best admission essay writing service




In this paper, we present PACK (Predictive ACKs), a novel end-to-end traffic redundancy elimination (TRE) system, designed for cloud computing customers. Cloud-based TRE needs to apply a judicious use of cloud resources so that the bandwidth cost reduction combined with the additional cost of TRE computation and storage would be optimized. PACK’s main advantage is its capability of offloading the cloud-server TRE effort to endclients, thus minimizing the processing costs induced by the TRE algorithm. Unlike previous solutions, PACK does not require the server to continuously maintain clients’ status. This makes PACK very suitable for pervasive computation environments that combine client mobility and server migration to maintain cloud elasticity. PACK is based on a novel TRE technique, which allows the client to use newly received chunks to identify previously received chunk chains, which in turn can be used as reliable predictors to future transmitted chunks.We present a fully functional PACKimplementation, transparent to all TCP-based applications and network devices. Finally, we analyze PACK benefits for cloud users, using traffic traces from various sources.


Traffic redundancy stems from common end-users’ activities, such as repeatedly accessing, downloading, uploading (i.e., backup), distributing, and modifying the same or similar information items (documents, data, Web, and video). TRE is used to eliminate the transmission of redundant content and, therefore, to significantly reduce the network cost. In most common TRE solutions, both the sender and the receiver examine and compare signatures of data chunks, parsed according to the data content, prior to their transmission. When redundant chunks are detected, the sender replaces the transmission of each redundant chunk with its strong signature. Commercial TRE solutions are popular at enterprise networks, and involve the deployment of two or more proprietary-protocol, state synchronized middle-boxes at both the intranet entry points of data centers and branch offices, eliminating repetitive traffic between them (e.g., Cisco, Riverbed, Quantum, Juniper, Blue Coat, Expand Networks, and F5). While proprietary middle-boxes are popular point solutions within enterprises, they are not as attractive in a cloud environment. Cloud providers cannot benefit from a technology whose goal is to reduce customer bandwidth bills, and thus are not likely to invest in one. The rise of “on-demand” work spaces, meeting rooms, and work-from-home solutions detaches the workers from their offices. In such a dynamic work environment, fixed-point solutions that require a client-side and a server-side middle-box pair become ineffective. On the other hand, cloud-side elasticity motivates work distribution among servers and migration among data centers.


  • It end-to-end TRE solutions are sender-based.
  • Its solutions require that the server continuously maintain clients’ status.


We present a novel receiver-based end-to-end TRE solution that relies on the power of predictions to eliminate redundant traffic between the cloud and its end-users. In this solution, each receiver observes the incoming stream and tries to match its chunks with a previously received chunk chain or a chunk chain of a local file. Using the long-term chunks’ metadata information kept locally, the receiver sends to the server predictions that include chunks’ signatures and easy-to-verify hints of the sender’s future data. The sender first examines the hint and performs the TRE operation only on a hint-match. The purpose of this procedure is to avoid the expensive TRE computation at the sender side in the absence of traffic redundancy. When redundancy is detected, the sender then sends to the receiver only the ACKs to the predictions, instead of sending the data. On the receiver side, we propose a new computationally lightweight chunking (fingerprinting) scheme termed PACK chunking. PACK chunking is a new alternative for Rabin fingerprinting traditionally used by RE applications. Experiments show that our approach can reach data processing speeds over 3 Gb/s, at least 20% faster than Rabin fingerprinting.


  • It demonstrates a cloud cost reduction achieved at a reasonable client effort.
  • It eliminate redundancy without significantly affecting the computational effort of the sender.



üProcessor                  -        Pentium –IV

ü Speed                        -        1.1 Ghz

ü RAM                         -        512 MB(min)

ü Hard Disk                 -        40 GB

ü Key Board                -        Standard Windows Keyboard

ü Mouse                       -        Two or Three Button Mouse

ü Monitor                     -        LCD/LED


  • Operating system :         Windows XP
  • Coding Language :         Java
  • Data Base             :         MySQL
  • Tool                     :         Net Beans IDE


Eyal Zohar, Israel Cidon, and Osnat Mokryn, “PACK: Prediction-Based Cloud Bandwidth and Cost Reduction System” IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 22, NO. 1, Feb. 2014.

buy essays online

© Copyright 2014. Powered by LansA Informatics Pvt Ltd

Contact us

We're not around right now. But you can send us an email and we'll get back to you, asap.

Questions, issues or concerns? I'd love to help you!

Click ENTER to chat