Number Of Replicas Research Articles

Improving Performance of Cloud Storage Systems Using Support-Based Replication Algorithm

Data replication is a mechanism for creating a copy of the same file block on many sites. It is used in cloud storage systems to improve the performance of data availability. The current replication technique helps users fix the number of replicas required. The problem with the existing approaches is that the user cannot determine which file should be duplicated and how many copies are necessary, causing the cloud storage system's performance to suffer. The problem mentioned above has an impact on the performance of cloud storage systems. Thus, our proposed replication method determines the replication factor based on the support values of the file blocks to determine the precise number of duplicates to be replicated. We have also proposed an efficient technique to place the replicas based on the local support values to increase the performance of the cloud storage system. Our results indicate that our proposed replication algorithm performs better than the algorithm used in the Hadoop distributed file system.

Exchanging Replicas with Unequal Cost, Infinitely and Permanently.

We developed a replica exchange method that is effectively parallelizable even if the computational cost of the Monte Carlo moves in the parallel replicas are considerably different, for instance, because the replicas run on different types of processor units or because of the algorithmic complexity. To prove detailed-balance, we make a paradigm shift from the common conceptual viewpoint in which the set of parallel replicas represents a high-dimensional superstate, to an ensemble-based criterion in which the other ensembles represent an environment that might or might not participate in the Monte Carlo move. In addition, based on a recent algorithm for computing permanents, we effectively increase the exchange rate to infinite without the steep factorial scaling as a function of the number of replicas. We illustrate the effectiveness of this replica exchange methodology by combining it with a quantitative path sampling method, replica exchange transition interface sampling (RETIS), in which the costs for a Monte Carlo move can vary enormously as paths in a RETIS algorithm do not have the same length and the average path lengths tend to vary considerably for the different path ensembles that run in parallel. This combination, coined ∞RETIS, was tested on three model systems.

Enhanced sampling using replica exchange with nonequilibrium switches: A case study on simple models.

Configurational sampling is central to characterize the equilibrium properties of complex molecular systems, but it remains a significant computational challenge. The conventional molecular dynamics (MD) simulations of limited duration often result in inadequate sampling and thus inaccurate equilibrium estimates. Replica exchange with nonequilibrium switches (RENS) is a collective variable-free computational technique to achieve extensive sampling from a sequence of equilibrium and nonequilibrium MD simulations without modifying the underlying potential energy surface of the system. Unlike the conventional replica exchange molecular dynamics (REMD) simulation, which demands a significant number of replicas for better accuracy, RENS employs nonequilibrium heating (forward) and cooling (reverse) work simulations prior to configurational swaps to improve the acceptance probability for replica exchange by using only a few replicas. Here, we have implemented the RENS algorithm on four model systems and examined its performance against the conventional MD and REMD simulations. The desired equilibrium distributions were generated by RENS for all the model systems, whereas REMD and MD simulations could not do so due to inadequate sampling on the same timescales. The calculated work distributions from RENS obeyed the expected nonequilibrium fluctuation theorem. The results indicate that the switching time of the nonequilibrium simulations can be systematically altered to optimize the acceptance probability and the reduced work of switching. The modular implementation of RENS algorithm not only enables us to readily extend it to multiple replicas but also paves the way for extension to larger molecular systems in the future.

SPIDER: An availability‐aware framework for the service function chain placement in distributed scenarios

AbstractThe network function virtualization (NFV) paradigm replaces hardware‐dependent network functions by virtual network functions (VNFs) that can be deployed in commodity hardware, including legacy servers. Consequently, the use of NFV is expected to reduce operating and capital expenses, as well as improve service deployment operation and management flexibility. For many use cases, the VNFs must be visited and invoked following a specific order of execution in order to compose a complete network service, named service function chain (SFC). Nonetheless, despite the benefits from NFV and SFC virtualization technologies, their introduction must not harm network performance and service availability. On the one hand, redundancy is seen by network service planners as a mechanism well established to combat availability issues. At same time, there is a goal to optimize resource utilization in order to reduce operational expenditure. In this article, we share our experience in the design use of a framework, named SPIDER, focused on SFC placement that considers the network infrastructure condition and the required SFC availability to define the placement strategy. The SPIDER monitors the status of infrastructure nodes and links and defines which servers the VNFs should be placed on and the number of redundant replicas needed. We present a proof‐of‐concept of SPIDER using Kubernetes to launch the VNFs as containers. We also use Kubernetes to forward the traffic between the VNFs, composing the service chain. We perform experiments to evaluate the runtime of SPIDER and the SFC delay under different network conditions.

