Coded Caching for Heterogeneous Systems: An Optimization Perspective
Coded Caching for Heterogeneous Systems: An Optimization Perspective
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DOI：
10.1109/tcomm.2019.2914393
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发表时间：
2019-08-01
影响因子：
8.3
通讯作者：
Yener, Aylin
中科院分区：
文献类型：
--
作者：
Ibrahim, Abdelrahman M.;Zewail, Ahmed A.;Yener, Aylin
在高速缓存辅助网络中，服务器在低业务量期间在用户处填充该高速缓存存储器，以便在业务量高峰时段减少递送负载。反过来，在服务器上的传递负载和用户处的该高速缓存大小之间存在基本的折衷。在本文中，我们研究这种权衡在多播网络中，服务器连接到用户具有不平等的缓存大小和用户的数量小于或等于库文件的数量。我们提出了集中式未编码的位置和线性交付计划，通过求解线性规划进行优化。此外，我们推导出一个下限的交付内存权衡与未编码的位置，占高速缓存大小的异质性。我们明确地描述这种权衡的情况下，三个最终用户，以及任意数量的最终用户时，在用户的总内存大小是小的，当它是大的。接下来，我们考虑一个系统，其中服务器通过不同容量的速率受限链路连接到用户，并且服务器根据总缓存预算分配用户的缓存大小。我们的特点是最佳的缓存大小，最大限度地减少交付完成时间与未编码的放置和线性交付。特别是，最佳的内存分配之间的平衡分配较大的高速缓存大小的用户与低容量的链接和统一的内存分配。
In cache-aided networks, the server populates the cache memories at the users during low-traffic periods in order to reduce the delivery load during peak-traffic hours. In turn, there exists a fundamental tradeoff between the delivery load on the server and the cache sizes at the users. In this paper, we study this tradeoff in a multicast network, where the server is connected to users with unequal cache sizes and the number of users is less than or equal to the number of library files. We propose centralized uncoded placement and linear delivery schemes which are optimized by solving a linear program. Additionally, we derive a lower bound on the delivery memory tradeoff with uncoded placement that accounts for the heterogeneity in cache sizes. We explicitly characterize this tradeoff for the case of three end-users, as well as an arbitrary number of end-users when the total memory size at the users is small, and when it is large. Next, we consider a system where the server is connected to the users via rate-limited links of different capacities and the server assigns the users' cache sizes subject to a total cache budget. We characterize the optimal cache sizes that minimize the delivery completion time with uncoded placement and linear delivery. In particular, the optimal memory allocation balances between assigning larger cache sizes to users with low capacity links and uniform memory allocation.