tag Manager track every action you take. You’re presented with special offers and coupons based on your viewing and buying patterns. If you find something you want for your birthday, a third party manages your wish list, which you can share through multiple social- media outlets or email to a friend. When you select something to buy, you find yourself presented with similar items as kind suggestions. And when you finally check out, you’re offered the ability to pay with promo codes, gifts cards, PayPal or a variety of credit cards.Get the Guide