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A growing number of organizations are hosting their software applications in distributed data centers (DCs) in the cloud, for faster response time and higher energy efficiency. The dramatic increase of user tasks, however, poses a significant challenge on DC providers to retain users’ expectations on both aspects. To tackle this challenge, this work first formulates the problem into a constrained biobjective optimization problem. A biobjective algorithm, named simulated-annealing-based adaptive differential evolution (SADE), is presented to simultaneously reduce both the response time of tasks and energy cost. Meanwhile, a method of minimal Manhattan distance is adopted to search for a final knee, for achieving a good balance between response time minimization and energy cost reduction. Experimental results on real-life datasets, i.e., the electricity prices and tasks collected from a Google cluster trace, have proved that SADE yields less task response time and lower energy cost compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Sparse span array is a critical communication technology for detecting microwave signal, yet it is difficult to simultaneously satisfy both reducing antenna elements' number and maintaining maximum side lobe level. Towards this problem, we propose a multi-objective optimization approach for self-organizing limited-area sparse span array, termed MOSSA. Overall, a uniform framework of multi-objective sparse span array is proposed. Specially, two objectives, number of selected antenna and peak side lobe level, are established for exploring the optimal array distribution in the framework. Based on the framework, for the problem of global-optimum array distribution, we propose a multi-objective particle swarm optimization searching pattern and design a MOSSA algorithm; Furthermore, for the problem of flexibly-adjusted self-organizing array structure, we present a multi-objective genetic programming searching pattern and design a MOSSA-gp algorithm. Moreover, a limited-region mode supplements to the framework. Finally, combination decision strategy assists users to screen out suitable solutions under the guidance of fuzzy-range indexes and then select the optimal solution by a triangle-approximating approach based on minimum Manhattan distance. Numerous experiments demonstrate that the proposed MOSSA outperforms other state-of-the-art algorithms in terms of both antenna elements' number and maximum side lobe level. [ABSTRACT FROM AUTHOR]