The rapid rise of IoT devices, which are expected to reach over 75 billion by 2025 and generate 175 zettabytes of data each year, has shown that traditional cloud computing has problems with latency and bandwidth. This means that fog-cloud architectures are needed for IoT processing in real time. This paper suggests the DLSFC-Enhanced (DLSFC-E) algorithm, which builds on the Data-Locality Aware Job Scheduling in Fog-Cloud (DLSFC) technique and uses a multi-objective optimization framework to solve these problems. DLSFC-E uses a Directed Acyclic Graph (DAG) to show how tasks depend on each other, adds dynamic data replication based on how people use the system, and includes realistic network dynamics (bandwidth 10–100 Mbps ± 20%, latency 1–10 ms ± 15%) in simulations of a three-layer IoT-fog-cloud system with 10 fog nodes. CloudSim 4.0 simulations and real-world traffic statistics from Amsterdam on a 5-node physical testbed are used to check the method. The results reveal that DLSFC-E is 85% in line with the best Linear Programming (LP) solutions, cuts the makespan by 2.8 to 5.2 times compared to centralized methods, and lowers migration expenses by 15% compared to DLSFC. It improves runtime scalability by 40% for 1000 or more activities $$\:O\left(n\text{log}n\right)$$ complexity and energy efficiency by 14%, with 92% of tasks keeping latency below 10 ms. These results, which were tested on datasets ranging from 30 to 600 MB, show that DLSFC-E is strong enough for IoT installations on a broad scale. The study finds that DLSFC-E is a scalable and efficient way to schedule things, but there are still problems to solve, such as making sure it works when the network goes down and adjusting the weight of tasks as needed.

The rapid adoption of cloud computing has driven extensive research into data replication methods and their practical applications. Data replication is a vital process in cloud systems, ensuring data availability, improving performance, and maintaining system stability. This is especially crucial for data-intensive applications that require the distribution and sharing of large volumes of information across geographically dispersed centers. However, managing this process presented significant challenges. As the number of data replicas increases and they are distributed across multiple locations, the associated costs and complexity of maintaining system usability, performance, and stability also rise. In this study, we initially randomized the distribution of data replication files across the cloud infrastructure to simulate a realistic scenario where data already exists within the system before the application of replication algorithms. This approach allowed the algorithms to optimize the replication process based on the initial data distribution and adapt to the evolving demands of incoming workloads. To address the challenges of dynamic data replication in cloud environments, this paper introduced two algorithms: the Firefly Optimization Algorithm for Data Replica Selection (FFO-S) and the Firefly Optimization Algorithm for Replica Placement (FFO-P). A detailed simulation study was performed using the CloudSim platform to assess the effectiveness of the proposed FFO-S and FFO-P algorithms. The simulation environment was designed to closely emulate real-world cloud infrastructures, ensuring the practical applicability of the results.

In cloud computing, ensuring the high availability and reliability of data is dominant for efficient content delivery. Content replication across multiple clouds has emerged as a solution to achieve the above. However, managing optimal replication while considering dynamic changes in data popularity and cloud resource availability remains a formidable challenge. In order to address these challenges, this article employs TFT-based Dynamic Data Replication Strategy (TD2RS), leveraging the Temporal Fusion Transformer (TFT), a deep learning temporal forecasting model. This proposed system collects historical data on content popularity and resource availability from multiple cloud sources, which are then used as input to TFT. Then TFT is used to capture temporal patterns and forecasts future data demands. An intelligent replication is performed to optimize content replication across multiple cloud environments based on these forecasts. The framework's performance was validated through extensive experiments using synthetic time-series data simulating with varied cloud resource characteristics. Some of the findings include that the proposed TFT approach improves the availability of data by 20% when compared to traditional replication techniques and also cuts down the latency level by 15%. These outcomes indicate that the TFT-based replication strategy targets to improve content delivery efficiency in the dynamic cloud computing environment, thus providing effective solution to dynamically address the availability, reliability, and performance challenges.

The cloud environment has garnered significant attention due to its crucial role as a supportive framework in computer science and engineering activities. This ever-growing adoption has increased among users, necessitating ongoing efforts to maintain and enhance cloud performance. A widely recognised approach to improving the user experience in cloud environments is data replication. This article proposes a Locality-Aware Dynamic Data Replication Algorithm (LADRE) designed to balance network load and enhance performance. The proposed model identifies the most appropriate data files for replication and selects optimal storage nodes for placing replicas. Simulations conducted using the CloudSim library demonstrate that LADRE improves cloud network load balancing by 5% compared to prior methods. Furthermore, LADRE outperforms existing approaches regarding network response time, storage efficiency, and the frequency of data removal operations.

