scholarly journals Efficient and Optimal Service Component Distribution across Multiple Clouds

2018 ◽  
Vol 6 (3) ◽  
pp. 44 ◽  
Author(s):  
Driss Riane ◽  
Ahmed Ettalbi
Keyword(s):  
Cloud Services ◽  
Main Step ◽  
Service Component ◽  
Component Distribution ◽  
Cloud Broker ◽  
Optimal Service ◽  
Multiple Clouds ◽  
Lock In ◽  
Better Than

<span class="fontstyle0">Cloud computing technology is one of the key considerations for business willing to access to different cloud services over the Internet and to benefit from the diversity of IaaS offers and pricing models. Although several solutions are available in the market, there are still some issues to solve. The main important aspect to address is the user’s request complexity, the vendor lock-in risk and the SLA fulfillment. In this paper, we propose a Multi-Cloud Broker called MCB that allows an efficient and optimal service component distribution among different clouds in flexible and dynamic infrastructure provisioning environment, in order to achieve better Quality of Service and cost efficiency. The request partitioning is the main step of our approach, this step is performed using Gomory-Hu tree based algorithm. Our simulation results show how our algorithm is better than existing partitioning algorithms in terms of running time.</span>

scholarly journals BSS: A Brokering Model for Service Selection using Integrated Weighting approach in Federated Cloud

2021 ◽  
Author(s):  
Sameer Singh Chauhan ◽  
Emmanuel S Pilli ◽  
R C Joshi
Keyword(s):  
Service Selection ◽  
Cloud Service ◽  
Entropy Method ◽  
Cloud Services ◽  
Total Weight ◽  
Reversal Problem ◽  
Cloud Broker ◽  
Subjective Weight ◽  
Access Patterns ◽  
Application Requirements

Abstract Federated Cloud is a multi-cloud platform that integrates various cloud providers either through standardization or an agreement. The services offered by different federation members possess different access patterns, similar characteristics, varied performance levels, different costs, etc. This heterogeneity creates a challenging task for users to decide a suitable service as per their application requirements. Cloud broker, an inter-mediator is required in service and federation management to help both service provider and users. Cloud broker has to store all the information related to services and feedback of users on those services in order to provide the best services to end-users. Brokering model for service selection (BSS) has been proposed which employs integrated weighting approach in cloud service selection. Subjective and objective weights of QoS attributes are combined to compute integrated total weight. Subjective weight is obtained from users’ feedback on QoS attributes of a cloud service while objective weight is computed from benchmark tested data of cloud services. Users' feedback and preferences given to QoS parameters are employed in subjective weight computation. Objective weight is computed using Shannon's Entropy method. Total weight is obtained by combining subjective and objective weights. BSS method is employed to rank cloud services. Simulation with a case study on real dataset has been done to validate the effectiveness of BSS. The obtained results demonstrate the consistency of model for handling rank reversal problem and provides better execution time than other state-of-the art solutions.

scholarly journals A Semi-Markov decision model-based brokering mechanism for mobile cloud market

2021 ◽  
Author(s):  
Elena Degtiareve
Keyword(s):  
Service Providers ◽  
Cloud Service ◽  
Cloud Services ◽  
Iteration Algorithm ◽  
Value Iteration ◽  
Security Risk ◽  
System Cost ◽  
Cloud Broker ◽  
Markov Decision ◽  
Markov Decision Model

As the multitude and complexity of cloud market increases the evaluation and selection of cloud services becomes a burdensome task for the users. With the increased rise of available services from various Cloud Service Providers (CSP), the role of cloud brokers becomes more and more important. In this thesis, the challenge of optimally allocating multiple cloud system resources to multiple mobile user’s requests with different requirements is investigated and an optimal Cloud Broker model is proposed. The cloud brokering mechanism is formulated as a Semi-Markov Decision Process (SMDP) model under the average system cost criteria, taking into consideration the cost of the occupying computing resources, the communication costs, the request traffic, and some security risk degrees and resource requirements from the multiple mobile users. Through minimizing the overall system cost, the optimal resource allocation policy is derived by using the Value Iteration Algorithm. Simulation results are provided, demonstrating the efficiency of the proposed Cloud Broker design.

scholarly journals BSS: a brokering model for service selection using integrated weighting approach in cloud environment

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Sameer Singh Chauhan ◽  
Emmanuel S. Pilli ◽  
R. C. Joshi
Keyword(s):  
Service Management ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Services ◽  
Total Weight ◽  
Cost Models ◽  
Reversal Problem ◽  
Cloud Broker ◽  
Subjective Weight ◽  
Cloud Providers

AbstractCloud providers shares their resources and services through collaboration in order to increase resource utilization, profit and quality of services. The offered services with different access patterns, similar characteristics, varied performance levels and cost models create a heterogeneous service environment. It becomes a challenging task for users to decide a suitable service as per their application requirements. Cloud broker, an inter-mediator is required in service management to help both cloud providers and users. Cloud broker has to store all the information related to services and feedback of users on those services in order to provide the best services to end-users. Brokering model for service selection (BSS) has been proposed which employs integrated weighting approach in cloud service selection. Subjective and objective weights of QoS attributes are combined to compute integrated total weight. Subjective weight is obtained from users’ feedback on QoS attributes of a cloud service while objective weight is computed from benchmark tested data of cloud services. Users’ feedback and preferences given to QoS parameters are employed in subjective weight computation. Objective weight is computed using Shannon’s Entropy method. Total weight is obtained by combining subjective and objective weights. BSS method is employed to rank cloud services. Simulation with a case study on real dataset has been done to validate the effectiveness of BSS. The obtained results demonstrate the consistency of model for handling rank reversal problem and provides better execution time than other state-of-the art solutions.