Replicas, averaging and factorization in the IIB matrix model

We study the partition functions of multiple replicas (copies) of D-brane configurations in the type IIB (IKKT) matrix model. We consider the quenched regime, where small fluctuations of the matrices are superimposed onto the slow (quenched) dynamics of the background, so the partition function is an ensemble average over the background. Interacting D-branes always factorize in a simple way. On the other hand, the non-interacting BPS configurations may or may not factorize depending on the number of replicas, and their factorization mechanism is more involved as the corresponding saddle-point solutions (half-wormholes) break the replica symmetry. We argue that the simple factorization mechanism of interacting branes is actually more interesting as it carries the specific signatures of quantum gravity, which are absent from disordered field theories like the SYK model.

Fundamental Redesign of the TIGER2hs Kernel to Address Severe Parameter Sensitivity.

Replica exchange molecular dynamics simulations are one of the most popular approaches to enhance conformational sampling of molecular systems. Applications range from protein folding to protein-protein or other host-guest interactions, as well as binding free energy calculations. While these methods are computationally expensive, highly accurate results can be obtained. We recently developed TIGER2hs, an improved version of the temperature intervals with global exchange of replicas (TIGER2) algorithm. This method combines the replica-based enhanced sampling in an explicit solvent with a hybrid solvent energy evaluation. During the exchange attempts, bulk water is replaced by an implicit solvent model, allowing sampling with significantly less replicas than parallel tempering (REMD). This enables accurate enhanced sampling calculations with only a fraction of computational resources compared to REMD. Our latest results highlight several issues with sampling imbalance and parameter sensitivity within the original TIGER2 exchange algorithms that affect the overall state populations. A high sensitivity on replica number and maximum temperature is eliminated by changing to a pairwise exchange kernel (PE) without additional sorting. Simulations are controlled by adjusting the average temperature change per exchange ⟨ΔT/χ⟩ to below 30 K to mimic a controlled temperature mixing of replicas similar to REMD. Thus, this parameter provides an applicable property for selecting combinations of replica number and maximum temperature to adjust simulations for best accuracy, with flexible resource investment. This increases the robustness of the method and ensures results in excellent agreement with REMD, as demonstrated for three different peptides.

A Blockchain Storage Architecture Based on Information-Centric Networking

Blockchain technology is a unique distributed ledger technology that has been widely used in various areas. With the increase in data on the blockchain and the append-only nature of the blockchain, the traditional blockchain’s full replica storage technique leads to blockchain storage scalability problem. Existing methods prioritize minimizing the storage strain on blockchain nodes while ignoring the availability of data, resulting in a lengthy average response time for users to access the blockchain. To address the shortcomings, this paper proposes an Information-Centric Networking-based blockchain storage architecture. The architecture uses the enhanced resolution system for community division to build blockchain node partitions and store blockchain ledgers in the underlying network. It introduces virtual chain for rapid blockchain indexing and adopts a collaborative block replica deletion algorithm across neighboring partitions, including replica number decision based on blockchain access decay characteristics and replica deletion based on resource relationship. Finally, we compare and analyze the proposed blockchain storage architecture with BC-store and KASARASA, and the results demonstrate that this architecture has significantly lower average access time than others. The replica data volume of this method is reduced by 57.2% compared to the full replica policy, but the access time is only 5.2% slower when compared to the full replica policy, which substantially increases the replica storage utilization.

Entanglement Estimation in Tensor Network States via Sampling

We introduce a method for extracting meaningful entanglement measures of tensor network states in general dimensions. Current methods require the explicit reconstruction of the density matrix, which is highly demanding, or the contraction of replicas, which requires an effort exponential in the number of replicas and which is costly in terms of memory. In contrast, our method requires the stochastic sampling of matrix elements of the classically represented reduced states with respect to random states drawn from simple product probability measures constituting frames. Even though not corresponding to physical operations, such matrix elements are straightforward to calculate for tensor network states, and their moments provide the Rényi entropies and negativities as well as their symmetry-resolved components. We test our method on the one-dimensional critical XX chain and the two-dimensional toric code in a checkerboard geometry. Although the cost is exponential in the subsystem size, it is sufficiently moderate so that—in contrast with other approaches—accurate results can be obtained on a personal computer for relatively large subsystem sizes.4 MoreReceived 21 March 2022Accepted 15 June 2022DOI:https://doi.org/10.1103/PRXQuantum.3.030312Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement detectionEntanglement measuresQuantum information theoryTechniquesProjected entangled pair statesReplica methodsTensor network methodsQuantum InformationCondensed Matter, Materials & Applied Physics

How to achieve adaptive security for asynchronous BFT?

