Cloud Computing
Gowri S; Jaganathan Rathi
Abstract
Background and Objectives: Cloud computing can play a vital role in promoting environmental sustainability by leveraging eco-friendly dedicated servers that adhere to green computing standards. The concept of "green cloud computing" revolves around harnessing cutting-edge technologies to minimize the ...
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Background and Objectives: Cloud computing can play a vital role in promoting environmental sustainability by leveraging eco-friendly dedicated servers that adhere to green computing standards. The concept of "green cloud computing" revolves around harnessing cutting-edge technologies to minimize the environmental footprint of computing systems. One of the significant challenges in cloud-based systems is task scheduling, which must be optimized to enhance system efficiency, user experience, and environmental sustainability.Method: This paper proposes a novel Hybrid HEES (Hierarchical Energy-Efficient Scheduling) method that optimizes energy consumption and task scheduling in cloud computing environments. By combining genetic algorithm optimization, workflow-based scheduling, and energy-aware resource allocation, HEES achieves significant reductions in energy consumption and average task completion time.Results: The method is evaluated through simulations, demonstrating its effectiveness in optimizing energy efficiency and task scheduling performance. The Hybrid HEES method has the potential to reduce energy consumption, improve computing performance, and enhance sustainability in cloud computing environments.Conclusion: To evaluate a proposed HEES method through cloudsim 3.0 simulations, the numerical results confirm the effectiveness of HEES algorithm, which achieves average Energy consumption performance improvements of around 12% compared to GP and 8% compared to RR existing methods.
Software Systems
Z. Rahmani Ghobadi; H. Rashidi; S. H. Alizadeh
Abstract
Background and Objectives: Applications and systems software that are running constantly become obsolete due to the accumulation of error conditions or the depletion of resources like physical memory or performance degradation. In this regard, software rejuvenation has been proposed to deal with such ...
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Background and Objectives: Applications and systems software that are running constantly become obsolete due to the accumulation of error conditions or the depletion of resources like physical memory or performance degradation. In this regard, software rejuvenation has been proposed to deal with such a phenomenon and prevent software failure in the future. This paper proposes a multiple objective of software rejuvenation models with several policies. The purpose is to identify the right rejuvenation policy in practical situations.Methods: We model software system with four policies using the Markov process. These policies are: (a) Software system without rejuvenation; (b) Software system with partial rejuvenation; (c) Software system with partial and full rejuvenation; and (d) Software system with four different types of rejuvenation. In the models and each policy, we consider assigning the level of performance on which the availability and operating costs are calculated. Results: To evaluate the models with the four policies, many numerical experiments were performed. For each policy, we evaluated and compared three objectives, namely performance, availability and operating costs. The experimental results states that for Software System with the policy of four different type of rejuvenation has about 18 and 16 percent improvement in performance and availability, respectively, compared with those other policies. Moreover, the operating cost of the software system with partial rejuvenation policy is lower and more efficient than other policies.Conclusion: According to the calculated objectives and the results of the policies, it can be concluded that in systems with lower operational costs, the most appropriate policy is the software system with four different types of rejuvenation because this policy bring the maximum possible value for the performance and availability.
