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Essay on Social Media's Role in Network Management in Big Data

Decent Essays
Network Management in Big Data
In day today world social media and social networking has received much attention from every people, like almost everyone has a Facebook account. This is where huge amount of data is being processed every day, in fact every second where Social networks accounts for large amount of consumer "big data". The average global Internet user spends two and a half hours daily on social media, in this scenario just consider how much data is being generated every minute by every user. The leading social networking sites are handling this big data in efficient way, when it reaches a comparison stage there's no beating Facebook in driving traffic to publishers. According to the data form US news the world's largest social
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Schedule computation or schedule communication helps to optimize utilization and keep running time low. Several works propose to improve job scheduling by preserving data locality maintaining fair allocation among multiple resource types or discarding time-consuming tasks. Even with optimal computation scheduling, the cluster network can still become a blockage. The optimization of network transfers can be done by improving the flow bandwidth allocation or by dynamically changing paths in response to demand. These approaches need accurate and timely application demand information, obtained either from the application itself through instrumentation, which is quick and accurate but intrusive, or from the network through monitoring , which does not require application involvement, but can be expensive, slow, and detects changes in demand only after they have occurred.
FlowComb also uses MapReduce framework to influence the design of the system. MapReduce provides a divide and conquer data processing model, where large workloads are split into smaller tasks, each processed by a single server in a cluster (the map phase). The results of each task are sent over the cluster network (the shuffle phase) and merged to obtain the final result (the reduce phase). The network footprint of a MapReduce job consists pre dominantly of traffic sent during the shuffle
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