QoS Based Framework for Configurable IaaS Cloud Services Discovery

Hajlaoui, Jalel Eddine; Omri, Mohamed Nazih; Benslimane, Djamal; Barhamgi, Mahmoud

doi:10.1109/icws.2017.53

Cited by 16 publications

(2 citation statements)

References 17 publications

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“…Service discovery in cloud computing involves locating the required services according to service descriptions ( Hajlaoui et al, 2017 ). Effective and efficient service discovery is essential in cloud environments.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…These public repositories allow service providers to publish services with their specification, which includes service description in natural language, service signature, URL, and search keywords (labels or tags). The key of software reuse is to find the required services in the repositories to satisfy the requirements based on the service specifications, which is the primary concern in service discovery [9].…”

Section: Introductionmentioning

confidence: 99%

ServeNet: A Deep Neural Network for Web Services Classification

Yang,

Qamar,

Liu

et al. 2018

Preprint

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Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a deep neural network to automatically abstract low-level representation of service description to high-level features without feature engineering and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy than other machine learning methods.

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Selection of Cloud Computing Services: Contribution of Intelligent Methods

Settouti

2021

Intelligent Network Management and Control

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QoS Based Framework for Configurable IaaS Cloud Services Discovery

Cited by 16 publications

References 17 publications

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

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

ServeNet: A Deep Neural Network for Web Services Classification

Selection of Cloud Computing Services: Contribution of Intelligent Methods

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