Replication Policy Research Articles

Load balancing policies without feedback using timed replicas

Dispatching policies such as join the shortest queue (JSQ), join the queue with smallest workload (JSW), and their power of two variants are used in load balancing systems where the instantaneous queue length or workload information at all queues or a subset of them can be queried. In situations where the dispatcher has an associated memory, one can minimize this query overhead by maintaining a list of idle servers to which jobs can be dispatched. Recent alternative approaches that do not require querying such information include the cancel-on-start and cancel-on-complete replication policies. The downside of such policies however is that the servers must communicate either the start or the completion time instant of each service to the dispatcher and must allow the coordinated and instantaneous cancellation of all redundant replicas. In practice, the requirements of query messaging, memory, and replica cancellation pose challenges in their implementation and their advantages are not clear. In this work, we consider load-balancing policies that do not need to query load information, do not need memory, and do not need to cancel replicas. Our policies allow the dispatcher to append a timer to each job or its replica. A job or a replica is discarded if its timer expires before it starts receiving service. We analyze several variants of this policy which are novel and simple to implement. We numerically observe that the variants of the proposed policy outperform popular feedback-based policies for low arrival rates, despite no feedback from servers to the dispatcher.

Leveraged funds: robust replication and performance evaluation

Leveraged and inverse exchange-traded funds seek daily returns equal to fixed multiples of indexes' returns, but the ensuing rebalancing costs create a tension between a high correlation with the index and a low average deviation from the leveraged index' performance. With proportional trading costs, we find that the optimal replication policy is robust to the index' dynamics and obtain a sufficient statistic for index replication performance, the implied spread, which is insensitive to risk-premia and enables comparisons of funds tracking different factors of an index. Overall, the impact of trading costs on replication performance is comparable to or higher than the effect of management fees.

Energy efficiency in multicore shared cache by fault tolerance using a genetic algorithm based block reuse predictor

Aggressive voltage scaling to reduce energy consumption in Multicore causes exponential cell failures in SRAM. Last-level-cache (LLC), the major contender of chip area, exhibits highest sensitivity to low voltage parametric failures and limits energy efficiency. To break the energy barrier, exclusive fault protection mechanism is solicited to ensure on-chip data recovery out of the low voltage failures. This work proposes Cache evolution for energy efficiency by protecting cache blocks from SRAM cell failures due to voltage scaling. A set of coherence and reuse aware in-cache selective replication policies are proposed to ensure on-chip data recovery. Reuse likelihood is predicted through a vector optimization technique using Genetic Algorithm (GA). Reuse aware selective invalidation is employed to balance cache load due to replications. A replication aware replacement policy is also developed that victimizes the lowest reusable cache block. The proposed scheme is evaluated in Multi2Sim 5.0 simulation framework with SPEC CPU benchmark programs. Experimental results claim 43.66% and 38.80% reductions in miss rate and 59.10% and 52.73% reductions in vulnerability for integer and floating point benchmarks respectively. Energy reduction of 39.21% is observed for integer and 25.73% is observed for floating point benchmarks. Up to 34.78% and 26.98% power reductions in integer and floating point benchmarks are also observed. The minimum supply voltage of 350 mV is achieved at the cost of 7.05% area overhead and 3% performance drop-off.

Dynamic Replication Policy on HDFS Based on Machine Learning Clustering

Data growth in recent years has been swift, leading to the emergence of big data science. Distributed File Systems (DFS) are commonly used to handle big data, like Google File System (GFS), Hadoop Distributed File System (HDFS), and others. The DFS should provide the availability of data and reliability of the system in case of failure. The DFS replicates the files in different locations to provide availability and reliability. These replications consume storage space and other resources. The importance of these files differs depending on how frequently they are used in the system. So some of these files do not deserve to replicate many times because it is unimportant in the system. This paper introduces a Dynamic Replication Policy using Machine Learning Clustering (DRPMLC) on HDFS, which uses Machine Learning to cluster the files into different groups and apply other replication policies to each group to reduce the storage consumption, improve the read and write operations time and keep the availability and reliability of HDFS as a High-Performance Distributed Computing (HPDC).

