Replication Algorithm Research Articles

How Interoperability Can Enable Artificial Intelligence in Clinical Applications.

This paper explores the critical role of Interoperability (IOP) in the integration of Artificial Intelligence (AI) for clinical applications. As AI gains prominence in medical analytics, its application in clinical practice faces challenges due to the lack of standardization in the medical sector. IOP, the ability of systems to exchange information seamlessly, emerges as a fundamental solution. Our paper discusses the indispensable nature of IOP throughout the Data Life Cycle, demonstrating how interoperable data can facilitate AI applications. The benefits of IOP encompass streamlined data entry for healthcare professionals, efficient data processing, enabling the sharing of data and algorithms for replication, and potentially increasing the significance of results obtained by medical data analytics via AI. Despite the challenges of IOP, its successful implementation promises substantial benefits for integrating AI into clinical practice, which could ultimately enhance patient outcomes and healthcare quality.

Study of data replication process using Raft replication algorithm to maintain consistency in server cluster

The article considers the issues of ensuring availability and fault tolerance of a server cluster. The analysis of methods and technologies for increasing system reliability and improving performance, such as data replication, automatic switching between servers and fast recovery after failures, is carried out. It is indicated that the key element in modern distributed systems is replication-based clustering. The types of replication are analyzed. It is shown that during the operation of the system, there is a choice between the stability of the received data and the speed or scaling limitation. It is shown that the Raft algorithm is one of the reliable mechanisms with a high level of availability for achieving consensus and data management. An analysis of the literature on the use of the Raft consensus algorithm is carried out. The replication process using the Raft algorithm is analyzed. It is substantiated that it is possible to ensure strong consistency and high availability of the cluster using the management and control mechanism on the Kubernetes platform while maintaining cluster configuration synchronization. The Raft consensus process is studied using the M/M/s QS model. The exponential distribution of time between client requests and servicing with a certain number of servers is considered. The program code for the mathematical model in Python has been developed. Modeling has been performed in the Visual code environment in Python. The following parameters have been studied: the number of servers that operate at a certain average speed, the expected time of a client request in the system, and the probability of message delay. It has been proven that as the arrival speed increases, the expected time of a client request increases. The analysis provides an understanding of how the Raft algorithm works in different contexts and is applicable to optimizing system design processes.

A hybrid chaos-based algorithm for data object replication in distributed systems

One of the primary challenges in distributed systems, such as cloud computing, lies in ensuring that data objects are accessible within a reasonable timeframe. To address this challenge, the data objects are replicated across multiple servers. Estimating the minimum quantity of data replicas and their optimal placement is considered an NP-complete optimization problem. The primary objectives of the current research include minimizing data processing costs, reducing the quantity of replicas, and maximizing the applied algorithms’ reliability in replica placement. This paper introduces a hybrid chaos-based swarm approach using the modified shuffle-frog leaping algorithm with a new local search strategy for replicating data in distributed systems. Taking into account the algorithm’s performance in static settings, the introduced method reduces the expenses associated with replica placement. The results of the experiment conducted on a standard data set indicate that the proposed approach can decrease data access time by about 33% when using approximately seven replicas. When executed several times, the suggested method yields a standard deviation of approximately 0.012 for the results, which is lower than the result existing algorithms produce. Additionally, the new approach’s success rate is higher in comparison with existing algorithms used in addressing the problem of replica placement.

