Author: Neftaly Malatjie

An overnight backup process that spills into working hours can easily clog up a network. This can be reduced through user education or by taking technical measures, suggests Atkinson. For example, locking down PCs to prevent users installing software will reduce the number of files that change from one day to the next.

Backup software may respect a time window and prioritise any missed files during the next run if correctly configured. For greater flexibility, look for software that will limit itself to a certain fraction of the available bandwidth during particular hours, that way it can run at full speed during quiet times, and throttle back to a trickle feed during the working day to complete the backup as soon as possible without causing disruption. This can also be implemented through QoS features.

It’s important to ensure that your hardware is fast enough for the job. Can the backup server do virus checking and compression in real time? Can it write to tape at least as fast as the data is arriving? 

One way of improving perceived performance is to ensure that the most important applications get priority. Typically, applications are allocated to classes of service (typically platinum, gold, silver, and bronze), and then policies are set for each class. For example, platinum traffic might be guaranteed at least 50 percent of the available bandwidth.

You can improve throughput for virtual hard disks by using the following techniques:

• Use a hard disk solution that allows fast access, such as a SCSI hard disk, a redundant array of independent disks (RAID), or storage area network (SAN).
• Put each virtual hard disk on a dedicated volume, SCSI hard disk, RAID, or SAN. It is easiest to put virtual hard disks together with their associated virtual machine configuration files on a RAID or SAN because this keeps everything in one place.
• Put virtual hard disks on a different physical disk than the host operating system. In particular, you want to put virtual hard disks on a different physical disk than the host page file.
• Reduce disk fragmentation. Defragment the physical disk on a regular basis, especially if you are using a dynamically expanding virtual hard disk. The data stored on a dynamically expanding virtual hard disk will grow increasingly fragmented as the size of the disk grows, because storage space is used only as it is needed. As the size grows it is less likely that the space will be contiguous. By contrast, a fixed-size virtual hard disk uses a reserved block of storage space, which means that data is less likely to be fragmented as it is stored.
• Compact virtual hard disks to free more physical disk space.

The following are methods of improving network performance

Protocol acceleration

Satellite links involve an additional round-trip latency of approximately one second, and this limits the speed of TCP/IP communication. Wastie cites a real-life example of a 1Mbit/sec line with a latency of 1.1 seconds that achieves a maximum throughput of 100Kbit/sec. TCP acceleration removes that bottleneck and allows the line to run at its nominal speed.

Adjusting packet sizes can also help. As mentioned above, large file transfer packets can block small packets from interactive applications. The problem is that even if the small packets are prioritised, they may be delayed for the time it takes to send a large packet. The answer is to split the large packet into smaller pieces. This can be achieved by configuring the client, server or router.

Increasing the window size so the sender doesn’t wait for an acknowledgement of one packet before sending the next can reduce the effects of high latency, and incorporating error-correction information can reduce or eliminate the need for retransmission when an error does occur.

Once network data has been collected, the next crucial step is to analyse the data. Data analysis is a process used to inspect, clean, transform and remodel data with a view to reach to a certain conclusion for a given situation. Data analysis is typically of two kinds: qualitative or quantitative. The type of data dictates the method of analysis. In qualitative research, any non-numerical data like text or individual words are analysed. Quantitative analysis, on the other hand, focuses on measurement of the data and can use statistics to help reveal results and conclusions. The results are numerical. In some cases, both forms of analysis are used hand in hand. For example, quantitative analysis can help prove qualitative conclusions.

Among the many benefits of data analysis, the more important ones are:

• Data analysis helps in structuring the findings from different sources of data.
• Data analysis is very helpful in breaking a macro problem into micro parts.
• Data analysis acts like a filter when it comes to acquiring meaningful insights out of huge data set.
• Data analysis helps in keeping human bias away from the conclusion with the help of proper network problem resolution.

When discussing data analysis it is important to mention that a methodology to analyse data needs to be picked. If a specific methodology is not selected data can neither be collected nor analyzed.

The methodology should be present in the dissertation as it enables the reader to understand which methods have been used during the research and what type of data has been collected and analyzed throughout the process.