An adaptive semantics-aware replacement algorithm for web caching

Abstract

As the web expands its overwhelming presence in our daily lives, the pressure to improve the performance of web servers increases. An essential optimization technique that enables Internet-scale web servers to service clients more efficiently and with lower resource demands consists in caching requested web objects on intermediate cache servers. At the core of the cache server operation is the replacement algorithm, which is in charge of selecting, according to a cache replacement policy, the cached pages that should be removed in order to make space for new pages. Traditional replacement policies used in practice take advantage of temporal reference locality by removing the least recently/frequently requested pages from the cache. In this paper we propose a new solution that adds a spatial dimension to the cache replacement process. Our solution is motivated by the observation that users typically browse the Web by successively following the links on the web pages they visit. Our system, called SACS, measures the distance between objects in terms of the number of links necessary to navigate from one object to another. Then, when replacement takes place, objects that are distant from the most recently accessed pages are candidates for removal; the closest an object is to a recently accessed page, the less likely it is to be evicted. We have implemented a cache server using SACS and evaluated our solution against other cache replacement strategies. In this paper we present the details of the design and implementation of SACS and discuss the evaluation results obtained.

Application caching strategy based on in-memory using Redis server to accelerate relational data access

“…Redis ini mendukung beberapa struktur data, seperti string, hashes, lists, sets, sorted sets hingga data geospasial, dan bitmaps [18]. Redis dan IMDB lain, seperti memcached dan Ehcache [19], dapat berkolaborasi dengan beberapa bahasa pemrograman, seperti C/C++, PHP, Java, dan Python [20]- [22] dan telah teruji berjalan dengan baik pada sistem operasi Linux [23], [24]. Oleh karena itu, penelitian ini menggunakan Redis yang dipasang pada Linux turunan Ubuntu 16.04, yaitu Linux LiteOS 4.…”

CBPCS

“…Least Recently Used (LRU) and Least Frequently Used (LFU) are two well-known replacement algorithms. These strategies have many variants by combining them with some value functions to measure the values of objects [2,20,24]. In this article, we develop suitable value functions and design an efficient replacement strategy to decide which block's cached objects should be replaced.…”

Integrated Cache Scheduling Replacement Algorithm to Reduce Cache Pollution

“…If any requested object is existing in the cache, it is moved at the MRU position in the queue and rest of the object is shifted to LRU side. If requested object is not exist and cache is full, then the object at the LRU position is evicted and the new object is inserted into the MRU position [4]. This algorithm only considers the time parameter to make a choice of object for replacement.…”