Service Measurement Index Research Articles

Application of deterministic, stochastic, and hybrid methods for cloud provider selection

Cloud Computing popularization inspired the emergence of many new cloud service providers. The significant number of cloud providers available drives users to complex or even impractical choice of the most suitable one to satisfy his needs without automation. The Cloud Provider Selection (CPS) problem addresses that choice. Hence, this work presents a general approach for solving the CPS problem using as selection criteria performance indicators compliant with the Cloud Service Measurement Initiative Consortium - Service Measurement Index framework (CSMIC-SMI). To accomplish that, deterministic (CPS-Matching and CPS-DEA), stochastic (Evolutionary Algorithms: CPS-GA, CPS-BDE, and CPS-DDE), and hybrid (Matching-GA, Matching-BDE, and Matching-DDE) selection optimization methods are developed and employed. The evaluation uses a synthetic database created from several real cloud provider indicator values in experiments comprising scenarios with different user needs and several cloud providers indicating that the proposed approach is appropriate for solving the cloud provider selection problem, showing promising results for a large-scale application. Particularly, comparing which approach chooses the most appropriate cloud provider the better, the hybrid one presents better results, achieving the best average hit percentage, dealing with simple and multi-cloud user requests.

An Efficient Hybrid Ranking Method for Cloud Computing Services Based on User Requirements

An increase in the number of cloud services makes service selection a challenging issue for cloud users. It is important to determine the best service that can fulfill user requirements. To this end, this paper proposes a hybrid multiple-attribute decision-making (MADM) model. The proposed method considers service measurement index cloud (SMICloud) structure for qualitative attributes of cloud services as well as user requirements based on fuzzy values to consider vague user requirements. Analytical hierarchy process (AHP) and fuzzy logic are used to rank cloud services. Furthermore, a fuzzy Delphi filtering method is proposed to decrease the execution time of ranking cloud services. In experiments, different aspects such as accuracy, execution time, scalability, and sensitivity analysis are investigated. The results confirm that the proposed method outperforms available methods in terms of execution time and scalability. Furthermore, the experiments show that the proposed method has achieved an accuracy of 96%.

Airport service quality dimension and measurement: a systematic literature review and future research agenda

PurposeThe purpose of this paper is to outline the evolution of research on airport service quality and measurement index of passenger satisfaction to explore opportunities for future research direction.Design/methodology/approachA systematic literature review was conducted involving a total final sample 27 articles published during 2000–2020, the source of the database used in this study is Emerald, ScienceDirect, Harzing's Publish or Perish with API Key based on set of inclusion/exclusion criteria for analysis and synthesis to meet the purpose of the paper.FindingsDimensions of measuring airport service quality are currently based on a process approach. There are eight dimensions of ASQ measurement practiced by the industry, which is different from the five dimensions of service quality measurement generally. There is still a theoretical and empirical gap, so one of the challenges in applying the ASQ measurement dimensions is bridging research with applications in the airport industry. Other findings, research on airport service quality measurement is currently focused on passenger satisfaction. The integration of expectation-disconfirmation theory and service profit chain models can be used in service quality, passenger satisfaction and profitability.Research limitations/implicationsThis paper seeks to contribute to and analyze limited articles on service quality at airports and identify further research areas.Originality/valueThis paper tries to explain the development of research on the dimensions of measuring service quality at airports. The author identifies a gap in airport service quality measurement dimensions used by researchers and the industry. The author believes that this study can provide a comprehensive thought on using airport service quality measurement dimensions for future research.

Budget optimized dynamic virtual machine provisioning in hybrid cloud using fuzzy analytic hierarchy process

Among various deployment models, Hybrid cloud is the preferred model allowing customers to maximize cost savings and performance by leveraging advantage of quick provisioning capabilities of public cloud. However, due to vast diversity of cloud services, today’s struggle for the customers is to evaluate and discover the best fit Cloud Service Provider (CSP) for an efficient Virtual Machine (VM) provisioning in various hybrid cloud contracts of multi cloud environment. Currently there is no framework that allows customers to evaluate CSPs based on application workload, application environment and employ an budget optimized VM provisioning with dynamically varying factors while adhering to Service Level Agreement (SLA). Hence, the proposed VM Provisioning framework is a Multi Criteria Decision-Making (MCDM) model with an objective to provision the VMs from the well suited CSP in hybrid cloud. The framework initially derives weights for each decision-making input criteria considered as Quality of Service (QoS) Service Measurement Index (SMI) attributes using extent analysis method of Fuzzy Analytical processing (FHP). It ranks the CSP by aggregating weights of decision makers among the alternatives. The framework then proposes a budget optimized algorithm called BOPVM to provision VMs in the hybrid cloud based on the order of assigned rank to CSPs. The framework is evaluated and implemented on real time workload among the CSPs – Amazon, Azure and Openstack. The evaluation results shows significant provisioning cost savings of 50% with reduced provisioning time by 65% when compared with existing algorithms.

