Replacement Policy Research Articles

AN OPTIMAL REPLACEMENT PROBLEM FOR A SHOCK MODEL WITH DIFFERENT MODES OF FAILURES USING ALPHA SERIES PROCESSES

This paper studies a shock model for a repairable system with two-type failures by assuming that two kinds of shocks in a sequence of random shocks will make the system failed, one based on the inter-arrival time between two consecutive shocks less than a given positive value and the other based on the shock magnitude of single shock more than a given positive value . Further it is assumed that the system after repair is not ‘as good as new’, but the consecutive repair times of the system form a stochastic increasing α-seires process. Under these assumptions, we determine an explicit expression for the average cost rate and an optimal placement policy N* based on the number of failure of the system is determined such that the long-run average cost per unit time is minimized. The explicit expression of long-run average cost per unit time is derived, and the corresponding optimal replacement policy can be determined analytically or numerically. Finally, a numerical example is given.

An Effective and Lightweight Countermeasure Scheme to Multiple Network Attacks in NDN

In Named Data Networking, cache pollution, cache poisoning and interest flooding are three popular types of attacks that can drastically degrade the network performance. However, previous methods for mitigating these attacks are not sufficiently effective or efficient. Also, they cannot simultaneously handle the three attacks, or the case that core routers or edge routers are compromised. To handle these problems, we propose an effective and lightweight countermeasure scheme. It consists of token-based router monitoring policy (TRM), hierarchical consensus-based trust management (HCT), and popularity-based probabilistic caching and caching replacement policy (PPC). In TRM, each edge router monitors and evaluates each data requester’s probability of launching the cache pollution attack and each data provider’s probability of launching the cache poisoning attack, and accordingly assigns, rewards and penalizes tokens to them to control their data request and data provision activities. Thus, the interest flooding attack can also be mitigated by limiting the consumption of tokens. In HCT, each core router manages its directly connected edge routers using TRM, and the core routers trust each other through adopting the concept of consensus in Blockchain. Thus, the edge and core routers executing monitoring and evaluation are trustable. PPC uses probabilistic caching and caching replacement based on the popularity of received content to further mitigate the attacks and reduce caching and data verification overhead. Results from simulation experiments demonstrate that our proposed scheme has better performance, in terms of interest satisfaction ratio and average end-to-end delay than current mechanisms.

COVID-19 and the second exams fiasco across the UK: four nations trying to avoid immediate policy failure

In 2021, the UK and devolved governments tried to avoid the school exams fiasco of 2020. Their immediate marker of success was to prevent a similar U-turn on their COVID-19 school exams replacement policies. They still cancelled the traditional exam format, and sought teacher assessments to determine their grades, but this time without using an algorithm to standardise the results. The outcomes produced some concerns about inequity, since the unequal exam results are similar to those experienced in 2020. However, we did not witness the same sense of acute political crisis. We explain these developments by explaining this year’s ‘windows of opportunity’ overseen by four separate governments, in which the definition of the problem, feasibility of each solution, and motive of policymakers to select one, connects strongly to the previous U-turn. A policy solution that had been rejected during the first window became a lifeline during the second and a likely choice during the third. This action solved an immediate crisis despite exacerbating the problem that ministers had previously sought to avoid (‘grade inflation’). It produced another year of stark education inequity, but also ensured that inequity went from part of an acute political crisis to its usual status as a chronic low-attention policy problem.

NDN Content Store and Caching Policies: Performance Evaluation

Among various factors contributing to performance of named data networking (NDN), the organization of caching is a key factor and has benefited from intense studies by the networking research community. The performed studies aimed at (1) finding the best strategy to adopt for content caching; (2) specifying the best location, and number of content stores (CS) in the network; and (3) defining the best cache replacement policy. Accessing and comparing the performance of the proposed solutions is as essential as the development of the proposals themselves. The present work aims at evaluating and comparing the behavior of four caching policies (i.e., random, least recently used (LRU), least frequently used (LFU), and first in first out (FIFO)) applied to NDN. Several network scenarios are used for simulation (2 topologies, varying the percentage of nodes of the content stores (5–100), 1 and 10 producers, 32 and 41 consumers). Five metrics are considered for the performance evaluation: cache hit ratio (CHR), network traffic, retrieval delay, interest re-transmissions, and the number of upstream hops. The content request follows the Zipf–Mandelbrot distribution (with skewness factor α=1.1 and α=0.75). LFU presents better performance in all considered metrics, except on the NDN testbed, with 41 consumers, 1 producer and a content request rate of 100 packets/s. For the level of content store from 50% to 100%, LRU presents a notably higher performance. Although the network behavior is similar for both skewness factors, when α=0.75, the CHR is significantly reduced, as expected.

