Can machine learning (ML) be used to improve on existing cache replacement strategies?
We propose a general framework called LeCaR that uses the ML technique of regret …

Data-intensive platforms such as Hadoop and Spark are routinely used to process massive
amounts of data residing on distributed file systems like HDFS. Increasing memory sizes and …

Massive data generated by connected smart devices, particularly in distributed computer
networks, contributed to the large network traffic burden caused by the ever-increasing use …

In the past few decades, much research has been conducted on the design of cache
replacement policies. Prior work frequently relies on manually-engineered heuristics to …

Internet users tend to skip and look for alternative websites if they have slow response times.
For cloud network managers, implementing a caching strategy on the edge network can …

Modern cache hierarchies are tangled webs of complexity. Multiple tiers of heterogeneous
physical and virtual devices, with many configurable parameters, all contend to optimally …

An optimization strategy for cached data offloading plays a crucial role in the edge network
environment. This strategy can improve the performance of edge nodes with limited cache …

The gargantuan uses of web access in various types of applications, such as text, image,
audio, and video, across the globe have caused the limitation for service providers to …

With the popularity of cloud services, cloud block storage (CBS) systems have been widely
deployed by cloud providers. Cloud cache plays a vital role in maintaining high and stable …

Caching web objects in the proxy server is a well-known approach to improving the
performance of web access. Cache replacement policies play a crucial role in the …