Equilibrium in cloud computing market

Mazalov, Vladimir V.; Lukyanenko, Andrey; Luukkainen, Sakari

doi:10.1016/j.peva.2015.07.002

Cited by 10 publications

(12 citation statements)

References 23 publications

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“…Aiming at the problem of that load balancing is not fully taken into account and scheduling error is large when using the current algorithm to establish the massive data balance scheduling model in the cloud computing environment [14,15], a balance scheduling method for massive data in cloud computing environment based on the improved clonal selection algorithm is proposed. According to the principle of clonal selection, the attributes of the massive data in the cloud computing environment are defined as antigens, and the antibody encoding mode of the massive data balance scheduling is designed.…”

Section: Optimization Principle Of Massive Data Balance Scheduling Inmentioning

confidence: 99%

Massive Data Balance Scheduling in Cloud Computing Environment

2019

IJOMAM

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At present, in the cloud computing environment, the massive data has different attribute characteristics, which cannot be made balance scheduling. There are often long scheduling time, and the balance of CPU, memory and bandwidth is poor. Based on the improved clonal selection algorithm, the balance scheduling method for massive data in cloud computing environment is proposed. Firstly, the massive data balance loading and scheduling, as well as the minimized task execution time are taken as the target, to construct the data scheduling model in the cloud computing environment. According to the principle of clonal selection, the attributes of the massive data in the cloud computing environment are defined as antigens, and the antibody encoding mode of the massive data balance scheduling is designed. Meanwhile, the individual data with the higher affinity to the antigens are selected from the antibody for mutation treatment, and the balance of massive data scheduling is quantified in cloud computing environment. After obtaining the quantization function, the control parameters of massive data scheduling are analyzed and studied, so as to build a balance scheduling model for massive data in cloud computing environment. The simulation results show that the proposed algorithm can make effectively balance scheduling for the massive data in the cloud computing environment, its scheduling time is short, the balance of CPU, memory and bandwidth is higher and the reliability is strong.

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Section: Optimization Principle Of Massive Data Balance Scheduling Inmentioning

confidence: 99%

Massive Data Balance Scheduling in Cloud Computing Environment

2019

IJOMAM

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“…The other issue is when IT services used by CPA firms and audit clients come from different vendors, meaning there is incompatibility between different systems and products. The interoperability between user interface standardizations and APIs (application programming interfaces) over different clouds presents a major hurdle for the introduction and functioning of cloud audit platforms (Lee, Park, & Yang, 2015;Mazalov, Lukyanenko, & Luukkainen, 2015). CCIF (Cloud Computing Interoperability Forum), as initiated by ITU-T (International Telecommunication Union's Telecommunication Standardization Sector), aims to create a unified cloud interface (UCI) and develops an open and standardized cloud interface for the integration of APIs (Lee, Park, & Yang, 2013).…”

Section: Availabilitymentioning

confidence: 99%

“…(2011) Cloud auditing providers provide an interface to access cloud data, and this interface is compatible with the interfaces from other providers. Lee et al (2013Lee et al ( , 2015; Mazalov et al (2015) Processing integrity (C)…”

Section: Questionnaire Development and Data Collection Proceduresmentioning

confidence: 99%

Cpa Firm’s Cloud Auditing Provider for Performance Evaluation and Improvement: An Empirical Case of China

Chen

Tzeng

2018

Technological and Economic Development of Economy

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While CPA (Certified Public Accountant) firms utilize cloud auditing technologies to generate auditing reports and convey information to their clients in the Internet of Things (IoT) Era, they often cannot determine whether cloud auditing is a secure and effective form of communication with clients. Strategies related to cloud auditing provider evaluation and improvement planning are inherently multiple attribute decision making (MADM) issues and are very important to the auditor industry. To overcome these problems, this paper proposes an evaluation and improvement planning model to be a reference for CPA firms selecting the best cloud auditing provider, and illustrates an application of such a model through an empirical case study. The DEMATEL (decisionmaking trial and evaluation laboratory) approach is first used to analyze the interactive influence relationship map (IIRM) between the criteria and dimensions of cloud auditing technology. DANP (DEMATEL-based ANP) is then employed to calculate the influential weights of the dimensions and criteria. Finally, the modified VIKOR method is utilized to provide improvement priorities for performance cloud auditing provider satisfaction. Based on expert interviews, the recommendations for improvement priorities are privacy, security, processing integrity, availability, and confidentiality. This approach is expected to support the auditor industry to systematically improve their cloud auditing provider selection.

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“…The model brings four benefits: (1) user's fair use of computing resources [8], (2) adjusting the balance of supply and demand of resources in cloud computing. When demand exceeds supply, raising the price of resources could help to reduce the number of users and tasks, on the other hand, when supply exceeds demand, lower price could help to TELKOMNIKA ISSN: 1693-6930 …”

Section: Cloud Computing Scheduling Market Modelmentioning

confidence: 99%

Research on Batch Scheduling in Cloud Computing

Jiao¹,

Yu²

2016

TELKOMNIKA

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In the existing cloud computing environment, batch scheduling strategies mainly focus on the management of resources allocation. This paper provides the task scheduling algorithm based on service quality which fully considers priority and scheduling deadline. IntroductionCloud computing is a new business model, and it is a product of information technology [1]. In cloud computing market, user scale is very large, and the QoS(Quality of Service) requests of each task are not identical. These factors make task schedule in cloud computing very complicated. So it is a technical difficulty to schedule the massive tasks in cloud computing in order to get a better scheduling results with the constrain of budget and deadline.The main batch scheduling algrithms are Min-min [2] and DBCT [3] (under the deadline and budget constrain time optimization algorithm). The idea of Min-min algorithm is that the best resources will be allocated to the minimum task, thus the system throughput rate can be improved and the minimum time of the job can be obtained. While the idea of DBCT is to schedule the task on the resource that can complete the task earliest, also request the cost no more than user's budget and the finish time no more than deadline. With the popularity of cloud computing, some users with less budget may also want to use cloud computing; also there are some tasks with sufficient budget and intensive demand of deadline. For these cases, the number of completed tasks of Min-min and DBCT algorithms will be less. There are also a lot of people put forward other algorithms, but some did not consider the deadline [4], some did not consider the user's budget [5]; they did not balance the relationship between the deadline and the budget.From the perspective of QoS, this paper proposes DBCMNCT (deadline and budget constrained maximized number of completed tasks scheduling algorithm) algorithm in order to meet the budget and deadline constrains. Compared with Min-min and DBCT algorithm, DBCMNCT algorithm can accomplish higher priority tasks better, at the same time the new algorithm can maximize the number of completed tasks. So the user's demands can be meet; on the other hand, the cloud computing can attract more consumers and get more benefits.

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Equilibrium in cloud computing market

Cited by 10 publications

References 23 publications

Massive Data Balance Scheduling in Cloud Computing Environment

Massive Data Balance Scheduling in Cloud Computing Environment

Cpa Firm’s Cloud Auditing Provider for Performance Evaluation and Improvement: An Empirical Case of China

Research on Batch Scheduling in Cloud Computing

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