Capacity optimization reduces storage needs and costs by archiving data, compressing it using lossy or non-lossy methods, and identifying redundancies through deduplication. It can increase compression ratios up to 20:1 and is used in WAN scenarios to improve transmission efficiency.
Capacity optimization consists of different and often complementary methods of both archiving data and reducing storage needs when performing backups. Corporations and individual businesses often back up more than their work, and the need to archive, index, and retrieve data requires optimization to reduce the amount of hardware and subsequent overhead needed to manage all that data. When backups are done, there are often redundancies and only minor changes between backups. In light of the redundancies, capacity optimization strategies devise solutions that reduce storage costs and the size of backups reduced from the originals by up to 95%. Capacity optimization is sometimes known as bandwidth optimization when it is used in a wide area network (WAN) application to allow for higher throughput when transmitting and receiving data over a network.
Data compression generally makes use of coding techniques to reduce the size of the data being stored or transmitted. Depending on whether some data is discarded in the process, it can be characterized as lossy, lossy, or non-lossy. Scanning the data for redundancy or repetition and replacing it with cross-referenced and indexed tokens allows for large reductions in the amount of storage needed. Data suppression codes guide accelerators in communicating to synchronize and use memory or a hard drive to write compression histories to an archive repository by allowing a Transmission Control Protocol (TCP) proxy to be used as a buffer of packets or sessions in so that transmission speeds are not reduced. Another method of data compression reduces the data size in real time as it moves to the first backup, and then through further optimization, resulting in greater savings in both space and time.
Using traditional means of compression it is possible to reduce the size of archived data in a ratio of 2:1; using capacity optimization can increase this reduction up to 20:1. Looking for redundancy in byte sequences through match windows and using cryptographic hash functions for unique sequences in deduplication algorithms allows segmentation of data streams. These stream segments are then assigned unique identifiers and indexed for retrieval. This way, only new datasets are archived before being further compressed using standard compression algorithms. Some deduplication methods are hardware-based, and combining them with traditional software compression algorithms allows the functions of both to produce significant space and time savings.
Many approaches focus on cost and space reduction of storage capacity to reduce costs associated with storage infrastructure, and similar considerations arise in WAN scenarios. A layer known as the transport layer must exist between applications and the underlying network structures during transmissions, allowing data to be sent and received efficiently and quickly, however the transport layer is still the one created in 1981 when it was TCP was created and it was running at 300 baud rate. Therefore, accelerators use TCP proxies, reducing transmission losses and confirming packet growth using advanced data compression methods to deliver more data per time segment. To overcome obstacles during transmission, these techniques work together to improve application performance and reduce the amount of bandwidth consumption.
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