bigdata has received considerable attentions in recent years because of massive data volumes in multifarious fields. Considering various "V" features, bigdatatasks are usually highly complex and computati...
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bigdata has received considerable attentions in recent years because of massive data volumes in multifarious fields. Considering various "V" features, bigdatatasks are usually highly complex and computational intensive. These tasks are generally performed in parallel in data centers resulting in massive energy consumption and Green House Gases emissions. Therefore, efficient resource allocation considering the synergy of the performance and energy efficiency is one of the crucial challenges today. In this paper, we aim to achieve maximum energy efficiency by combining thermal-aware and dynamic voltage and frequency scaling (DVFS) techniques. This paper proposes: (a) a thermal-aware and power-aware hybrid energy consumption model synchronously considering the computing, cooling, and migration energy consumption;(b) a tensor-based task allocation and frequency assignment model for representing the relationship among different tasks, nodes, time slots, and frequencies;and (c) a big data task scheduling algorithm based on Thermal-aware and DVFS-enabled techniques (TSTD) to minimize the total energy consumption of data centers. The experimental results demonstrate that the proposed TSTD algorithm significantly outperforms the state-of-the-art energy efficient algorithms from total, computing, and cooling energy consumption perspectives, as well as cooling energy consumption proportion and total energy consumption savings.
Effective taskscheduling is recognized as one of the main critical challenges in cloud computing;it is an essential step for effectively exploiting cloud computing resources, as several tasks may need to be efficient...
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Effective taskscheduling is recognized as one of the main critical challenges in cloud computing;it is an essential step for effectively exploiting cloud computing resources, as several tasks may need to be efficiently scheduled on various virtual machines by minimizing makespan and maximizing resource utilization. taskscheduling is an NP-hard problem, and consequently, finding the best solution may be difficult, particularly for bigdata applications. This paper presents an intelligent big data task scheduling approach for IoT cloud computing applications using a hybrid Dragonfly Algorithm. The Dragonfly algorithm is a newly introduced optimization algorithm for solving optimization problems which mimics the swarming behaviors of dragonflies. Our algorithm, MHDA, aims to decrease the makespan and increase resource utilization, and is thus a multi-objective approach. beta-hill climbing is utilized as a local exploratory search to enhance the Dragonfly Algorithm's exploitation ability and avoid being trapped in local optima. Two experimental studies were conducted on synthetic and real trace datasets using the CloudSim toolkit to compare MHDA to other well-known algorithms for solving taskscheduling problems. The analysis, which included the use of a t-test, revealed that MHDA outperformed other well-known algorithms: MHDA converged faster than other methods, making it useful for big data task scheduling applications, and it achieved 17.12% improvement in the results.
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