Self‐adaptive brainstorming for jobshop scheduling in multicloud environment

Bhatt, Ashutosh; Dimri, Priti; Aggarwal, Ambika

doi:10.1002/spe.2819

Cited by 15 publications

(7 citation statements)

References 35 publications

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“…Task Scheduling Algorithms. Various algorithms have been explored to schedule tasks in a cloud computing environment and these approaches can be further divided into the heuristic approach, hybrid approach, and energy-efficient approach, which are explained in this section [9,10].…”

Section: Task Scheduling In Cloud Computingmentioning

confidence: 99%

Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling

Aggarwal

Kumar

Bhatt³

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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Cloud computing is a procedure of stockpiling as well as retrieval of data or computer services over the Internet that allows all its users to remotely access the data centers. Cloud computing provides all required services to the users, but every platform has its share of pros and cons, and another major problem in the cloud is task scheduling or workflow scheduling. Multiple factors are becoming a challenge for scheduling in cloud computing namely the heterogeneity of resources, tasks, and user priority. User priority has been encountered as the most challenging problem during the last decade as the number of users is increasing worldwide. This issue has been resolved by an advanced encryption standard (AES) algorithm, which decreases the response time and execution delay of the user-request. There are multifarious tasks, for instance, deploying the data on the cloud, that will be executed according to first come first serve (FCFS) and not on the payment basis, which provides an ease to the users. These investigated techniques are 30.21%, 25.20%, 25.30%, 30.25%, 24.26%, and 36.9 8% improved in comparison with the traditional FFOA, DE, ABC, PSO, GA, and ETC, respectively. Moreover, during iteration number 5, this approach is 15.20%, 20.22%, 30.56%, 26.30%, and 36.23% improved than that of the traditional techniques FFOA, DE, ABC, PSO, GA, and ETC, respectively. This investigated method is more efficient and applicable in certain arenas where user priority is the primary concern and can offer all the required services to the users without any interruption.

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Section: Task Scheduling In Cloud Computingmentioning

confidence: 99%

Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling

Aggarwal

Kumar

Bhatt³

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

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“…They expounded multicloud‐aware service composition so that services offered in multiple cloud settings will be interoperable and can be semantically matched. Bhatt et al 33 improve the self‐adaptive brain storm optimization scheme for job shop scheduling in multicloud environments. Like the above articles, our work also contributes to multicloud computing.…”

Section: Related Workmentioning

confidence: 99%

Architecture of a time‐sensitive provisioning system for cloud‐native software

2021

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Application development paradigms and composition of technology services are decisively moving in the direction of hybrid and multi clouds. Enterprises are stitching new cloud-native business models that leverage containerized multi-tier microservice architecture, heterogeneity of cloud deployment models, and diversity of cloud providers. Scalability and resiliency are key components of this new world architecture, but these are also functions of the predictability of provisioning the underpinning compute instances on cloud. Thus, a major challenge to surmount before complex multicloud aware applications can be designed is the problem of unpredictable latencies associated with the provisioning of compute services on cloud. In the first part of this article, we develop a technique for time-sensitive provisioning of virtual compute on demand, while also allowing deprovisioning on demand. Using the technique we propose, a cloud broker will be able to operate on a pool of reserved instances sourced from cloud providers and multiplex them profitably across cloud customers with associated provisioning time guarantees, but without usage commitment restrictions. We articulate this challenge in the form of the Reserved Instance Allocation Problem (RIAP), which we first prove to be NP-hard. We then design a heuristic-based method to solve the intractable RIAP in polynomial time. We evaluate the effectiveness of our heuristic-based mechanism through a combination of deep simulations and practical validation on mainstream public clouds. We demonstrate that our algorithm consistently yields a high profit-to-investment ratio for a broker who seeks to operate a commerce of virtual machines with time-sensitive provisioning. In the second part of this article, we tackle the problem of unpredictable provisioning latencies in bare metal commerce. We build and evaluate an allocation model to calculate optimal supporting bare metal inventory to maximize cost-sensitive fulfillment of bare metal provisioning requests in a time-sensitive manner.

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“…In the last decade, application of scheduling algorithms in the cloud computing environment took the momentum with the aim of improving the resource utilization 22 . Gill and Buyya 23 discussed the resource provisioning for workloads in the parallel computing environment of clouds.…”

Section: Related Workmentioning

confidence: 99%

HEART: Unrelated parallel machines problem with precedence constraints for task scheduling in cloud computing using heuristic and meta‐heuristic algorithms

et al. 2020

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Summary Cloud computing is becoming a profitable technology because of it offers cost‐effective IT solutions globally. A well‐designed task scheduling algorithm ensures the optimal utilization of clouds resources and reducing execution time dynamically. This research article deals with the task scheduling of inter‐dependent subtasks on unrelated parallel computing machines in a cloud computing environment. This article considers two variants of the problem‐based on two different objective function values. The first variant considers the minimization of the total completion time objective function while the second variant considers the minimization of the makespan objective function. Heuristic and meta‐heuristic (HEART) based algorithms are proposed to solve the task scheduling problems. These algorithms utilize the property of list scheduling algorithm of unrelated parallel machine scheduling problem. A mixed integer linear programming (MILP) formulation has been provided for the two variants of the problem. The optimal solution is obtained by solving MILP formulation using A Mathematical Programming Language (AMPL) software. Extensive numerical experiments have been performed to evaluate the performance of proposed algorithms. The solutions obtained by the proposed algorithms are found to out‐perform the existing algorithms. The proposed algorithms can be used by cloud computing service providers (CCSPs) for enhancing their resources utilization to reduce their operating cost.

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Self‐adaptive brainstorming for jobshop scheduling in multicloud environment

Cited by 15 publications

References 35 publications

Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling

Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling

Architecture of a time‐sensitive provisioning system for cloud‐native software

HEART: Unrelated parallel machines problem with precedence constraints for task scheduling in cloud computing using heuristic and meta‐heuristic algorithms

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