A Novel-Scheduling Algorithm for Cloud Computing based on Fuzzy Logic

Mehranzadeh, Amin; Hashemi, Seyyed Mohsen

doi:10.5120/ijais13-450939

Cited by 12 publications

(4 citation statements)

References 8 publications

(7 reference statements)

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“…The algorithm results are compared with the first fit (FF) and round robin (RR) algorithms, which have higher GPA performance than FF and RR. Mehranzadeh et al [8] proposed fuzzy logic for task scheduling. This scheduling method is capable of scheduling data center VMs.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Security Model for Cloud based Task Scheduling

Devi¹,

Paulraj²,

Muthusenthil³

2020

KSII TIIS

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Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms

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Section: Related Workmentioning

confidence: 99%

Deep Learning Based Security Model for Cloud based Task Scheduling

Devi¹,

Paulraj²,

Muthusenthil³

2020

KSII TIIS

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“…The output of such a fuzzy controller will be the virtual machine id that is assigned to that of the host. By using the scheduling scheme, these virtual machines that are fairly assigned to that of the host in accordance to the time slot of the waiting in the queue along with their precedence [19]. In case of simulation, every task will have the precedence value from 0 to 3 and this AGE value will range between 0 and 3 for four different virtual machines.…”

Section: Proposed Fuzzymentioning

confidence: 99%

A fuzzy energy and security aware scheduling in cloud

Swamy¹,

Mandapati²

2017

IJET

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The cloud computing is the one that deals with the trading of the resources efficiently in accordance to the user's need. A Job scheduling is the choice of an ideal resource for any job to be executed with regard to waiting time, cost or turnaround time. A cloud job scheduling will be an NP-hard problem that contains n jobs and m machines and every job is processed with each of these m machines to minimize the make span. The security here is one of the top most concerns in the cloud. In order to calculate the value of fitness the fuzzy inference system makes use of the membership function for determining the degree up to which the input parameters that belong to every fuzzy set is relevant. Here the fuzzy is used for the purpose of scheduling energy as well as security in the cloud computing.

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“…The authors emphasize that there is a need of measuring and evaluating cloud performance to help users in making their decisions. Fuzzy logic in Cloud platforms: Many researchers have attempted in using fuzzy logic for scheduling resources [37] [38] [39]. Amin et.…”

Section: Significance and Related Workmentioning

confidence: 99%

“…al. [37] have presented and evaluated a new scheduling algorithm that is an efficient technique for scheduling virtual machines between data centers using fuzzy logic. A similar work [38] focuses on the allocation and scheduling of resources as well as improving the reliability of cloud computing.…”

Section: Significance and Related Workmentioning

confidence: 99%

Trusted resource allocation in volunteer edge-cloud computing for scientific applications

Pandey¹

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Data-intensive science applications in fields such as e.g., bioinformatics, health sciences, and material discovery are becoming increasingly dynamic and demanding with resource requirements. Researchers using these applications which are based on advanced scientific workflows frequently require a diverse set of resources that are often not available within private servers or a single Cloud Service Provider (CSP). For example, a user working with Precision Medicine applications would prefer only those CSPs who follow guidelines from HIPAA (Health Insurance Portability and Accountability Act) for implementing their data services and might want services from other CSPs for economic viability. With the generation of more and more data these workflows often require deployment and dynamic scaling of multi-cloud resources in an efficient and high-performance manner (e.g., quick setup, reduced computation time, and increased application throughput). At the same time, users seek to minimize the costs of configuring the related multi-cloud resources. While performance and cost are among the key factors to decide upon CSP resource selection, the scientific workflows often process proprietary/confidential data that introduces additional constraints of security postures. Thus, users have to make an informed decision on the selection of resources that are most suited for their applications while trading off between the key factors of resource selection which are performance, agility, cost, and security (PACS). Furthermore, even with the most efficient resource allocation across multi-cloud, the cost to solution might not be economical for all users which have led to the development of new paradigms of computing such as volunteer computing where users utilize volunteered cyber resources to meet their computing requirements. For economical and readily available resources, it is essential that such volunteered resources can integrate well with cloud resources for providing the most efficient computing infrastructure for users. In this dissertation, individual stages such as user requirement collection, user's resource preferences, resource brokering and task scheduling, in lifecycle of resource brokering for users are tackled. For collection of user requirements, a novel approach through an iterative design interface is proposed. In addition, fuzzy interference-based approach is proposed to capture users' biases and expertise for guiding their resource selection for their applications. The results showed improvement in performance i.e. time to execute in 98 percent of the studied applications. The data collected on user's requirements and preferences is later used by optimizer engine and machine learning algorithms for resource brokering. For resource brokering, a new integer linear programming based solution (OnTimeURB) is proposed which creates multi-cloud template solutions for resource allocation while also optimizing performance, agility, cost, and security. The solution was further improved by the addition of a machine learning model based on naive bayes classifier which captures the true QoS of cloud resources for guiding template solution creation. The proposed solution was able to improve the time to execute for as much as 96 percent of the largest applications. As discussed above, to fulfill necessity of economical computing resources, a new paradigm of computing viz-a-viz Volunteer Edge Computing (VEC) is proposed which reduces cost and improves performance and security by creating edge clusters comprising of volunteered computing resources close to users. The initial results have shown improved time of execution for application workflows against state-of-the-art solutions while utilizing only the most secure VEC resources. Consequently, we have utilized reinforcement learning based solutions to characterize volunteered resources for their availability and flexibility towards implementation of security policies. The characterization of volunteered resources facilitates efficient allocation of resources and scheduling of workflows tasks which improves performance and throughput of workflow executions. VEC architecture is further validated with state-of-the-art bioinformatics workflows and manufacturing workflows.

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A Novel-Scheduling Algorithm for Cloud Computing based on Fuzzy Logic

Cited by 12 publications

References 8 publications

Deep Learning Based Security Model for Cloud based Task Scheduling

Deep Learning Based Security Model for Cloud based Task Scheduling

A fuzzy energy and security aware scheduling in cloud

Trusted resource allocation in volunteer edge-cloud computing for scientific applications

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