scholarly journals A Semi-Markov decision model-based brokering mechanism for mobile cloud market

2021 ◽  
Author(s):  
Elena Degtiareve
Keyword(s):  
Service Providers ◽  
Cloud Service ◽  
Cloud Services ◽  
Iteration Algorithm ◽  
Value Iteration ◽  
Security Risk ◽  
System Cost ◽  
Cloud Broker ◽  
Markov Decision ◽  
Markov Decision Model

As the multitude and complexity of cloud market increases the evaluation and selection of cloud services becomes a burdensome task for the users. With the increased rise of available services from various Cloud Service Providers (CSP), the role of cloud brokers becomes more and more important. In this thesis, the challenge of optimally allocating multiple cloud system resources to multiple mobile user’s requests with different requirements is investigated and an optimal Cloud Broker model is proposed. The cloud brokering mechanism is formulated as a Semi-Markov Decision Process (SMDP) model under the average system cost criteria, taking into consideration the cost of the occupying computing resources, the communication costs, the request traffic, and some security risk degrees and resource requirements from the multiple mobile users. Through minimizing the overall system cost, the optimal resource allocation policy is derived by using the Value Iteration Algorithm. Simulation results are provided, demonstrating the efficiency of the proposed Cloud Broker design.

scholarly journals Decision Making Models on the Market of Cloud Services

2021 ◽  
pp. 53-64
Author(s):  
Vasyl Gorbachuk ◽  
Maksym Dunaievskyi ◽  
Seit-Bekir Suleimanov ◽  
Lyudmyla Batih ◽  
Denys Symonov
Keyword(s):  
Data Storage ◽  
Capacity Planning ◽  
Business Models ◽  
Optimization Problems ◽  
Cloud Service ◽  
Cloud Services ◽  
Third Party ◽  
Management Tools ◽  
Cloud Broker ◽  
Service Broker

Introduction. Optimization can be applied in developing profitability management tools for a cloud service broker working according to a certain business model. On behalf of the managing telecommunications holding company (telecommunications operator), this broker integrates, aggregates and configures software and data storage services of third-party Internet software vendors. Such a broker receives only fixed commissions from this company, based on the subscription fee, but does not pay royalties to an Internet software vendor and does not receive payments from the sale of service packages. The purpose. The cloud broker faces the problem of limited human resources required to carry out the relevant legal, technical and economic activities. In addition, the broker faces the problem of uncertainty in sales, service prices, the share of resource use, or the risk of losing operational and financial goals. Results. To run a broker?s business efficiently, one needs to find services and their bundles that increase profitability and reduce financial risk by solving certain optimization problems. Information on such services is needed to support negotiations on fixed and variable commissions, as well as to prioritize services and their packages to be provided. Thus, for the cloud services broker, both profitability management tools and services portfolio development tools are useful. In general, a cloud service broker is an organization that negotiates the relationships between cloud service clients and Internet software vendors. Cloud broker can be created on the basis of different business models regarding the type of service (platform, infrastructure, software), type of clients (enterprise, household), functions performed (identity management, accounting, billing, location, etc.), the degree of rebranding, measures of aggregation of services and other criteria. Conclusions. Different cloud brokers have different attitudes to choice of important solutions for their businesses. Solutions can relate to pricing, capacity planning and utilization in combination with service quality, security, scalability and other issues. Keywords: optimization, portfolio, uncertainty, Boolean variables, revenue generation.

scholarly journals Profit Maximization for Cloud Broker in Cloud Computing

2021 ◽  
Vol 9 (VII) ◽  
pp. 354-359
Author(s):  
Prof. M. S. Namose
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Service Providers ◽  
Current System ◽  
Profit Maximization ◽  
Cloud Service ◽  
Cloud Services ◽  
Market Demand ◽  
Cloud Broker ◽  
The Cost

As cloud computing evolves, more and more applications are moving to the cloud. Cloud brokers are are like Middlemen between cloud service providers and cloud users. Thus, cloud brokers can significantly reduce the cost of consumers. In addition to reducing the cost per user, the cloud broker can also accommodate the price difference between on-demand virtual machines and dedicated virtual machines. The problem with the current system is that if many customers request a large amount of cloud services at once, the cloud service broker cannot purchase enough cloud services from CSP to meet the needs of all customers. Then there is a peak demand problem where the customer cannot complete the job. As a result, dynamic conditions not only lead to financial problems, but can also negatively impact the customer experience. To solve this problem, the system focuses on guaranteed quality of service for all requests, reduces waste of resources, increases security and maximizes revenue. All jobs are scheduled by the job scheduler and assigned to different VMs in a centralized way. Many factors such as market demand, application volume, SLA, service rental cost, etc. are taken into account to formulate an optimal configuration problem of profit maximization.

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