Dynamic Data Replication and Scheduling Using Fuzzy-CSO Algorithm for IoT-Clouds

Recently, the concept of the internet of things and its services has emerged with cloud computing. Cloud computing is a modern technology for dealing with big data to perform specified operations. The cloud addresses the problem of selecting and placing iterations across nodes in fog computing. Previous studies focused on original swarm intelligent and mathematical models; thus, we proposed a novel hybrid method based on two modern metaheuristic algorithms. This paper combined the Aquila Optimizer (AO) algorithm with the elephant herding optimization (EHO) for solving dynamic data replication problems in the fog computing environment. In the proposed method, we present a set of objectives that determine data transmission paths, choose the least cost path, reduce network bottlenecks, bandwidth, balance, and speed data transfer rates between nodes in cloud computing. A hybrid method, AOEHO, addresses the optimal and least expensive path, determines the best replication via cloud computing, and determines optimal nodes to select and place data replication near users. Moreover, we developed a multi-objective optimization based on the proposed AOEHO to decrease the bandwidth and enhance load balancing and cloud throughput. The proposed method is evaluated based on data replication using seven criteria. These criteria are data replication access, distance, costs, availability, SBER, popularity, and the Floyd algorithm. The experimental results show the superiority of the proposed AOEHO strategy performance over other algorithms, such as bandwidth, distance, load balancing, data transmission, and least cost path.

Abstract With the evolution of geographically distributed data centers in the Cloud Computing landscape along with the amount of data being processed in these data centers, which is growing at an exponential rate, processing massive data applications become an important topic. Since a given task may require many datasets for its execution and the datasets are spread over several different data centers, finding an efficient way to manage the datasets storage across nodes of a Cloud system is a difficult problem. In fact, the execution time of a task might be influenced by the cost of data transfers, which mainly depends on two criterias. The first one is the initial placement of the input datasets during the build‐time phase, while the second is the replication of the datasets during the runtime phase. The replication is explicitly considered when datasets are being migrated over the data centers in order to make them locally available wherever needed. Data placement and data replication are important challenges in Cloud Computing. Nevertheless, many studies focus on data placement or data replication exclusively. In this paper, a combination of a data placement strategy followed by a dynamic data replication management strategy is proposed, with the purpose of reducing the associated cost of all data transfers between the (distant) data centers. Our proposed data placement approach considers the main characteristics of a data center such asstorage capacityandread/write speedsto efficiently store the datasets, while our dynamic data replication management approach considers three parameters: thenumber of replicasin the system, thedependency between datasetsand tasks and thestorage capacityof data centers. The decision of when and whether to keep or to delete replicas is determined by the fulfillment of those three parameters. Our approach estimates the total execution time of the tasks as well as the monetary cost, considering the data transfers activity. Our experiments are conducted using Cloudsim simulator. The obtained results show that our proposed strategies produce an efficient data management by reducing the overheads of the data transfers, compared to both a data placement without replication (by 76%) and the selected data replication approach from Kouidri et al. (by 52%), and by improving the financial cost.

Data correlation and fuzzy inference system-based data replication in federated cloud systems

In recent years, there has been increasing interest in cloud computing research, especially replication strategies and their applications.When the number of replicas is increased and placed in different places, maintaining the system's data availability, performance and reliability will increase the cost.In this paper, two multi-objectives swarm intelligence algorithms are used to optimize the data replication selection and placement in a cloud environment.These algorithms are namely, multi-objective particle swarm optimization (MOPSO) and multi-objective ant colony optimization (MOACO).The first algorithm, (MOPSO), is used to find the best selected data replica according to the most popular data replication strategy.The improved time-based decay function (ITBDF), is used to enhance the proposed model.The second algorithm, (MOACO), is used to find the best data replica placement according to the minimum distance, the number of data transmissions and the availability of data replication.A simulation of the suggested strategy has been performed using CloudSim.the Cloud is formed to simulate different kinds of datacenters (DCs) with different structures.Moreover, 21 DCs are used.Each DC consists of a host that contains a set of virtual machines (VMs) that provides blocks of available data replications.Three different data placements for high datacenters were created.A total of one thousand cloudlets are randomly confirmed for the data replication order.All replication files are placed in high datacenters and randomly distributed in the suggested system.The performance of proposed strategy was evaluated relative to many well-known strategies such as, Enhance Fast Spread (EFS), Dynamic Cost-aware Re-replication and Re-balancing Strategy (DCR2S), Genetic Algorithm (GA), Genetic adaptive Selection Algorithm (GASA), Replica Selection and Placement (RSP), Dynamic Replica Selection Ant Colony Optimization (DRSACO), Adaptive Replica Dynamic Strategy (ARDS), Popular File Replication First (PFRF).The experimental results show that MOPSO, achieves better data replication than compared algorithms.Additionally, MOACO, achieves higher data availability, lower cost, and less bandwidth consumption than compared algorithms.

