Towards Agents and Ontology for Cloud Service Discovery

Kang, Jaeyong; Sim, Kwang Mong

doi:10.1109/cyberc.2011.84

Cited by 58 publications

(43 citation statements)

References 3 publications

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“…Numerical similarity gained less interest from researchers than Semantic similarity. One of the early works on the numerical matching problem for cloud services is presented in [4]. Proposed algorithm increase the search time by considering min and max value of cloud attribute when calculating matching score ms between user request and cloud service.…”

Section: Numerical Similarity Algorithmsmentioning

confidence: 99%

“…In [4] Kang and Sim presented an algorithm (We refer it as SNSim in this paper) to calculate the Numerical Similarity between two attribute values as follows:…”

Section: Sim Numerical Similarity (Snsim)mentioning

confidence: 99%

“…Furthermore, we compare between proposed algorithm and two existing algorithms. First one SNSim is presented in [4] depends on the max and min values of the service attribute and the second one is MNSim algorithm that depends on the max value of the service attribute only. Comparison is done based on four parameters (number of matched services, execution time, average Score and recall) to find the advantages and disadvantages of each one.…”

Section: Introductionmentioning

confidence: 99%

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Numerical Similarity Algorithms for Cloud Service Discovery and Selection System

Hasan¹,

Kumari²

2017

IJIES

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Cloud computing delivers computing services over the Internet based on pay as you use financial model. Cloud service providers publish service advertisements in different formats on the Internet. Thus, cloud consumers should explore all provider websites using available search engines to find the appropriate cloud service. Unfortunately, existing search engines give a huge list of unrelated results that makes consumers spend a lot of time and effort to find the best matched cloud service. In this paper, we present a layered architecture for cloud service discovery and selection system to automate cloud service discovery and selection process, and remove the barriers between cloud service providers and consumers. Additionally, we present novel numerical algorithm for cloud services matching and compare it with existing algorithms. Proposed algorithm (XNSim) is independent of any external attribute value, while existing algorithm (SNSim) depends on the max and min values of the service attribute and (MNSim) algorithm depends on the max value of the service attribute. Comparison is done based on four parameters (number of matched services, execution time, average score and recall) to find the advantages and disadvantages of each one. XNSim algorithm showed better performance and more effectiveness over MNSim and SNSim.

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Section: Numerical Similarity Algorithmsmentioning

confidence: 99%

“…In [4] Kang and Sim presented an algorithm (We refer it as SNSim in this paper) to calculate the Numerical Similarity between two attribute values as follows:…”

Section: Sim Numerical Similarity (Snsim)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Numerical Similarity Algorithms for Cloud Service Discovery and Selection System

Hasan¹,

Kumari²

2017

IJIES

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“…In [9] a four-stage agent-based Cloud service discovery protocol was presented. The authors developed MAS that coordinates efficiently by recommending a adaptable ontology-based matching utilizing an ontology-based definition where resources were defined semantically and comparatively to other resources.…”

Section: Literature Survey 31 a Review On Applications Of Mass In CLmentioning

confidence: 99%

Dynamic Consolidation of Virtual Machines with Multi-Agent System

Barlaskar¹,

Singh²

2014

IJCA

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Cloud computing systems supply massive infrastructures for high-performance computing that are flexible as they are able to acclimate to user and application requirements. Cloud Computing offers on demand services which are used by virtue of a service-oriented interface that execute the anything-as-a-service archetype. The enlargement of Cloud computing has resulted in the establishment of large-scale data centers around the world containing thousands of compute nodes and these data centers consume excessive amounts of electrical energy resulting in high operating costs. Therefore, to cut down the cost of energy consumption the cloud providers must optimize resource usage by performing dynamic consolidation of virtual machines (VMs) in an effective way to improve energy efficiency in cloud data center. The problem of VM consolidation can be split into four sub-problems namely physical machine overload detection; physical machine under-load detection; VM selection and VM placement. Each of the afore-stated subproblems must operate in an optimized manner to maintain the tradeoff between energy and performance. In this research paper a new multi-agent system (MAS) for dynamic consolidation of VMs is proposed with the aim of making the cloud system smarter by incorporating the five traits of multiagent systems which are ubiquity, interconnection, intelligence, delegation and human orientation. The Cloud Computing systems require intelligent and perceptive based software with progressive, elastic, self-ruling style which can be provided by MASs. The proposed method has significantly reduced energy consumption and at the same time ensures a high level of constancy to the Service Level Agreements (SLA).

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“…Our cloud service brokering system [22] consists of multiple broker agents, user agents, provider agents, applications and resources. Thus, the proposed model can be described into four-stage in terms of its architecture.…”

Section: The Conceptual Model Of Cloud Service Brokering System mentioning

confidence: 99%

A MAS-Based Cloud Service Brokering System to Respond Security Needs of Cloud Customers

Talbi¹,

Haqiq²

2017

IJIMAI

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-Cloud computing is becoming a key factor in computer science and an important technology for many organizations to deliver different types of services. The companies which provide services to customers are called as cloud service providers. The cloud users (CUs) increase and require secure, reliable and trustworthy cloud service providers (CSPs) from the market. So, it's a challenge for a new customer to choose the highly secure provider. This paper presents a cloud service brokering system in order to analyze and rank the secured cloud service provider among the available providers list. This model uses an autonomous and flexible agent in multi-agent system (MASs) that have an intelligent behavior and suitable tools for helping the brokering system to assess the security risks for the group of cloud providers which make decision of the more secured provider and justify the business needs of users in terms of security and reliability.Keywords -Cloud Computing, Brokering System, Multi-agent System, Security Risk.I. InTROducTIOn c lOud computing [1] is a new paradigm of utility computing and enormously growing phenomenon in the present IT industry hype .Many companies, enterprises and organizations outsource some of their information systems to benefit from the cloud services which are Platform as a Service (PaaS), Infrastructure as a Service (IaaS) and Software as a Service (SaaS). The main interesting features of a cloud are the cost decrease and a faster time to market. Based on sharing resources, the cloud computing changes the user concerns from managing an infrastructure to only focusing on their core business. Currently there are many numbers of providers, but finding the best cloud service provider is difficult. Thus, it is a challenge for the users to choose the more secured cloud provider for fulfilling their requirements.Nowadays, a few efforts have been devoted to building tools and frameworks that can permit customers to evaluate cloud offerings and rank them based on their ability to meet the user's quality of service (QoS) and security requirements. This is a major problem for every user, especially those who are more concerned about data security and privacy from CSP. For this purpose, cloud brokers [2] have emerged; they can help cloud consumers to select adequate solutions by comparing existing offers, essentially against their prices.A secure computer system provides guarantees regarding the confidentiality, integrity, availability, non-repudiation and authenticity of its objects (such as data, processes or services). Security is related to vulnerabilities in software, and these are hard to foresee or detect before an actual attack; security involves personal aspects (e.g., user or operator issues) and aspects of the operational environment that are often beyond the control of the development teams. As cloud computing presents new kinds of security risks [3], [4], they need to be treated before wider adoption. Accordingly, we have to dispose a system that measure and rank the secured c...

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Towards Agents and Ontology for Cloud Service Discovery

Cited by 58 publications

References 3 publications

Numerical Similarity Algorithms for Cloud Service Discovery and Selection System

Numerical Similarity Algorithms for Cloud Service Discovery and Selection System

Dynamic Consolidation of Virtual Machines with Multi-Agent System

A MAS-Based Cloud Service Brokering System to Respond Security Needs of Cloud Customers

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