An effectiveness-based adaptive cache replacement policy

In this paper, a two-unit system with failure interactions is studied. The system is subject to two types of shocks (I and II) and the probabilities of these two shock types are age-dependent. A type I shock, rectified by a minimal repair, causes a minor failure of unit A and type II shock causes a complete system failure that calls for a replacement. Each unit A minor failure also results in an amount of damage to unit B. The damages to unit B caused by type I shocks can be accumulated to trigger a preventive replacement or a corrective replacement action. Besides, unit B with cumulative damage of level z may become minor failed with probability \(\pi (z)\) at each unit A minor failure and rectified by a minimal repair. We consider a more general replacement policy. Under this policy, the system is preventively replaced at the Nth type I shock, or at the time when the total damage to unit B exceeds a pre-specified level Z (but less than a failure level K where \(K>Z\)) or is replaced correctively at first type II shock or when the total damage to unit B exceeding a failure level K, whichever occurs first. To minimize the expected cost per unit time, the optimal replacement policy \((N^{*}\), \(Z^{*})\) is derived analytically and determined numerically. We also show that several previous maintenance models in the literature are special cases of our model.

This paper investigates the effects of unsuccessful repairs on the optimal periodic replacement policy for a repairable product with multiple attempts of minimal repair under the free-repair warranty. The motivation for this study is based on the novel notion of ‘multi-attempt minimal repair’, first introduced by Cha and Finkelstein (2024a, 2024b), in which they highlighted various practical reasons and examples to illustrate that minimal repair attempts may sometimes fail and need to be repeated until success is achieved. Their repair model and useful properties served as the motivation for this study. For both the warranted and non-warranted products, cost models are developed from the user’s perspective, and the corresponding optimal replacement policies are derived to minimize the long-run expected cost rates. By comparing these optimal policies, we show that the optimal planned time for preventive replacement should be adjusted toward the end of the warranty period, and the effects of unsuccessful minimal repair produce different degrees of impact, respectively, on the warranted and non-warranted product. Finally, detailed numerical examples and computation results illustrate our findings, and some insightful observations about practical and managerial implications are concluded.

This article describes how the named data networking (NDN) has recently received a lot of attention as a potential information-centric networking (ICN) architecture for the future Internet. The NDN paradigm has a great potential to efficiently address and solve the current seminal IP-based IoT architecture issues and requirements. NDN can be used with different sets of caching algorithms and caching replacement policies. The authors investigate the most suitable combination of these two features to be implemented in an IoT environment. For this purpose, the authors first reviewed the current research and development progress in ICN, then they conduct a qualitative comparative study of the relevant ICN proposals and discuss the suitability of the NDN as a promising architecture for IoT. Finally, they evaluate the performance of NDN in an IoT environment with different caching algorithms and replacement policies. The obtained results show that the consumer-cache caching algorithm used with the Random Replacement (RR) policy significantly improve NDN content validity in an IoT environment.

The conception rate in dairy cows is dependent on a number of cow factors such as days in milk and insemination number. Unfortunately, some of these factors were not accounted for in optimal insemination and replacement decision models. By using wrong estimates of the conception rate, the calculated optimal insemination and replacement policy might differ from the real optimal insemination and replacement policy. The objective of this study was to evaluate different sets of conception rates with an increasing level of accuracy to determine the insemination policy. An existing dynamic program for optimal insemination and replacement was used to compare three different scenarios in the estimation of conception rates, based on the reproductive performance of Dutch dairy cattle: (i) constant conception rate throughout lactation, (ii) conception rate dependent on parity and months in milk, and (iii) conception rate dependent on parity, months in milk and insemination number. The time step of the model was 30.4 days (1 month). The discounted future cash flow of culling a cow at each time step (replace a heifer immediately) was compared with keeping that cow under optimal future decisions. The difference between immediate culling and optimal decisions is defined as the retention pay-off. The insemination value was calculated as the difference between the future cash flow between immediate insemination of a cow and waiting one time-step. The results show that the difference in the insemination values and the optimal time to stop insemination depend on parity, lactation stage and the relative milk yield. In older cows with equal milk yields and at the same months in milk, the insemination value was lower than in younger cows. Within a parity, the insemination value was higher for cows with a higher milk yield. On individual cow level, using more accurate conception rate as input in the optimal insemination and replacement model might reduce miscalculation of the economic consequences for at least of €20-€38 per cow per year. Basing insemination decisions on less accurate input of the probabilities of conception, however, did not have an economic consequence at the herd level. In conclusion, using the appropriate conception rate as input in the optimal insemination and replacement model would increase the precise decision for the optimal time to stop insemination and hence improve the reproductive management efficacy.

A bandwidth and effective hit optimal cache scheme for wireless data access networks with client injected updates

Structural properties of optimal tool replacement policy in a machining center

We consider the problem of on-chip L2 cache management and replacement policies. We propose a new adaptive cache replacement policy, called Dueling CLOCK (DC), that has several advantages over the Least Recently Used (LRU) cache replacement policy.

