A CLOSENESS-ALERT INTEREST-GATHERED P2P FOLDER SHARING SYSTEM

Clustering peers by their physical closeness will frequently increase file query performance. However, number of current works can cluster peers according to both peer interest and physical closeness. Although structured P2Ps provide greater file query efficiency than unstructured P2Ps, it is sometimes complicated to understand it because of their strictly defined topologies. During this work, we introduce a Closeness-Aware and Interest-clustered P2P file discussing System with assorted structured P2P, which forms physically-close nodes in a cluster and additional groups physically-close and customary-interest nodes in a sub-cluster with assorted hierarchical topology. Clustering peers by their common interests can considerably enhance the efficiency of file query. PAIS relies on a smart file replication formula to help enhance file query efficiency. Thinking about the lately visited file is usually visited again, the blossom filter based approach is enhanced by only analyzing the recently added blossom filter information to lessen file searching delay. Trace-driven experimental is due to the specific-world Planet Lab test bed show PAIS significantly reduces overhead and boosts the efficiency of file discussing with and without churn. Further, the experimental results show the very best effectiveness within the intra-sub-cluster file searching approaches in enhancing file searching efficiency. PAIS develops an overlay for every group that connects lower capacity nodes to greater capacity nodes for distributed file querying while remaining from node overload. To lessen the overhead within the file information collection, PAIS uses blossom filter based file information collection and corresponding distributed file searching. To improve the file discussing efficiency, PAIS ranks the blossom filter leads to order

FBRC: Optimization of task scheduling in Fog-based Region and Cloud

© 2017 IEEE. Fog computing preserves benefits of cloud computing and is strategically positioned to address effectively many local and performance issues because its resources and specific services are virtualized and located at the edge of the customer premises. Resource management is a critical issue affecting system performance significantly. Due to the complex distribution and high mobility of fog devices, computation resources still experience high latencies in fog's large coverage area. This paper considers a Fog-based Region and Cloud (FBRC) in which requests are locally handled not just by a region but multiple regions when additional resources are needed. An efficient task scheduling mechanism is thus essential to minimize the completion time of tasks and improve user experiences. To this end, two issues are investigated in the paper: 1) designing a fog-based region architecture to provide nearby computing resources; 2) investigating efficient scheduling algorithms to distribute tasks among regions and remote clouds. To deal with the complexity of scheduling tasks, a heuristic-based algorithm is proposed based on our formulation and validated by extensive simulations