Efficient dynamic resource allocation method for cloud computing environment

Belgacem, Ali; Beghdad-Bey, Kadda; Nacer, Hassina; Bouznad, Sofiane

doi:10.1007/s10586-020-03053-x

Cited by 40 publications

(17 citation statements)

References 39 publications

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“…(iii) After a specified time interval, VMs waiting for execution are allotted to the servers and energy consumption is calculated for each run. (iv) Once all VMs complete the tasks, resource utilization and makespan [28] value are calculated and can be used for improving the performance of servers. (v) EM switcher algorithm has the extra property of checking all other dynamic algorithms for list of VMs and implementing the algorithm which uses minimum energy.…”

Section: Proposed Approachesmentioning

confidence: 99%

Enhanced Virtualization-Based Dynamic Bin-Packing Optimized Energy Management Solution for Heterogeneous Clouds

Gupta

et al. 2022

Mathematical Problems in Engineering

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Cloud computing provides unprecedented advantages of using computing resources with very less efforts and cost. The energy utilization in cloud data centers has forced the cloud service providers to raise the expense of using its services and has increased the carbon footprints in the environment. Many static bin-packing algorithms exist which can reduce energy by some percentage, but with new era of digitization, advanced and dynamic techniques are required which can serve heterogeneous users and random users’ requests. Thus, in this paper, two new dynamic best-fit decreasing-based bin-packing algorithms are proposed wherein the first technique is for service providers and focuses on increasing server utilization and the second approach acts as a switcher to harness best results among all algorithms. Both techniques deliberately achieve high performance in terms of total energy consumption, resource utilization, and makespan along with serving continuous and varying requests from customers. The simulations are performed using Java. The results exhibited that DEE-BFD can escalate resource utilization by 96% and EM switcher can reduce total energy consumption by 49% and reduce makespan by 56%.

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Section: Proposed Approachesmentioning

confidence: 99%

Enhanced Virtualization-Based Dynamic Bin-Packing Optimized Energy Management Solution for Heterogeneous Clouds

Gupta

et al. 2022

Mathematical Problems in Engineering

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“…e results of EEWS were better than both HCRO and MPQGA, in terms of makespan, conserved energy, and fitness value. Belgacem et al [37] proposed a dynamic resource allocation model to meet customer demand for resources with improved responsiveness. eir model also proposes a multiobjective search algorithm called the spacing multiobjective ant lion algorithm (S-MOAL) to minimize both the makespan and the cost of using virtual machines.…”

Section: Related Workmentioning

confidence: 99%

“…Ben Alla et al [33] EATSD Reduce the energy consumption of the cloud resources and optimize the makespan under the deadlines constraints Peng et al [34] DQN e framework for trade-off optimization energy consumption and task makespan Yao et al [35] EnMORL Simultaneously minimize the makespan and energy consumption while meeting the budget constraint Singh et al [36] EEWS Minimizing makespan and maximizing energy conservation while scheduling workflow Belgacem et al [37] S-MOAL Minimize both the makespan and the cost of using virtual machines Importance factor determined by the end-user or service provider task j mki Task j assigned to core i of the virtual machine k in the node m time init j Initial time of the task j time setup j Setup time of the task j time end j End time of the task j T Prej A set of prerequisite tasks for the task j time pre j Start time of the task j prerequisite time end pre End time of task j prerequisites time transfe Data transfer times required to perform a task that is outside the virtual machine time exec j Processing time of a task j in the core i of the virtual machine k of the node m e taskmki Energy consumed by a task j e transfer mk Energy consumed by the data transfer of the task j e vmk Energy consumed by the virtual machine k e task mk Energy consumption by various tasks j in a virtual machine k e nm Energy consumption per node e net Average data transfer power in cloud infrastructure…”

Section: Problem Modelsmentioning

confidence: 99%

Energy-Aware Scheduling of Workflow Using a Heuristic Method on Green Cloud

Peng

Barzegar

Yarahmadi³

et al. 2020

Scientific Programming

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Energy consumption has been one of the main concerns to support the rapid growth of cloud data centers, as it not only increases the cost of electricity to service providers but also plays an important role in increasing greenhouse gas emissions and thus environmental pollution, and has a negative impact on system reliability and availability. As a result, energy consumption and efficiency metrics have become a vital issue for parallel scheduling applications based on tasks performed at cloud data centers. In this paper, we present a time and energy-aware two-phase scheduling algorithm called best heuristic scheduling (BHS) for directed acyclic graph (DAG) scheduling on cloud data center processors. In the first phase, the algorithm allocates resources to tasks by sorting, based on four heuristic methods and a grasshopper algorithm. It then selects the most appropriate method to perform each task, based on the importance factor determined by the end-user or service provider to achieve a solution designed at the right time. In the second phase, BHS minimizes the makespan and energy consumption according to the importance factor determined by the end-user or service provider and taking into account the start time, setup time, end time, and energy profile of virtual machines. Finally, a test dataset is developed to evaluate the proposed BHS algorithm compared to the multiheuristic resource allocation algorithm (MHRA). The results show that the proposed algorithm facilitates 19.71% more energy storage than the MHRA algorithm. Furthermore, the makespan is reduced by 56.12% in heterogeneous environments.

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“…The cloud is a large pool of shared resources with free access in a dynamic, scalable manner and with guaranteed quality of service [4][5][6]. It aims to provide users with resources for an agreed period of time and satisfy both the needs of users and service providers.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost

Belgacem

Beghdad-Bey

2021

Cluster Comput

Self Cite

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Recently, modern businesses have started to transform into cloud computing platforms to deploy their workflow applications. However, scheduling workflow under resource allocation is significantly challenging due to the computational intensity of the workflow, the dependency between tasks, and the heterogeneity of cloud resources. During resource allocation, the cloud computing environment may encounter considerable problems in terms of execution time and execution cost, which may lead to disruptions in service quality given to users. Therefore, there is a necessity to reduce the makespan and the cost at the same time. Often, this is modeled as a multi-objective optimization problem. In this respect, the fundamental research issue we address in this paper is the potential trade-off between the makespan and the cost of virtual machine usage. We propose a HEFT-ACO approach, which is based on the heterogeneous earliest end time (HEFT), and the ant colony algorithm (ACO) to minimize them. Experimental simulations are performed on three types of realworld science workflows and take into account the properties of the Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than basic ACO, PEFT-ACO, and FR-MOS.

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Efficient dynamic resource allocation method for cloud computing environment

Cited by 40 publications

References 39 publications

Enhanced Virtualization-Based Dynamic Bin-Packing Optimized Energy Management Solution for Heterogeneous Clouds

Enhanced Virtualization-Based Dynamic Bin-Packing Optimized Energy Management Solution for Heterogeneous Clouds

Energy-Aware Scheduling of Workflow Using a Heuristic Method on Green Cloud

Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost

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