Ant colony optimization applied to web service compositions in cloud computing

Yu, Qiang; Chen, Ling; Li, Bin

doi:10.1016/j.compeleceng.2014.12.004

Cited by 90 publications

(72 citation statements)

References 15 publications

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“…The proposed Resource Supervisor (RS) method is validated with various existing algorithms such as Ant Colony optimization (AC) [5], Honeybee Foraging Algorithm (HFA) [6], Inter Cloud Manager (ICM) [9] algorithm. On comparing the results, the Resource Supervisor (RS) algorithm executes the larger cloudlet with minimum execution time than existing algorithms.…”

Section: Comparison Of Various Algorithms With Proposed Rs Methodsmentioning

confidence: 99%

“…Cloudwatch, Azurewatch, Cloudkick, Nimsoft, Logic monitor, Aneka are some of the open sources available for monitoring the cloud resources. In traditional scheduling algorithms [5], scheduling can do with a limited number of tasks and leads to network traffic. The network problem is solved by implementing suitable and efficient scheduling process.…”

Section: Introductionmentioning

confidence: 99%

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A Preference-Based Supervising of Virtual Machines in Cloud Environment

Ezhilarasi¹,

Padmakumari²,

Umamakeswari³

2018

IJET

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Cloud Computing is a well-known technology in today's world. A large number of users are benefited from the cloud services. The cloud computing must provide efficient service on time for customer satisfaction. So, prominent resource monitoring and scheduling techniques are needed. To achieve the customer satisfaction and to reduce the communication overhead, a method called Resource Supervisor (RS) is proposed. The proposed algorithm assigns the preference for the tasks having highest length, monitor the status of the resource and schedule the tasks to various resources quickly. The proposed method is implemented using Cloud Simulator, and experimental results are validated by comparing RS method with existing algorithms, which provides better outcomes and reduces the communication overhead.

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Section: Comparison Of Various Algorithms With Proposed Rs Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Preference-Based Supervising of Virtual Machines in Cloud Environment

Ezhilarasi¹,

Padmakumari²,

Umamakeswari³

2018

IJET

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“…This limits the benefits of other clouds in responding to the user's complex needs, given that the needed services could be unavailable in a single cloud, or cannot totally satisfy the user requirements, such as security and compliance or business and technical needs . As composing services from several clouds was proved to be more beneficial than relying on services from one single cloud, the new approaches are now spanning multiple clouds …”

Section: Introductionmentioning

confidence: 99%

“…Taking as an example the combination of several SaaS applications (horizontal composition), which will be aggregated later with some IaaS services (eg, VMs and storage servers) that are requested by the user (vertical composition). In the literature, most works have treated the horizontal service composition, whereas few attempts have addressed the vertical service composition . However, there are some few works that have treated both vertical and horizontal service compositions …”

Section: Introductionmentioning

confidence: 99%

Bringing semantics to multicloud service compositions

2020

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Summary Over the last decade, cloud computing has emerged as a new paradigm for delivering various on‐demand virtualized resources as services. Cloud services have inherited not only the major characteristics of web services but also their classical issues, in particular, the interoperability issues and the heterogeneous nature of their hosting environments. This latter problem must be taken into account when composing various cloud services, in order to answer users' complex requirements. Moreover, leading cloud providers started to offer their services across multiple clouds. This adds a new factor of heterogeneity, as composition engines must take into consideration the heterogeneity not only at the service level (eg, service descriptions) but also at the cloud level (eg, pricing models, security policies). In this context, the semantics of multicloud actors must be incorporated into the multicloud service composition (MCSC) process. However, most existing approaches have treated the semantic service composition in traditional single‐cloud environments. The few works in multicloud settings have ignored the semantics of cloud zones and resources. Moreover, they often focus on the general aspect of MCSC (eg, horizontal or vertical compositions). Even the few researchers who have addressed both vertical and horizontal service compositions, conducted their research studies in the context of single‐ cloud environments, which were proven to be unrealistic and offer limited quality of service (QoS) and security support. To ensure a high interoperability when composing services from multiple heterogeneous clouds and to enable a horizontal/vertical semantic service compositions, we take advantage of a standardized and semantically enriched generic service description, including all aspects (technical, operational, business, semantic, contextual) and supporting different cloud service models (SaaS, PaaS, IaaS, etc). We also incorporate Semantic Web Rule Language into the MCSC process to enable not only rule‐based reasoning about various composition constraints (eg, QoS constraints, cloud zones constraints) but also to provide accurate semantic matching of cloud services' capabilities. Conducted experiments have proven the ability of our approach to combine high‐quality services from the optimal number of clouds.

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“…20 With the number of web services increasing, the selection of a web service for a composition is not an easy problem because many web services are equal in functionality but different in Quality of Service (QoS). [21][22][23][24][25][26][27][28][29][30] Web service composition aims to choose a combination service which meets the needs of a user's service quality. Currently, special attention has been given to the solution of the web service composition problem through SI algorithms, with the proposal of algorithms such as PSO, ACO, and firework.…”

Section: Algorithm Performance Test In Discrete Environmentsmentioning

confidence: 99%

A fruit fly optimization algorithm with a traction mechanism and its applications

Guo

Zhang

et al. 2017

International Journal of Distributed Sensor Networks

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The original fruit fly optimization algorithm, as well as some of its improved versions, may fail to find the function extremum when it falls far from the origin point or in the negative range. To address this problem, in this article, we propose a new fruit fly optimization algorithm, named as the traction fruit fly optimization algorithm, which is mainly based on the combination of ''traction population'' and dynamic search radius. In traction fruit fly optimization algorithm, traction population consists of the worst individual recorded in the iterative process, the individual in the center of the interval, and the best fruit flies individual through different transformations, which is used to avoid the algorithm stopping at a local optimal. Moreover, our dynamic search radius strategy will ensure a wide search range in the early stage and enhance the local search capability in the latter part of the algorithm. Extensive experiment results show that traction fruit fly optimization algorithm is superior to fruit fly optimization algorithm and its other improved versions in the optimization of extreme values of continuous functions. In addition, through solving the service composition optimization problem, we prove that traction fruit fly optimization algorithm can also obtain a better performance in the discrete environment.

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Ant colony optimization applied to web service compositions in cloud computing

Cited by 90 publications

References 15 publications

A Preference-Based Supervising of Virtual Machines in Cloud Environment

A Preference-Based Supervising of Virtual Machines in Cloud Environment

Bringing semantics to multicloud service compositions

A fruit fly optimization algorithm with a traction mechanism and its applications

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