Furthermore, we present two methods that use progressive stepwise data exchange between peers to better each peer's summary and therefore improve the system's performance. We finally examine the effect of these data exchange methods with respect to load balancing....
One feature of cloud storage systems is data fragmentation (or sharding) so that data can be distributed over multiple servers and subqueries can be run in parallel on the fragments. On the other hand, flexible query answering can enable a database system to find related information for a user...
The figures are typically depicted during the LPS period because the SINR distribution is the same for both methods during the non-LPS period. In these spatial plots, improvements are observed around the LPNER of LPNs using the proposed scheme. Owing to load balancing and subframe matching ...
Warehouse activities, which are of great importance in terms of reducing transportation and production costs, balancing supply and demand, and contributing to the production and marketing process, play a vital role in achieving the desired level of customer service at the lowest possible cost. In ...
The AUV traverses the cluster head nodes in turn along the path, efficiently completing data collection from the entire network while effectively reducing the length of the AUV movement path, improving energy efficiency, and balancing node energy consumption. Table 3. Results of the group ...
Distributed storage technology mainly uses location servers, namely, LBS, and a highly scalable system architecture to achieve load balancing of large-scale storage servers. This technology cannot improve system performance. Hardware resource virtualization technology is a key technology in cloud computing ...
We place the load balancing approach in the second category for solving heterogeneous clusters’ problem in parallelized clustering. A number of scientists investigated the total execution time by load balancing in parallel clustering. He et al. [6] used this approach to big data processing. They ...
and capable of balancing the complexities of across diverse types of data. Most importantly, CDSKNN exhibits higher operational efficiency on datasets at the million-cell scale, requiring an average of only 6.33 min for clustering 1.46 million single cells, saving 33.3% to 99% of running time co...
He et al. [18] proposed MR-DBSCAN, which first implemented distributed DBSCAN with Map/Reduce on the Hadoop platform. They focused on load balancing in large-scale datasets and efficient speed-up and scale-up for skewed big data. It has three levels: data partitioning, local clustering, and...
Importantly, no image pre-processing techniques were applied before the data balancing process. The latter allows for a comprehensive evaluation of the classifier’s ability to handle the inherent variations within the dataset, ensuring that performance assessments are executed in real conditions. 4.2. ...