The paradigm and domain of data security is the key point as per the current era in which the data is getting transmitted to multiple channels from multiple sources. The data leakage and security loopholes are enormous and there is need to enforce the higher levels of security, privacy and integrity. Such sections incorporate e-administration, long range interpersonal communication, internet business, transportation, coordinations, proficient correspondences and numerous others. The work on security and trustworthiness is very conspicuous in the systems based situations and the private based condition. This examination original copy is exhibiting the efficacious use of security based methodology towards the execution with blockchain programming utilizing robustness and different devices. The blockchain based mix is currently days utilized for e-administrations and military applications for the noticeable security based applications. To work with the high performance approaches and algorithms, the blockchain technology is quite prominent and used in huge performance aware patterns whereby the need to enforce the security is there. The work integrates the usage patterns of blockchain technologies so that the overall security and integrity can be improved in which there is immutability and strength based algorithms for enforce the security measures.

Data replications effectively replicate the same data to various multiple locations to accomplish the objective of zero loss of information in case of failures without any downtown. Dynamic data replication strategies (providing run time location of replicas) in clouds should optimize the key performance indicator parameters, like response time, reliability, availability, scalability, cost, availability, performance, etc. To fulfill these objectives, various state-of-the-art dynamic data replication strategies has been proposed, based on several criteria and reported in the literature along with advantages and disadvantages. This paper provides a quantitative analysis and performance evaluation of target-oriented replication strategies based on target objectives. In this paper, we will try to find out which target objective is most addressed, which are average addressed, and which are least addressed in target-oriented replication strategies. The paper also includes a detailed discussion about the challenges, issues, and future research directions. This comprehensive analysis and performance evaluation based-work will open a new door for researchers in the field of cloud computing and will be helpful for further development of cloud-based dynamic data replication strategies to develop a technique that will address all attributes (Target Objectives) effectively in one replication strategy.

Task scheduling and data replication are highly coupled resource management techniques that are widely used by cloud providers to improve the overall system performance and ensure service level agreement (SLA) compliance while preserving their own economic profit. However, balancing the trade-off between system performance and provider profit is very challenging. In this paper, we propose a novel scheduling algorithm called Bottleneck and Cost Value Scheduling (BCVS) algorithm coupled with a novel dynamic data replication strategy called Correlation and Economic Model-based Replication (CEMR). The main goal is to improve data access effectiveness in order to meet service level objectives in terms of response time SLORT and minimum availability SLOMA, while preserving the provider profit. The BCVS algorithm focuses on reducing system bottleneck situations caused by data transfer when the CEMR focuses on preventing future SLA violations and guaranteeing a minimum availability. An economic model is also proposed to estimate the cloud provider profit. Simulation results indicate that the proposed combination of scheduling and replication algorithms offers higher monetary profit for the cloud provider by up to 30% compared to existing strategies. Moreover, it allows better performance.

In recent years, cloud computing research, specifically data replication techniques and their applications, has been growing. If the replicas number is raised and put in multiple positions, it will be expensive to maintain the data usability, performance and stability of the application systems. In this paper, two bio- inspired algorithms were proposed to improve both selection and placement of data replicas in the cloud environment. The suggested algorithms for dynamic data replication are multi-objective particle swarm optimization (MO-PSO) and ant colony optimization (MO-ACO). The first suggested algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., MO-PSO, is employed to obtain the best selected data replica depend on the most frequent one. However, the second suggested algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., MO-ACO, is employed to obtain the best data replica placement depend on the shortest distance, and the replicas availability. A simulation of the suggested strategy was carried out using CloudSim. Each data center (DC) includes hosts with set of virtual machines (VMs). The data replication order is determined at random from a thousand cloudlets. All replication files are randomly distributed in the proposed architecture. The performance of suggested techniques was evaluated against several approaches including: Adaptive Replica Dynamic Strategy (ARDS), Enhance Fast Spread (EFS), Genetic Algorithm (GA), Replica Selection and Placement (RSP), Popular File Replication First (PFRF), and Dynamic Cost-aware Re-replication and Re-balancing Strategy (DCR2S). The simulation results prove that MOPSO gives improved data replication compared against other algorithms. Additionally, MOACO realizes higher data availability, lower cost, and less bandwidth consumption compared with other algorithms.

