Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

Panahi, Vahideh; Navimipour, Nima Jafari

doi:10.1002/cpe.5218

Cited by 39 publications

(27 citation statements)

References 54 publications

(84 reference statements)

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“…Virtualization can be leveraged to isolate the complexity of managing a multinetwork application from the rest of the network to provide assurance and safety. Finally, studying the impact of metaheuristics and nature‐inspired algorithms such as particle swarm optimization (PSO), imperialist competitive algorithm, bee colony algorithm, shark smell optimization, and world cup optimization algorithm on the virtualization techniques can be assessed in the future.…”

Section: Open Issues and Future Workmentioning

confidence: 99%

A comprehensive and systematic review of the network virtualization techniques in the IoT

Ramakrishnan

Shabbir

Kassim

et al. 2020

Int J Communication

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Internet of Things (IoT) as one of the last technology in the network domain is an environment of associated physical objects that are reachable through the Internet. Virtualization as the primary technology of IoT has a significant role in its popularization. The visualization strategies have a great impact on the IoT, but, as far as we know, there is not a comprehensive and systematic study in this field. Also, there is no systematic and complete way to discuss and analyze the related strategies. Henceforth, a systematic survey of virtualization techniques is presented in this paper. Different parameters based on the examination of the prevailing methods are considered in three main categorizations. Furthermore, the benefits and weaknesses connected with selected techniques have been discussed, and the significant issues of these techniques are addressed to improve the more efficient virtualization technique for the future. K E Y W O R D SCloud, Fog, network, Internet of Things, systematic literature review, virtualization

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Section: Open Issues and Future Workmentioning

confidence: 99%

A comprehensive and systematic review of the network virtualization techniques in the IoT

Ramakrishnan

Shabbir

Kassim

et al. 2020

Int J Communication

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“…GA is a method designed to simulate the evolution processes and natural selection in organisms, which follows the order as generating the initial population, assessment, selection, crossover, mutation, and regeneration. [33][34][35] The initial population becomes vital as it represents the solution and randomly generated. Employing a problem specific function, The execution time S j needs to process T i…”

Section: Genetic Algorithmmentioning

confidence: 99%

An energy‐aware approach for resource managing in the fog‐basedInternet of Thingsusing a hybrid algorithm

Ren

Zhang

Arefzadeh

2020

Int J Communication

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SummaryRegarding the recent information technology improvement, the fog computing (FC) emergence increases the ability of computational equipment and supplies modern solutions for traditional industrial applications. In the fog environment, Internet of Things (IoT) applications are completed by computing nodes that are intermediate in the fog, and the physical servers in data centers of the cloud. From the other side, because of resource constraints, dynamic nature, resource heterogeneity, and volatility of fog environment, resource management problems must be considered as one of the challenging issues of fog. The resource managing problem is an NP‐hard issue, so, in the current article, a powerful hybrid algorithm for managing resources in FC‐based IoT is proposed using an ant colony optimization (ACO) and a genetic algorithm (GA). GAs are computationally costly because of some problems such as the lack of guarantee for obtaining optimal solutions. Then, the precision and speed of convergence can be optimized by the ACO algorithm. Therefore, the powerful affirmative feedback pros of ACO on the convergence rate is considered. The algorithm uses GA's universal investigation power, and then it is transformed into ACO primary pheromone. This algorithm outperforms ACO and GA under equal conditions, as the simulation experiments showed.

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“…Competitive Algorithm (ICA), 44,45 Ant Colony Optimization (ACO), 46 Bee Colony Optimization (BCO), [47][48][49][50] Genetic Algorithm (GA), 51,52 and…”

Section: Overview Of Non-deterministic Mechanismmentioning

confidence: 99%

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Azhir

Navimipour

Hosseinzadeh

et al. 2019

Concurrency and Computation

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Query optimization is considered as one of the main challenges of query processing phases in the cloud environments. The query optimizer attempts to provide the most optimal execution plan by considering the possible query plans. Therefore, the execution cost of a query can be affected by some factors, including communication costs, unavailability of resources, and access to large distributed data sets. In addition, it is known as NP-hard problem and many researchers are focused on this problem in recent years. Some techniques are proposed for solving this problem. Deterministic and non-deterministic methods are two main categories to study these techniques. The deterministic and non-deterministic query optimization methods can be further divided into three subcategories, cost-based query plan enumeration, multiple query optimization, and adaptive query optimization methods. Moreover, this paper presents the advantages and disadvantages of the algorithms for solving the query optimization problems in the cloud environments. Moreover, these techniques are compared in terms of optimization, time, cost, efficiency, and scalability. Finally, some key areas are offered to improve the cloud query optimization mechanisms in the future. KEYWORDScloud computing, database, query optimization, review INTRODUCTIONThe data transfer operation and resource sharing are facilitated by rapid progress of the distributed IT-based systems. 1,2 Cloud computing supports several computers through a network. 3 The cloud computing has a large-scale distributed architecture and virtualized services to deliver the requests to users. 4,5 Moreover, the cloud computing provides important financial advantages and long level cooperation possibilities for organizations and institutions. 6 The cloud computing is defined as a distributed IT-based technology based on service business model. 7This paradigm provides many benefits for users, such as the provision of computing capabilities, heterogeneous network access, scalability, and elasticity with measured services. 8,9 The cloud computing gives shared access to a large pool of resources, including data storage, memory, processing, and virtual machines. 10 A cloud client, such as a web browser and mobile app can be helpful in accessing these services. 11 Enormous amounts of data are retrieved from geo-distributed data sources and cross-layer data-handling requirements to make a change in business model. 12 The cloud storage as one of the main services is provided by cloud computing, 13 which allows the users to store their data in virtual pools instead of their servers. 14 In addition, subscribers can access the data from any area of cloud. 15 Therefore, the reliability and availability are necessary to recover the information and query processing.The query processing involves three main steps, as shown in Figure 1. First, the query is translated into an expression of the relational algebra.Second, an optimal evaluation plan for the query plan is generated. The query optimization is the main part o...

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Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

Cited by 39 publications

References 54 publications

A comprehensive and systematic review of the network virtualization techniques in the IoT

A comprehensive and systematic review of the network virtualization techniques in the IoT

An energy‐aware approach for resource managing in the fog‐basedInternet of Thingsusing a hybrid algorithm

Deterministic and non‐deterministic query optimization techniques in the cloud computing

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