An Efficient Energy-Aware Tasks Scheduling with Deadline-Constrained in Cloud Computing

Alla, Said Ben; Touhafi, Abdellah; Ezzati, Abdellah

doi:10.3390/computers8020046

Cited by 18 publications

(13 citation statements)

References 20 publications

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“…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%

“…In contrast, the method proposed in this paper considers makespan and energy consumption for various elements such as cores, virtual machines, and nodes. Ben Alla et al [33] proposed efficient energy-aware task scheduling with deadline-constrained in cloud computing (EATSD). e main objective of their proposed solution is to reduce the energy consumption of the cloud resources, consider different end-user priorities, and optimize the makespan within the deadline constraints.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Problem Modelsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Peng

Barzegar

Yarahmadi³

et al. 2020

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…It also combines power‐aware list‐based scheduling algorithm with dynamic voltage and frequency scaling (DVFS) technique for real‐time tasks to maintain the quality of service while considering tasks deadlines 13 Energy aware for tasks scheduling with deadline constraint (EATSD): This approach is based on considering different users' priorities and deadlines constraints that can allocate the resources on the basis of their class as well as the class of received tasks so that the energy consumption is reduced 34 …”

Section: Performance Evaluationmentioning

confidence: 99%

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Khorsand

Ramezanpour

2020

Int J Communication

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The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA).Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well-known NPhard problem. To resolve this problem, this paper proposes an energy-efficient task-scheduling algorithm based on best-worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision-making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization. K E Y W O R D Sbest-worst method (BWM), cloud computing, energy consumption, multi-criteria decision making, TOPSIS method

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“…In some other algorithms, data-intensive tasks are scheduled within specific deadlines to guarantee the service quality and reduce energy consumption. In Ben et al, 23 tasks and virtual machines (VMs) are categorized into multiple classes by the dynamic classifier. The task scheduler at each level is mapped to a suitable VM class.…”

Section: Job Scheduling Algorithmsmentioning

confidence: 99%

“…The files are deleted from the top of the list until the needed storage space is made available. This valuation is performed according to Equation (23), where fileValue f,i , CT, LRT, FR, AFCost f,i,k , S f , and NFSQ i are respectively the value of file f in site i, the current time, the last request time, the file access frequency, the average cost of remote access to the file, the size of file f, and the number of existing access requests to file f in the current storage disk queue. w 1 , w 2 , and w 3 are the empirical weighting factors.…”

Section: Replica Replacementmentioning

confidence: 99%

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

Beigrezaei

Haghighat

Mirtaheri

2020

Int J Communication

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Summary In Data Grid systems, quick data access is a challenging issue due to the high latency. The failure of requests is one of the most common matters in these systems that has an impact on performance and access delay. Job scheduling and data replication are two main techniques in reducing access latency. In this paper, we propose two new neighborhood‐based job scheduling strategies and a novel neighborhood‐based dynamic data replication algorithm (NDDR). The proposed algorithms reduce the access latency by considering a variety of practical parameters for decision making and the access delay by considering the failure probability of a node in job scheduling, replica selection, and replica placement. The proposed neighborhood concept in job scheduling includes all the nodes with low data transmission costs. Therefore, we can select the best computational node and reduce the search time by running a hierarchical and parallel search. NDDR reduces the access latency through selecting the best replica by performing a hierarchical search established based on the access time, storage queue workload, storage speed, and failure probability. NDDR improves the load balancing and data locality by selecting the best replication place considering the workload, temporal locality, geographical locality, and spatial locality. We evaluate our proposed algorithms by using Optorsim Simulator in two scenarios. The simulations confirm that the proposed algorithms improve the results compared with similar existing algorithms by 11%, 15%, 12%, and 10% in terms of mean job time, replication frequency, mean data access latency, and effective network usage, respectively.

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An Efficient Energy-Aware Tasks Scheduling with Deadline-Constrained in Cloud Computing

Cited by 18 publications

References 20 publications

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

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

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling

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