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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. [ABSTRACT FROM AUTHOR]

One of the main problems of big distributed systems, like IoT, is the high access time to data objects. Replicating the data objects on various servers is a traditional strategy. Replica placement, which can be implemented statically or dynamically, is generally crucial to the effectiveness of distributed systems. Producing the minimum number of data copies and placing them on appropriate servers to minimize access time is an NP-complete optimization problem. Various heuristic techniques for efficient replica placement in distributed systems have been proposed. The main objectives of this research are to decrease the cost of data processing operations, decrease the number of copies, and improve the accessibility of the data objects. In this study, a discretized and group-based gray wolf optimization algorithm with swarm and evolutionary features was developed for the replica placement problem. The proposed algorithm includes swarm and evolutionary features and divides the wolves' population into subgroups, and each subgroup was locally searched in a different solution space. According to experiments conducted on the standard benchmark dataset, the suggested method provides about a 40% reduction in the data access time with about five replicas. Also, the reliability of the suggested method during different executions is considerably higher than the previous methods. [ABSTRACT FROM AUTHOR]

Availability and accessibility of data objects in a reasonable time is a main issue in distributed systems like cloud computing services. As a result, the reduction of data-related operation times in distributed systems such as data read/write has become a major challenge in the development of these systems. In this regard, replicating the data objects on different servers is one commonly used technique. In general, replica placement plays an essential role in the efficiency of distributed systems and can be implemented statically or dynamically. Estimation of the minimum number of data replicas and the optimal placement of the replicas is an NP-complete optimization problem. Hence, different heuristic algorithms have been proposed for optimal replica placement in distributed systems. Reducing data processing costs as well as the number of replicas, and increasing the reliability of the replica placement algorithms are the main goals of this research. This paper presents a discrete and swarm-evolutionary method using a combination of shuffle-frog leaping and genetic algorithms to data-replica placement problems in distributed systems. The experiments on the standard dataset show that the proposed method reduces data access time by up to 30% with about 14 replicas; whereas the generated replicas by the GA and ACO are, respectively, 24 and 30. The average reduction in data access time by GA and ACO 21% and 18% which shows less efficiency than the SFLA–GA algorithm. Regarding the results, the SFLA–GA converges on the optimal solution before the 10th iteration, which shows the higher performance of the proposed method. Furthermore, the standard deviation among the results obtained by the proposed method on several runs is about 0.029, which is lower than other algorithms. Additionally, the proposed method has a higher success rate than other algorithms in the replica placement problem. [ABSTRACT FROM AUTHOR]

Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments. [ABSTRACT FROM AUTHOR]

One of the key issues with large distributed systems, such as IoT platforms, is gaining timely access to data objects. As a result, decreasing the operation time of reading and writing data in distributed communication systems become essential demands for asymmetric system. A common method is to replicate the data objects across multiple servers. Replica placement, which can be performed statically or dynamically, is critical to the effectiveness of distributed systems in general. Replication and placing them on the best available data servers in an optimal manner is an NP-complete optimization problem. As a result, several heuristic strategies for replica placement in distributed systems have been presented. The primary goals of this research are to reduce the cost of data access time, reduce the number of replicas, and increase the reliability of the algorithms for placing replicas. In this paper, a discretized heuristic algorithm with artificial individuals and a hybrid imitation method were developed. In the proposed method, particle and gray-wolf-based individuals use a local memory and velocity to search for optimal solutions. The proposed method includes symmetry in both local and global searches. Another contribution of this research is the development of the proposed optimization algorithm for solving the data object replication problem in distributed systems. Regarding the results of simulations on the standard benchmark, the suggested method gives a 35% reduction in data access time with about six replicates. Furthermore, the standard deviation among the results obtained by the proposed method is about 0.015 which is lower than the other methods in the same experiments; hence, the method is more stable than the previous methods during different executions. [ABSTRACT FROM AUTHOR]

An increasing amount of data is stored and managed in today's information systems. For this purpose, relational databases are used very often. Fast access to data is becoming increasingly important and great emphasis is placed on its improvement. The data access time can be significantly improved by creating partitions and index structures, or their combinations. From the point of view of efficiency, it is not appropriate or necessary to access all data. They can be divided into smaller parts, which will facilitate the execution of certain operations and bring efficiency, whether in terms of time or performance. As far as indexing is concerned, there is also an opportunity to access only the data sought, thus avoiding the Table Access Full method. This paper deals with the effect of partitioning and indexing on data access time, which is compared in seven different scenarios of different combinations of partitions created over tables and indexes.