We consider how to build practical asynchronous BFT protocols with adaptive security. In particular, we build two protocols in both the computational setting (where the adversary is limited to polynomial-time) and the stronger information-theoretic model (where the adversary is unbounded). In the computational model, we provide EPIC using adaptively secure key generation and common coin protocols. In the information-theoretical model, we provide HALE leveraging the classic local coin protocol of Bracha. HALE is more robust than EPIC and does not need distributed key generation.Via a five-continent deployment on Amazon EC2, we show EPIC is slightly slower for small and medium-sized networks than the most efficient asynchronous BFT protocols with static security. As the number of replicas is smaller than 46, EPIC's throughput is stable, achieving peak throughput of 8,000–12,500 tx/sec with a transaction size of 250 bytes. When the network size grows larger, EPIC is not as efficient as asynchronous BFT protocols with static security, with throughput of 4,000–6,300 tx/sec. We also show while HALE is in general less efficient than EPIC, HALE is reasonably fast, achieving 42,000 tx/sec and 3,400 tx/sec for the 4-server setting in the LAN/WAN environments, respectively. Remarkably, HALE outperforms EPIC in LANs when the number of replicas is smaller than 16.

Delay-aware distributed program caching for IoT-edge networks.

Edge computing is a novel network architecture that is in proximity to the end devices in an Internet of Things (IoT). As the IoT becoming a major factor in our daily life, provisioning a low response time of the services to IoT users through edge computing is an important problem. Caching necessary program data for the task in an edge node effectively reduces the response time of the computation task. However, due to the increase of IoT users and devices, it is noteworthy that limited-resource edge nodes would receive a number of tasks, having a heavy burden of processing the requests. Therefore, the limited resource and caching space at cloudlet need the careful design of the caching algorithm to utilize the space of multiple edge nodes and relieve the burden of computations. In this paper, we propose a cooperative program caching system that makes different edge nodes cooperatively store program data and cache the replicas of the data requested frequently to handle a number of requests from IoT users. In particular, we develop a cooperative caching algorithm that caches the appropriate number of data replicas depending on the number of requests on each cloudlet and the popularity of the data to minimize the response time. The simulation results show that the proposed cooperative caching algorithm can effectively reduce the response time for IoT users compared to other existing algorithms.

Energy-Aware Irregular Slotted Aloha Methods for Wireless-Powered IoT Networks

This article considers radio-frequency (RF) energy harvesting devices that use an irregular slotted Aloha (IRSA) channel access protocol to transmit their data to a hybrid access point (HAP). Specifically, it addresses the fundamental problem of optimizing the number of packet replicas transmitted by each device in each time frame. Unlike prior works, it considers a learning approach to optimize the number of replicas according to the energy level of devices. This article first uses a model-based Markov decision process (MDP) to study the problem at hand. Then, it proposes a model-free, centralized, and a distributed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning-based solution that aims to maximize the number of successful transmissions in each time frame. Our simulation results show that our centralized and distributed solutions, respectively, achieve up to 38% and 29% more successful transmissions than conventional Aloha.

Machine Learning-Assisted Phase Transition Temperatures from Generalized Replica Exchange Simulations of Dry Martini Lipid Bilayers.

Accurate estimation of phase transition temperatures has been a longstanding challenge for molecular simulations. Recently, the generalized Replica Exchange technique for estimating phase transition temperatures has allowed for improved sampling of the phase transition; however, it requires a significant number of simultaneous replicas both inside and outside of the transition region leading to costly computational expense. In this work, the recently developed machine learning-assisted lipid phase analysis technique for learning the phase of individual lipids has been combined with generalized Replica Exchange Molecular Dynamics to reduce the overall computational expense of evaluating transition temperatures. This technique is then applied to eight different Dry Martini lipids to demonstrate its ability to describe transition temperatures as a function of chain length and tail saturation.

Does Hamiltonian Replica Exchange via Lambda-Hopping Enhance the Sampling in Alchemical Free Energy Calculations?

