PACK:
Prediction-Based Cloud Bandwidth and Cost Reduction System
ABSTRACT:
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 end clients, 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 PACK implementation,
transparent to all TCP-based applications and network devices. Finally, we
analyze PACK benefits for cloud users, using traffic traces from various
sources.
EXISTING SYSTEM:
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.
DISADVANTAGES
OF EXISTING SYSTEM:
n Cloud
providers cannot benefit from a technology whose goal is to reduce customer
bandwidth bills, and thus are not likely to invest in one.
n 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.
n cloud
load balancing and power optimizations may lead to a server-side process and
data migration environment, in which TRE solutions that require full
synchronization between the server and the client are hard to accomplish or may
lose efficiency due to lost synchronization
n Current
end-to-end solutions also suffer from the requirement to maintain end-to-end
synchronization that may result in degraded TRE efficiency.
PROPOSED SYSTEM:
In this paper, 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. 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.
ADVANTAGES
OF PROPOSED SYSTEM:
v Our approach can reach data processing
speeds over3 Gb/s, at least 20% faster than Rabin fingerprinting.
v The receiver-based TRE solution addresses
mobility problems common to quasi-mobile desktop/ laptops computational
environments.
v One of them is cloud elasticity due to
which the servers are dynamically relocated around the federated cloud, thus
causing clients to interact with multiple changing servers.
v We implemented, tested, and performed
realistic experiments with PACK within a cloud environment. Our experiments
demonstrate a cloud cost reduction achieved at a reasonable client effort while
gaining additional bandwidth savings at the client side.
v Our implementation utilizes the TCP Options
field, supporting all TCP-based applications such as Web, video streaming, P2P,
e-mail, etc.
v We demonstrate that our solution achieves
30% redundancy elimination without significantly affecting the computational
effort of the sender, resulting in a 20% reduction of the overall cost to the cloud
customer.
SYSTEM ARCHITECTURE:
Fig. 1. From stream to chain.
Figure
2- Overview of the PACK implementation.
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
ü Processor - Pentium –IV
ü Speed - 1.1
Ghz
ü RAM - 256
MB(min)
ü Hard Disk -
20 GB
ü Key Board -
Standard Windows Keyboard
ü Mouse - Two
or Three Button Mouse
ü Monitor - SVGA
SOFTWARE CONFIGURATION:-
ü Operating System : Windows XP
ü Programming Language :
JAVA
ü Java Version :
JDK 1.6 & above.
REFERENCE:
Eyal Zohar, Israel Cidon, and Osnat
Mokryn-“ PACK: Prediction-Based Cloud Bandwidth and Cost Reduction System”-IEEE/ACM TRANSACTIONS ON NETWORKING 2013.
