Number Of Replicas Research Articles

Impact of Replicas and Simulation Length on In Silico Behaviors of a Protein Domain.

Molecular dynamics (MD) simulations are immensely valuable for studying protein structure, function and dynamics. Their ability to capture atomic-level behavior of molecules and describe their evolution over time makes it a powerful synergistic tool for biochemistry, structural biology and other life sciences. To advance research and knowledge on reasonable timescales, researchers must optimize the amount of useful information extracted from simulation data while often frugally managing computational resources. Often, this involves balancing the length of MD trajectories with the number of replicas of a given system, with the aim of maximizing sampling of the conformational landscape. However, identifying this balance is not always intuitive, and the lack of standards among researchers can produce large variability in results and predictions from MD measurements. Here, we investigate the variability in MD results when simulation length and replica numbers are varied. Using a 231-amino acid domain, we compare measurements from independent trajectories to a benchmark trajectory of 3, 1000-ns replicates. We perform these simulations on 27 protein-ligand complexes, allowing us to compare ligand-specific rankings of complexes across independent replicas. Our results reveal that some MD measurements are accurately ranked by single trajectories, while others are not. We uncover similar variability in the effects of trajectory lengths on measurements. Our findings suggest that a one-size-fits-all approach to MD simulations is not necessarily the best approach, and depending on the intended measurements and research question, it may be advantageous sometimes to prioritize longer trajectories over multiple replicas. This work provides important considerations for researchers while designing simulation studies.

Public Authentic-Replica Sampling Mechanism in Distributed Storage Environments

With the rapid development of wireless communication and big data analysis technologies, the storage of massive amounts of data relies on third-party trusted storage, such as cloud storage. However, once data are stored on third-party servers, data owners lose physical control over their data, making it challenging to ensure data integrity and security. To address this issue, researchers have proposed integrity auditing mechanisms that allow for the auditing of data integrity on cloud servers without retrieving all the data. To further enhance the availability of data stored on cloud servers, multiple replicas of the original data are stored on the server. However, in existing multi-replica auditing schemes, there is a problem of server fraud, where the server does not actually store the corresponding data replicas. To tackle this issue, this paper presents a formal definition of authentic replicas along with a security model for the authentic-replica sampling mechanism. Based on time-lock puzzles, identity-based encryption (IBE) mechanisms, and succinct proof techniques, we design an authentic replica auditing mechanism. This mechanism ensures the authenticity of replicas and can resist outsourcing attacks and generation attacks. Additionally, our schemes replace the combination of random numbers and replica correspondence tables with Linear Feedback Shift Registers (LFSRs), optimizing the original client-side generation and uploading of replica parameters from being linearly related to the number of replicas to a constant level. Furthermore, our schemes allow for the public recovery of replica parameters, enabling any third party to verify the replicas through these parameters. As a result, the schemes achieve public verifiability and meet the efficiency requirements for authentic-replica sampling in multi-cloud environments. This makes our scheme more suitable for distributed storage environments. The experiments show that our scheme makes the time for generating copy parameters negligible while also greatly optimizing the time required for replica generation. As the amount of replica data increases, the time spent does not grow linearly. Due to the multi-party aggregation design, the verification time is also optimal. Compared to the latest schemes, the verification time is reduced by approximately 30%.

Replica Rényi wormholes and generalised modular entropy in JT gravity

We consider the problem of computing semi-classical Rényi entropies of CFT on AdS2 backgrounds in JT gravity with nongravitating baths, for general replica number n. Away from the n → 1 limit, the backreaction of the CFT twist fields on the geometry is nontrivial. For one twist field insertion and general n, we show that the quantum extremal surface (QES) condition involves extremisation of the generalised modular entropy, consistent with Dong’s generalisation of the Ryu-Takayanagi formula for general n. For multiple QES we describe replica wormhole geometries using the theory of Fuchsian uniformisation, explicitly working out the analytically tractable case of the n = 2 double trumpet wormhole geometry. We determine the off-shell dependence of the gravitational action on the QES locations and boundary map. In a factorisation limit, corresponding to late times, we are able to relate this action functional to area terms given by the value of the JT dilaton at the (off-shell) QES locations, with computable corrections. Applied to the two-sided eternal black hole, we find the n-dependent Page times for Rényi enropies in the high temperature limit.

