Efficient Algorithm for Identification and Cache Based Discovery of Cloud Services

Quadir, Abdul; Varadarajan, Vijayakumar; Mandal, Karan

doi:10.1007/s11036-019-01256-0

Cited by 21 publications

(16 citation statements)

References 35 publications

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“…Md, et al [57] proposed an algorithm to discover cloud services based on the QoS using a decision tree classification algorithm. A cloud service resource registry algorithm was developed to enable service providers to register their services with the QoS attributes.…”

Section: Cloud Service Discovery Solutions: State Of the Artmentioning

confidence: 99%

“…Agents were used to search and check the similarities between the inquired service by the user and the available cloud services based on the ontology. Moreover, other researchers applied intelligent and machine learning techniques to identify the appropriate cloud service that matches the user's needs [34,50,51,53,[56][57][58]. Mainly, solutions were applied to extract services of all cloud models (SaaS, PaaS, and IaaS), but some were specific for one model [5,36].…”

Section: Cloud Service Discovery Solutions: State Of the Artmentioning

confidence: 99%

“…A cloud service requester ontology is used to request executing the application demanding resources, and a service provider ontology is used to allocate resources in the form of services. Examples of this type of approach include the models proposed by Kang and Sim [60], Boukadi, Rekik, Rekik and Ben-Abdallah [52], Parhi, Pattanayak and Patra [54], Parhi, Pattanayak and Patra [38], Kanagasabai [34], Nabli, Djemaa and Amor [53], Md, Varadarajan and Mandal [57] and Alkalbani, Hussain and Kim [58].…”

Section: Centralized Semantic-based Approachesmentioning

confidence: 99%

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Cloud Computing Services: Taxonomy of Discovery Approaches and Extraction Solutions

et al. 2020

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Cloud computing offers new features of sharing resources and applications to meet users’ computing requirements. It is a model by which the users can access computing resources as services offered on the Internet (cloud services). Cloud service providers offer a highly diverse range of asymmetric cloud services with heterogeneous features, which makes it difficult for the users to find the best service that fits his needs. Many research studies have been done on cloud service discovery, and several models and solutions that applied different techniques have been proposed. This paper aims at presenting the state of the art in the area of cloud services discovery by exploring the current approaches, techniques, and models. Furthermore, it proposes a taxonomy of cloud service discovery approaches. An integrative review approach was used to explore the related literature. Then, by analyzing the existing cloud service discovery solutions, a taxonomy of discovery approaches was suggested based on several perspectives including the discovery environment and the discovery process methods. The proposed taxonomy allows easily classifying and comparing cloud services discovery solutions. Moreover, it may reveal issues and gaps for further research and expose new insights for more innovative and effective cloud services discovery solutions.

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Section: Cloud Service Discovery Solutions: State Of the Artmentioning

confidence: 99%

Section: Cloud Service Discovery Solutions: State Of the Artmentioning

confidence: 99%

Section: Centralized Semantic-based Approachesmentioning

confidence: 99%

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Cloud Computing Services: Taxonomy of Discovery Approaches and Extraction Solutions

et al. 2020

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“…While the complexity and network size grow dynamically, the service discovery becomes an NP-hard problem in which efficient algorithms are required to discover optimum resources ( Ezugwu & Adewumi, 2017 ; Md, Varadarajan & Mandal, 2019 ). For this reason, service discovery is one of the indispensable challenges in the cloud, which finds suitable resources based on the requested job ( Navimipour et al, 2014 ; Rajendran & Swamynathan, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

A new SLA-aware method for discovering the cloud services using an improved nature-inspired optimization algorithm

Heidari

Navimipour

2021

PeerJ Computer Science

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Cloud computing is one of the most important computing patterns that use a pay-as-you-go manner to process data and execute applications. Therefore, numerous enterprises are migrating their applications to cloud environments. Not only do intensive applications deal with enormous quantities of data, but they also demonstrate compute-intensive properties very frequently. The dynamicity, coupled with the ambiguity between marketed resources and resource requirement queries from users, remains important issues that hamper efficient discovery in a cloud environment. Cloud service discovery becomes a complex problem because of the increase in network size and complexity. Complexity and network size keep increasing dynamically, making it a complex NP-hard problem that requires effective service discovery approaches. One of the most famous cloud service discovery methods is the Ant Colony Optimization (ACO) algorithm; however, it suffers from a load balancing problem among the discovered nodes. If the workload balance is inefficient, it limits the use of resources. This paper solved this problem by applying an Inverted Ant Colony Optimization (IACO) algorithm for load-aware service discovery in cloud computing. The IACO considers the pheromones’ repulsion instead of attraction. We design a model for service discovery in the cloud environment to overcome the traditional shortcomings. Numerical results demonstrate that the proposed mechanism can obtain an efficient service discovery method. The algorithm is simulated using a CloudSim simulator, and the result shows better performance. Reducing energy consumption, mitigate response time, and better Service Level Agreement (SLA) violation in the cloud environments are the advantages of the proposed method.

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“…The contribution in [6] proposes (i) an efficient algorithm for identifying cloud services based on Quality of Service (QoS) metrics given by the cloud consumer using the decision tree classification algorithm, (ii) an efficient algorithm for cloud service resource registry aiming to enable cloud service providers to register their services with its QoS attributes, and (iii) a cloud service resource discovery for searching in the cloud service registry for cloud services using the Split and Cache (SAC) algorithm. An error regarding the authors names in [6] are corrected in the Erratum [7].…”

mentioning

confidence: 99%

Editorial: Mobile Networks in the Era of Big Data

Varadarajan

Neelanarayanan

Doyle³

et al. 2019

Mobile Netw Appl

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There is an exceptional rupture of research activities in big data, which illustrate enormous potential in making decisions, optimizing operations, mitigating security threats and also in a variety of fields, such as retail, advertisement, manufacturing, healthcare and insurance. Data from Mobile devices and networks like smart phones, cellular networks, Sensor networks, Vehicular Networks is an important source of big data. Thus, Mobile networks play a critical role in big data generation, delivery, and processing. Big Data will have profound impacts on the design and operation of Mobile networks. We received 46 paper submissions, from which 7 submissions have been transferred and 8 papers withdrawn. After a rigorous single blinded peer review process only 14 papers have been accepted and 17 papers have been rejected, i.e., our acceptance rate is about 45% not considering the transferred and withdrawn submissions. Hence, this special issue features 14 high quality research papers that can demonstrate proofs-of-concept, services, solutions for research challenges, case studies, analytics, real world examples and successful deliveries of Big Data and Mobile Networks.

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Efficient Algorithm for Identification and Cache Based Discovery of Cloud Services

Cited by 21 publications

References 35 publications

Cloud Computing Services: Taxonomy of Discovery Approaches and Extraction Solutions

Cloud Computing Services: Taxonomy of Discovery Approaches and Extraction Solutions

A new SLA-aware method for discovering the cloud services using an improved nature-inspired optimization algorithm

Editorial: Mobile Networks in the Era of Big Data

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