Qualitative SMI based Cloud Service Selection using Intuitionistic Fuzzy TOPSIS

Cloud Computing allows access to a public resource pool on demand and easy network connection for the same. Due to the popularity and profits of using Cloud Services, many organizations are moving to Cloud .So selecting a suitable and best Cloud Provider is a challenge for all the users. Many ranking approaches had been proposed for solving this multicriteria decision making problem like AHP, TOPSIS etc. But many of the works focused on quantitative QoS attributes .But qualitative attributes are also important in the case of many application scenarios where the user may be more concerned about the qualitative attributes. CSMIC has released Service Measurement Index attributes for effectively comparing the Cloud services. The comparison of Cloud Service providers based on SMI attributes which are qualitative in nature by using a ranking approach that handles fuzziness in the dataset is the objective of this paper. The proposed approach uses the MCDM algorithm called Technique for Order Preference by Similarity to ideal Solution and uncertainty is handled by Intuitionistic fuzzy values. The qualitative SMI attributes are used as criteria for ranking the Cloud Services.

Cloud Service Selection using DEA based on SMI Attributes

Cloud computing allows on-demand access and fast network connection to a shared resource pool. Most companies are switching to Cloud due to the popularity and benefits of using Cloud Services. So finding a suitable and best cloud provider is a challenge for all users. Several ranking methods, such as AHP, TOPSIS, had been suggested to solve this problem by multicriteria decision making techniques. But, many of the works focused on a subset of the main QoS attributes for ranking. Cloud Services Measurement Initiatives Consortium (CSMIC) has released Service Measurement Index attributes for effectively comparing the Cloud services. The comparison of services provided by cloud based on SMI attributes which are qualitative as well as quantitative in nature is studied in this paper by one of the non-parametric methods called Data Envelopment Analysis (DEA) and ranked the cloud services based on the efficiency scores obtained by DEA. The cloud users can select the best suitable Cloud service using the proposed approach that best suit their QoS requirements.

Is underground coal mining causing land degradation and significantly damaging ecosystems in semi‐arid areas? A study from an Ecological Capital perspective

AbstractCoal mining often brings negative impacts to land and eco‐environments. How to quantitatively evaluate this impact at a regional scale, and assess its related influences in mining and nonmining areas from an ecological viewpoint is important and a challenge. The Shendong Mining Area (SMA) at the junction region of Shaanxi Province and Inner Mongolia Autonomous region in northern China is a typical semi‐arid region. Remote sensing (RS) and Geographic Information System (GIS) were used to construct an ecosystem service measurement and evaluation index system in a typical high‐intensity coal mining area. Ecological Capital was estimated using remote sensing and statistical data from 2005, 2010 and 2015. The results show that (a) EC distribution has gradually increased from southeast to northwest (with an average annual growth rate of 2.32%); (b) water and soil conservation are the largest among ecosystem service functions, and the EC per unit area for the shrub ecosystem is the highest; and (c) the EC within the SMA grew steadily with an average annual growth rate of 3.09%. We can conclude that under high‐intensity mining, EC has not decreased, and the EC growth rate in some mining areas were even greater than nonmining areas, indicating that underground mining impacts were under tolerable threshold in this region. Through the remote sensing quantitative evaluation model, this article reveals the impact of regional ecological assets under different mining intensity, and provides an important basis for evaluating regional sustainable development and ecological environment management.