Positive energy district stakeholder perceptions and measures for energy vulnerability mitigation

100 Positive Energy Districts (PEDs) are to be created in Europe by 2025, with a stated goal of urban decarbonization. These are highly energy efficient residential urban areas, powered entirely through renewables. PED creation is to be guided by principles of quality of life, sustainability, and inclusiveness (specifically focusing on affordability and energy poverty prevention). Although there is research into the decarbonization aspects of PEDs, there has been little focus on the guiding principles, and their potential to reduce energy vulnerability. Using energy vulnerability factors and an energy justice framework, this article examines how the topic of energy vulnerability mitigation is perceived by professional PED stakeholders. Stakeholders from multiple countries were interviewed in order to determine how and to what extent they approached the topic of inclusivity and energy vulnerability. The contribution of this paper to academic research is in helping to frame energy vulnerability in European smart city urban areas, focusing on the perceptions of key stakeholders. This contributes to research on the identification and evaluation of innovations such as PEDs which offer a potential model for an inclusive transition. Furthermore, this article offers a contribution for policymakers, informing PED replication policies with a focus on the synergistic aims of decarbonization and energy vulnerability mitigation.

Payoff-based Dynamic Segment Replication and Graph Classification Method with Attribute Vectors Adapted to Urban VANET

Due to the number of constraints and the dynamic nature of vehicular ad hoc networks (VANET), effective video broadcasting always remains a difficult task. In this work, we proposed a quality of video visualization guarantee model based on a feedback loop and an efficient algorithm for segmenting and replicating video segments using the Payoff-based Dynamic Segment Replication Policy (P-DSR). In the urban VANET environment, P-DSR is defined by taking into account the position of the vehicles, the speed, the direction, the number of neighboring vehicles, and the reputation of each node to stabilize the urban VANET topology. However, the management of various load control parameters between the different components of the urban VANET network remains a problem to be studied. This work uses a multi-objective problem that takes the parameters of our algorithm based on the Graph Classification Method with Attribute Vectors (GCMAV) as input. This algorithm aims to provide an improved class lifetime, an improved video segment delivery rate, a reduced inter-class overload, and an optimization of a global criterion. A scalable algorithm is used to optimize the parameters of the GCMAV. The simulations were carried out using the NetSim simulator and Multi-Objective Evolutionary Algorithms framework to optimize parameters. Experiments were carried out with realistic maps of Open Street Maps and its results were compared with other algorithms such as Seamless and Authorized Multimedia Streaming and P-DSR. The survey suggests that the proposed methodology works well concerning the average lifetime of the inter-classes and the delivery rate of video segments.

Synergy via Redundancy: Adaptive Replication Strategies and Fundamental Limits

The maximum possible throughput (or the rate of job completion) of a multi-server system is typically the sum of the service rates of individual servers. Recent work shows that launching multiple replicas of a job and canceling them as soon as one copy finishes can boost the throughput, especially when the service time distribution has high variability. This means that redundancy can, in fact, create synergy among servers such that their overall throughput is greater than the sum of individual servers. This work seeks to find the fundamental limit of the throughput boost achieved by job replication and the optimal replication policy to achieve it. While most previous works consider upfront replication policies, we expand the set of possible policies to delayed launch of replicas. The search for the optimal adaptive replication policy can be formulated as a Markov Decision Process, using which we propose two myopic replication policies, MaxRate and AdaRep, to adaptively replicate jobs. In order to quantify the optimality gap of these and other policies, we derive upper bounds on the service capacity, which provide fundamental limits on the throughput of queueing systems with redundancy.

Infant and Child Development: A journal for open, transparent and inclusive science from prenatal through emerging adulthood

<i>Infant and Child Development</i>: A journal for open, transparent and inclusive science from prenatal through emerging adulthood

Replicating Smart Cities: The City-to-City Learning Programme in the Replicate EC-H2020-SCC Project

This article addresses the problem of replication among smart cities in the European Commission’s Horizon 2020: Smart Cities and Communities (EC-H2020-SCC) framework programme. This article initially sets the general policy context by conducting a benchmarking about the explicit replication strategies followed by each of the 17 ongoing EC-H2020-SCC lighthouse projects. This article aims to shed light on the following research question: Why might replication not be happening among smart cities as a unidirectional, hierarchical, mechanistic, solutionist, and technocratic process? Particularly, in asking so, it focuses on the EC-H2020-SCC Replicate project by examining in depth the fieldwork action research process implemented during 2019 through a knowledge exchange webinar series with participant stakeholders from six European cities—three lighthouse cities (St. Sebastian, Florence, and Bristol) and three follower-fellow cities (Essen, Lausanne, and Nilüfer). This process resulted in a City-to-City Learning Programme that reformulated the issue of replication by experimenting an alternative and an enhanced policy approach. Thus, stemming from the evidence-based policy outcomes of the City-to-City Learning Programme, this article reveals that a replication policy approach from the social innovation lenses might be enabled as a multidirectional, radial, dynamic, iterative, and democratic learning process, overcoming the given unidirectional, hierarchical, mechanistic, solutionist, and technocratic approach.