Due to the increasing gap between CPU and memory speed, cache performance plays an increasingly critical role in determining the overall performance of microprocessor systems. One of the important factors that a affect cache performance is the cache replacement policy. Despite the importance, current analytical cache performance models ignore the impact of cache replacement policies on cache performance. To the best of our knowledge, this paper is the first to propose an analytical model which predicts the performance of cache replacement policies. The input to our model is a simple circular sequence profiling of each application, which requires very little storage overhead. The output of the model is the predicted miss rates of an application under different replacement policies. The model is based on probability theory and utilizes Markov processes to compute each cache access' miss probability. The model realistic assumptions and relies solely on the statistical properties of the application, without relying on heuristics or rules of thumbs. The model's run time is less than 0.1 seconds, much lower than that of trace simulations. We validate the model by comparing the predicted miss rates of seventeen Spec2000 and NAS benchmark applications against miss rates obtained by detailed execution-driven simulations, across a range of different cache sizes, associativities, and four replacement policies, and show that the model is very accurate. The model's average prediction error is 1.41%,and there are only 14 out of 952 validation points in which the prediction errors are larger than 10%.

The Thread Synchronization Unit (TSU) is a hardware mechanism that provides data-driven thread synchronization and data consistency for multi-threaded architectures built with control-flow (i.e. commodity) microprocessors. The TSU design is based on the Decoupled Data-Driven model of execution. This model decouples the synchronization from the computation portions of a program and allows them to execute asynchronously. At compile time a program is partitioned into a number of threads of variable granularity and the Data-Driven thread synchronization graph is also constructed. The TSU is responsible for maintaining the synchronization graph implicitly, it determines when a thread is ready for execution without interruption and then feeds it to the microprocessor for execution. The TSU-based machines exhibit the tolerance to long memory and communication latencies, of the data-driven model, with very little overhead and also exploits short-term optimal cache placement and replacement policies.

Condition-based maintenance using the inverse Gaussian degradation model

A (τ, T) replacement policy is commonly used to prevent system failure. In previous research, it was assumed that the maintenance time can be ignored since it is negligibly short compared with operation time. However, neglecting maintenance time when optimizing maintenance policies for systems for which the maintenance time is not relatively short can lead to sub-optimal or even incorrect solutions to the problem, resulting in higher maintenance costs. An optimal (τ, T) replacement policy is presented for series systems in which the units deteriorate following different stochastic processes independently. The failure of each unit in the time interval [τ, T) occurs in accordance with a nonhomogeneous Poisson process, the failure is immediately removed by a minimal repair and the system continues operating. The failure of any unit in time interval [τ, T) causes the entire system to fail, which is immediately detected, and corrective maintenance is performed on the whole system. If a failure does not occur during time interval [τ, T), the system receives preventive replacement at T A (τ, T) replacement policy was formulated that considers both preventive and corrective maintenance times and that determines an optimal preventive replacement interval that minimizes the expected long-run cost per unit time. A numerical example illustrates that implementing the proposed (τ, T) replacement policy can effectively reduce maintenance costs.

Caching web pages at proxies and in web servers' memories can greatly enhance performance. Proxy caching is known to reduce network and both proxy and server caching can significantly decrease latency. Web caching problems have different properties than traditional operating systems caching, and replacement can benefit by recognizing and exploiting these differences. We address two aspects of the predictability of traffic patterns: the overall experienced by large proxy and web servers, and the distinct access patterns of individual pages. We formalize the notion of cache load under various replacement policies, includingLRU andLFU, and demonstrate that the trace of a large proxy server exhibits regular load. Predictable allows for improved design, analysis, and experimental evaluation of replacement policies. We provide a simple and (near) optimal replacement policy when each page request has an associated distribution function on the next request time of the page. Without the predictable assumption, no such online policy is possible and it is known that even obtaining an offline optimum is hard. For experiments, predictable enables comparing and evaluating replacement policies using partial traces, containing requests made to only a subset of the pages. Our results are based on considering a simpler caching model which we call the interval caching model .W e relate traditional and interval caching policies under predictable load, and derive (near)-optimal replacement policies from their optimal interval caching counterparts.

It is well-known in the standard age replacement policy that a finite preventive replacement time does not exist when the failure time is exponential and the optimal preventive replacement time is nonrandom. It is shown that when the failure time is exponential, a finite time exists by introducing the shortage and excess costs. In addition, the random age replacement is proposed and similar discussions are made. Furthermore, the periodic and random inspection policies are taken up, and their optimal policies are shown to correspond theoretically to those of the age replacement ones. It is shown finally that when the random inspection cost is the half of the periodic one, two expected costs are almost the same.

Optimal replacement policy for a deteriorating system with increasing repair times

In this paper, a cold standby repairable system consisting of two dissimilar components and one repairman is studied. When failures occur, the repair of both component 1 and component 2 are not ‘as good as new’. The consecutive operating times of component 1 after repair constitute a decreasing geometric process, while the repair times of component 1 are independent and identically distributed. For component 2, its failure is rectified by minimal repair, and the repair time is negligible. Component 1 has priority in use when both components are good. The replacement policy N is based on the failure number of component 1. Under policy N, we derive the explicit expression of the long-run average cost rate C(N) as well as the average number of repairs of component 2 before the system replaced. The optimal replacement policy N*, which minimises the long-run average cost rate C(N), is obtained theoretically. If the failure rate r(t) of component 2 is increasing, the existence and uniqueness of the optimal policy N* is also proved. Finally, a numerical example is given to validate the developed theoretical model. Some sensitivity analyses are provided to show the influence of some parameters, such as the costs for replacement and repair, and the parameters of the lifetime and repair time distributions of both components, to the optimal replacement policy N* and corresponding average cost rate C(N*).