An optimized scheduling algorithm for cloud broker using adaptive cost model

Tiwari, Ashish; Nagaraju, A.; Mahrishi, Mehul

doi:10.1109/iadcc.2013.6506810

Cited by 26 publications

(11 citation statements)

References 8 publications

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“…Output of algorithm show in above figure 6 and 7. Figure 6 is showing selection of CSP on basis of rough set workload allocation algorithm [1]. Figure 7 is showing selection of CSP on basis of that paper algorithm (Priority based rough set workload allocation algorithm).…”

Section: Results and Simulationmentioning

confidence: 99%

“…Simulation results show that the proposed algorithm reduces the SLA violations. [1] Infrastructure-as-a-Service, Cloud service providers, such as Amazon EC2 and Rackspace, allow users to lease their computing resources over the Internet, and invest their money into developing and maintaining the infrastructure. Hence, maximizing profit, right pricing, and rightsizing are vital elements to their business.…”

Section: Literature Surveymentioning

confidence: 99%

“…According to user feedback Threshold value would be decide. [1] Threshold value γi represent minimum satisfaction of user for particular attribute and ρ shows the value of attribute provided by particular CSP. Table 2 has found after applying threshold value for each parameter for their respective CSPs.…”

Section: Set Approximation and Rough Setmentioning

confidence: 99%

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Optimal adaptive CSP scheduling on basis of priority of specific service using cloud broker

Srivastava

Singh

2014

2014 9th International Conference on Industrial and Information Systems (ICIIS)

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That paper focus on how to select cloud service provider (CSP) for the user which provide the best service to the user. Selection of the proper CSP depends on the rating of CSP. If rating of CSP is higher than that CSP would be select for the first user and so on. Paper has focused on both rating of CSP and rating of particular attribute of CSP. In this Paper selection of CSP depends on both rating of CSP and rating of particular attribute of CSP. Now a day's lot of cloud service provider present in the market and day by day these number are being exceed. These are following CSP example Amazon elastic computing (EC2) cloud offers virtual machine0.1$ per hour, Google compute cloud offer virtual machine 0.5$ and so on. This is being hard for user to select the proper CSP for the service which provide the best service in low cost and effective quality. Cloud broker work on behalf of user. Users send his requirement and budget to Cloud Broker than its deal with the Cloud Service Provider and select it for user. In this paper Rough set model is being use for selecting the optimal CSP.

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Section: Results and Simulationmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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Optimal adaptive CSP scheduling on basis of priority of specific service using cloud broker

Srivastava

Singh

2014

2014 9th International Conference on Industrial and Information Systems (ICIIS)

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“…Relate work Key point Cost-aware resource scheduling 63-66 63 Allocation cost for task scheduling 64 Multiobjective task scheduling 65 Optimal resource scheduling 66 Workload balancing Efficiency-aware resource scheduling 67-69 67 Task scheduling 68 To enhance the efficiency of resource scheduling 69 Providing the cloud resources dynamically Energy-aware resource scheduling 70-72 70 VM scheduling and allocation of task 71 VM scheduling 72 Reducing power consumption of a data center scheduling to control the order of execution. The tasks are executed in the VM according to a predefined policy sequence.…”

Section: Work Areamentioning

confidence: 99%

Resource management for meteorological service in cloud‐edge computing: A survey

Tang¹,

Zhang

et al. 2020

Trans Emerging Tel Tech

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The ever‐increasing volume of data and the higher demand for real‐time services in the meteorological industry have made the relevant departments face some problems such as how to process data efficiently and deliver services instantly. Hence, by intervening in cloud computing and building meteorological cloud platform, the data are effectively managed and processed which means reliable services could be satisfied. However, a large amount of complex heterogeneous data generated by meteorological stations remote from the cloud center is sent to the cloud for processing to impose bandwidth pressure on the cloud. It will bring substantial access delays in case of frequent access. Meanwhile, the data in the public cloud will encounter security problems for the sensitivity. Therefore, appropriate assistive techniques such as edge computing are urgent to be chosen to solve the aforementioned problems. The collaborative work of edge computing and cloud makes meteorological data sent to the edge nodes for processing, which effectively cuts down the bandwidth pressure, reduces the transmission delay, and advances data security of the cloud. In this paper, we first describe the architecture, classification, and definition of meteorological services in the cloud. Then, the resource management of meteorological services in edge computing and cloud computing is summarized respectively. Furthermore, meteorological applications in cloud computing is introduced. Besides, we explore the future work of meteorological service provisioning in collaborative edge computing and cloud.

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“…So improving current specifications is granted to make sure the required interoperability. For instance, the OGF expertise might be very to accomplish this task [34] [36].…”

Section: Standardizationmentioning

confidence: 99%

Eagle Techniques in Cloud Computational Formulation

2019

IJITEE

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The eagle expresses of cloud computing plays a pivotal role in the development of technology. In this computing world everyone is active so the end users and providers use various applications which are working as a broker for providing and managing the services. The aim of the paper is to solve the problems in such a way that brokers will provide an optimized solution for cloud service providers and the end users. The key role of allocating the efficient resources by making the algorithm which works for the time and cost optimization keeping in consideration of its quality of services and characteristics. These both are affecting the performance of these techniques is a major drawback due to low accuracy and large computational complexity of the algorithms. As per the scenario, the approach of the research is based on the rough set theory (RST). It’s a strategy to found the information revelation and handle the issues like number of parameters (Virtualization, resource sharing, cloud standardization etc). The rough set theory is the new method in cloud service selection so that the best services to provide for cloud users and efficient service improvement for cloud providers.

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An optimized scheduling algorithm for cloud broker using adaptive cost model

Cited by 26 publications

References 8 publications

Optimal adaptive CSP scheduling on basis of priority of specific service using cloud broker

Optimal adaptive CSP scheduling on basis of priority of specific service using cloud broker

Resource management for meteorological service in cloud‐edge computing: A survey

Eagle Techniques in Cloud Computational Formulation

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