Generalized periodic replacement policies for repairable systems subject to two types of failures

The purpose of this current research is to schedule generalized periodic replacement policies for a single unit system executing random working jobs. The system is subject to two types of failures when it has failed, including a minor failure (Type I failure), which can be thoroughly removed by the minimal repair and a catastrophic failure (Type II failure), which should be rectified by the corrective replacement. To be specific, four distinct periodic replacement models including a periodic replacement first model (Model A1), a modified periodic replacement first model (Model A2), a periodic replacement last model (Model B1), and a modified periodic replacement last model (Model B2) are investigated. The long run average cost rate (ACR) over an infinite time span under different situations is obtained theoretically and optimal periodic replacement interval for each condition is derived analytically. Numerical examples are exhibited to verify the derived results.

Mean Field and Refined Mean Field Approximations for Heterogeneous Systems

Mean field approximation is a powerful technique to study the performance of large stochastic systems represented as n interacting objects. Applications include load balancing models, epidemic spreading, cache replacement policies, or large-scale data centers. Mean field approximation is asymptotically exact for systems composed of n homogeneous objects under mild conditions. In this paper, we study what happens when objects are heterogeneous. This can represent servers with different speeds or contents with different popularities. We define an interaction model that allows obtaining asymptotic convergence results for stochastic systems with heterogeneous object behavior, and show that the error of the mean field approximation is of order $O(1/n)$. More importantly, we show how to adapt the refined mean field approximation, developed by Gast et al., and show that the error of this approximation is reduced to O(1/n^2). To illustrate the applicability of our result, we present two examples. The first addresses a list-based cache replacement model, RANDOM(m), which is an extension of the RANDOM policy. The second is a heterogeneous supermarket model. These examples show that the proposed approximations are computationally tractable and very accurate. They also show that for moderate system sizes (30) the refined mean field approximation tends to be more accurate than simulations for any reasonable simulation time.

When CCN meets MCGDM: optimal cache replacement policy achieved by PRSRV with Pythagorean fuzzy set pair analysis

When CCN meets MCGDM: optimal cache replacement policy achieved by PRSRV with Pythagorean fuzzy set pair analysis

Mining voice of customers and employees in insurance companies from online reviews: a text analytics approach

PurposeAlthough insurance companies contribute to about $600 billion of the US gross domestic product and employ 2.7 million people, the overall perception of clients and workers employed in this domain is poor. To the best of our knowledge, this study is the first to propose recommendations to improve the service quality of insurance companies using the voice of customers and employees that are available online.Design/methodology/approachThe proposed methodology consists of four stages: extracting text reviews posted by employees and customers, reviewing feedback using bigrams and trigrams, determining topics and identifying quality-associated managerial recommendations using the Strengths, Weaknesses, Opportunities and Threats (SWOT) and root cause analysis techniques.FindingsOur results indicate that primary causes for client dissatisfaction are improper client assistance services, inefficient claims processing, issues related to payments/fees and unresponsive ancillary services. Offering different types of payment methods, immediate car replacement policy and features such as smartphone applications are commended very well. The major reasons for workforce frustrations include long and overly complex training, lack of social events and incentive programs. Coworkers, workplace facilities, clean work environment and other amenities are shown to have a positive impact on employees' satisfaction level.Originality/valueTo the best of our knowledge, this study is the first to propose recommendations to improve the service quality of insurance companies using the voice of customers and employees that are available online. The proposed quality-related insights can assist insurance companies in improving the outlook of their workers as well as customers.

