Camul: Online Caching on Multiple Caches with Relaying and Bypassing

ECC: Edge Collaborative Caching Strategy for Differentiated Services Load-Balancing

“…According to the experimental test results of Maheshwari et al on edge cloud system in 2018, we set t l = 1, t r = 10, t b = 100, and the unit of time was simply set as milliseconds (ms) [19]. Refer to Camul [8], the ratio of operation cost of FETCH and bypass is 10, so set t f = 1000 ms. Due to the huge difference between the two poles of queueing delay in Google Cluster Data 2011-2, which is 1us ∼ 10 13 us, analysis of its "queue delay-ranking" shows that it has a strong long tail effect, so we adopted the approximate average value of the long tail and set it as t p = 100 ms. In the actual scenario, queueing delay is closely related to machine I/O capability, data/request arrival rate, etc., and there is no universal value.…”

“…In recent years, many excellent works were proposed on edge caching research. According to the use of tools or the field of studies, it can be divided into three kinds: D2D (Device-to-Device) communication aided edge caching [12,13], Game theory aided edge caching which reduces operator cost or increases profits [14], and edge collaborative caching which reduces the service response/access time [8,9]. D2D communication aided edge caching.…”

“…Edge collaborative caching. Tan et al [8] studied online service caching in the multiple edge node collaboration scenario, and proposed an asymptotic optimal cache replacement algorithm with the goal of optimizing network traffic and other costs. Ma et al pointed out that due to the heterogeneity of edge resource capacity and the inconsistency of edge storage computing capacity, it is difficult to make full use of edge resource storage and computing capacity in the absence of collaboration between edge nodes.…”

“…Due to probabilistic selection existing in some baselines and the proposed algorithm, we conducted 10 times and analyzed the results of the proposed algorithm improvements. Specifically, the baselines are the advanced Camul [8] and the classic LRUwithRelay and LRUwithoutRelay algorithms. The baselines are used to test and analyze important performance indicators such as hit ratio, average access delay and so on.…”

“…In addition, most of the existing work does not consider the extra cost of a large number of sudden service requests on edge nodes, such as queuing delay, which will greatly increase the average access delay and lead to a bad experience for users. Caching services or applications which users frequently request to collaborative edge nodes can effectively reduce average access latency and network traffic [7][8][9].…”