A Cache Replacement Policy Based on Multi-Factors for Named Data Networking

Named Data Networking(NDN)is one of the most excellent future Internet architectures and every router in NDN has the capacity of caching contents passing by.It greatly reduces network traffic and improves the speed of content distribution and retrieval.In order to make full use of the limited caching space in routers,it is an urgent challenge to make an efficient cache replacement policy.However,the existing cache replacement policies only consider very few factors that affect the cache performance.In this paper,we present a cache replacement policy based on multi-factors for NDN(CRPM),in which the content with the least cache value is evicted from the caching space.CRPM fully analyzes multi-factors that affect the caching performance,puts forward the corresponding calculation methods,and utilize the multi-factors to measure the cache value of contents.Furthermore,a new cache value function is constructed,which makes the content with high value be stored in the router as long as possible,so as to ensure the efficient use of cache resources.The simulation results show that CPRM can effectively improve cache hit ratio,enhance cache resource utilization,reduce energy consumption and decrease hit distance of content acquisition.

A Prefetch-Adaptive Intelligent Cache Replacement Policy Based on Machine Learning

Hardware prefetching and replacement policies are two techniques to improve the performance of the memory subsystem.While prefetching hides memory latency and improves performance,interactions take place with the cache replacement policies,thereby introducing performance variability in the application.To improve the accuracy of reuse of cache blocks in the presence of hardware prefetching,we propose Prefetch-Adaptive Intelligent Cache Replacement Policy(PAIC).PAIC is designed with separate predictors for prefetch and demand requests,and uses machine learning to optimize reuse prediction in the presence of prefetching.By distinguishing reuse predictions for prefetch and demand requests,PAIC can better combine the performance benefits from prefetching and replacement policies.We evaluate PAIC on a set of 27 memory-intensive programs from the SPEC 2006 and SPEC 2017.Under single-core configuration,PAIC improves performance over Least Recently Used(LRU)replacement policy by 37.22%,compared with improvements of 32.93%for Signature-based Hit Predictor(SHiP),34.56%for Hawkeye,and 34.43%for Glider.Under the four-core configuration,PAIC improves performance over LRU by 20.99%,versus 13.23%for SHiP,17.89%for Hawkeye and 15.50%for Glider.

Optimal Age Replacement Policy with Replacement Occurrence While the Warranty is in Effect

This study considers an age replacement policy(ARP) for a repairable product with an increasing failure rate with and without a product warranty. As for the warranty policy to consider in association with such an age replacement policy, we adapt a renewable minimal repair-replacement warrant(MRRW) policy with 2D factors of failure time of the product and its corresponding repair time. The expected cost rate during the life cycle of the product is utilized as a criterion to find the optimal policies for both with and without the product warranty. We determine the optimal replacement age that minimizes the objective function which evaluates the expected cost rate during the product cycle and investigate the impact of several factors on the optimal replacement age. The main objective of this study lies on the generalization of the classical age replacement policy to the situation where a renewable warranty depending on 2D factors is in effect. We present some interesting observations regarding the effect of relevant factors based on numerical analysis.

Quasi-Periodic Replacement Policy for a Two-Unit Production System

Maintenance optimization of periodic replacement for mono-unit production system is a widely discussed topic.In practice,the replacement for a parallel production system with multi-unit served by one repairman is universal.The replacement action is often implemented in a maintenance window at random,although the replacement period appears as a quasi-periodic event.To optimize these maintenance policies,this study analyzes a bivariate quasi-periodic replacement policy (T,W) for a two-unit parallel production system with a maintenance window and one repairman.One unit of the system is replaced when either a failure occurs or a replacement plan comes in the maintenance window,whichever comes first.If one of the two units is in the replacement,the other may continue operating or waiting until the replacement is completed.An optimal replacement window [T*,T* + W*] can be obtained by jointly considering the system's long-running cost rate and the availability using the genetic algorithm.This study also introduces three examples of the production system with different types of units to illustrate the proposed policy.

