Replica Management Research Articles

Cloud Replica Management-Based Hybrid Optimization

This research addresses the challenges in cloud-based replica management by proposing a novel strategy employing a Genetically Implied Greywolf with Oppositional Learning (GIGOL) hybrid optimization technique. This approach optimizes multi-objectives such as response time, load balancing, availability, replication cost, and energy consumption, ensuring cost-effectiveness and energy efficiency. The GIGOL model integrates Genetic Algorithm, opposition learning, and Grey Wolf Optimization, aiming to achieve optimal replica placement. The study emphasizes resolving overhead issues through machine learning techniques for efficient cloud-based replica management. Performance evaluation showcases improvements in response time, load balancing, availability, replication cost, and energy consumption, highlighting the effectiveness of the proposed approach within budget constraints and management policies.

Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

At present, the secure campus network strategy adopts technical means such as distinguishing applications and limiting them separately, but they have triggered other new problems, greatly affecting the unity of network resources and data. The relatively dispersed network architecture will inevitably limit the further development and expansion of the campus network. Therefore, when universities plan their networks, they must consider whether the network is safe, complete, smooth, and sustainable for smooth upgrading and development. In order to improve the effect of campus network security intrusion detection, this paper combines feature segmentation and deep learning technology to construct a campus network security intrusion detection model. To reduce the transmission time of query requirements in the grid, this paper improves the replica management mechanism and requires the information server to cache the Bloom Filter structure of nodes in its successor node list. Moreover, this paper uses the Compressed Bloom Filter algorithm to compress the Bloom Filter structure that needs to be transmitted, therefore reducing the network traffic generated during the update process of the Bloom Filter structure copy and avoiding network congestion. It also constructs a campus network security intrusion detection model based on feature segmentation and deep learning. Through experimental verification, the effectiveness of the system in intrusion detection, user evaluation, information processing, and other aspects is verified, and it has certain advantages compared to traditional algorithms. Through experimental research, it can be seen that the campus network security intrusion detection model based on feature segmentation and deep learning proposed in the paper can effectively improve the effect of campus network security monitoring. The method proposed in this article can not only be applied to campus network security, but also to the network security management of enterprises and other units, with certain scalability

Fractional social optimization-based migration and replica management algorithm for load balancing in distributed file system for cloud computing

ABSTRACT Effective management of data is a major issue in Distributed File System (DFS), like the cloud. This issue is handled by replicating files in an effective manner, which can minimize the time of data access and elevate the data availability. This paper devises a Fractional Social Optimization Algorithm (FSOA) for replica management along with balancing load in DFS in the cloud stage. Balancing the workload for DFS is the main objective. Here, the chunk creation is done by partitioning the file into a different number of chunks considering Deep Fuzzy Clustering (DFC) and then in the round-robin manner the Virtual machine (VM) is assigned. In that case for balancing the load considering certain objectives like resource use, energy consumption and migration cost thereby the load balancing is performed with the proposed FSOA. Here, the FSOA is formulated by uniting the Social optimization algorithm (SOA) and Fractional Calculus (FC). The replica management is done in DFS using the proposed FSOA by considering the various objectives. The FSOA has the smallest load of 0.299, smallest cost of 0.395, smallest energy consumption of 0.510, smallest overhead of 0.358, and smallest throughput of 0.537.

Decentralized Replica Management in Latency-Bound Edge Environments for Resource Usage Minimization

Decentralized Replica Management in Latency-Bound Edge Environments for Resource Usage Minimization

RDRM: Real-time Dynamic Replica Management with Joint Optimization for Edge Computing

RDRM: Real-time Dynamic Replica Management with Joint Optimization for Edge Computing

Hybrid trust-based optimized virtual machine migration for dynamic load balancing and replica management in heterogeneous cloud

Cloud computing is an upcoming technology that has garnered interest from academic as well as commercial domains. Cloud offers the advantage of providing huge computing capability as well as resources that are positioned at multiple locations irrespective of time or location of the user. Cloud utilizes the concept of virtualization to dispatch the multiple tasks encountered simultaneously to the server. However, allocation of tasks to the heterogeneous servers requires that the load is balanced among the servers. To address this issue, a trust based dynamic load balancing algorithm in distributed file system is proposed. Load balancing is performed by predicting the loads in the physical machine with the help of the Rider optimization algorithm-based Neural Network (RideNN). Further, load balancing is carried out using the proposed Fractional Social Deer Optimization (FSDO) algorithm, where the virtual machine migration is performed based on the load condition in the physical machine. Later, replica management is accomplished for managing the replica in distributed file system with the help of the devised FSDO algorithm. Moreover, the proposed FSDO based dynamic load balancing algorithm is evaluated for its performance based on parameters, like predicted load, prediction error, trust, cost and energy consumption with values 0.051, 0.723, 0.390 and 0.431J correspondingly.