Application of Fuzzy Logic for Horizontal Scaling in Kubernetes Environments within the Context of Edge Computing

This paper presents a fuzzy logic-based approach for replica scaling in a Kubernetes environment, focusing on integrating Edge Computing. The proposed FHS (Fuzzy-based Horizontal Scaling) system was compared to the standard Kubernetes scaling mechanism, HPA (Horizontal Pod Autoscaler). The comparison considered resource consumption, the number of replicas used, and adherence to latency Service-Level Agreements (SLAs). The experiments were conducted in an environment simulating Edge Computing infrastructure, with virtual machines used to represent edge nodes and traffic generated via JMeter. The results demonstrate that FHS achieves a reduction in CPU consumption, uses fewer replicas under the same stress conditions, and exhibits more distributed SLA latency violation rates compared to HPA. These results indicate that FHS offers a more efficient and customizable solution for replica scaling in Kubernetes within Edge Computing environments, contributing to both operational efficiency and service quality.

NPPoS: Non-interactive practical proof-of-storage for blockchain

Blockchain full nodes are pivotal for transaction availability, as they store the entire ledger, but verifying their storage integrity faces challenges from malicious remote storage attacks such as Sybil, outsourcing, and generation attacks. However, there is no suitable proof-of-storage solution for blockchain full nodes to ensure a healthy number of replicas of the ledger. Existing proof-of-storage solutions are designed for general-purpose settings where a data owner uses secret information to verify storage, rendering them unsuitable for blockchain where proof-of-storage must be fast, publicly verifiable, and data owner-agnostic. This paper introduces a decentralised and quantum-resistant solution named Non-interactive Practical Proof of Storage (nPPoS) with an asymmetric encoding and decoding scheme for fast and secure PoStorage and Zero-Knowledge Scalable Transparent Arguments of Knowledge (zk-STARKs) for public variability in blockchain full nodes. The algorithm with asymmetric times for encoding and decoding creates unique block replicas and corresponding proofs for each storage node to mitigate malicious remote attacks and minimise performance degradation. The intentional resource-intensive encoding deters attacks, while faster decoding minimises performance overhead. Through zk-STARKs, nPPoS achieves public verifiability, enabling one-to-many verification for scalability, quantum resistance and decentralisation. It also introduces a two-phase randomisation technique and a time-weighted trustworthiness measurement for scalability and adaptability.

Malicious replica quarantining protocol for Mobile Wireless Sensor Networks using replica detection and identification

Since sensor networks are deployed in harsh and unattended environments, and the nodes are not tamper-resistant, security is crucial for all the impending sensor network applications. When physically compromised, confidential credential of sensor nodes may be revealed to adversaries. Upon obtaining the credentials, an adversary can produce a number of replicas of a compromised legitimate node. These replicas interfere with or prevent the normal operation of the network through attacks called replica or imposterattacks. Hence detection, identification and quarantining of replicas attacks are crucial. Existing schemes mostly focus on static sensor networks while solutions for MWSN’s are also available. Upon discovering that an id is used by multiple nodes, these schemes quarantine all nodes with the same id without distinguishing the legitimate node from its replicas. In this paper, we propose a novel distributed light-weight and memory efficient signature-based protocol for quarantining malicious replicas in the presence of Byzantine replicas in MWSN’s. This protocol quarantines only the replicas that present wrong sensed data in the presence of Byzantine replicas that modify and communicate its data and send false claims. The proposed protocol uses a signature generation, exchange and verification scheme to detect, uniquely identify and distinguish replicas with the same id among multiple replicas for a single legitimate node. The protocol quarantines only the malicious replicas that present wrong sensed data among all the detected replicas of each legitimate node by uniquely identifying the malicious replicas using their unique signatures.