Efficient cache replacement framework based on access hotness for spacecraft processors

A notable portion of cachelines in real-world workloads exhibits inner non-uniform access behaviors.However,modern cache management rarely considers this fine-grained feature,which impacts the effective cache capacity of contemporary high-performance spacecraft processors.To harness these non-uniform access behaviors,an efficient cache replacement framework featuring an auxiliary cache specifically designed to retain evicted hot data was proposed.This framework reconstructs the cache replacement policy,facilitating data migration between the main cache and the auxiliary cache.Unlike traditional cacheline-granularity policies,the approach excels at identifying and evicting infrequently used data,thereby optimizing cache utilization.The evaluation shows impressive performance improvement,especially on workloads with irregular access patterns.Benefiting from fine granularity,the proposal achieves superior storage efficiency compared with commonly used cache management schemes,providing a potential optimization opportunity for modern resource-constrained processors,such as spacecraft processors.Furthermore,the framework complements existing modern cache replacement policies and can be seamlessly integrated with minimal modifications,enhancing their overall efficacy.

Evolutionary Computation for Image Feature Extraction

Modifications to an image feature extraction approach involving evolutionary computation and autonomous agents are proposed. The described algorithm allows extraction of features with certain specified characteristics, while omitting other undesirable details in the image. Experimental results are presented with remarks.

Optimal chunk caching in network coding-based qualitative communication

Network processing in the current Internet is at the entirety of the data packet,which is problematic when encountering network congestion.The newly proposed Internet service named Qualitative Communication changes the network processing paradigm to an even finer granularity,namely chunk level,which obsoletes many existing networking policies and schemes,especially the caching algorithms and cache replacement policies that have been extensively explored in Web Caching,Content Delivery Networks(CDN)or Information-Centric Networks(ICN).This paper outlines all the new factors that are brought by random linear network coding-based Qualitative Communication and proves the importance and necessity of considering them.A novel metric is proposed by taking these new factors into consideration.An optimization problem is formulated to maximize the metric value of all retained chunks in the local storage of network nodes under the constraint of storage limit.A cache replacement scheme that obtains the optimal result in a recursive manner is proposed correspondingly.With the help of the introduced intelligent cache replacement algorithm,the performance evaluations show remarkably reduced end-to-end latency compared to the existing schemes in various network scenarios.

Fast convergence caching replacement algorithm based on dynamic classification for content-centric networks

One of the key research fields of content-centric networking （CCN） is to develop more efficient cache replacement policies to improve the hit ratio of CCN in-network caching. However, most of existing cache strategies designed mainly based on the time or frequency of content access, can not properly deal with the problem of the dynamicity of content popularity in the network. In this paper, we propose a fast convergence caching replacement algorithm based on dynamic classification method for CCN, named as FCDC. It develops a dynamic classification method to reduce the time complexity of cache inquiry, which achieves a higher caching hit rate in comparison to random classification method under dynamic change of content popularity. Meanwhile, in order to relieve the influence brought about by dynamic content popularity, it designs a weighting function to speed up cache hit rate convergence in the CCN router. Experimental results show that the proposed scheme outperforms the replacement policies related to least recently used （LRU） and recent usage frequency （RUF） in cache hit rate and resiliency when content popularity in the network varies.

THE OPTIMAL REPLACEMENT FOR ADDITIVE DAMAGE MODELS IN DISCRETE SETTING

A system receives shocks at successive random points of discrete time, and each shock causes a positive integer-valued random amount of damage which accumulates on the system one after another. The system is subject to failure and it fails once the total cumulative damage level first exceeds a fixed threshold. Upon failure the system must be replaced by a new and identical one and a cost is incurred. If the system is replaced before failure, a lower cost is incurred.On the basis of some assumptions, we specify a replacement rule which minimizes the longrun (expected) average cost per unit time and possesses the control limit property, Finally, an algorithm is discussed in a special case.

Reliability analysis for a cold standby system under stepwise Poisson shocks

In this paper,a two-dissimilar-component cold standby repairable system maintained by a repairmanis investigated.The system fails due to either intrinsic factors or external shocks.Assume that shocks arrive according to a stepwise Poisson process,the intensity of which varies with respect to the number of failures of the working component.Repair after failure is imperfect.Using the supplementary variable method and the vector Markov process theory,we obtain some important reliability index for the system.By the renewal reward theorem,an optimal replacement policy N∗is derived by minimising the average cost rate of the system.Numerical examples are presented to validate the theoretical results.

SOME RESULTS ON COMPARISON OF MAINTENANCE POLICIES

In this paper, we study the stochastic comparisons of minimal repair replacement policies in repairable case. We give some results on comparisons of the same unit for different policies and on comparisons of the same policies for different units.