Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both execution time and execution cost. In this paper, we adopt a model that optimizes the execution cost while meeting deadline constraints. In solving this problem, we propose an Improved Ant Colony System (IACS) approach featuring two novel strategies. Firstly, a dynamic heuristic strategy is used to calculate a heuristic value during an evolutionary process by taking the workflow topological structure into consideration. Secondly, a double search strategy is used to initialize the pheromone and calculate the heuristic value according to the execution time at the beginning and to initialize the pheromone and calculate heuristic value according to the execution cost after a feasible solution is found. Therefore, the proposed IACS is adaptive to the search environment and to different objectives. We have conducted extensive experiments based on workflows with different scales and different cloud resources. We compare the result with a particle swarm optimization (PSO) approach and a dynamic objective genetic algorithm (DOGA) approach. Experimental results show that IACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions

Which is better to preserve pulmonary function: Short-term or prolonged leukocyte depletion during cardiopulmonary bypass?

ObjectivesNeutrophils are crucial in the development of acute lung injuries during cardiopulmonary bypass. However, the efficacy of leukocyte depletion on pulmonary protection remains controversial, possibly owing to different filtration strategies used in the literature. In this study, we investigated whether short-term leukocyte depletion strategy is more efficacious than prolonged leukocyte depletion in preserving pulmonary function.MethodsEighteen adult dogs were randomized equally into 3 groups. Leukocyte-depleting filters were used for 10 minutes in the LD-S group, throughout cardiopulmonary bypass in the LD-T group, and not used in the control group. Neutrophil counts, elastase, and interleukin-8 concentrations in plasma, myeloperoxidase and interleukin-8 concentrations in pulmonary tissue, and pulmonary vascular resistance and oxygen index were determined to evaluate the inflammatory response and damage to pulmonary function.ResultsAlthough the neutrophil count and pulmonary parenchymal myeloperoxidase contents were significantly lower in both LD-S and LD-T groups than that in the control group, lower pulmonary parenchymal interleukin-8 level, lower pulmonary vascular resistance (113 ± 33 dyne · s/cm5), higher oxygen index (366 ± 82.3 mm Hg), and thinner alveolus wall thickness were seen only in the LD-S group, and the pulmonary parenchymal interleukin-8 levels were also lower in the LD-S group after cardiopulmonary bypass. The plasma elastase and interleukin-8 levels were significantly lower in the LD-S group, but they were significantly higher in the LD-T group compared with the control group after cardiopulmonary bypass.ConclusionsShort-term rather than prolonged leukocyte depletion during cardiopulmonary bypass appears to be more efficacious in protecting pulmonary function via attenuation of the extracorporeal circulation–induced inflammatory response

Visual characterization of associative quasitrivial nondecreasing operations on finite chains

In this paper we provide visual characterization of associative quasitrivial nondecreasing operations on finite chains. We also provide a characterization of bisymmetric quasitrivial nondecreasing binary operations on finite chains. Finally, we estimate the number of functions belonging to the previous classes.Comment: 25 pages, 18 Figure

Cooperative coevolutionary bare-bones particle swarm optimization with function independent decomposition for large-scale supply chain network design with uncertainties

Supply chain network design (SCND) is a complicated constrained optimization problem that plays a significant role in the business management. This article extends the SCND model to a large-scale SCND with uncertainties (LUSCND), which is more practical but also more challenging. However, it is difficult for traditional approaches to obtain the feasible solutions in the large-scale search space within the limited time. This article proposes a cooperative coevolutionary bare-bones particle swarm optimization (CCBBPSO) with function independent decomposition (FID), called CCBBPSO-FID, for a multiperiod three-echelon LUSCND problem. For the large-scale issue, binary encoding of the original model is converted to integer encoding for dimensionality reduction, and a novel FID is designed to efficiently decompose the problem. For obtaining the feasible solutions, two repair methods are designed to repair the infeasible solutions that appear frequently in the LUSCND problem. A step translation method is proposed to deal with the variables out of bounds, and a labeled reposition operator with adaptive probabilities is designed to repair the infeasible solutions that violate the constraints. Experiments are conducted on 405 instances with three different scales. The results show that CCBBPSO-FID has an evident superiority over contestant algorithms