In the context of computational drug design, we examine the effectiveness of the enhanced sampling techniques in state-of-the-art free energy calculations based on alchemical molecular dynamics simulations. In a paradigmatic molecule with competition between conformationally restrained E and Z isomers whose probability ratio is strongly affected by the coupling with the environment, we compare the so-called -hopping technique to the Hamiltonian replica exchange methods assessing their convergence behavior as a function of the enhanced sampling protocols (number of replicas, scaling factors, simulation times). We found that the pure -hopping, commonly used in solvation and binding free energy calculations via alchemical free energy perturbation techniques, is ineffective in enhancing the sampling of the isomeric states, exhibiting a pathological dependence on the initial conditions. Correct sampling can be restored in -hopping simulation by the addition of a “hot-zone” scaling factor to the -stratification (FEP+ approach), provided that the additive hot-zone scaling factors are tuned and optimized using preliminary ordinary replica-exchange simulation of the end-states.

Optimal Replica-Exchange Molecular Simulations in Combination with Evolution Strategies.

We have incorporated Evolution Strategies into the Replica-Exchange Monte Carlo simulation method to predict the phase behavior of several example fluids. The replica-exchange method allows one system to exchange temperatures with its neighbors to search for the most stable structure relatively efficiently in a single simulation. However, if the temperature intervals of the replicas are not positioned carefully, there is an issue that local exchange does not occur. Our results for a simple Lennard-Jones fluid and the liquid-crystal Yukawa model demonstrate the utility of the approach when compared to conventional methods. When Evolution Strategies were applied to the Replica-Exchange Monte Carlo simulation, the problem of a significant localized decrease in exchange probability near the phase transition was avoided. By obtaining the optimal temperature intervals, the system efficiently traverses a broader parameter space with a small number of replicas. This is equivalent to accelerating molecular simulations with limited computational resources and can be useful when attempting to predict the phase behavior of complex systems.

Shape deformations of charged Rényi entropies from holography

Charged and symmetry-resolved Rényi entropies are entanglement measures quantifying the degree of entanglement within different charge sectors of a theory with a conserved global charge. We use holography to determine the dependence of charged Rényi entropies on small shape deformations away from a spherical or planar entangling surface in general dimensions. This dependence is completely characterized by a single coefficient appearing in the two point function of the displacement operator associated with the Rényi defect. We extract this coefficient using its relation to the one point function of the stress tensor in the presence of a deformed entangling surface. This is mapped to a holographic calculation in the background of a deformed charged black hole with hyperbolic horizon. We obtain numerical solutions for different values of the chemical potential and replica number n in various spacetime dimensions, as well as analytic expressions for small chemical potential near n = 1. When the Rényi defect becomes supersymmetric, we demonstrate a conjectured relation between the two point function of the displacement operator and the conformal weight of the twist operator.

Reliability-Ensured Efficient Scheduling With Replication in Cloud Environment

Server consolidation in a virtualized cloud environment improves resource utilization by hosting many virtual machines (VMs) on a single machine. The host server failure induces a serious risk of the interruption of services provided through VMs hosted on the failed server. Ensuring high reliability and availability from the unpredicted host server failures is a challenging task, and this assurance guarantee is the quality of service to the users. Executing latency-sensitive workloads within a time frame, multireplica strategy-based reliability assurance is used in general. In this article, we propose two reliability-ensured scheduling approaches by strategically allocating the jobs to machines to satisfy their reliability and deadline requirements. The proposed approaches “Replication with equal failure rate” and “Efficient reliability replication method” are used to schedule the jobs to machines with equal and different failure rates, respectively, while satisfying both the deadline and reliability requirements of the jobs. Both the scheduling approaches dynamically select the machines for the jobs to minimize the number of replicas based on job characteristics and failure rate of machines. Also, we adopt an overlapping mechanism to further minimize the resource utilization. The extensive simulation results on randomly generated and real-world data at different scales show that the proposed approaches perform better than other state-of-the-art approaches.

Energy Efficiency Optimization for Irregular Repetition Slotted ALOHA-Based Massive Access

Irregular repetition slotted ALOHA (IRSA) is a promising mechanism to support massive access in Internet of Things (IoT). As IoT devices are usually battery capacity-limited, the energy efficiency (EE) should be paid close attention to when operating the IRSA scheme. This letter improves the EE of devices by adjusting the transmit power and maximum number of packet replicas based on two high-throughput transmission probability distributions of IRSA scheme. Specifically, the optimal transmit power and the optimal packet replica number are found explicitly following individual parameter optimizations. Based on the obtained individual optimums, an iterative algorithm is proposed for finding the global optimum quickly. Numerical results show that the EE of devices can be enhanced significantly by using the found optimums.