Copycat perceptron: Smashing barriers through collective learning.

We characterize the equilibrium properties of a model of y coupled binary perceptrons in the teacher-student scenario, subject to a suitable cost function, with an explicit ferromagnetic coupling proportional to the Hamming distance between the students' weights. In contrast to recent works, we analyze a more general setting in which thermal noise is present that affects each student's generalization performance. In the nonzero temperature regime, we find that the coupling of replicas leads to a bend of the phase diagram towards smaller values of α: This suggests that the free entropy landscape gets smoother around the solution with perfect generalization (i.e., the teacher) at a fixed fraction of examples, allowing standard thermal updating algorithms such as Simulated Annealing to easily reach the teacher solution and avoid getting trapped in metastable states as happens in the unreplicated case, even in the computationally easy regime of the inference phase diagram. These results provide additional analytic and numerical evidence for the recently conjectured Bayes-optimal property of Replicated Simulated Annealing for a sufficient number of replicas. From a learning perspective, these results also suggest that multiple students working together (in this case reviewing the same data) are able to learn the same rule both significantly faster and with fewer examples, a property that could be exploited in the context of cooperative and federated learning.

PA-SPS: A predictive adaptive approach for an elastic stream processing system

Stream Processing Systems (SPSs) dynamically process input events. Since the input is usually not a constant flow, presenting rate fluctuations, many works in the literature propose to dynamically replicate SPS operators, aiming at reducing the processing bottleneck induced by such fluctuations. However, these SPSs do not consider the problem of load balancing of the replicas or the cost involved in reconfiguring the system whenever the number of replicas changes. We present in this paper a predictive model which, based on input rate variation, execution time of operators, and queued events, dynamically defines the necessary current number of replicas of each operator. A predictor, composed of different models (i.e., mathematical and Machine Learning ones), predicts the input rate. We also propose a Storm-based SPS, named PA-SPS, which uses such a predictive model, not requiring reboot reconfiguration when the number of operators replica change. PA-SPS also implements a load balancer that distributes incoming events evenly among replicas of an operator. We have conducted experiments on Google Cloud Platform (GCP) for evaluation PA-SPS using real traffic traces of different applications and also compared it with Storm and other existing SPSs.

The equilibrium-value convergence for the multiple-partners game

We study the assignment game (Shapley and Shubik, 1972) and its generalization of the multiple-partners game (Sotomayor, 1992), the simplest many-to-many extension. Our main result is that the Shapley value of a replicated multiple-partners game converges to a competitive equilibrium payoff when the number of replicas tends to infinity. The result also holds for a large subclass of semivalues since we prove that they converge to the same value as the replica becomes large. Furthermore, in supermodular and monotonic assignment games, the asymptotic Shapley value coincides with the mean stable imputation. The proof of our theorem relies on Hall's theorem.

Generalized extreme value analysis of efficient evaluation of extreme values in random media criticality calculations

The uncertainty of fuel debris criticality can be estimated by Monte Carlo criticality calculations repeated over independent replicas of some properly modelled random medium. Incomplete randomized Weierstrass function (IRWF) is a modelling framework for such calculations. Under these approaches at hand, the theme of this paper is how to efficiently analyse extreme realizations of neutron effective multiplication factor (keff) over IRWF random media replicas. To this end, a new bounded amplification (BA) technique is applied to IRWF. The numerical results clearly indicate that the BA-applied IRWF reduces a required number of random media replicas at least by an order of magnitude. To rigorously validate this efficiency gain, generalized extreme value (GEV) analysis is applied to a data set of keff values obtained without applying BA. It turns out that the extreme values of these keff values follow the Weibull distribution. Therefore, the theory of GEV guarantees the existence of an upper limit for these keff values, and the actually computed upper limit is indeed smaller than or equal to the top two keff values obtained from an order-of-magnitude reduced number of BA-applied IRWF random media replicas. This means that the efficiency gain brought by BA has been confirmed by the GEV methodology. In addition, a method of rejecting spurious estimation values due to statistical fluctuation is demonstrated concerning the extreme value index in the GEV analysis.

