Task Scheduling Strategy Based on Queueing Model in Cloud Computing and Its Energy Consumption Optimization Analysis

Recently, the rapid development in processing speeds, fast storage devices and better network connectivity, hasaccelerated the popularization of cloud computing. Cloud computing is an on-demand-servicewhich provides users with high end servers,storage and processing capabilities where the user need not be concerned with its infrastructure.Although, there are abundant resources in the cloud infrastructure, for the efficient working and execution of tasks, task scheduling plays a crucial role. Task scheduling results in better performance (throughput) of the system along with better resource utilization which ultimately results inreduced energy consumption. At any given time, a processor should never be in idle state, as it still consumes some amount of energy. In this paper, the use of Quantum Genetic Algorithm has led to the reduction in energy consumption. The objective is to find a scheduling sequencewhich can be implemented ina cloud computing environment. Along with minimizing energy consumption, the algorithm helps reduce makespan time of a processor as well.The results show a decrease in energy consumption by 10-15% under different test scenarios involving a variable number of tasks, processors, and the number of iterations (generations) for which the algorithm was run. The algorithm converges to the desired result within 10-15 iterations, as can be seen from the results published in this paper.

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Utilization and Energy Consumption Optimization for Cloud Computing Environment

10.1007/978-981-16-3961-6_50 ◽

2021 ◽

pp. 609-619

Author(s):

Rajeev Tiwari ◽

Roohi Sille ◽

Nilima Salankar ◽

Pardeep Singh

Keyword(s):

Cloud Computing ◽

Energy Consumption ◽

Computing Environment ◽

Cloud Computing Environment ◽

Energy Consumption Optimization

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Crow-penguin optimizer for multiobjective task scheduling strategy in cloud computing

International Journal of Communication Systems ◽

10.1002/dac.4467 ◽

2020 ◽

Vol 33 (14) ◽

pp. e4467

Author(s):

Harvinder Singh ◽

Sanjay Tyagi ◽

Pardeep Kumar

Keyword(s):

Cloud Computing ◽

Task Scheduling ◽

Scheduling Strategy

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Task Scheduling Based on a Hybrid Heuristic Algorithm for Smart Production Line with Fog Computing

Sensors ◽

10.3390/s19051023 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1023 ◽

Cited By ~ 14

Author(s):

Juan Wang ◽

Di Li

Keyword(s):

Energy Consumption ◽

Fault Detection ◽

Real Time ◽

Task Scheduling ◽

Fog Computing ◽

Superior Performance ◽

Smart Manufacturing ◽

Hybrid Heuristic ◽

Scheduling Strategy ◽

Terminal Equipment

Fog computing provides computation, storage and network services for smart manufacturing. However, in a smart factory, the task requests, terminal devices and fog nodes have very strong heterogeneity, such as the different task characteristics of terminal equipment: fault detection tasks have high real-time demands; production scheduling tasks require a large amount of calculation; inventory management tasks require a vast amount of storage space, and so on. In addition, the fog nodes have different processing abilities, such that strong fog nodes with considerable computing resources can help terminal equipment to complete the complex task processing, such as manufacturing inspection, fault detection, state analysis of devices, and so on. In this setting, a new problem has appeared, that is, determining how to perform task scheduling among the different fog nodes to minimize the delay and energy consumption as well as improve the smart manufacturing performance metrics, such as production efficiency, product quality and equipment utilization rate. Therefore, this paper studies the task scheduling strategy in the fog computing scenario. A task scheduling strategy based on a hybrid heuristic (HH) algorithm is proposed that mainly solves the problem of terminal devices with limited computing resources and high energy consumption and makes the scheme feasible for real-time and efficient processing tasks of terminal devices. Finally, the experimental results show that the proposed strategy achieves superior performance compared to other strategies.

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An Energy Efficient Task Scheduling Strategy in a Cloud Computing System and its Performance Evaluation using a Two-Dimensional Continuous Time Markov Chain Model

Electronics ◽

10.3390/electronics8070775 ◽

2019 ◽

Vol 8 (7) ◽

pp. 775

Author(s):

Wenjuan Zhao ◽

Xiushuang Wang ◽

Shunfu Jin ◽

Wuyi Yue ◽

Yutaka Takahashi

Keyword(s):

Cloud Computing ◽

Task Scheduling ◽

Performance Measures ◽

Energy Efficient ◽

System Optimization ◽

Computing System ◽

Continuous Time Markov Chain ◽

Scheduling Strategy ◽

Cloud Computing System ◽

Waking Up

With ongoing energy shortages and rises in greenhouse emissions worldwide, increasing academic attention is being turned towards ways to improve the efficiency and sustainability of cloud computing. In this paper, we present a performance analysis and a system optimization of a cloud computing system with an energy efficient task scheduling strategy directed towards satisfying the service level agreement of cloud users while at the same time improving the energy efficiency in cloud computing system. In this paper, we propose a novel energy-aware task scheduling strategy based on a sleep-delay timer and a waking-up threshold. To capture the stochastic behavior of tasks with the proposed strategy, we establish a synchronous vacation queueing system combining vacation-delay and N-policy. Taking into account the total number of tasks and the state of the physical machine (PM), we construct a two-dimensional continuous-time Markov chain (CTMC), and produce an infinitesimal generator. Moreover, by using the geometric-matrix solution method, we analyze the queueing model in the steady state, and then, we derive the system performance measures in terms of the average sojourn time and the energy conservation level. Furthermore, we conduct system experiments to investigate the proposed strategy and validate the system model according to performance measures. Statistical results show that there is a compromise between the different performance measures when setting strategy parameters. By combining different performance measures, we develop a cost function for the system optimization. Finally, by dynamically adjusting the crossover probability and the mutation probability, and initializing the individuals with chaotic equations, we present an improved genetic algorithm to jointly optimize the sleep parameter, the sleep-delay parameter and the waking-up threshold.

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EOTS: An Energy-consumption Optimization-oriented Task Scheduling Algorithm for Wireless Sensor Networks

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v12i07.5904 ◽

2016 ◽

Vol 12 (07) ◽

pp. 59

Author(s):

Zeyu Sun ◽

Yuanbo Li ◽

Chuanfeng Li ◽

Yalin Nie

Keyword(s):

Wireless Sensor Networks ◽

Energy Consumption ◽

Sensor Networks ◽

Task Scheduling ◽

Energy Cost ◽

Iterative Refinement ◽

Sensor Nodes ◽

Wireless Sensor ◽

Utilization Rate ◽

Energy Consumption Optimization

The mismatch of task scheduling results in rapid network energy consumption during data transmission in wireless sensor networks. To address this issue, the paper proposed an Energy-consumption Optimization-oriented Task Scheduling Algorithm (EOTS algorithm) which formally described the overall power dissipation in the network system. On this basis, a network model was built up such that both the idle energy consumption in sensor nodes and energy consumption during the execution of tasks were taken into account, with which the whole task was effectively decomposed into sub-task sequences. They underwent simulated annealing and iterative refinement, with the intention of improving sensor nodes’ utilization rate, reducing local idle energy cost, as well as cutting down the overall energy consumption accordingly. The experiment result shows that under the environment of multi-task operation, from the perspective of energy cost optimization, the proposed scheduling strategy recorded an increase of 21.24% compared with the FIFO algorithm, and an increase of 16.77% in comparison to the EMRSA algorithm; while in light of network lifetimes, the EOTS algorithm surpassed the ECTA algorithm by a gain of 19.21%. Therefore, the effectiveness of the proposed EOTS algorithm is verified.

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