A Lightweight and Adaptive Cache Partitioning Scheme for Content Delivery Networks
Peng Wang, Zhelong Zhao, Yu Liu, Ke Zhou, Zhihai Huang, Yanxiong Chen · 2022 IEEE 40th International Conference on Computer Design (ICCD) · 2022
Allocating exclusive resources for different applications in content delivery networks (CDNs) allows for a higher overall hit ratio. The cache partitioning schemes on Last-Level Cache (LLC) are promising solutions that dynamically split cache sizes into partitions corresponding to threads by the miss ratio curve (MRC). Nonetheless, due to the sheer number of applications and various item sizes in CDNs, partitioning via MRC will cause high computational overheads and performance fluctuations. As a result, in this paper, we propose a lightweight and adaptive cache partitioning scheme (LAP) for CDNs. LAP establishes a shadow cache for each partition, where the size of the partition and its shadow cache is equal to the size of the integral cache. The average number of hits on the granularity unit in the shadow caches, where the size of the granularity equals the size of the probable largest item, is used to sort N partitions in decreasing order. When resizing partitions, LAP transfers a capacity of the size of granularity from the (N – k + 1)-th $\left( {k \leq \frac{N}{2}} \right)$ partition into the k-th partition. Meanwhile,we provide a threshold that neglects partition resizing and improves partitioning efficiency. This lightweight scheme can enhance resource utilization by progressively adapting to workload variations. We have deployed LAP in PicCloud of Company-T and LAP can improve the OHR by 9.34% and reduce the average user access latency by 12.5ms. Then, we verify LAP in the public trace from Akamai and the real trace from PicCloud. Experimental results demonstrate that LAP outperforms other cache partitioning schemes and tackles the performance cliff problem with little overhead.