APPLICATION OF RENEWAL REWARD PROCESSES IN HOMOGENEOUS DISCRETE MARKOV CHAIN

A renewal process which is a special type of a counting process, which counts the number of events that occur up to (and including) time has been investigated, in order to provide some insight into the performance measures in renewal process and sequence such as, the mean time between successive renewals, ; Laplace-Stiltjes transform (LST) of the mean time, ; the Laplace-Stieltjes transform (LST) of the mean time distribution function, ; Laplace-Stiltjes transform (LST) of fold convolution of distribution function, ; the time at which the renewal occurs, the average number of renewals per unit time over the interval (0, t], and expected reward, . Our quest is to analyse the distribution function of the renewal process and sequence using the concept of discrete time Markov chain to obtain the aforementioned performance measures. Some properties of the Erlang , exponential and geometric distributions are used with the help of some existing laws, theorems and formulas of Markov chain. We concluded our study through illustrative examples that, it is not possible for an infinite number of renewals to occur in a finite period of time; Also, the expected number of renewals increases linearly with time; and from the uniqueness property, we affirmed that, the Poisson process is the only renewal process with a linear mean-value function; and lastly, we obtained the optimal replacement policy for a manufacturing machine which showed that, the exponential property of the lifetime

Analysis of time-to-failure data for a repairable system subject to degradation

In this paper, a gradually deteriorating system with imperfect repair is considered. The deterioration is modeled by a stationary stochastic process. The system fails once the deterioration level exceeds a given threshold L. At failure, an imperfect repair is performed and the deterioration level is reduced to a fixed value r, say. The system can be repaired n−1 times and will be replaced after the nth failure. The article aims to estimate the parameters of the proposed deterioration process based on the observed failures. To this end we consider the Wiener and Gamma processes which are the most common used stochastic process models. In Wiener process, an explicit expression for the estimators is obtained. Birnbaum–Saunders approximation is extended to estimate the parameters in Gamma process. An optimal replacement policy is also discussed. Finally, a Monte-Carlo simulation is conducted to investigate the performance of estimators.

ARMOR: A Reliable and Mobility-Aware RPL for Mobile Internet of Things Infrastructures

Mobile portable embedded devices are becoming an integral part of our daily activities in the vision of Internet of Things (IoT). Nevertheless, due to lack of mobility support in the IPv6 routing protocol for low-power and lossy networks (RPLs), which is standardized for multihop IoT infrastructures, providing reliable communications in terms of packet delivery ratio (PDR) in mobile IoT applications has become significantly challenging. While several studies tried to enhance the adaptability of RPL to network dynamics, their utilized routing metrics have prevented them from establishing long-lasting reliable paths. Furthermore, the stochastic parent replacement policy in the standard version of RPL has intensified this challenge. Aside from this, due to the existing tradeoff between reliability and power efficiency, most of the existing approaches have only concentrated on one of these concerns without paying attention to the other one. To address these issues, this article introduces ARMOR, a routing mechanism built upon RPL, which employs a novel mobility-aware routing metric, i.e., time to reside (TTR), and a corresponding parent replacement policy. According to the motion characteristics of the mobile objects, TTR provides an estimation of how long the nodes will be in the transmission range of each other. This enables ARMOR to select nodes, which provide longer connection period and consequently higher reliability. In comparison with the state of the art, while keeping the power consumption constant, ARMOR significantly improves the amount of PDR in the network by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5\times $ </tex-math></inline-formula> , while it enhances the reliability against the original version of this protocol by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.2\times $ </tex-math></inline-formula> .

Effectiveness Evaluation of Replacement Policies for On-Chip Caches in Multiprocessors

A Cache plays a vital role in improving performance in the multicore environment, especially the Last Level Cache (LLC). The improvements in performance are based on the block size, associativity, and replacement policies. Most of the papers concentrate on traditional Least Recently Used (LRU) based replacement policies for their replacement decisions. Unfortunately, the replacement decisions do not enhance performance of the cache as expected. An enhanced modified Pseudo LRU policy is proposed, which is an approximation of LRU. The proposed methodology uses counters to enhance the confidence of replacement decisions based on the history of the replaceable blocks in cache. It is very clear from the Simulation results that the replacement scheme proposed exhibits better performance improvement in terms of miss ratio of about 3% and energy efficiency of about 2% on an average.

