Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache.

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Published
2025-03-07
DOI
https://doi.org/10.1109/csnt64827.2025.10968993
Primary Topic
Caching and Content Delivery
Type
article
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article

Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs

Rohit Kumar Gupta, Arka Prokash Mazumdar
Caching and Content Delivery
article

Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs

Rohit Kumar Gupta, Arka Prokash Mazumdar
article en

Abstract

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache.

Manipal Academy of Higher Education (IN), Malaviya National Institute of Technology Jaipur (IN)
Affordable and clean energy
Openalex Percentile: Top 92%
Caching and Content Delivery
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Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs — Rohit Kumar Gupta, Arka Prokash Mazumdar · (2025) | TGRS Research Map | TGRS