An efficient load balancing technique using CAViaR-HHO enabled VM migration and replica management in cloud computing

Cloud computing is immense technology that offers distributed resources to a number of users who are present throughout the world. Cloud model is comprised of numerous virtual machines (VMs) and physical machines (PMs) to carry out user tasks effectively in a parallel manner but in some cases, the demand of the users may be high that resulting in the overloading of PMs and this condition deteriorates the performance of cloud network. For achieving effective virtualization in the cloud paradigm, energy and resource utilization are major properties that should be handled effectively and such properties are accomplished through effective management of workload by distributing load equivalently among VMs. By doing so, resource utilization of the network is enhanced and it only requires minimum energy to process the tasks. Numerous load-balancing algorithms have been introduced earlier to maintain load in a cloud environment, nevertheless, they are devoid of mitigating the number of task migrations. Hence, this research proposes an effective load balancing algorithm and replica management method using the proposed Conditional Autoregressive Value at risk by Regression Quantiles-Horse Herd Optimization (CAViaR-HHO) model. Here, the load is computed by considering some factors like Central Processing Unit (CPU), Million Instructions per Second (MIPS), bandwidth, memory, and frequency. VM migration and replica migration is effectively carried out using the proposed CAViaR-HHO model. Meanwhile, the developed method is devised by integration of Conditional Autoregressive Value at risk by Regression Quantiles (CAViaR) with Horse Herd Optimization Algorithm (HOA). However, the proposed CAViaR-HHO has achieved a load with a minimum value of 0.109, capacity with a maximum value of 0.591, resource utilization with a maximum value of 0.467, and minimum cost of 0.344. Using setup-1, when the number of tasks is 500, the capacity of the proposed method is 5.58%, 3.89%, 2.87%, 1.52%, and 0.67% higher when compared to the existing approaches namely, C-FDLA, K-means clustering + LB, Adaptive starvation threshold, EIMORM, and Dynamic replica creation method.

A new hyper-heuristic based on ant lion optimizer and Tabu search algorithm for replica management in cloud environment

A new hyper-heuristic based on ant lion optimizer and Tabu search algorithm for replica management in cloud environment

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.

Cost-aware automatic scaling and workload-aware replica management for edge-cloud environment

As the mobile edge computing develops continually, many emerging and diversified edge applications, such as Internet of vehicles, virtual reality games and lightweight deep learning tasks, are emerged. These applications are latency sensitive and require a large number of network connections. As the data size and business requests processed per unit of time continue to increase, the pressure of edge cloud load increases so much that the edge cloud cannot provide timely and effective services for users. In order to meet the high requirement of delay sensitive application service, on the one hand, the energy consumption of edge devices should be reduced, and the modules with high computational load should be scheduled to the remote server for execution. On the other hand, the automatic scaling model is proposed to improve the total cost of the tenanted instances. In the dynamic replica management model, the response time and the energy consumption are reduced, moreover, the workload in the hosts are balanced. In the experiment of automatic scaling, the cumulative total cost and the power consumption of our proposed algorithm are lower than that of CAAS and MLC algorithm. Two hours after the experiment, the CPU utility of our proposed algorithm is higher than the CPU utilizations of CAAS and MLC. In the dynamic replica placement experiment, the average response time and load balancing value of the data migration algorithm in our paper are lower than that of BA and LA. The available storage space of our replica placement method is more balanced than both the DDD and WA.

MetaCat - metadata catalog for data management systems

Metadata management is one of three major areas of scientific data management along with replica management and workflow management. Metadata is the information describing the data stored in a data item, a file or an object. It includes the data item provenance, recording conditions, format and other attributes. MetaCat is a metadata management database designed and developed for High Energy Physics experiments. As a component of a data management system, it’s main objectives are to provide efficient metadata storage and management and fast data selection functionality. MetaCat is required to work on the scale of 100 million files (or objects) and beyond. The article will discuss the functionality of MetaCat and technological solutions used to implement the product.

