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Friday, 2 February 2018

Content Caching and Scheduling in Wireless Networks With Elastic and Inelastic Traffic(2014)

Content Caching and Scheduling in Wireless Networks With Elastic and Inelastic Traffic(2014)

ABSTRACT:
The rapid growth of wireless content access implies the need for content placement and scheduling at wireless base stations. We study a system under which users are divided into clusters based on their channel conditions, and their requests are represented by different queues at logical front ends. Requests might be elastic (implying no hard delay constraint) or inelastic (requiring that a delay target be met). Correspondingly, we have request queues that indicate the number of elastic requests, and deficit queues that indicate the deficit in inelastic service. Caches are of finite size and can be refreshed periodically from a media vault. We consider two cost models that correspond to inelastic requests for streaming stored content and real-time streaming of events, respectively. We design provably optimal policies that stabilize the request queues (hence ensuring finite delays) and reduce average deficit to zero [hence ensuring that the quality-of-service (QoS) target is met] at small cost. We illustrate our approach through simulations.
EXISTING SYSTEM:
The past few years have seen the rise of smart handheld wireless devices as a means of content consumption. Content might include streaming applications in which chunks of the file must be received under hard delay constraints, as well as file downloads such as software updates that do not have such hard constraints. The core of the Internet is well provisioned, and network capacity constraints for content delivery are at the media vault (where content originates) and at the wireless access links at end-users. Hence, a natural location to place caches for a content distribution network (CDN) would be at the wireless gateway, which could be a cellular base station through which users obtain network access. Furthermore, it is natural to try to take advantage of the inherent broadcast nature of the wireless medium to satisfy multiple users simultaneously. There are multiple cellular base stations (BSs), each of which has a cache in which to store content. The content of the caches can be periodically refreshed through accessing a media vault. We divide users into different clusters, with the idea that all users in each cluster are geographically close such that they have statistically similar channel conditions and are able to access the same base stations. Note that multiple clusters could be present in the same cell based on the dissimilarity of their channel conditions to different base stations. The requests made by each cluster are aggregated at a logical entity that we call a front end (FE) associated with that cluster.
DISADVANTAGES OF EXISTING SYSTEM:
·        The wireless network between the caches to the users has finite capacity.
·         Refreshing content in the caches from the media vault incurs a cost.
PROPOSED SYSTEM:
In this paper, we develop algorithms for content distribution with elastic and inelastic requests. We use a request queue to implicitly determine the popularity of elastic content. Similarly, the deficit queue determines the necessary service for inelastic requests. Content may be refreshed periodically at caches. We study two different kinds of cost models, each of which is appropriate for a different content distribution scenario. The first is the case of file distribution (elastic) along with streaming of stored content (inelastic), where we model cost in terms of the frequency with which caches are refreshed. The second is the case of streaming of content that is generated in real-time, where content expires after a certain time, and the cost of placement of each packet in the cache is considered.
ADVANTAGES OF PROPOSED SYSTEM:
·        It stabilizes the system  load within the capacity region.
·        Minimizes the average expected cost while stabilizing the deficit queues
MODULES:
] Creating System Model
] Content Caching System Module
] Elastic Traffic Module
] Inelastic Traffic Module
MODULES DESCRIPTION:
Creating System Model
ü In this module, we create the System model, with Socket programming technique
ü Create Wireless Nodes (Base Stations) with Cache
ü Media Vault
ü There are multiple cellular base stations (BSs), each of which has a cache in which to store content.
ü Users can make two kinds of requests, namely: 1) elastic requests that have no delay constraints, and 2) inelastic requests that have a hard delay constraint.
Content Caching System Module
ü In this module we design Scheduling methodology that is what is to be broadcasted from caches. In this module we also develop Content caching methodology, which is what to be loaded in caches.
ü The content of the caches can be periodically refreshed through accessing a media vault. We divide users into different clusters, with the idea that all users in each cluster are geographically close such that they have statistically similar channel conditions and are able to access the same base stations. Note that multiple clusters could be present in the same cell based on the dissimilarity of their channel conditions to different base stations. The requests made by each cluster are aggregated at a logical entity that we call a front end (FE) associated with that cluster. The front end could be running on any of the devices in the cluster or at a base station, and its purpose is to keep track of the requests associated with the users of that cluster.
Elastic Traffic Module
ü In this module, we develop elastic traffic module, where there should be No delay constraint.
ü Stored in Request Queues at frontends.
ü Elastic requests are stored in a request queue at each front end, with each type of request occupying a particular queue. Here, the objective is to stabilize the queue, so as to have finite delays.
Inelastic Traffic Module
ü In this module, we develop Inelastic traffic module for Hard Delay Constraint.
ü Drop if not served by the deadline.
ü Need a minimum delivery ratio.
ü For inelastic requests, we adopt the model proposed wherein users request chunks of content that have a strict deadline, and the request is dropped if the deadline cannot be met.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø System                          :         Pentium IV 2.4 GHz.
Ø Hard Disk                      :         40 GB.
Ø Floppy Drive                 :         1.44 Mb.
Ø Monitor                         :         15 VGA Colour.
Ø Mouse                            :         Logitech.
Ø Ram                               :         512 Mb.
SOFTWARE REQUIREMENTS:
Ø Operating system           :         Windows XP/7.
Ø Coding Language         :         C#.net
Ø Tool                                  :         Visual Studio 2010

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