A static replication approach for callable interest rate derivatives: mathematical foundations and efficient estimation of SIMM–MVA

The computation of credit risk measures such as exposure and Credit Value Adjustments (CVA) requires the simulation of future portfolio prices. Recent metrics, such as dynamic Initial Margin (IM) and Margin Value Adjustments (MVA) additionally require the simulation of future conditional sensitivities. For portfolios with non-linear instruments that do not admit closed-form valuation formulas, this poses a significant computational challenge. This problem is addressed by proposing a static replication algorithm for interest rate options with early-exercise features under an affine term-structure model. Under the appropriate conditions, we can find an equivalent portfolio of vanilla options that replicate these products. Specifically, we decompose the product into a portfolio of European swaptions. The weights and strikes of the portfolio are obtained by regressing the target option value with interpretable, feed-forward neural networks. Once an equivalent portfolio of European swaptions is determined, we can leverage on closed-form expressions to obtain the conditional prices and sensitivities, which serve as an input to exposure and SIMM-driven MVA quantification. For a consistent forward sensitivity estimation, this involves the differentiation of the portfolio-weights. The accuracy and convergence of the method is demonstrated through several representative numerical examples, benchmarked against the established least-square Monte Carlo method.

A Semi-Static Replication Method for Bermudan Swaptions under an Affine Multi-Factor Model

We present a semi-static replication algorithm for Bermudan swaptions under an affine, multi-factor term structure model. In contrast to dynamic replication, which needs to be continuously updated as the market moves, a semi-static replication needs to be rebalanced on just a finite number of instances. We show that the exotic derivative can be decomposed into a portfolio of vanilla discount bond options, which mirrors its value as the market moves and can be priced in closed form. This paves the way toward the efficient numerical simulation of xVA, market, and credit risk metrics for which forward valuation is the key ingredient. The static portfolio composition is obtained by regressing the target option’s value using an interpretable, artificial neural network. Leveraging the universal approximation power of neural networks, we prove that the replication error can be arbitrarily small for a sufficiently large portfolio. A direct, a lower bound, and an upper bound estimator for the Bermudan swaption price are inferred from the replication algorithm. Additionally, closed-form error margins to the price statistics are determined. We practically study the accuracy and convergence of the method through several numerical experiments. The results indicate that the semi-static replication approaches the LSM benchmark with basis point accuracy and provides tight, efficient error bounds. For in-model simulations, the semi-static replication outperforms a traditional dynamic hedge.

Efficient linearizability checking for actor‐based systems

AbstractRecent demand for distributed software had led to a surge in popularity in actor‐based frameworks. However, even with the stylized message passing model of actors, writing correct distributed software is still difficult. We present our work on linearizability checking in DS2, an integrated framework for specifying, synthesizing, and testing distributed actor systems. The key insight of our approach is that often subcomponents of distributed actor systems represent common algorithms or data structures (e.g., a distributed hash table or tree) that can be validated against a simple sequential model of the system. This makes it easy for developers to validate their concurrent actor systems without complex specifications. DS2 automatically explores the concurrent schedules that system could arrive at, and it compares observed output of the system to ensure it is equivalent to what the sequential implementation could have produced. We describe DS2's linearizability checking and test it on several concurrent replication algorithms from the literature. We explore in detail how different algorithms for enumerating the model schedule space fare in finding bugs in actor systems, and we present our own refinements on algorithms for exploring actor system schedules that we show are effective in finding bugs.

A Novel Honestly Adjustable Replication Algorithm to Minimize Data Replication and Enhance Data Reliable Transport in Wireless Sensor Networks

A Novel Honestly Adjustable Replication Algorithm to Minimize Data Replication and Enhance Data Reliable Transport in Wireless Sensor Networks

ST-CenterNet: Small Target Detection Algorithm with Adaptive Data Enhancement.