Efficient Replica Migration Scheme for Distributed Cloud Storage Systems

With the wide adoption of large-scale internet services and big data, the cloud has become the ideal environment to satisfy the ever-growing storage demand. In this context, data replication has been touted as the ultimate solution to improve data availability and reduce access time. However, replica management systems usually need to migrate and create a large number of data replicas over time between and within data centers, incurring a large overhead in terms of network load and availability. In this paper, we propose CRANE, an effiCient Replica migrAtion scheme for distributed cloud Storage systEms. CRANE complements any replica placement algorithm by efficiently managing replica creation in geo-distributed infrastructures in order to (1) minimize the time needed to copy the data to the new replica location, (2) avoid network congestion, and (3) ensure the minimum desired availability for the data. Through simulation and experimental results, we show that CRANE provides a sub-optimal solution for the replica migration problem with lower computational complexity than its integer linear program formulation. We also show that, compared to OpenStack Swift, CRANE is able to reduce by up to 60 percent the replica creation and migration time and by up to 50 percent the inter-data center network traffic while ensuring the minimum required data availability.

An intelligent energy efficient storage system for cloud based big data applications

Storage technology has emerged as an indispensable paradigm for processing various applications in cloud data centers. The storage infrastructure consisting of Hard Disk Drives (HDDs) and Solid-State Drives (SSDs) accounts for high energy consumption. Also, the trade-offs between HDDs and SSDs in terms of cost and energy consumption are extremely high. Therefore, disk-based storage subsystems need to be more energy efficient. This paper proposes an intelligent energy-efficient hybrid disk storage system. The proposed system recognizes the frequently used data from traces of applications. Replica management along with data layout is used to allocate frequently used files in hot disks and the other files in cold disks. The request is executed using an intelligent scheduling technique that searches and selects the disk based on its states. The scheduling technique selects either an idle or active disk without spinning the standby disks. The selection procedure also considers minimum waiting time for the active disk and maximum remaining idle time for the idle disk. The proposed system has been implemented by adding disk management in the cloud environment, which proved to be highly effective in achieving a 39% power savings with an 18.26% decrease in execution time.

Effective replica management for improving reliability and availability in edge-cloud computing environment

The multi-replica strategy can create multiple data replicas for the edge cloud system and store them in different DataNodes, which improves data availability and data service quality. However, the storage resources of DataNodes are limited and the user demand for data is time-varying, the unreasonable number of data replicas will cause a high storage burden on the file system or low data service quality. Therefore, the number of data replicas needs to be dynamically adjusted according to the actual situation. Based on this, a dynamic replica creation strategy based on the gray Markov chain is proposed. If the number of replicas needs to be increased, the newly added replicas need to be placed on the DataNodes. Considering the problem of load balancing of the DataNode during replica placement, this paper proposes a replica placement strategy based on the Fast Non-dominated Sorting Genetic algorithm. In addition, considering the problem of data replica synchronization and the data recovery of failed DataNodes in the edge cloud system, this paper proposes a delay-adaptive replica synchronization strategy and a load-balancing based replica recovery strategy. Finally, the experiments prove the effectiveness of the proposed strategies.

An Efficient Multi-Objective Model for Data Replication in Cloud Computing Environment

The main aim of the proposed methodology is to design a multi-objective function for replica management system using oppositional gravitational search algorithm (OGSA), in which we analyze the various factors influencing replication decisions such as mean service time, mean file availability, energy consumption, load variance, and mean access latency. The OGSA algorithm is hybridization of oppositional-based learning (OBL) and gravitational search algorithm (GSA), which is change existing solution, and to adopt a new good solution based on objective function. Here, firstly we create a set of files and data node to generate a population by assigning the file to data node randomly and evaluate the fitness which is minimizing the objective function. Secondly, we regenerate the population to produce optimal or suboptimal population using OGSA. The experimental results show that the performance of the proposed methods is better than the other methods of data replication problem.