A Unified Framework for User-Preferred Multi-Level Ranking of Cloud Computing Services Based on Usability and Quality of Service Evaluation

As networking and Cloud computing technologies have evolved, a wide range of Cloud services has been introduced by different Cloud service providers. Many organizations and individuals are using these services as a part of their regular work. Thus, the performance of users’ systems is significantly dependent upon the performance of the services they employ. Therefore, it becomes crucial for Cloud users to thoroughly evaluate and compare the available Cloud services to select the best of them. However, different Cloud service models, range of pricing and feature schemes, different performance attributes used by service providers, fuzzy nature of some of the performance attributes, etc. make Cloud service performance analysis and comparison a challenging task. In addition, different user preferences regarding Cloud service attributes make this analysis even more complex. This situation leads to ambiguity and indecisiveness in selecting a Cloud service that best matches the end-user’s needs and thus leads to degraded performance of user’s systems and financial losses. To this end, this article proposes a unified Cloud service measurement index to provide a single comprehensive framework for multi-level evaluation of Cloud services. For a detailed and effective performance evaluation, we identified 8 top-level attributes of Cloud services and 65 detailed key performance indicators to evaluate these attributes. For an analytical ranking of the target Cloud services, we employed “Multi-Attribute Global Inference of Quality”, which considers the hierarchical relationship of performance attributes. Our method considers user preferences for Cloud service attributes in terms of attribute weights and is flexible to select all or only user-preferred Cloud service attributes. We show the application of the proposed framework and the ranking method using a case study.

CROSA: Context‐aware cloud service ranking approach using online reviews based on sentiment analysis

SummaryThe explosion of cloud services over the Internet has raised new challenges in cloud service selection and ranking. The existence of a great variety of offered cloud services made the users think deeply about the most appropriate services that meet their needs and at the same time are adaptable to their context. Nowadays, online reviews are used for the purpose of enhancing the effectiveness of finding useful product information, having impact on the consumers' decision‐making process. In this context, the current paper suggests a context‐aware cloud service ranking approach using online reviews and based on sentiment analysis (CROSA). Its main objective is to ease the cloud service selection. The CROSA approach analyzes sentiments associated with service measurement index (SMI)–based service properties for each alternative cloud service. Moreover, it enhances the cloud service decision‐making by supporting fuzzy sentiments through the intuitionistic fuzzy set theory and PROMETHEE II. The experimental results presented in this paper show that this approach is efficient and performing.

Framework for ranking the cloud service providers of federated cloud architecture using probability ranking methodology

To select the best cloud provider in federated cloud architecture. It is also aimed to resolve various problems faced by the user and providers in cloud environment. The proposed innovation method shortlists the cloud service provider based on the quality of service (QoS) and SLA parameters. It can be used to select the best possible service provider using service measurement index (SMI). These SMI parameters are designed and implemented by Cloud Service Measurement Index Consortium (CSMIC). In this paper, a unique provider discovery algorithm and new classical probability ranking principle technique (NCPRPT) model is proposed in the modified federated architecture and then the performance is evaluated. Users can select the optimum cloud provider according to the required levels of services is very difficult and time consuming. This federated architecture can be used to the new ranking mechanism for ranking the providers using classical probability ranking on the basis of the SMI parameters. All the shortlisted providers are examined and then ranking is done on the basis of the present and past values of SMI.

Framework for ranking the cloud service providers of federated cloud architecture using probability ranking methodology

To select the best cloud provider in federated cloud architecture. It is also aimed to resolve various problems faced by the user and providers in cloud environment. The proposed innovation method shortlists the cloud service provider based on the quality of service (QoS) and SLA parameters. It can be used to select the best possible service provider using service measurement index (SMI). These SMI parameters are designed and implemented by Cloud Service Measurement Index Consortium (CSMIC). In this paper, a unique provider discovery algorithm and new classical probability ranking principle technique (NCPRPT) model is proposed in the modified federated architecture and then the performance is evaluated. Users can select the optimum cloud provider according to the required levels of services is very difficult and time consuming. This federated architecture can be used to the new ranking mechanism for ranking the providers using classical probability ranking on the basis of the SMI parameters. All the shortlisted providers are examined and then ranking is done on the basis of the present and past values of SMI.

A Hybrid Model is Proposed Based in The Combination of Genetic and MAFS in Cloud Environment

Cloud computing is being heralded as an important trend in information technology throughout the world. Data security has a major issue in cloud computing environment; An intrusion detection system (IDS) is a component that helps to detect various types of malicious network traffic which cannot be detected by a conventional firewall. Many IDS have been developed based on machine learning techniques. In recent growth, advanced detection approaches created by combining or integrating multiple learning techniques have shown better detection performance than general single learning technique. The feature representation method is an important pattern classifier that facilitates correct classifications, however, there have been very few related studies focusing how to extractor representative features for normal connections and effective detection of attacks. The objective of this paper is to suggest new security mechanisms using various trust approaches in broker based federated cloud architecture, ranking the providers with the help of regression tree approach using Service Measurement Index security attributes and new hybrid computation intelligence built on the combination of genetic with Artificial Fish Swarm in Intrusion Detection system.