Replicating Smart Cities: The City-to-City Learning Programme in the Replicate EC-H2020-SCC Project

This article addresses the problem of replication among smart cities in the European Commission’s Horizon 2020: Smart Cities and Communities (EC-H2020-SCC) framework programme. This article initially sets the general policy context by conducting a benchmarking about the explicit replication strategies followed by each of the 17 ongoing EC-H2020-SCC lighthouse projects. This article aims to shed light on the following research question: Why might replication not be happening among smart cities as a unidirectional, hierarchical, mechanistic, solutionist, and technocratic process? Particularly, in asking so, it focuses on the EC-H2020-SCC Replicate project by examining in depth the fieldwork action research process implemented during 2019 through a knowledge exchange webinar series with participant stakeholders from six European cities—three lighthouse cities (St. Sebastian, Florence, and Bristol) and three follower-fellow cities (Essen, Lausanne, and Nilüfer). This process resulted in a City-to-City Learning Programme that reformulated the issue of replication by experimenting an alternative and an enhanced policy approach. Thus, stemming from the evidence-based policy outcomes of the City-to-City Learning Programme, this article reveals that a replication policy approach from the social innovation lenses might be enabled as a multidirectional, radial, dynamic, iterative, and democratic learning process, overcoming the given unidirectional, hierarchical, mechanistic, solutionist, and technocratic approach.

Replicability of Research on International Relations As a Global Trend

This article is dedicated to the replication policy of leading world journals on international relations. The opportunity to replicate (reproduce) research results presented in foreign periodicals is viewed as an important factor of publication ethics. The experience of leading world journals and the US research infrastructure in the sphere of international relations are analyzed. The special role of the Interuniversity Consortium for Political and Social Research is shown, as well as that of the rising alternative Dataverse project, which is being developed by the Harvard Institute for Quantitative Social Science. A cross-cultural analysis of the use of research data in the leading countries of Europe and the world has been carried out. The recent efforts of Chinese universities to develop a scientific infrastructure are highlighted. The conclusion is formulated that Russia needs to create an integrated research infrastructure to generate and use databases in international political science.

Performance Analysis of Workload Dependent Load Balancing Policies

Load balancing plays a crucial role in achieving low latency in large distributed systems. Recent load balancing strategies often rely on replication or use placeholders to further improve latency. However assessing the performance and stability of these strategies is challenging and is therefore mostly simulation based. In this paper we introduce a unified approach to analyze the performance and stability of a broad class of workload dependent load balancing strategies. This class includes many replication policies, such as replicate below threshold, delayed replication and replicate only small jobs, as well as strategies for fork-join systems. We consider systems with general job size distributions where jobs may experience server slowdown. We show that the equilibrium workload distribution of the cavity process satisfies a functional differential equation (FDE) and conjecture that the cavity process captures the limiting behavior of the system as its size tends to infinity. The cavity process method assumes that the workload at the queues is independent and identically distributed. This way, one may focus on the behaviour of one queue, this method was introduced in [1] and is shown to yield exact results for SQ(d) with job size distribution that have a decreasing hazard rate and LL(d) with general job sizes.

Asymptotics of Replication and Matching in Large Caching Systems

We consider a generic model of distributed caching systems, where the cache servers are constrained by two main resources: memory size and bandwidth. Content distribution networks (CDNs) providing video contents and peer-to-peer video-on-demand services are a few examples of such systems. The throughput of these systems crucially depends on how these resources are managed, i.e., how contents are replicated across servers and how requests of specific contents are matched to servers storing the contents. In this paper, we formulate the problem of computing the replication policy and the matching policy, which jointly maximizes the throughput of the caching system. It is shown that computing the optimal replication policy for a given finite system is an NP-hard problem. A greedy replication scheme is then proposed and is shown to achieve a constant factor approximation guarantee when combined with the optimal matching policy. We note that the optimal matching policy has the problem of interruption in service of the ongoing requests due to re-assignment or repacking of the existing requests. To avoid this problem, we propose a simple randomized online matching scheme and analyze its performance in conjunction with the proposed replication scheme. We consider a limiting regime, where the number of servers is large and the arrival rates of the contents are scaled proportionally, and show that the proposed policies achieve asymptotic optimality. Extensive simulation results are presented to evaluate the performance of different policies and study the behavior of the caching system under different service time distributions of the requests.