Resource and replica management strategy for optimizing financial cost and user experience in edge cloud computing system

Edge cloud computing can provide resources that are close to users and reduce response time. However, the edge cloud computing system still faces many challenges on addressing the overload problem due to its limited capacity. In this paper, a resource management strategy is proposed to satisfy the workloads of the edge cloud, while minimizing the financial cost of the rented nodes. Furthermore, the replica management strategy, which consists of the replica allocation and consistency preservation strategy, is studied. A dynamic replica allocation strategy is proposed to satisfy the user experience while reducing the storage overheads. In addition, the replica consistency preservation strategy is proposed for guaranteeing the data consistency and correctness. Finally, extensive experiments are conducted based on a real-world dataset. With the increase in the time, the proposed resource management algorithm can significantly reduce the total financial cost of the rented nodes and SLA default rate and improve the CPU utilization. For instance, the total financial cost of the proposed algorithm averagely achieves up to 32.27% and 53.65% reduction over that of CAAS algorithm and DRM algorithm, respectively. In addition, the proposed replica allocation algorithm can effectively reduce the data transmission time and the storage overhead.

Adaptive Replica Creation and Selection Strategies for Latency-Aware Application in Collaborative Edge-Cloud System

Abstract There are many research problems in cloud replica management such as low data reliability, unbalanced node load and large resource consumption. The strategy and status of replica creation, replica placement and replica selection are analyzed. The replica creation based on access tendency (DRC-AT), the replica placement based on user request response time and storage capacity (DRP-RS) and the replica selection based on response time (DRS-RT) are proposed. The DRC-AT algorithm introduces the two parameters of file popularity and period value of file popularity, calculates the file access tendency periodically and decides the creation and deletion of the replica of the file according to the size of the file access tendency. The DRP-RS algorithm evaluates the user’s request response time and storage capacity to select the best node set to place the replica. The DRS-RT algorithm returns to the user the node with the strongest service capability that contains the user’s requested data. Experiments show that the algorithm can improve the speed of data reading by the client, improve the resource utilization, balance the load of the node and improve the overall performance of the system.

Node Reputation-Based Replica Replication Strategy in P2P Networks

Replica technology increases the reliability and availability of data by increasing the redundancy of data in the system. The existing replica replication algorithm only depends on the access traffic of the node when selecting the replication node, ignoring the non-cooperation of the node, resulting in a decrease in the utilization of the replica. This paper proposes an adaptive replica management policy PCA based on node reputation. PCA selects nodes with large query traffic and high reputation as replication nodes to ensure high query hit rate. Because PCA sets the copy according to the cooperation of the node, it avoids the waste of copy caused by setting the copy on the non-cooperative node, and guarantees high copy utilization.

Method for replica selection in the Internet of Things using a hybrid optimisation algorithm

Internet of Things (IoT) as a new technological revolution has been proposed recently wherein the things are connected over the Internet. Because of the inherent characteristics of IoT for storage of data at untrusted and heterogeneous hosts, data replication across large geographic distances for efficient data management is unavoidable. The selection of appropriate replication things in the IoT, which reduces response time and cost is one of the most important issues of data management. Since this problem is an NP-hard problem, classic approaches are not efficient to solve this issue, and evolutionary algorithm such as ant colony optimisation (ACO) and genetic algorithm (GA) seems to be very useful. This study offers a method based on a combination of ACO and a GA to solve this problem. In the proposed method, the ACO has been used to create diversity, and afterwards, the GA is performed to provide a full search over the search space. The obtained results have shown the better performance of the proposed method in comparison with ACO, the High-QoS First-Replication (HQFR), and the Response Time-based Replica Management algorithms with regard to waiting time. In addition, the obtained results have revealed the better performance of the hybrid method in comparison with HQFR and the Dynamic Cost-aware Re-replication and Re-balancing Strategy.

Using data mining techniques to improve replica management in cloud environment

Effective data management is a crucial problem in distributed systems such as data grid and cloud. This can be achieved by replicating file in a wise manner, which reduces data access time, increases data availability, reliability and system load balancing. Determining a reasonable number and appropriate location of replicas is essential decision in cloud computing. In this paper, a new dynamic replication strategy called Data Mining-based Data Replication (DMDR) is proposed, which determines the correlation of the data files accessed using the file access history. We focus particularly on how extracted knowledge with maximal frequent correlated pattern mining improves data replication. We can group files with high dependency in the same replica set. Through the DMDR strategy, replicas can be stored in the suitable locations, with reduced access latency according to the centrality factor. In addition, due to the finite storage space of each node, replicas that are useful for future tasks can be wastefully deleted and replaced with less beneficial ones. Results of simulation using CloudSim indicate that DMDR strategy has a relative advantage in effective network usage, average response time, hit ratio in comparison with current methods. It can be concluded from this investigation that data mining technique is effective and helpful in the finding of users’ future access behavior in cloud environment.