General target detection with deep learning has made tremendous strides in the past few years. However, small target detection sometimes is associated with insufficient sample size and difficulty in extracting complete feature information. For safety during autonomous driving, remote signs and pedestrians need to be detected from driving scenes photographed by car cameras. In the early period of a medical lesion, because of the small area of the lesion, target detection is of great significance to detect masses and tumors for accurate diagnosis and treatment. To deal with these problems, we propose a novel deep learning model, named CenterNet for small targets (ST-CenterNet). First of all, due to the lack of visual information on small targets in the dataset, we extracted less discriminative features. To overcome this shortcoming, the proposed selective small target replication algorithm (SSTRA) was used to realize increasing numbers of small targets by selectively oversampling them. In addition, the difficulty of extracting shallow semantic information for small targets results in incomplete target feature information. Consequently, we developed a target adaptation feature extraction module (TAFEM), which was used to conduct bottom-up and top-down bidirectional feature extraction by combining ResNet with the adaptive feature pyramid network (AFPN). The improved new network model, AFPN, was added to solve the problem of the original feature extraction module, which can only extract the last layer of the feature information. The experimental results demonstrate that the proposed method can accurately detect the small-scale image of distributed targets and simultaneously, at the pixel level, classify whether a subject is wearing a safety helmet. Compared with the detection effect of the original algorithm on the safety helmet wearing dataset (SHWD), we achieved mean average precision (mAP) of 89.06% and frames per second (FPS) of 28.96, an improvement of 18.08% mAP over the previous method.

Inverted Ant Colony Optimization Algorithm for Data Replication in Cloud Computing

Inverted Ant Colony Optimization Algorithm for Data Replication in Cloud Computing

Efficient Heuristic Replication Techniques for High Data Availability in Cloud

Most social networks allow connections amongst many people based on shared interests. Social networks have to offer shared data like videos, photos with minimum latency to the group, which could be challenging as the storage cost has to be minimized and hence entire data replication is not a solution. The replication of data across a network of read-intensive can potentially lead to increased savings in cost and energy and reduce the end-user’s response time. Though simple and adaptive replication strategies exist, the solution is non-deterministic; the replicas of the data need to be optimized to the data usability, performance, and stability of the application systems. To resolve the non-deterministic issue of replication, metaheuristics are applied. In this work, Harmony Search and Tabu Search algorithms are used optimizing the replication process. A novel Harmony-Tabu search is proposed for effective placement and replication of data. Experiments on large datasets show the effectiveness of the proposed technique. It is seen that the bandwidth saving for proposed harmony-Tabu replication performs better in the range of 3.57% to 18.18% for varying number of cloud datacenters when compared to simple replication, Tabu replication and Harmony replication algorithm.

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.

Data-Plane-Assisted State Replication With Network Function Virtualization

Modern 5G networks are capable of providing ultralow-latency and highly scalable network services by employing modern networking paradigms, such as software-defined networking (SDN) and network function virtualization. The latter enables performance-critical network applications to be run in a distributed fashion directly inside the infrastructure. Being distributed, those applications rely on sophisticated state replication algorithms to synchronize states among each other. Nevertheless, current implementations of such algorithms do not fully exploit the potential of the modern infrastructures, thus leading to suboptimal performance. In this article, we propose STARE, a novel state replication system tailored for 5G networks. At its core, STARE exploits stateful SDN to offload replication-related processes to the data plane, ultimately leading to reduced communication delays and processing overhead for virtual network functions. We provide a detailed description of the STARE architecture alongside a publicly-available P4-based implementation. Furthermore, our evaluation shows that STARE is capable of scaling to big networks while introducing low overhead in the network.

Hihooi: A Database Replication Middleware for Scaling Transactional Databases Consistently

With the advent of the Internet and Internet-connected devices, modern business applications can experience rapid increases as well as variability in transactional workloads. Database replication has been employed to scale performance and improve availability of relational databases but past approaches have suffered from various issues including limited scalability, performance versus consistency tradeoffs, and requirements for database or application modifications. This paper presents Hihooi, a replication-based middleware system that is able to achieve workload scalability, strong consistency guarantees, and elasticity for existing transactional databases at a low cost. A novel replication algorithm enables Hihooi to propagate database modifications asynchronously to all replicas at high speeds, while ensuring that all replicas are consistent. At the same time, a fine-grained routing algorithm is used to load balance incoming transactions to available replicas in a consistent way. Our thorough experimental evaluation with several well-established benchmarks shows how Hihooi is able to achieve almost linear workload scalability for transactional databases.

