Maximizing Page-Level Cache Hit Ratios in LargeWeb Services

Wang, Justin; Berg, Benjamin Wagner vom; Berger, Daniel; Sen, Siddhartha

doi:10.1145/3305218.3305253

Cited by 8 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, we analyze their effectiveness of these games in terms of cache hit ratio and energy efficiency (EE) cases at the steady state. The cache hit ratio is a measurement of how many content requests a cache is able to fill successfully, compared with how many requests it receives . On the other hand, the EE is typically defined as the radio capacity in the system to the total energy consumption ratio .…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The cache hit ratio is a measurement of how many content requests a cache is able to fill successfully, compared with how many requests it receives. 29 On the other hand, the EE is typically defined as the radio capacity in the system to the total energy consumption ratio. 30 In others words, the EE is equal to the total achieved throughput R in the network to the total transmit power at the RRHs plus the circuit powers ratio.…”

Section: Simulation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic theoretic proactive caching placement game for energy saving in H‐CRAN

Mrad

Hamouda

2020

Int J Communication

View full text Add to dashboard Cite

Summary Edge caching is an effective feature of the next 5G network to guarantee the availability of the service content and a reduced time response for the user. However, the placement of the cache content remains an issue to fully take advantage of edge caching. In this paper, we address the proactive caching problem in Heterogeneous Cloud Radio Access Network (H‐CRAN) from a game theoretic point of view. The problem is formulated as a bargaining game where the remote radio heads (RRHs) dynamically negotiate and decide which content to cache in which RRH under energy saving and cache capacity constraints. The Pareto optimal equilibrium is proved for the cooperative game by the iterative Nash bargaining algorithm. We compare between cooperative and noncooperative proactive caching games and demonstrate how the selfishness of different players can affect the overall system performance. We also showed that our cooperative proactive caching game improves the energy consumption of 40% as compared with noncooperative game and of 68% to no‐game strategy. Moreover, the number of satisfied requests at the RRHs with the proposed cooperative proactive caching scheme is significantly increased.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Dynamic theoretic proactive caching placement game for energy saving in H‐CRAN

Mrad

Hamouda

2020

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Other related caching algorithms, but not TTL specific [35,36], attempt to minimize the average cost of misses, where the cost of an object is given by, for example, the variability in latency or computation cost. Other approaches that consider cache optimization such as [37][38][39][40][41], attempt to optimize given cache utility offline. This is different from the approach of the paper at hand as we model caching under non-zero random object fetching delays.…”

Section: Analysis Of Ttl Cache Hierarchiesmentioning

confidence: 99%

On the Impact of Network Delays on Time-to-Live Caching

El‐Sayed¹,

Rizk²

2022

Preprint

View full text Add to dashboard Cite

We consider Time-to-Live (TTL) caches that tag every object in cache with a specific (and possibly renewable) expiration time. State-of-the-art models for TTL caches assume zero object fetch delay, i.e., the time required to fetch a requested object that is not in cache from a different cache or the origin server. Particularly, in cache hierarchies this delay has a significant impact on performance metrics such as the object hit probability. Recent work suggests that the impact of the object fetch delay on the cache performance will continue to increase due to the scaling mismatch between shrinking inter-request times (due to higher data center link rates) in contrast to processing and memory access times.In this paper, we analyze tree-based cache hierarchies with random object fetch delays and provide an exact analysis of the corresponding object hit probability. Our analysis allows understanding the impact of random delays and TTLs on cache metrics for a wide class of request stream models characterized through Markov arrival processes. This is expressed through a metric that we denote delay impairment of the hit probability. In addition, we analyze and extend state-of-the-art approximations of the hit probability to take the delay into account. We provide numerical and trace-based simulation-based evaluation results showing that larger TTLs do not efficiently compensate the detrimental effect of object fetch delays. Our evaluations also show that unlike our exact model the state-of-the-art approximations do not capture the impact of the object fetch delay well especially for cache hierarchies. Surprisingly, we show that the impact of this delay on the hit probability is not monotonic but depends on the request stream properties, as well as, the TTL.

show abstract