Social-aware routing for cognitive radio–based vehicular ad hoc networks

Cognitive radio–based vehicular ad hoc networks can solve the problem of limited spectrum resource and growing vehicular communication service demands in intelligent transportation systems, and thus, it receives much concern recently. In cognitive radio–based vehicular ad hoc networks, the high mobility of vehicles and the dynamic spectrum activity of cognitive radio make routing in such networks a great challenge. Some routing researches have been proposed in cognitive radio–based vehicular ad hoc networks with single-objective optimization and neglecting the nodes’ social behaviors which can improve the network performance. From this perspective, we propose a social-aware routing scheme for cognitive radio–based vehicular ad hoc networks, with the purpose of increasing the packet delivery ratio and decreasing the overhead ratio. First, we analyze the social centrality of primary users to offer an accuracy spectrum hole measurement. Moreover, we develop a social community partition algorithm to divide secondary users into intra-community and inter-community groups. Furthermore, considering the tradeoff between the packet delivery ratio and the overhead ratio, we adopt different replication policies and forwarding ranks in different community communication processes. In the intra-community communication process, we employ the single-copy policy and the contact duration rank. In the inter-community communication process, we utilize the optimized-binary-tree replication policy and the bridge coefficient rank. Simulation results show that our social-aware routing scheme achieves the higher package delivery ratio and the lower overhead ratio when compared with the existing cognitive radio–based vehicular ad hoc networks routing schemes and other standard routing schemes.

Resolution of Expression of Concern: Tuning In, Not Turning Out: Evaluating the Impact of Ethnic Television on Political Participation

AbstractResolution of Expression of Concern: This is an update to the Expression of Concern published on 25 June 2019 (the original text is included below) for the article “Tuning In, Not Turning Out: Evaluating the Impact of Ethnic Television on Political Participation” by Yamil Ricardo Velez &amp; Benjamin J. Newman (https://doi.org/10.1111/ajps.12427), published online on 15 April 2019 in Wiley Online Library (wileyonlinelibrary.com). It is published at the request of the Journal Co‐Editors Kathleen Dolan and Jennifer L. Lawless, in agreement with Wiley Periodicals, Inc. The Resolution of Expression of Concern is published to alert readers that, while the authors of the paper believed they had sufficient authorization to use the 2012 Latino Decisions Election Eve Survey data in the article's analysis, the appropriate Latino Decisions principals had not granted that permission. Following an investigation, the Journal Co‐Editors have decided to let the published article stand and will take no further action. Latino Decisions agrees with this decision. The dataset has been removed from the Journal's Harvard Dataverse webpage.This Expression of Concern relates to the article “Tuning In, Not Turning Out: Evaluating the Impact of Ethnic Television on Political Participation” by Yamil Ricardo Velez &amp; Benjamin J. Newman (https://doi.org/10.1111/ajps.12427).This Expression of Concern is for the above article, published online on 15 April 2019 in Wiley Online Library (wileyonlinelibrary.com), which has been published at the request of journal Editor Jan Leighley, in agreement with Wiley Periodicals, Inc. The Expression of Concern is published due to the possibility that the authors did not have sufficient authorization from Latino Decisions to use the 2012 Latino Decisions Election Eve Survey data used in the analysis. This concern arose following notification to the Editor that the data posted on the AJPS Harvard Dataverse webpage (https://dataverse.harvard.edu/dataverse/ajps) were proprietary and had not been authorized for use in the article at the time the manuscript was submitted for peer review, nor approved for posting upon publication, consistent with the AJPS Replication Policy. Further actions by the Journal will be taken once the results of an investigation into the authors’ rights to use the data during peer review and in the published manuscript have been announced.