Enhancing replica management in a cloud environment using data mining based dynamic replication algorithm

Cloud computing has recently gained a lot of popularity and attention from the research community. One of the many on-demand services that large-scale applications provide to cloud customers is storage, which accumulates more generated data and subsequently leads to the need for storage. Despite the fact that users can use the cloud to store and provide the type of storage that desire, it still takes a significant amount of time to store and retrieve data due to the large accumulation of data. Due to the need to improve data availability, response time, reliability, and migration costs, the current storage engine needs to be replicated across multiple sites. When copies are properly distributed, data replication speeds up execution. The biggest challenges in data replication are choosing which data to replicate, where to put it, how to manage replication, and how many replicas it needs. Therefore, various studies have been carried out on some data mining-based data replication systems to evaluate replication issues and manage cloud storage. In most cases, data replication in a data mining environment is done using data mining along with a replication algorithm and a data grid policy. In addition, this paper addresses replica management issues and proposes affordable data replication in the cloud that satisfies all Quality of Service (QoS) requirements.

Distributed Heuristic Algorithm for Migration and Replication of Self-organized Services in Future Networks

Nowadays, the mobile communication networks have become a consistent part of our everyday life by transforming huge amount of data through communicating devices, that leads to new challenges. According to the Cisco Networking Index, more than 29.3 billion networked devices will be connected to the network during the year 2023. It is obvious that the existing infrastructures in current networks will not be able to support all the generated data due to the bandwidth limits, processing and transmission overhead. To cope with these issues, future mobile communication networks must achieve high requirements to reduce the amount of transferred data, decrease latency and computation costs. One of the essential challenging tasks in this subject area is the optimal self-organized service placement. In this paper a heuristic-based algorithm for service placement in future networks was presented. This algorithm achieves the ideal placement of services replicas by monitoring the load within the server and its neighborhood, choosing the node that contributes with the highest received load, and finally replicating or migrating the service to it based on specific criteria, so that the distance of requests coming from clients becomes as small as possible because of placing services within nearby locations. It was proved that our proposed algorithm achieves an improved performance by meeting the services within a shorter time, a smaller bandwidth, and thus a lower communication cost. It was compared with the traditional client-server approach and the random placement algorithm. Experimental results showed that the heuristic algorithm outperforms other approaches and meets the optimal performance with different network sizes and varying load scenarios.

An Authorization Framework for Preserving Privacy of Big Medical Data via Blockchain in Cloud Server

In recent years, cloud-based medical record sharing has greatly improved the process of researching the disease and patient diagnosis. However, since cloud systems are centralized, there is serious concern about data security and privacy. Blockchain technology is viewed as a promising method of dealing with privacy issues and data security because of its exclusive features of distributed ledgers, secrecy, verifiability, and enhanced security. The literature review has shown significant works on integrating blockchain technology with cloud system for managing and sharing healthcare data. It has been analyzed that previous works are primarily dependent on the centralized data storage approach, which raises privacy concerns. The previous works also do not emphasize handling big medical data and lack the reliability of the end-to-end security features system. This paper has presented an authorization framework for ensuring data security and privacy preservation using blockchain technology with IPFS as decentralized file storage and sharing system. The proposed study devises a proof of replication algorithm using smart contracts to provide a better access control mechanism. The implementation of the proposed framework is based on the symmetric encryption and Ethereum blockchain platform. The study outcome illustrates the efficiency and availability of the proposed scheme compared to the typical cloud-based blockchain method.