PROACTIVE HORIZONTAL SCALING METHOD FOR KUBERNETES

Context. The problem of minimizing redundant resource reservation while maintaining QoS at an agreed level is crucial for modern information systems. Modern information systems can include a large number of applications, each of which uses computing resources and has its own unique features, which require a high level of automation to increase the efficiency of computing resource management processes.
 Objective. The purpose of this paper is to ensure the quality of IT services at an agreed level in the face of significant dynamics of user requests by developing and using a method of proactive automatic application scaling in Kubernetes.
 Method. This paper proposes a proactive horizontal scaling method based on the Prophet time series prediction algorithm. Prometheus metrics storage is used as a data source for training and validating forecasting models. Based on the historical metrics, a model is trained to predict the future utilization of computation resources using Prophet. The obtained time series is validated and used to calculate the required number of application replicas, considering deployment delays.
 Results. The experiments have shown the effectiveness of the proposed proactive automated application scaling method in comparison with existing solutions based on the reactive approach in the selected scenarios. This method made it possible to reduce the reservation of computing resources by 47% without loss of service quality compared to the configuration without scaling.
 Conclusions. A method for automating the horizontal scaling of applications in Kubernetes is proposed. Although the experiments have shown the effectiveness of this solution, this method can be significantly improved. In particular, it is necessary to consider the possibility of integrating a reactive component for atypical load patterns.

Converting acoustical demonstrations into in-class experiments

Teaching acoustical principles to non-science students invites extensive use of classroom demonstrations to help clarify concepts. Ideally, a separate laboratory course opens up many more opportunities for active learning for improved student understanding. However, observing and hearing a demonstration is distinct from performing the activity directly. Moreover, the addition of a laboratory course impacts departmental teaching load and may impact enrollment. In this paper I will discuss laboratory and demonstration activities that have been converted into group activities that are implemented as a classroom activity. The opportunity to add these elements to the course hinge on being able to build or purchase a large number of replicas of equipment needed for the planned activities. The two examples I will discuss in detail are the use of two stringed monochords and pvc trumpets. In addition, I will share a simple mechanical demonstration based on vibrating bars to visualize the location dependent resonances of the basilar membrane.

Implementation and evaluation of a decentralized medical data exchange system based on the IOTA Tangle and approximate algorithms for optimal data placement


 
 
 
 
 
 
 
 © Voronin V., Rybalchenko A., Shmatko O., Kolomiitsev O., Tretiak V., Kliuchka Ya., 2023
 
 
 
 
 
 
 
 
 Facilitating the transfer of patient medical information across healthcare providers is crucial for ensuring high-quality care. However, this process encounters obstacles pertaining to privacy, security, and centralised control. This article outlines the creation of a decentralised system for exchanging medical data. The system utilises Distributed Ledger Technology, specifically the IOTA Tangle (open protocol (and network) for data and value transfer). This document outlines the structure and fundamental elements of a system designed for the secure and unalterable storage and transfer of medical records. The system employs masking and encryption methodologies to protect patient confidentiality while enabling healthcare practitioners to access complete information with patient authorization. The IOTA Tangle facilitates transactions without any fees and ensures the verification of data integrity through its Directed Acyclic Graph (DAG) topology. Simulation trials validate the system’s capability to securely communicate medical data on a large scale, while incurring lower resource costs than typical blockchain systems. The system demonstrates the feasibility of a decentralised and self-governing method for efficient and confidential sharing of medical data, utilising Distributed Ledger Technology (DLT). The secure data sharing platform facilitates the implementation of novel care and research frameworks, all the while upholding patient confidentiality and adhering to healthcare ethical standards. Also, in the development of billing Online Transaction Processing (OLTP) systems, which are designed for input, structured storage and processing of information in real time, the use of cloud technology is proposed. The problems that arise and their relevance to the solution of an integer linear programming problem with Boolean variables are shown. Approximate algorithms for optimal data placement and mathematical models for optimizing the structure of a distributed database of a cloud system are proposed, taking into account the limitations on the amount of node memory, the available costs of renting cloud resources, and the number of replicas of fragments of the distributed database.

