Resource discovery in the peer to peer networks using an inverted ant colony optimization algorithm

Concurrency and Computation

Hosseinzadeh

et al. 2019

Self Cite

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...

“…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

Concurrency and Computation

Hosseinzadeh

et al. 2019

Self Cite

“…In this regard, job execution time and network traffic are reduced. Finally, studying the role of meta‐heuristics and nature‐inspired algorithms such as particle swarm optimization (PSO), genetic algorithm, imperialist competitive algorithm, ant colony optimization (ACO), memetic algorithm, bee colony algorithm, world cup optimization algorithm with the combination of fuzzy logic, and neural networks are very interesting lines for future research.…”

Section: Open Issuesmentioning

confidence: 99%

Data replication mechanisms in the peer‐to‐peer networks

Mohammadi

2019

Int J Communication

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Peer-to-peer (P2P) network is a distributed network in which nodes with similar capabilities exchange information with each other. Due to the nature of the P2P network distribution, numerous network message transformation is required to exchange the data between nodes over the network, which may increase access latency. Because of the high amount of the stored data in the P2P networks, the replication of data is very important. A large amount of data is handled using data replication to increase data access, reduce access latency, and increase data availability. This issue has an important role in the P2P networks, but there is no complete and systematic research in this field. Therefore, this paper aims to provide a comprehensive study of data replication mechanisms in the P2P networks. The 1027 papers have been identified and have been reduced to 213 main studies using the paper selection process. Moreover, in this paper, the major advances are reviewed in four main groups (structured, unstructured, super-peer, and hybrid networks), and the new challenges are also highlighted. Moreover, the open issues and guidelines for future studies are presented.

“…79 The IACO algorithm is a variation of the basic ACO algorithm that converts its logic by inverting the attraction of ants towards pheromones into a repulsion effect. 125 At first step, for dealing with the problem of a cold start, the selection criteria are used. In this method, ants start the graph traversal from the start node, and the best node is selected by the first ant to move and the minimum number of clouds that satisfy user requirements are acquired.…”

Section: Overview Of the Selected Mechanismsmentioning

confidence: 99%

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

Asghari

2018

Int J Communication

Self Cite

Summary Many sorts of services in the cloud environments must be composited based on the user's requests to meet the requirements. Thus, the distributed services are joined to the cloud services through service composition. Also, it is known as NP‐hard problems and many researchers significantly are focused on this problem in recent years. Therefore, many different nature‐inspired meta‐heuristic techniques are proposed for solving this problem. The nature‐inspired meta‐heuristic techniques have an important role in solving the service composition problem in the cloud environments, but there is not a wide‐ranging and detailed paper about reviewing and studying the important mechanisms in this field. Therefore, this study presents a comprehensive analysis of the nature‐inspired meta‐heuristic techniques for the service composition issue in the cloud computing. The review also contains a classification of the important techniques. These classifications include Ant Colony Optimization, Bee Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Cuckoo Optimization Algorithm, Bat Algorithm, greedy algorithm, and hybrid algorithm. An important aim of this paper is to highlight the emphasis on the optimization algorithms, and the benefits to tackle the challenges are encountered in the cloud service composition. Also, this paper presents the advantages and disadvantages of the nature‐inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments. Moreover, this paper aims to provide more efficient service composition algorithms in the future. Finally, the obtained results have shown that the discussed algorithms have an important effect in solving the cloud service composition problem, and this effect has been increased in recent years.