One of the key issues with large distributed systems, such as IoT platforms, is gaining timely access to data objects. As a result, decreasing the operation time of reading and writing data in distributed communication systems become essential demands for asymmetric system. A common method is to replicate the data objects across multiple servers. Replica placement, which can be performed statically or dynamically, is critical to the effectiveness of distributed systems in general. Replication and placing them on the best available data servers in an optimal manner is an NP-complete optimization problem. As a result, several heuristic strategies for replica placement in distributed systems have been presented. The primary goals of this research are to reduce the cost of data access time, reduce the number of replicas, and increase the reliability of the algorithms for placing replicas. In this paper, a discretized heuristic algorithm with artificial individuals and a hybrid imitation method were developed. In the proposed method, particle and gray-wolf-based individuals use a local memory and velocity to search for optimal solutions. The proposed method includes symmetry in both local and global searches. Another contribution of this research is the development of the proposed optimization algorithm for solving the data object replication problem in distributed systems. Regarding the results of simulations on the standard benchmark, the suggested method gives a 35% reduction in data access time with about six replicates. Furthermore, the standard deviation among the results obtained by the proposed method is about 0.015 which is lower than the other methods in the same experiments; hence, the method is more stable than the previous methods during different executions.

Abstract— High-performance computing and vast storage are two key factors required for executing data-intensive applications. In comparison with traditional distributed systems like data grid, cloud computing provides these factors in a more affordable, scalable and elastic platform. Furthermore, accessing data files is critical for performing such applications. Sometimes accessing data becomes a bottleneck for the whole cloud workflow system and decreases the performance of the system dramatically. Job scheduling and data replication are two important techniques which can enhance the performance of data-intensive applications. It is wise to integrate these techniques into one framework for achieving a single objective. In this paper, we integrate data replication and job scheduling with the aim of reducing response time by reduction of data access time in cloud computing environment. This is called data replication-based scheduling (DRBS). Simulation results show the effectiveness of our algorithm in comparison with well-known algorithms such as random and round-robin.

Metallic carbon nanotubes (m-CNs) cause malfunction by shorting the source and drain terminals in carbon nanotube transistors. To achieve high-yield with robust read and write operations, a new nine carbon nanotube MOSFET (9-CN-MOSFET) static random-access memory (SRAM) cell that can tolerate the removal of m-CNs is proposed in this paper. A functional yield model considering the spatial correlations of carbon nanotubes in channel arrays of CN-MOSFETs is developed. The yield of the m-CN-removal-tolerant 9-CN-MOSFET SRAM array is increased by 21309\times as compared to a design that does not consider process imperfections in carbon-based electronics. Due to the increased strength of read and write ports, the read delay and worst-case write delay of the m-CN-removal-tolerant 9-CN-MOSFET SRAM circuit are reduced by 29.05% and 22.30%, respectively, as compared to the previously published memory circuit that does not consider process imperfections in a 16-nm CN-MOSFET technology. [ABSTRACT FROM PUBLISHER]

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations. [ABSTRACT FROM AUTHOR]

Abstract: Data broadcasting is an efficient data dissemination method in a wireless client–server system. A data server broadcasts data items periodically, and mobile clients cache data items to save communication bandwidth, resource usage, and data access time. The server also broadcasts invalidation reports (IRs) to maintain the consistency between server data and the clients’ cached data. Most existing cache invalidation policies in a wireless environment based on IRs simply purge the entire cache after a client has been disconnected long enough to miss a certain number (window size) of IRs. We present a cache invalidation scheme that provides better cache reusability and better data access time after a long disconnection. Our scheme attempts to increase cache reusability by respecting update rates at a server, broadcast intervals, the communication bandwidth, and data sizes, as well as disconnection time. Simulation results show that the increased cache reusability of our scheme can improve the data access time after a long disconnection. [Copyright &y& Elsevier]