Negativity and its capacity in JT gravity

We study the refined Rényi negativity in the matrix model of Jackiw-Teitelboim (JT) gravity. We first consider the JT gravity with dynamical branes, which serves as a toy model of the evaporating black hole. By including the backreaction of branes, we find that the refined Rényi negativity monotonically decreases at late time of the evaporation. Next we define a novel quantity, which we call “capacity of negativity,” as a derivative of the refined Rényi negativity with respect to the replica number. We find that the capacity of negativity has two peaks as a function of time, which comes from the exchange of dominance of the different types of replica wormholes.

Principle of minimal singularity for Green’s functions

Analytic continuations of integer-valued parameters can lead to profound insights, such as angular momentum in Regge theory, the number of replicas in spin glasses, the number of internal degrees of freedom, the spacetime dimension in dimensional regularization, and Wilson’s renormalization group. In this work, we consider a new kind of analytic continuation of correlation functions, inspired by two recent approaches to underdetermined Dyson-Schwinger equations in D-dimensional spacetime. If the Green’s functions Gn=⟨ϕn⟩ admit analytic continuation to complex values of n, the two different approaches are unified by a novel principle for self-consistent problems: Singularities in the complex plane should be minimal. This principle manifests as the merging of different branches of Green’s functions in the quartic theories. For D=0, we obtain the closed-form solutions of the general gϕm theories, including the cases with complex coupling constant g or noninteger power m. For D=1, we derive rapidly convergent results for the Hermitian quartic and non-Hermitian cubic theories by minimizing the complexity of the singularity at n=∞. Published by the American Physical Society 2024

Customizing the minimum number of replicas for achieving fault tolerance in a cloud/grid environment

Networks consist of numerous resources; it is crucial not to overlook fault tolerance and consider it during planning. This is because errors during implementation can result in wasted time and effort, thereby squandering these resources. One solution to address this issue effectively is to implement the task on multiple resources to minimize the occurrence of failed tasks. However, employing an unspecified or fixed number of resources can lead to the depletion of network resources and the overall failure of the network. Replication plays a pivotal role in enhancing data availability in distributed systems. By storing data in multiple locations, users can still access it even if some copies are unavailable due to site failure. Many replication-based algorithms utilize a predetermined number of iterations per function, which may consume excessive network resources, even if the ongoing task does not require such abundant resources. This paper proposes task replication as a viable mechanism for an efficient and fault-tolerant scheduling system. We introduce an algorithm that dynamically selects the optimal and minimal number of replicas based on the network's failure history. This approach aims to minimize the failure rate during task execution.

Online dynamic replication and placement algorithms for cost optimization of online social networks in two-tier multi-cloud

In social media, a huge number of worldwide data objects are posted every day. The contents of these data objects include text, links, images, audio, and videos which could be small, medium, or large and accessed across the world. Moving these data objects into a single cloud service provider (CSP) is risky and results in four-fold obstacles: vendor lock-in, service availability, cost-ineffective use, and increasing latency. Using multiple CSPs to replicate and distribute the data object solves such obstacles. However, replicating data objects among multiple CSPs increases the cost of creating and maintaining this replication. This study focuses on three issues of Online Social Network (OSN) which include: (1) determining the appropriate number of replicas of each data object based on its popularity on the OSN, (2) identifying the suitable datacenters that host the replicas according to latency time of different regions, and (3) deciding the suitable storage class for the data object at a specific time of its lifetime. Two algorithms are proposed to adapt the replication and placement of the data object according to its popularity in the OSN. The first algorithm is Dynamic Fixed Time (DFT) which uses fixed time periods to adapt replication and placement. The second algorithm is Dynamic Exponential Time (DET) which determines the data object replication and placement based on exponential time periods. A simulation using a synthesized workload generated based on a real Facebook statistic dataset shows that the proposed algorithms produce a monetary cost savings of more than 23% compared to the Static Replication and Local Placement (SRLP) algorithm.