Integration of analytic network process with service measurement index framework for cloud service provider selection

SummaryThe growing popularity of cloud services has attracted large number of business organizations to enter into the cloud market. The varied availability of cloud service providers, for various services, leaves the cloud customers into skepticism. Service measurement index (SMI) framework, which is based on the analytic hierarchical process, is developed for the ranking of cloud services. As there are multiple dependencies among the criteria for the selection of cloud service provider, analytic hierarchical process formulation is not efficient. In the proposed work, analytic network process that captures the cloud service‐provider selection problem in a more realistic way is integrated in the ranking component of SMI framework. In this method, interactions among the criteria are used for the ranking of the cloud services. Few new criterions are also added into the existing SMI framework to make the framework more effective. A case study is conducted to analyze the proposed technique for the cloud provider selection.

Broker based trust architecture for federated healthcare cloud system

AbstractCloud healthcare is the challenging, growing exponentially and heterogeneous technical environment involving various roles such as medical practitioners, pharmacists, patients and IT professionals to access the information through different technologies and types of devices. In this paper, federated healthcare cloud broker architecture is proposed where the independent health service providers can be integrated together to form a large healthcare federation, fulfill the requirements of users by sharing the available resources at different locations with affordable prices and provides Quality of Service (QoS) to all end users. Different trust mechanisms such as Policy based trust; SLA verification trust and reputation based trust are computed to ensure the security and privacy of the users, who accessing the services in the proposed architecture. Service Measurement Index (SMI) attributes suggested by the Cloud Service Index Measurement Consortium (CSIMC) used to calculate trust values of the healt...

Application based brokering algorithm for optimal resource provisioning in multiple heterogeneous clouds

In recent years, adoption of cloud computing for computational needs is growing significantly due to various factors such as no upfront cost and access to latest service. In general, cloud infrastructure providers offer a wide range of services with different pricing models, instance types and a host of value-added features. Efficient selection of cloud services constitutes significant management challenges for cloud consumer, which is tedious and involves large information processing. To overcome this, the cloud brokers provide resource provisioning options that ease the task of choosing the best services based on consumers requirements and also provide a uniform management interface to access cloud services. This paper proposes a novel cloud brokering architecture that provides an optimal deployment plan for placement of virtual resources in multiple clouds. The objective of the deployment plan is to select the best cloud services with optimal cost, taking into account various attributes defined in service measurement index (SMI) with additional physical and logical constraints. The proposed cloud brokering architecture has been modeled using mixed integer programming formulation and Benders decomposition algorithm to solve efficiently. Efficacy of the proposed algorithm has been verified by extensive numerical studies and sensitivity analysis.

Semantic‐based representation, enrichment and computation of product breakdown structures

AbstractProduct breakdown structures (PBS) are considered a cornerstone in the development of safety critical systems. In this context, the dramatic increase of the amount of applications, management platforms or services imply a tangled environment of data formats, models or protocols that prevent a proper re‐use of data and information resources. Recent times have seen the development of the Open Services for Lifecycle Collaboration (OSLC) initiative to exchange domain‐based knowledge under a common and shared data model (RDF) and to access services under web‐based protocols. The present paper introduces a method and a model to represent a PBS applying a profile‐based approach using RDF‐based vocabularies. Moreover, a hybrid method to validate data, information and structure of a PBS is also introduced. Finally, a case study is conducted to compare traditional PBS management methods to a Linked Data based PBS using the Analytical Hierarchy Process (AHP) and the Service Measurement Index (SMI).

An Enhanced Quality Model for IaaS Provider with Non-Quantifiable Key Performance Indicators

 Abstract—Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources Cloud computing is significant because it encapsulates a range of different technologies that have developed through the history of commercial computing. The model analyzed is known as Service Measurement Index (SMI) Model, which is a set of business-relevant Key Performance Indicators (KPI's) that provide a standardized method for measuring and comparing a business service regardless of whether that service is internally provided or sourced from an outside company. The SMI model analyzed is only applicable to the quantifiable attributes, thus in this research, we have concentrated on the Non-quantifiable attributes (KPIs) which, once incorporated, would not only enrich the customer experience but also provide more accurate customer results together with improved customer satisfaction.