看了两篇论文：retention time && interval - request time---process of evicting files
in process of evicting files, choosing which file to be discarded is important.
This means, the optimal objective is max hit rate
* Hit rate=Probability(request interval < residual lifetime of content)
request interval ~ Exp(e_parameter * zipf_parameter(file))
- Paper1:《optimizing TTL caches under heavy-tailed demand(对比Proactive rention aware caching)》
the probability of storing the content is just the probability that the time eslapsed since the last request is less than the timer Tn(the time is stored in the cache for a time Tn)
| optimizing TTL caches under heavy-tailed demand | Proactive rention aware caching | |
|---|---|---|
| objective | max hit rate | min storage cost+ download cost |
| variable | content storing time/TTL | |
| request process | request inter-arrival time is heavy tailed distribution | - PP - 请求间隔服从指数分布（parameter 为uniform && sum=1） |
-
Paper2:《马尔科夫：a reinforcement-learning approach to proactive caching in wireless networks(对比accurate learning or fast mixing? synamic adaptability of caching algorithms)》
解决：which contents and at what time, should be pushed to the cache
神经网络和强化学习 -
Paper3:《Analysis and Design of Hierarchical Web Caching Systems》
cache miss rate=(cache miss interval)^(-1)
设置rentention time为： retention time of file(file,cache node)=inter-arrival time between two successive cache miss for file f at cacche node m=successive hit interval + a miss interval(最后一次) -
Paper 4: 《Exploiting Caching and Multicast for 5G Wireless Networks》他娘的终于看明白是怎么回事了：
- ｛0，1｝->[0,1]，通过一般方法解的最优解｛x*, y*｝,此时x，y为实数
- Randomized rounding: 定义新变量u=[0,1/2]，m=[1/2-u,1/2+u];y=1 if y* >m,else...; x=0 if y=1
-
小结： poisson process 也是一种连续时间的马尔科夫链
poisson process 状态离散 ，时间连续。状态实际就是第几个time interval事件发生了多少次。
下一个状态
反思
看了好久，终于TM看明白了。
Problem I：
request process 的模拟过程到底需要不需要online，或者是否需要模拟成随机过程？
Solution：问题本质是cache online还是static(间接static，即一个time window内的请求，time window可以设置足够大)
Problem II：
with static caching, a set of files is stored in the cache and the cache content does not change in the event of a cache hit or miss. in other words, the caching strategies use last a period request information to predict next period reqests.Specifically,
| 问题&&抽象 | |||
|---|---|---|---|
| 用户请求arrival indensity-->channel/delay? | 除了CP影响delivery; 带宽影响deliver。 |
||
| last period?什么时候更新一次 | |||
带宽影响deliver：
| 处理方式： | 参考 | idea |
|---|---|---|
| Method 1:congestion sensitive | 'Mostafa'中是be sensitive of the traffic load on the link to back-end server | sensitive of the traffic load on the link to cache nodes(到达service cache 有多跳路径，路径选择的影响因素有traffic congetstion, node stability) |
| Method 2: a distribute algorithm | DR-Jian Li: 根据reverse path 的前 多跳节点信息（delay, stability, popularity,CP）来决定当前节点是否缓存 |
节点在不同time unit内中请求数量不同，当请求数量较大时，在缓存节点的覆盖范围内generate traffic congestion：请求策略可以转向其他节点，从而避免因congestion而发生响应请求延时过大。-->congestion?
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