Adaptive data placement in the Fog infrastructure of IoT applications with dynamic changes

Time-sensitive Internet of Things (IoT) applications need to provide instant responses to end users. Seeking to reduce the latency of IoT applications is challenging task. Due to the dynamic changes that can occur over time such as failure of IoT devices or integration of new services and devices, the IoT application constantly requires new storage resources. To ensure the high performance of an IoT application, it is common today to place its produced data in the Fog infrastructure while satisfying its requirements over time. The main challenge in this regard is to ensure optimal data placement in this heterogeneous and distributed infrastructure with low latency. In fact, with continuous changes, new data assignments to target storage nodes can lead to data migration and increased latency. In this paper, we propose an adaptive approach to place the IoT data with multiple replicas in the Fog infrastructure while estimating and optimizing the overall latency. To cope with the IoT application changes, we establish a ℓ-backtracking greedy algorithm called adaptive multiple data replicas placement of IoT applications with external event changes in the Fog infrastructure (iFogDPE). It is a stable many-to-many matching algorithm that minimizes the write, read and migration latency using a few number of data replicas. Then, it assigns each data consumer with its appropriate data replica. To model and to simulate the IoT application and Fog infrastructure enactment as well as to test and to evaluate our solution we used iFogSim simulator. Our experimental results highlight the efficiency of our approach in reducing the storage and access latency with fewer data replicas while maintaining low data migration latency over time.

Load-Balancing of Kubernetes-Based Edge Computing Infrastructure Using Resource Adaptive Proxy.

Kubernetes (K8s) is expected to be a key container orchestration tool for edge computing infrastructures owing to its various features for supporting container deployment and dynamic resource management. For example, its horizontal pod autoscaling feature provides service availability and scalability by increasing the number of replicas. kube-proxy provides traffic load-balancing between replicas by distributing client requests equally to all pods (replicas) of an application in a K8s cluster. However, this approach can result in long delays when requests are forwarded to remote workers, especially in edge computing environments where worker nodes are geographically dispersed. Moreover, if the receiving worker is overloaded, the request-processing delay can increase significantly. To overcome these limitations, this paper proposes an enhanced load balancer called resource adaptive proxy (RAP). RAP periodically monitors the resource status of each pod and the network status among worker nodes to aid in load-balancing decisions. Furthermore, it preferentially handles requests locally to the maximum extent possible. If the local worker node is overloaded, RAP forwards its requests to the best node in the cluster while considering resource availability. Our experimental results demonstrated that RAP could significantly improve throughput and reduce request latency compared with the default load-balancing mechanism of K8s.

Three-Dimensional (3D) Stereolithographic Tooth Replicas Accuracy Evaluation: In Vitro Pilot Study for Dental Auto-Transplant Surgical Procedures.

After immediate tooth extraction or after alveolar socket healing, tooth transplants are increasingly used for functional restoration of edentulous maxillary areas. Recent studies have shown the periodontal ligament (PDL) viability and the tooth housing time in the adapted neo-alveolus as key factors for transplantation success. During surgical time, 3D stereolithographic replicas are used for fitting test procedures. In this paper, the accuracy of 3D dental replicas, compared with the corresponding natural teeth, is assessed in surgical transplantation. Lamb skulls were selected and submitted to Cone Beam Computer Tomography (CBCT). Scanning information, converted into Standard Digital Imaging and Communications in Medicine (DICOM) and Standard Triangulation Language (STL), was sent to the Volux X-ray Centre for 3D replica printing. After the tooth extractions, all lambs’ incisors were measured with a digital caliber and compared with the 3D replicas. Volume and dimensional error values were evaluated. All replicas showed macroscopically smaller volume (45.54%). Root replicas showed higher variations compared with the crown areas, with several unreplicated apical root areas. The cement–enamel junction tooth area was replicated quite faithfully, and the base area relative error showed 9.8% mean value. Even further studies with a larger number of replicas are needed. Data obtained confirmed high volumes of macroscopic discrepancies with several unreproduced apical root sites. The achieved accuracy (90.2%) confirmed that the 3D replicas cannot be used to reduce the surgical time during transplantation predictable procedures.