Replication in Financial Economics

The existing replication policies at top finance journals are far weaker than the policies at top economics journals. This paper explores both the costs and benefits of having a stronger replication policy in the context of my failed 2010 initiative to develop a unified policy across all top finance journals. For example, the most obvious cost of a replication policy is the additional burden it imposes on authors in answering questions about both the code and data. Indeed, this cost is disproportionately placed on our most productive researchers – potentially leading to less innovation. On the other hand, having a strong policy would likely reduce research misconduct – in particular, soft misconduct such as p-hacking. I present a framework to mitigate the costs associated with replication and maximize the benefits.

Performance Analysis of Workload Dependent Load Balancing Policies

Load balancing plays a crucial role in achieving low latency in large distributed systems. Recent load balancing strategies often rely on replication or use placeholders to further improve latency. However assessing the performance and stability of these strategies is challenging and is therefore often simulation based. In this paper we introduce a unified approach to analyze the performance and stability of a broad class of workload dependent load balancing strategies. This class includes many replication policies, such as replicate below threshold, delayed replication and replicate only small jobs, as well as strategies for fork-join systems. We consider systems with general job size distributions where jobs may experience server slowdown. We show that the equilibrium workload distribution of the cavity process satisfies a functional differential equation and conjecture that the cavity process captures the limiting behavior of the system as its size tends to infinity. We study this functional differential equation in more detail for a variety of load balancing policies and propose a numerical method to solve it. The numerical method relies on a fixed point iteration or a simple Euler iteration depending on the type of functional differential equation involved. We further show that additional simplifications can be made if certain distributions are assumed to be phase-type. Various numerical examples are included that validate the numerical method and illustrate its strength and flexibility.

Effect of Recommendations on Serving Content with Unknown Demand

We consider the task of content replication in distributed content delivery systems used by Video-on-Demand (VoD) services with large content catalogs. The prior work in this area focuses on the setting where each request is generated independent of all past requests. Motivated by the fact that most popular VoD services offer recommendations to users based on their viewing history, in a departure from existing studies, we study the setting with time-correlation in requests coming from each user. We use a Markovian process to model each user’s request process. In addition to introducing time-correlation in user requests, our model is consistent with empirically observed properties of the request process for VoD services with recommendation engines. In the setting where the underlying Markov Chain is unknown and has to be learned from the very requests the system is trying to serve, we show that separating the task of estimating content popularity and using the estimates to design a static content replication policy is strictly sub-optimal. To prove this, we show that an adaptive policy, which jointly performs the task of estimation and content replication, outperforms all policies that separate the task of estimation and content replication.

PTRE: a probabilistic two-phase replication elimination policy in large-scale distributed storage platforms

To support large-scale data-intensive applications, massive distributed storage platform are being widely deployed in more and more IT-infrastructures. One of the most mentioned issues on distributed storage platform is how to maintain desirable data availability without too many extra costs. Therefore, data replication service plays a key role to achieve this goal. Unfortunately, many existing replication policies are designed for small-scale or centralised storage platforms, and their performance tends to be dramatically degraded when a system consists of thousands of autonomous storage nodes. In this paper, we present a novel replication policy that allows a storage platform to eliminate useless replicas and maintain sufficient data availability at the same time. Through theoretical analysis, we have proven that the costs of the proposed policy is linearly increased with the number of underlying storage nodes, which means that it can be easily applied in large-scale distributed storage platform. The experimental results indicate that the proposed replication scheme can significant improve the effective utilisation of storage resources comparing with other existing policies. In addition, it exhibits a better robustness when the underlying storage platform is in presence of dramatically fluctuant workload.

PTRE: a probabilistic two-phase replication elimination policy in large-scale distributed storage platforms

To support large-scale data-intensive applications, massive distributed storage platform are being widely deployed in more and more IT-infrastructures. One of the most mentioned issues on distributed storage platform is how to maintain desirable data availability without too many extra costs. Therefore, data replication service plays a key role to achieve this goal. Unfortunately, many existing replication policies are designed for small-scale or centralised storage platforms, and their performance tends to be dramatically degraded when a system consists of thousands of autonomous storage nodes. In this paper, we present a novel replication policy that allows a storage platform to eliminate useless replicas and maintain sufficient data availability at the same time. Through theoretical analysis, we have proven that the costs of the proposed policy is linearly increased with the number of underlying storage nodes, which means that it can be easily applied in large-scale distributed storage platform. The experimental results indicate that the proposed replication scheme can significant improve the effective utilisation of storage resources comparing with other existing policies. In addition, it exhibits a better robustness when the underlying storage platform is in presence of dramatically fluctuant workload.