Cloud computing environment is getting more interesting as a new trend of data management. Data replication has been widely applied to improve data access in distributed systems such as Grid and Cloud. However, due to the finite storage capacity of each site, copies that are useful for future jobs can be wastefully deleted and replaced with less valuable ones. Therefore, it is considerable to have appropriate replication strategy that can dynamically store the replicas while satisfying quality of service (QoS) requirements and storage capacity constraints. In this paper, we present a dynamic replication algorithm, named hierarchical data replication strategy (HDRS). HDRS consists of the replica creation that can adaptively increase replicas based on exponential growth or decay rate, the replica placement according to the access load and labeling technique, and finally the replica replacement based on the value of file in the future. We evaluate different dynamic data replication methods using CloudSim simulation. Experiments demonstrate that HDRS can reduce response time and bandwidth usage compared with other algorithms. It means that the HDRS can determine a popular file and replicates it to the best site. This method avoids useless replications and decreases access latency by balancing the load of sites.

Distributed/parallel file systems commonly suffer from load imbalance and resource contention due to the bursty characteristic exhibited in scientific applications. This article presents an adaptive scheme supporting dynamic block data replication and an efficient replica placement policy to improve the I/O performance of a distributed file system. Our goal is not only to yield a balanced data replication among storage servers but also a high degree of data access parallelism for the applications. We first present mathematical cost models to formulate the cost of data block replication by considering both the overhead and reduced data access time to the replicated data. To verify the validity and feasibility of the proposed cost model, we implement our proposal in a prototype distributed file system and evaluate it using a set of representative database-relevant application benchmarks. Our results demonstrate that the proposed approach can boost the usage efficiency of the data replicas with acceptable overhead of data replication management. Consequently, the overall data throughput of storage system can be noticeably improved. In summary, the proposed replication management scheme works well, especially for the database-relevant applications that exhibit an uneven access frequency and pattern to different parts of files.

SummaryIn Data Grid systems, quick data access is a challenging issue due to the high latency. The failure of requests is one of the most common matters in these systems that has an impact on performance and access delay. Job scheduling and data replication are two main techniques in reducing access latency. In this paper, we propose two new neighborhood‐based job scheduling strategies and a novel neighborhood‐based dynamic data replication algorithm (NDDR). The proposed algorithms reduce the access latency by considering a variety of practical parameters for decision making and the access delay by considering the failure probability of a node in job scheduling, replica selection, and replica placement. The proposed neighborhood concept in job scheduling includes all the nodes with low data transmission costs. Therefore, we can select the best computational node and reduce the search time by running a hierarchical and parallel search. NDDR reduces the access latency through selecting the best replica by performing a hierarchical search established based on the access time, storage queue workload, storage speed, and failure probability. NDDR improves the load balancing and data locality by selecting the best replication place considering the workload, temporal locality, geographical locality, and spatial locality. We evaluate our proposed algorithms by using Optorsim Simulator in two scenarios. The simulations confirm that the proposed algorithms improve the results compared with similar existing algorithms by 11%, 15%, 12%, and 10% in terms of mean job time, replication frequency, mean data access latency, and effective network usage, respectively.

Adaptive Dynamic Data Replication with Load-balancing in Distributed Systems

International audience

SLA-aware task scheduling and data replication for enhancing provider profit in clouds

Effective data management is very challenging to cloud providers, whose business model relies on maintaining an economic profit while satisfying the tenants' performance requirements. To address these challenges, many data replication strategies have been proposed. In this paper, we propose a new dynamic data replication strategy for cloud systems called RCPP1. In order to satisfy performance requirements, the proposed strategy exploits the valuable knowledge extracted from the tenants' past access history. Therefore, it uses the mathematical triadic concept analysis approach to determine correlated data to be replicated. Furthermore, the cloud provider's profit is taken into account. Hence, an economic model is proposed to estimate the revenues and expenditures of the provider. Experimental studies show the efficiency and effectiveness of RCPP compared to state-of-the-art strategies. RCPP is indeed proven able to reduce the total expenditures of the cloud provider significantly while achieving better performances.