In this work, we propose an adaptive redundancy scheme - PaRS - for in-memory datasets. In-memory datasets are characterized by skewed popularity, it is prudent to apply customized redundancy schemes with dynamic memory efficiency and access parallelisms to different in-memory datasets. To be resilient to data loss incurred by transient failures, redundancy strategies are incorporated into in-memory stores. In-memory store has become a key component for an increasing number of data-intensive applications like OLTP and OLAP. The experimental results indicate the superiority of the proposed approaches compared to both LFU and WRP, in terms of improvement in cache performance. We added this factors to two famous approaches LFU and Weighting Replacement Policy (WRP) to strength their performance. The proposed approach considers the internal requests generated in each web site. This work proposes a replacement approach with better characteristics as it is easier to be implemented than the previous approaches. Although most of the proposed schemes could overcome the disadvantages of the LFU, they still have lots of overhead and are difficult to implement. Most of these systems and policies are mainly based on the enhancement of a well-known scheme called the Least Frequently Used (LFU) scheme. Many web cashing systems and policies have been proposed to determine which objects to evict from the cache memory to accommodate new ones. The increasing demand for World Wide Web (Thus, web caching is crucial for reducing the load on network, shorten network latency and improve clients' waiting time. The article does an extensive evaluation of both inclusive and non-inclusive cache hierarchies and shows that the proposed global schemes outperform existing local dead-block management schemes. We extend the cache controllers at both private and shared cache levels to use the aforementioned information to evict dead blocks. When memory regions are deemed dead at some cache level(s), all the associated cache blocks are evicted from the corresponding level(s). In the proposed global management schemes, static information (e.g., when tasks start/finish, and what data regions tasks produce/consume) is combined with dynamic information to detect when/where blocks become dead. This article introduces runtime-orchestrated global dead-block management, in which static and dynamic information about tasks available to the runtime system is used to effectively detect and manage dead blocks across the cache hierarchy. Existing dead-block prediction schemes take decisions locally for each cache level and do not efficiently manage the entire cache hierarchy. Dead blocks may occupy cache space in multiple cache levels for a long time without providing any utility until they are finally evicted. Task-parallel programs inefficiently utilize the cache hierarchy due to the presence of dead blocks in caches. This will lead to a new way of thinking about replacement policies in general. Third, we show when the proposed techniques do not work, and the charac-teristics of an application that makes use of these tech-niques. Second, we propose several global replacement policies that tackle the short-comings of the localized version. First, we show the shortcomings of the localized replacement policies. The contribution of this paper is threefold. So, is a global replacement algorithm needed? In this paper we study the feasibility of having a global replacement algorithm. For example, if the level 2 cache decides to evict an LRU block, one or more blocks at level 1, possibly not LRU, can be evicted, therefore affecting the hit rate of level 1 and the overall performance. If each cache in the hierar-chy decides which block to replace in an independent manner, the performance of the whole memory hierar-chy will suffer, especially if inclusion is to be maintained. The memory system does not consist of a single cache, but a hierarchy of caches. However, all the proposed schemes are local in nature. Therefore, a lot of work has been proposed, both in industry and academia, to enhance the performance of the replacement policy. The higher the associativity of the cache, the more vital the replacement policy be-comes. The efficiency of the replace-ment policy affects both the hit rate and the access la-tency of a cache system. Cache replacement policy is a major design parameter of any memory hierarchy.
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