PLVER: Joint Stable Allocation and Content Replication for Edge-Assisted Live Video Delivery

Live streaming services have gained extreme popularity in recent years. Due to the spiky traffic patterns of live videos, utilizing distributed edge servers to improve viewers' quality of experience (QoE) has become a common practice nowadays. Nevertheless, the current client-driven content caching mechanism does not support pre-caching from the cloud to the edge, resulting in a considerable amount of cache misses in live video delivery. By jointly considering the features of live videos and edge servers, we propose PLVER, a proactive live video push scheme to address the cache miss problem in live video delivery. Specifically, PLVER first conducts a one-to-multiple stable allocation between edge clusters and user groups to balance the load of live traffic over the edge servers. It then adopts proactive video replication algorithms to speed up video replication among the edge servers. We conduct extensive trace-driven evaluation, covering 0.3 million Twitch viewers and more than 300 Twitch channels. The results demonstrate that with PLVER, edge servers can carry 28 and 82 percent more traffic than the auction-based replication (ABR) method and the caching on requested time (CORT) method, respectively.

A replication strategy for mobile opportunistic networks based on utility clustering

Dynamic replication is a wide-spread multi-copy routing approach for efficiently coping with the intermittent connectivity in mobile opportunistic networks. According to it, a node forwards a message replica to an encountered node based on a utility value that captures the latter’s fitness for delivering the message to the destination. The popularity of the approach stems from its flexibility to effectively operate in networks with diverse characteristics without requiring special customization. Nonetheless, its drawback is the tendency to produce a high number of replicas that consume limited resources such as energy and storage. To tackle the problem we make the observation that network nodes can be grouped, based on their utility values, into clusters that portray different delivery capabilities. We exploit this finding to transform the basic forwarding strategy, which is to move a packet using nodes of increasing utility, and actually forward it through clusters of increasing delivery capability. The new strategy works in synergy with the basic dynamic replication algorithms and is fully configurable, in the sense that it can be used with virtually any utility function. We also extend our approach to work with two utility functions at the same time, a feature that is especially efficient in mobile networks that exhibit social characteristics. By conducting experiments in a wide set of real-life networks, we empirically show that our method is robust in reducing the overall number of replicas in networks with diverse connectivity characteristics without at the same time hindering delivery efficiency.

A packet replication algorithm for delay tolerant networks based on multi-metric

Utility function of packet is often used to decide how to replicate packet in DTN networks. But this method has more uncertainty and relies on a single performance metric. For this problem, in order to reduce the impact of a single metric uncertainty, multiple utility metrics are introduced in the new algorithm. A packet replication probability calculation method based on entropy weight is designed. By calculating the entropy weight of each metric, the algorithm obtains the replication probability of each packet and takes the probability as the priority of packet replication. Because of considering the two metrics of packet expected transmission delay and node encounter possibility, the algorithm effectively reduces the influence of encounter time distribution problem and direction prediction problem in the original algorithm, and reduces the uncertainty of utility function. Simulation results show that the algorithm reduces the packet replication number and the average delay, improves the successful packet delivery rate. The overall performance of the network is further improved.

An infrastructure-assisted job scheduling and task coordination in volunteer computing-based VANET

Vehicular networks as the key enablers in Intelligent Transportation Systems (ITS) and the Internet of Things (IoT) are key components of smart sustainable cities. Vehicles as a significant component of smart cities have emerging in-vehicle applications that can assist in good governance for sustainable smart cities. Most of these applications are delay sensitive and demand high computational capabilities that are provided by emerging technologies. Utilizing the distributed computational resources of vehicles with the help of volunteer computing is an efficient method to fulfill the high computational requirements of vehicles itself and the other components of smart cities. Vehicle as a resource is an emerging concept that must be considered to address the future challenges of sustainable smart cities. In this paper, an infrastructure-assisted job scheduling and task coordination mechanism in volunteer computing-based VANET called RSU-based VCBV is proposed, which enhances the architecture of VANET to utilize the surplus resources of vehicles for task execution. We propose job scheduling and task coordination algorithms for different volunteer models. Further, we design and implement an adaptive task replication method to seek fault tolerance by avoiding task failures due to locations of vehicles. We propose a task replication algorithm called location-based task replication algorithm. Extensive simulations validate the performance of our proposed volunteer models while comparing average task execution time and weight ratios with existing work.