Secure hybrid replacement policy: Mitigating conflict-based cache side channel attacks

Conflict-based attacks on the last-level cache (LLC) are the most threatening cache side channel attack scenarios, where attackers exploit cache set conflicts as a channel to obtain sensitive cache accesses of victims. In this paper, we observe that conflict-based attacks cause exploited cache sets to experience an abnormally large number of accesses. Protecting these sets with suspicious access patterns effectively interferes with the attacker’s observation and thus mitigates such attacks. Based on this insight, we propose Secure Hybrid Replacement Policy (SHRP). SHRP captures suspicious sets by counting the number of accesses and alters their replacement policy to introduce noise into the attacker’s probe. Specifically, When a request occurs an LLC hit, SHRP has a certain probability of sending an invalidation request to the suspicious set. If a request triggers an LLC replacement, SHRP has a certain probability of turning the request uncacheable. Because of these probabilistic obscuring actions, attackers are impossible to accurately infer the victim’s behaviors on the protected cache set. The evaluation demonstrates that SHRP efficiently mitigates conflict-based attacks and induces negligible performance impacts and acceptable storage overhead.

Learning Future Reference Patterns for Efficient Cache Replacement Decisions

This study proposes a cache replacement policy technique to increase the cache hit rate. This policy can improve the efficiency of cache management and performance. Heuristic cache replacement policies are mechanisms that are designed empirically in advance to determine what needs to be replaced. This study explains why the heuristic policy does not achieve a high accuracy for certain patterns of data. A machine learning method is proposed to predict the blocks that need to be requested in the future to prevent erroneous decisions. The core operation of the proposed method is that when a cache miss occurs, the machine learning model predicts a future block reference sequence that is based on the block reference sequence of the input sequence. The predicted block is added to the prediction buffer and the predicted block is removed from the non-access buffer if it exists in the non-access buffer. After filling the prediction buffer, the conventional replacement policy can be replaced with a time complexity of O(1) by replacing the block with a non-access buffer. The proposed method improves the least recently used (LRU) algorithm by 77%, the least frequently used (LFU) algorithm by 65%, and the adaptive replacement cache (ARC) by 77% and shows a hit rate similar to that of state-of-the-art research. The proposed method reinforces the existing heuristic policy and enables a consistent performance for LRU- and LFU-friendly workloads.

AGE-BASED REPLACEMENT WITH IMPERFECT REPAIR AND RISK AS LOW AS REASONABLY PRACTICABLE

Maintenance policies must consider system reliability and the risk of accidents in systems where equipment failures represent a risk. In this context, this work proposes an age replacement policy with Bayesian imperfect repair and considers the “as low as reasonably practicable” (ALARP) principle. The policy determines the age of replacement that minimizes the long-run cost per unit time when the failure rate is ALARP. The model also supposes that failures are either minimally or perfectly repaired, depending on the skill of the maintainer. Numerical applications are performed with and without the disproportion factor in ALARP, both for infinite and one-replacement-cycle horizons. The results show that considering imperfect repair leads to an increase in replacement costs and a decrease in the optimal replacement age when considering the ALARP principle. The model applies to situations where there are conflicts of interest between maintenance management and risk; that is, cases where the aim is to reduce the cost of replacing equipment and minimize the risks. Maintenance policies must consider system reliability and the risk of accidents in systems where equipment failures represent a risk. In this context, this work proposes an age replacement policy with Bayesian imperfect repair and considers the “as low as reasonably practicable” (ALARP) principle. The policy determines the age of replacement that minimizes the long-run cost per unit time when the failure rate is ALARP. The model also supposes that failures are either minimally or perfectly repaired, depending on the skill of the maintainer. Numerical applications are performed with and without the disproportion factor in ALARP, both for infinite and one-replacement-cycle horizons. The results show that considering imperfect repair leads to an increase in replacement costs and a decrease in the optimal replacement age when considering the ALARP principle. The model applies to situations where there are conflicts of interest between maintenance management and risk; that is, cases where the aim is to reduce the cost of replacing equipment and minimize the risks.

A Remaining Useful Life Model for Optimizing Maintenance cost and Spare-parts replacement of Production Systems in the Context of Sustainability