Replication control strategy based on a simple game of life in opportunistic networks

Although flooding-based message dissemination in opportunistic mobile networks yields high delivery performance, the nodes' resources, such as energy and buffer, are rapidly depleted due to the enormous quantity of message replicas dispersed over the networks. This study aims to reduce the number of message replicas in the networks while maintaining an acceptable delivery rate. Inspired by Conway's Game of Life, which uses cellular automaton-based neighbor conditions to regulate the total population, we propose RiGoL, a replication control-based technique for determining how many neighbors hold the same messages. We utilize a counter to keep track of the number of neighbors and two thresholds, namely forward and drop thresholds. Our strategy works as follows: when a pair of nodes meet, the node checks to see if the peer has the same message. The counter is increased by one if the peer does not hold the same message. On the other hand, if the peer already has the message, the counter is decremented by one. Only when the counter exceeds the forward threshold, then the message forwarded to another node. In contrast, the message is deleted from the node's buffer if the counter value is less than the drop threshold. We conduct extensive simulations using ONE Simulator to evaluate our proposed strategy. The simulation results show that although RiGoL yields higher latency than Epidemic, RiGoL successfully reduces the message replication in the network and prolongs the hub node's lifetime.

DCSP: A delay and cost-aware service placement and load distribution algorithm for IoT-based fog networks

The service placement problem in a fog-cloud pertains to the intricate task of ascertaining the optimal placement locations for diverse service replicas that constitute an Internet of Things (IoT) application within a distributed computing architecture. Nonetheless, due to the heterogeneous nature of fog nodes and cloud data centers, the constantly evolving nature of IoT applications, the pressing demand for real-time data processing with minimal latency, and the imperative of cost optimization, service placement remains an arduous and intricate challenge. Heuristic algorithms are widely employed to derive near-optimal solutions. The present study introduces a novel heuristic approach to service placement and load distribution in fog-cloud environments called Delay and Cost-aware Service Placement (DCSP). This approach takes into account both delay and cost and thereby enables the optimal utilization of resources, delay, and cost for delay-sensitive services. DCSP features continual adjustment of the number of service replicas based on workload and resource availability, ensuring efficient usage of fog and cloud computing resources and optimal delay for each request. The results of simulation experiments that compare various algorithms demonstrate the superiority of DCSP, which outperforms other methods in cost, average delay, and resource utilization. The experimental results not only illustrate the exceptional superiority of our method over MOHGA, MVC, and the Local method in terms of delay and utilization but also demonstrate our efforts to maintain cost at a comparable level. Our method surpasses all of them in utilization, achieving remarkable improvements ranging from 7.4 % to 49.7 %. Furthermore, it outperforms them in terms of delay, with significant enhancements ranging from 2.1 % to 19.3 %.

Dealing with Induced Fit, Conformational Selection, and Secondary Poses in Molecular Dynamics Simulations for Reliable Free Energy Predictions.

In this study, we have tested the performance of standard molecular dynamics (MD) simulations, replicates of shorter standard MD simulations, and Hamiltonian Replica Exchange (HREM) simulations for the sampling of two macrocyclic hosts for guest delivery, characterized by induced fit (phenyl-based host) and conformation selection (naphthyl-based host) and of the ODR-BRD4(I) drug-receptor system where the ligand can assume two main poses. For the optimization of the HREM simulation, we have proposed and tested an on-the-fly iterative scheme for equalizing the acceptance ratio along the replica progression at a constant replica number resulting in a moderate impact of the sampling efficiency. Concerning standard MD, we have found that, while splitting the total allocated simulation time in short MD replicates can reproduce the sampling efficiency of HREM in the phenyl-based host and in the ODR-BRD4(I) complex, in the naphthyl-based macrocycle, characterized by long-lived metastable states, enhanced sampling techniques are the only viable alternative for a reliable canonical sampling of the rugged conformational landscape.