Business Case for Energy Efficiency in Support of Climate Change Mitigation, Economic and Societal Benefits in India

This study seeks to provide policymakers and other stakeholders with actionable information towards a road map for reducing energy consumption cost-effectively. We focus on individual end use equipment types (hereafter referred to as appliance groups) that might be the subject of policies - such as labels, energy performance standards, and incentives - to affect market transformation in the short term, and on high-efficiency technology options that are available today. the high efficiency or Business Case scenario is constructed around a model of cost-effective efficiency improvement. Our analysis demonstrates that a significant reduction in energy consumption and emissions is achievable at net negative cost, that is, as a profitable investment for consumers. Net savings are calculated assuming no additional costs to energy consumption such as carbon taxes. Savings relative to the base case as calculated in this way is often referred to as “economic savings potential”. So far, the Indian market has responded favorably to government efficiency initiatives, with Indian manufacturers producing a higher fraction of high-efficiency equipment than before program implementation. This study highlights both the financial benefit and the scope of potential impact for adopting this equipment, all of which is already readily available on the market. The approach of the study is to assess the impact of short-term actions on long-term impacts. “Short-term” market transformation is assumed to occur by 2015, while “long-term” energy demand reduction impacts are assessed in 2030. In the intervening years, most but not all of the equipment studied will turn over completely. The Business Case concentrates on technologies for which cost-effectiveness can be clearly demonstrated

Design of the PMT underwater cascade implosion protection system for JUNO

Photomultiplier tubes (PMTs) are widely used underwater in large-scale neutrino experiments. As a hollow glass spherelike structure, implosion is unavoidable during long-term operation under large water pressure. There is a possibility of cascade implosion to neighbor PMTs due to shockwave. Jiangmen Underground Neutrino Observatory designed a protection structure for each 20-inch PMT, consisting of a top cover, a bottom cover, and their connection. This paper introduces the requirement and design of the PMT protection system, including the material selection, investigation of manufacture technology, and prototyping. Optimization and validation by simulation and underwater experiments are also presented.Comment: 10 pages, 15 figure

Reply to: Mobility overestimation in MoS2_2 transistors due to invasive voltage probes

In this reply, we include new experimental results and verify that the observed non-linearity in rippled-MoS$_2$ (leading to mobility kink) is an intrinsic property of a disordered system, rather than contact effects (invasive probes) or other device issues. Noting that Peng Wu's hypothesis is based on a highly ordered ideal system, transfer curves are expected to be linear, and the carrier density is assumed be constant. Wu's model is therefore oversimplified for disordered systems and neglects carrier-density dependent scattering physics. Thus, it is fundamentally incompatible with our rippled-MoS$_2$, and leads to the wrong conclusion

A Novel Evolutionary Algorithm with Column and Sub-Block Local Search for Sudoku Puzzles

Sudoku puzzles are not only popular intellectual games but also NP-hard combinatorial problems related to various real-world applications, which have attracted much attention worldwide. Although many efficient tools, such as evolutionary computation (EC) algorithms, have been proposed for solving Sudoku puzzles, they still face great challenges with regard to hard and large instances of Sudoku puzzles. Therefore, to efficiently solve Sudoku puzzles, this paper proposes a genetic algorithm (GA)-based method with a novel local search technology called local search-based GA (LSGA). The LSGA includes three novel design aspects. First, it adopts a matrix coding scheme to represent individuals and designs the corresponding crossover and mutation operations. Second, a novel local search strategy based on column search and sub-block search is proposed to increase the convergence speed of the GA. Third, an elite population learning mechanism is proposed to let the population evolve by learning the historical optimal solution. Based on the above technologies, LSGA can greatly improve the search ability for solving complex Sudoku puzzles. LSGA is compared with some state-of-the-art algorithms at Sudoku puzzles of different difficulty levels and the results show that LSGA performs well in terms of both convergence speed and success rates on the tested Sudoku puzzle instances