In reliability and maintenance engineering, predictive methods play a major role for estimating the remaining useful life (RUL) of equipment. However, in the most cases the RUL estimation is based on two types of approaches: model-based solution and data-driven solution. Data-driven solution is a very realistic solution, but requires the availability of large quantity of collected data using sensors networks, big storage capacity, supercomputers for processing, and high-level algorithms such as machines learning, convolutional Neural Networks, Hidden Markov Models. While model-based solution methods are less difficult to deploy, they use techniques based on the mean residual life (MRL) value. This paper proposes an integrated decision support model for minimizing the maintenance expected cost, and for reducing the excess of spare-parts usage of multi-component production systems. This decision support model includes three performance indicators that are: the renewal function (RF), the mean residual lifetime (MRL) and the renewal mean residual lifetime (RMLR). The contribution of this research consists of (1) introducing the MRL function for a class of useful probability distributions, (2) proposing the Renewal MRL (RMRL) as a predictive maintenance strategy, (3) applying the approach-MRL to maintenance scheduling problems, (4) dressing a comparative numerical study between the different proposed models using an industrial case study. Besides, the comparative study performing the RF, MRL, and RMRL is carried out using basic and modified replacement strategies consisting of an age replacement policy (ARP) and an opportunistic maintenance solution. In addition, a case study from an Electrical power system is proposed by providing numerical results that discuss and illustrate the outcome gains in terms of maintenance costs saving and spare parts replacements’ minimization. However, the proposed solution is demonstrated to have a positive effect to enhance sustainable development systems, and it is provided soon to cover other theoretical and application aspects of the MRL.

Estimation of Age Replacement Policy when Maintenance Cost is Linear and Nonlinear

Estimation of Age Replacement Policy when Maintenance Cost is Linear and Nonlinear

PROWL: A Cache Replacement Policy for Consistency Aware Renewable Powered Devices

Energy harvesting systems powered by renewable energy sources employ hybrid volatile-nonvolatile memory to enhance energy efficiency and forward progress. These systems have unreliable power sources and energy buffers with limited capacity, so they complete long-running applications across multiple power outages. However, a power outage might cause data inconsistency, because the data in nonvolatile memories are persistent, while the data in volatile memories are unsteady. State of the art studies proposed various memory architectures and compiler-based techniques to tackle the data inconsistency in these systems. These approaches impose too many unnecessary check-points on the system to avoid data inconsistency. These studies did not consider the effect of cache memory to mask and postpone the imposed check-points on the system for the sake of consistency. In this article, we utilize the cache memory and propose PROWL, a consistency aware cache replacement policy to avoid data inconsistency with fewer check-points. The results show that PROWL has by up to 85 percent fewer check-points compare to the state of the art approaches, and PROWL improves the average response time of the system by up to 65 percent.

Towards Dependency-Aware Cache Management for Data Analytics Applications

Memory caches are being used aggressively in today's data analytics systems such as Spark, Tez, and Piccolo. The significant performance impact of caches and their limited sizes call for efficient cache management in data analytics clusters. However, prevalent data analytics systems employ rather simple cache management policies-notably Least Recently Used (LRU) and Least Frequently Used (LFU)-that are oblivious to the application semantics of data dependency, expressed as directed acyclic graphs (DAGs). Without this knowledge, cache management can, at best, be performed by guessing the future data access patterns based on history, which frequently results in inefficient, erroneous caching with a low hit rate and a long response time. Worse still, the lack of data dependency knowledge makes it impossible to retain the all-or-nothing cache property of cluster applications, in that a compute task cannot be sped up unless all the dependent data has been kept in the main memory. In this paper, we propose a novel cache replacement policy, named Least Reference Count (LRC), which exploits the application's data dependency information to optimize the cache management. LRC keeps track of the reference count of each data block, defined as the number of dependent child blocks that have not been computed yet, and always evicts the block with the smallest reference count. Furthermore, we incorporate the all-or-nothing requirement into LRC by coordinately managing the reference counts of all the input data blocks for the same computation. We demonstrate the efficacy of LRC through both empirical analysis and cluster deployments against popular benchmarking workloads. Our Spark implementation shows that, the proposed policies well address the all-or-nothing requirement and significantly improve the cache performance. Compared with LRU and a recently proposed caching policy called MEMTUNE, LRC improves the caching performance of typical workloads in production clusters by 22% and 284%, respectively.

Prescriptive block replacement policy for production degrading systems

Most of the current research in production management is related to the question of integrating data analytics in optimization models. This leads to some very nice models, especially for maintenance planning. Nevertheless, from our point of view, the question of integrating such data-based maintenance optimization models in production planning context remains challenging. The objective of this work is to investigate first insights into prescriptive maintenance within production management context. In this paper, we propose to elaborate a maintenance decision model for a degrading production system. The model allows to optimize both the expected capacity for production planning without maintenance interruption and a condition preventive replacement setting out of the production time. The maintenance decision is also extended to ensure the production objectives with a new prescriptive decision: the acceleration or deceleration production rate which also impacts the degradation speed.