Packet compression software

Microsoft Outlook, the email client used by over million users, restricts file attachments to 20 to 25 MB. For clerks whose agenda packets and accompanying documentation could include hundreds of pages of reports, photos, maps, schematics, and other materials, creating a complete packet that meets a 20 MB file size limit is like trying to prepare a meal with only one ingredient—it cannot be done. As municipalities depend less on printed packets and more on digital delivery, optimizing file size has become more important.

While there are other ways to share files within your administration other than emails, such as intranets, document sharing services like Google Drive, and your agenda and meeting management software , you have to build files that can be easily accessed, reviewed, and shared outside your office.

The portable document format PDF is one of the most frequently used and shared digital document file types. PDFs can be viewed in many popular browsers, such as Chrome. Each file compression service uses a proprietary algorithm to identify and eliminate redundancies, find patterns, and target content that it can reduce. It also looks to identify high-resolution images and minimize their file size by lowering the pixel density.

Overall, the process aims to make these size-saving changes without compromising on the quality of the file. If you have any reason to think you might want to refer to a high-resolution version of an agenda packet in the future, save a copy of the original, high-resolution document version. While there are dozens of free file compression tools available online, know that some are more reliable than others. You do not want to risk compressing a large file down to the point of being so size-reduced that you have lost resolution on photos and images and even text, resulting in a poor quality document that is grainy and hard to read.

Depending on the file compression tool that you use, you may be given the option to choose the level of image file compression, ranging from low to high. With CivicClerk, as staff members upload PDF attachments to their agenda items, the system automatically compresses those files. Different packets are usually written into the same packet store in time generated order. This causes more mixing of parameters and data types. As result of this mixing, it is difficult to compress using simple algorithms; so much so that RICE — the state of the art CCSDS recommendation — actually expanded some housekeeping telemetry packet stores in our tests.

We propose a simple data pre-processing in order to exploit the existing data redundancy more efficiently. First, we reduce the amount of mix by grouping together the packets of the same type and order them by generation time. Then we read these packets using the binary at bit level transposed feed instead of using the traditional feed.

The transposed feed experiences far less transitions than the traditional feed. It exploits the structure of the housekeeping telemetry packets automatically. For example, if a parameter is allocated 16 bits of a packet and its value evolves slowly many of those 16 bits will remain identical in the transposed feed. This pre-processing technique allows a simple compression algorithm like the Run Length Encoding RLE to achieve performances close to the best compression technique on the market 7zip.

The combination of this pre-processing technique with Run Length Encoding has been validated with all Rosetta playback housekeeping TM data. It on-board feasibility has been demonstrated by implementing this technique on an on-board hardware processor LEON2. Without additional measures, the risk level increases along with increasing compression. However when combined with risk mitigation techniques we can actually increase robustness.

The benefits of this housekeeping packet compression technique are many in addition of the obvious reduction in bandwidth requirements:. When compressing packets, these systems must choose between writing small packets to the network and performing additional work to aggregate and encapsulate multiple packets. Neither option produces optimal results. This enables BIG-IP AAM to apply compression across a completely homogenous data set while addressing all application types, resulting in higher compression ratios than comparable packet-based systems.

Furthermore, by operating at the session layer, packet boundary and repacketization problems are eliminated. System throughput is also increased when compression is performed at the session layer through the elimination of the encapsulation stage. One limitation all compression routines have in common is limited storage space. Others techniques, such as disk-based compression systems, can store as much as 1 terabyte of data. To understand the impact of dictionary size, a basic understanding of cache management is required.

Zipf's and Heaps' laws are linguistics-derived mathematical equations used to predict the repetitiveness of a vocabulary subset in a finite text. Both laws are applicable outside linguistics to describe observed patterns of repetitiveness in data. Both are often used in data deduplication and compression algorithms as aids to predict and optimize the elimination of repeating byte patterns. Zipf's law provides a mathematical formula for determining the frequency distribution of words in a language.

Zipf's law states that the frequency of any word in a collection is inversely proportional to its rank in the frequency table. The most frequent word will occur twice as often as the second most frequent, and so on. A plot graph of data that exhibits Zipf's law will have a slope of All modern, dictionary-based compression systems leverage uneven distribution by storing more frequently accessed data and discarding less frequently accessed data.

Through this type of optimization, a dictionary that stores less than 10 percent of all the byte patterns can achieve a hit ratio well in excess of 50 percent. The effect of this uneven distribution of byte patterns is evident in the effectiveness of common compression programs.

For example, while gzip stores only 64 KB of history, it averages approximately 64 percent compression. However, bzip2 stores between KB and KB of history and averages 66 percent compression.

The Zipf's and Heaps' laws are linguistics-derived mathematical equations used to predict the repetitiveness of a vocabulary subset in a finite text. Block-based systems, such as Riverbed Technology's Steelhead appliances, store segments of previously transferred data flowing across the WAN.

When these blocks are encountered a second time, references to the blocks are transmitted to the remote appliance, which then reconstructs the original data. A critical shortcoming of block-based systems is that repetitive data almost never is exactly the length of a block.