.png){kind=icon}
.png){kind=icon}
Return
A new Weight Function for Highest random weight scheme and its efficient lookup implementations
.png){kind=icon}
PRE
AI
M
S
B
Q
A
DOI：10.1016/j.future.2026.108711.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Cloud infrastructures have become increasingly popular among distributed applications in the modern era due to their efficient failure recovery, global reach, mobility, and service integration. The Highest Random Weight (HRW) is one of the Consistent Hashing (CH) schemes that offers indefinite scalability while maintaining consistency, lookup, minimal dispersal, and load balancing in cloud environments. However, as systems scale, existing HRW and other CH variants face mounting challenges, including high memory usage, performance degradation due to rehashing and O(w) comparisons during lookup operations. This paper suggests a weight function for the Highest Random Weight (HRW) scheme, termed as Multiplication Modulo-based Highest Random Weight (MM-HRW) scheme. In the MM-HRW scheme, the entire key range can be divided into several nonoverlapping ranges, and each range corresponds to a working node. By applying binary search on the ranges, we can find out the working node of a key with O(logw) comparisons. Additionally, we present the corresponding algorithms, implementations, and mathematical validation to support the experimental findings. The results demonstrate that MM-HRW consistently achieves an affordable memory footprint, validating its scalability across various scenarios. Additionally, by considering a small, medium and large cluster size, MM-HRW scheme achieves a high and stable lookup rate while maintaining O(logw) comparisons across all scenarios.
Journal
F
IF:
6.1
Papers:
6.8K
Citations:
2.3W
Organization
Cited Papers