An energy‐aware method for data replication in the cloud environments using a Tabu search and particle swarm optimization algorithm

Ebadi, Yalda; Navimipour, Nima Jafari

doi:10.1002/cpe.4757

Cited by 78 publications

(46 citation statements)

References 33 publications

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“…Replication [11] is a very important subject of investigation in the environment of the cloud. Based on the actual number of replicas with their location which is a Non-Deterministic Polynomial (NP)-hard problem.…”

Section: P Sankara Rao R Usha Ranimentioning

confidence: 99%

A Cuckoo Search Based Heuristic for Replicating IoT Data in Cloud Edge System

2020

IJRTE

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The rapid development in information technology has rendered an increase in the data volume at a speed which is surprising. In recent times, cloud computing and the Internet of Things (IoT) have become the hottest among the topics in the industry of information technology. There are many advantages to Cloud computing such as scalability, low price, and large scale and the primary technique of the IoTs like the Radio-Frequency Identification (RFID) have been applied to a large scale. In the recent times, the users of cloud storage have been increasing to a great extent and the reason behind this was the cloud storage system bringing down the issues in maintenance and also has a low amount of storage when compared to other methods. This system provides a high degree of reliability and availability where redundancy is introduced to the systems. In the replicated systems, objects get to be copied many times and every copy resides in a different location found in distributed computing. So, replication of data has been posing some threat to the cloud storage for users and also for the providers since it has been a major challenge providing efficient storage of data. So, the work has been analysing different strategies of replication of data and have pointed out several issues that are affected by this. For the purpose of this work, replication of data has been presented by employing the Cuckoo Search (CS) and the Greedy Search. The research is proceeding in a direction to reduce the replications without any adverse effect on the reliability and the availability of data.

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Section: P Sankara Rao R Usha Ranimentioning

confidence: 99%

A Cuckoo Search Based Heuristic for Replicating IoT Data in Cloud Edge System

2020

IJRTE

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“…Particle Swarm Optimization (PSO) 18,[53][54][55][56] are some of the examples of population-based nature inspired metaheuristic algorithms. 57 The query optimization mechanisms regarding the non-deterministic algorithms and their anatomy are discussed in Section 3.1.2.…”

Section: Overview Of Non-deterministic Mechanismmentioning

confidence: 99%

“…In the cloud computing, the search space will be significantly larger as we have the opportunity to execute subqueries at any site and use any available copies of relations residing at different sites. Due to the replication of relations, 4,18 parallel execution of cloud storage, and unpredictable nature of cloud applications and services, the query processing system is more complicated. Two main costs in the distributed query processing are local processing cost and global communication cost.…”

mentioning

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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“…Cloud computing is a new paradigm of a service delivery model. 1,2 The primary aim of cloud computing is providing on-demand, reliable, scalable, and available services in distributed environments. [3][4][5] Recently, it has gained high popularity for its efficient resource usage and convenient service access.…”

Section: Introductionmentioning

confidence: 99%

A method for virtual machine migration in cloud computing using a collective behavior‐based metaheuristics algorithm

Sha

Ebadi

Mavaluru

et al. 2019

Concurrency and Computation

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Summary Due to the growth of applications and the integration of new customers into the world of computing systems, computing needs to be changed and to become more powerful and flexible than before. Meanwhile, cloud computing is presented as a model beyond a system that is currently capable of answering most request needs. Flexible infrastructure for cloud computing and virtualization technology provide new features to support business activities. Clouds are a very important topic that used secure management tools for storage, security and securing data centers in a flexible manner. One of the important matters in cloud technologies is virtual machine migration (VMM). There are different ways to implement the VMM, but because of the limitation of resources' energy, energy management is also very important and challenging. Due to the NP‐hard nature of this problem, this paper presents an energy‐aware VMM engine for cloud computing using the discrete bacterial foraging algorithm as a new collective behavior‐based metaheuristics algorithm. The CloudSim simulator is employed to investigate the efficiency of this method. The obtained results have shown that the proposed method improves the energy consumption and the migration count.

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An energy‐aware method for data replication in the cloud environments using a Tabu search and particle swarm optimization algorithm

Cited by 78 publications

References 33 publications

A Cuckoo Search Based Heuristic for Replicating IoT Data in Cloud Edge System

A Cuckoo Search Based Heuristic for Replicating IoT Data in Cloud Edge System

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

A method for virtual machine migration in cloud computing using a collective behavior‐based metaheuristics algorithm

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