A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing

Abstract: For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing, we propose a resource cost model that defines the demand of tasks on resources with more details. This model reflects the relationship between the user's resource costs and the budget costs. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan and the user's budget costs as constraints of the optimization problem, achieving multi-objective optimization of both performance and cost. An improved ant colony algorithm has been proposed to solve this problem. Two constraint functions were used to evaluate and provide feedback regarding the performance and budget cost. These two constraint functions made the algorithm adjust the quality of the solution in a timely manner based on feedback in order to achieve the optimal solution. Some simulation experiments were designed to evaluate this method's performance using four metrics: 1) the makespan; 2) cost; 3) deadline violation rate; and 4) resource utilization. Experimental results show that based on these four metrics, a multi-objective optimization method is better than other similar methods, especially as it increased 56.6% in the best case scenario

A multiqueue interlacing peak scheduling method based on tasks’ classification in cloud computing

In cloud computing, resources are dynamic, and the demands placed on the resources allocated to a particular task are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called interlacing peak is proposed. First, the resource load information, such as CPU, I/O, and memory usage, is periodically collected and updated, and the task information regarding CPU, I/O, and memory is collected. Second, resources are sorted into three queues according to the loads of the CPU, I/O, and memory: CPU intensive, I/O intensive, and memory intensive, according to their demands for resources. Finally, once the tasks have been scheduled, they need to interlace the resource load peak. Some types of tasks need to be matched with the resources whose loads correspond to a lighter types of tasks. In other words, CPU-intensive tasks should be matched with resources with low CPU utilization; I/O-intensive tasks should be matched with resources with shorter I/O wait times; and memory-intensive tasks should be matched with resources that have low memory usage. The effectiveness of this method is proved from the theoretical point of view. It has also been proven to be less complex in regard to time and place. Four experiments were designed to verify the performance of this method. Experiments leverage four metrics: 1) average response time; 2) load balancing; 3) deadline violation rates; and 4) resource utilization. The experimental results show that this method can balance loads and improve the effects of resource allocation and utilization effectively. This is especially true when resources are limited. In this way, many tasks will compete for the same resources. However, this method shows advantage over other similar standard algorithms

Dynamically weighted load evaluation method based on self-adaptive threshold in cloud computing

International audienceCloud resources and their loads possess dynamic characteristics. Current research methods have utilized certain physical indicators and fixed thresholds to evaluate cloud resources, which cannot meet the dynamic needs of cloud resources or accurately reflect their resource states. To address this challenge, this paper proposes a Self-adaptive threshold based Dynamically Weighted load evaluation Method (termed SDWM). It evaluates the load state of the resource through a dynamically weighted evaluation method. First, the work proposes some dynamic evaluation indicators in order to evaluate the resource state more accurately. Second, SDWM divided the resource load into three states, including Overload, Normal and Idle using the self-adaptive threshold. It then migrated those overload resources to a balance load, and releases the idle resources whose idle times exceeded a threshold to save energy, which could effectively improve system utilization. Finally, SDWM leveraged an energy evaluation model to describe energy quantitatively using the migration amount of the resource request. The parameters of the energy model were obtained from a linear regression model according to the actual experimental environment. Experimental results showed that SDWM is superior to other methods in energy conservation, task response time, and resource utilization, and the improvements are 31.5 %, 50 %, 50.8 %, respectively. These results demonstrate the positive effect of the dynamic self-adaptive threshold. More specially, SDWM shows great adaptability when resources dynamically join or exi

Preliminary Discussion on Comprehensive Research Method for Rock Burst in Coal Mine Based on Newton’s Second Law

Rock burst is one of the major dynamic disasters that directly threaten production safety in coal mines. According to the current research, the occurrence of rock burst can be described by the generalized Newton’s second law with three elements which are research object, force condition, and motion state. These three elements refer to the coal and rock mass in the mining area, concentrated static and dynamic loads, and dynamic instability of surrounding rock, respectively. On this basis, a comprehensive rock burst research method involving the three elements of Newton’s second law was proposed, which especially focuses on the investigation into geological conditions of mining areas. The research procedure of this method specifically includes the detailed exploration of engineering geological bodies, the classification and stability evaluation of surrounding rock, the measurement and inversion of in situ stress, the evolution analysis of mining-induced stress field, energy field, and fracture field, the study of multiscale failure mechanism of coal and rock mass, the establishment of theoretical failure model of coal and rock mass, the real-time monitoring and warning in potentially dangerous areas, and the reasonable prevention and control in key risk zones. As a preliminary discussion, the significant research progress in each aspect mentioned above has been reviewed and the feasible research directions of rock burst are presented in this paper

Study of Two-Step Parallel Cutting Technology for Deep-Hole Blasting in Shaft Excavation

In hard rock deep-hole blasting excavation, blastholes are rarely utilized due to the clamping effect of the lower rock, which affects excavation progress and restricts the development and application of deep-hole blasting technology. Cut blasting is a key to improving excavation speed. In this paper, a new cutting method designating the two-step cutting technology was presented. The blasthole was divided into upper and lower sections without changing the blasthole layout. The upper section was detonated first, creating sufficient free surface for the lower section, which was detonated secondly. This created a larger cavity and improved blasthole utilization. Results showed good blasting effects for two-step cutting technology through theoretical analysis and engineering applications. The blasthole utilization rate was 96.1% when the upper and lower specific charge ratio = 0.78. This paper provides a good reference for resolving the low blasthole utilization problem in deep-hole blasting of vertical shafts within a hard rock

Fucoidan from Fucus vesiculosus suppresses hepatitis B virus replication by enhancing extracellular signal-regulated Kinase activation

Abstract Background Hepatitis B virus (HBV) infection is a serious public health problem leading to cirrhosis and hepatocellular carcinoma. As the clinical utility of current therapies is limited, the development of new therapeutic approaches for the prevention and treatment of HBV infection is imperative. Fucoidan is a natural sulfated polysaccharide that extracted from different species of brown seaweed, which was reported to exhibit various bioactivities. However, it remains unclear whether fucoidan influences HBV replication or not. Methods The HBV-infected mouse model was established by hydrodynamic injection of HBV replicative plasmid, and the mice were treated with saline or fucoidan respectively. Besides, we also tested the inhibitory effect of fucoidan against HBV infection in HBV-transfected cell lines. Results The result showed that fucoidan from Fucus vesiculosus decreased serum HBV DNA, HBsAg and HBeAg levels and hepatic HBcAg expression in HBV-infected mice. Moreover, fucoidan treatment also suppressed intracellular HBcAg expression and the secretion of the HBV DNA as well as HBsAg and HBeAg in HBV-expressing cells. Furthermore, we proved that the inhibitory activity by fucoidan was due to the activation of the extracellular signal-regulated kinase (ERK) pathway and the subsequent production of type I interferon. Using specific inhibitor of ERK pathway abrogated the fucoidan-mediated inhibition of HBV replication. Conclusion This study highlights that fucoidan might be served as an alternative therapeutic approach for the treatment of HBV infection