Resource allocation and scheduling of multiple composite web services in cloud computing using cooperative coevolution genetic algorithm

In cloud computing, resource allocation and scheduling of multiple composite web services is an important and challenging problem. This is especially so in a hybrid cloud where there may be some low-cost resources available from private clouds and some high-cost resources from public clouds. Meeting this challenge involves two classical computational problems: one is assigning resources to each of the tasks in the composite web services; the other is scheduling the allocated resources when each resource may be used by multiple tasks at different points of time. In addition, Quality-of-Service (QoS) issues, such as execution time and running costs, must be considered in the resource allocation and scheduling problem. Here we present a Cooperative Coevolutionary Genetic Algorithm (CCGA) to solve the deadline-constrained resource allocation and scheduling problem for multiple composite web services. Experimental results show that our CCGA is both efficient and scalable

Daidzin decreases blood glucose and lipid in streptozotocin-induced diabetic mice

Purpose: To investigate the ameliorative effect of daidzin (DZ) on diabetes in streptozotocin (STZ)- induced diabetic Institute of Cancer Research (ICR) mice, with a view to determining its usefulness in the treatment of diabetes.Methods: The effect of DZ (100, 200 and 400 mg/kg) on blood glucose was investigated in both normal and STZ-induced diabetic mice with glibenclamide (3 mg/kg) and metformin (400 mg/kg) as positive control, respectively. Serum or hepatic levels of lipid, proinflammatory factors, malondialdehyde (MDA) and superoxide dismutase (SOD) were measured. Glucosidase activity assay and glucose uptake by C2C12 myotubes were performed in vitro and the expression of glucose transporter 4 (GLUT4) in C2C12 cells was determined by western blot.Results: DZ (200 and 400 mg/kg) did not decrease fasting blood glucose in normal mice but inhibited starch-induced postprandial glycemia. Oral administration of 400 mg/kg of DZ for 14 days significantly decreased mouse blood glucose (p < 0.01), as well as serum total cholesterol (TC, p < 0.01), triglycerides (TG, p < 0.01), low-density lipoprotein cholesterol (LDL-c, p < 0.01) levels in STZ-induced hyperglycemic mice and improved oral glucose tolerance. The serum and hepatic activity of SOD was enhanced (p < 0.01 and p < 0.001, respectively) while MDA level decreased (p < 0.001). Blood concentrations of interleukin-6 (IL-6, p < 0.001), tumor necrosis factor α (TNF-α, p < 0.01), monocyte chemotactic protein 1 (MCP-1, p < 0.01) were also significantly reduced. In vitro glucosidase activity results showed that DZ inhibited α-glucosidase with IC50 values of 82, 98 and 389 μg/mL for α- glucosidase from S. cerevisiae, Rhizopus sp. and rat intestines, respectively. It also stimulated glucose uptake and GLUT4 membrane translocation in C2C12 myotubes at 20 μM (p < 0.05).Conclusion: Oral administration of DZ is effective in alleviating diabetic hyperglycemia, dyslipidemia and inflammation. Inhibition of α-glucosidase and stimulation of glucose consumption by muscles may account for its inhibitory effect on blood glucose.Keywords: Daidzin, Diabetes, Inflammation, Superoxide dismutase (SOD), Malondialdehyde (MDA), Glucosidase, C2C12 myotubes, Glucose transporte

Association Analysis of MET

To investigate the association of MET SNPs with gender disparity in thyroid tumors, as well as the metastasis and prognosis of patients, 858 patients with papillary thyroid carcinoma (PTC), 556 patients with nodular goiter, and 896 population-based normal controls were recruited. The genotyping of MET SNPs was carried out using the Sequenom MassARRAY system. The distribution of MET SNPs (rs1621 and rs6566) was different among groups. Gender stratification analysis revealed a significant association between the rs1621 genotype and PTC in female patients (P=0.037), but not in male patients (P>0.05). For female patients, the rs1621 AG genotype was significantly higher in patients with PTC than in normal controls (P=0.01) and revealed an increasing risk of PTC (OR: 1.465, 95% CI: 1.118–1.92). However, association analysis of the rs1621 genotype with metastasis and prognosis revealed no significant correlation in both male and female patients. The findings of our study showed that polymorphism of SNP locus rs1621 in MET gene may be associated with gender disparity in PTC. Higher AG genotypes in rs1621 were correlated with PTC in female patients, but not in male patients

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

QoS-aware web service composition using genetic algorithms

Web service technology is increasingly being used to build various e-Applications, in domains such as e-Business and e-Science. Characteristic benefits of web service technology are its inter-operability, decoupling and just-in-time integration. Using web service technology, an e-Application can be implemented by web service composition — by composing existing individual web services in accordance with the business process of the application. This means the application is provided to customers in the form of a value-added composite web service. An important and challenging issue of web service composition, is how to meet Quality-of-Service (QoS) requirements. This includes customer focused elements such as response time, price, throughput and reliability as well as how to best provide QoS results for the composites. This in turn best fulfils customers’ expectations and achieves their satisfaction. Fulfilling these QoS requirements or addressing the QoS-aware web service composition problem is the focus of this project. From a computational point of view, QoS-aware web service composition can be transformed into diverse optimisation problems. These problems are characterised as complex, large-scale, highly constrained and multi-objective problems. We therefore use genetic algorithms (GAs) to address QoS-based service composition problems. More precisely, this study addresses three important subproblems of QoS-aware web service composition; QoS-based web service selection for a composite web service accommodating constraints on inter-service dependence and conflict, QoS-based resource allocation and scheduling for multiple composite services on hybrid clouds, and performance-driven composite service partitioning for decentralised execution. Based on operations research theory, we model the three problems as a constrained optimisation problem, a resource allocation and scheduling problem, and a graph partitioning problem, respectively. Then, we present novel GAs to address these problems. We also conduct experiments to evaluate the performance of the new GAs. Finally, verification experiments are performed to show the correctness of the GAs. The major outcomes from the first problem are three novel GAs: a penaltybased GA, a min-conflict hill-climbing repairing GA, and a hybrid GA. These GAs adopt different constraint handling strategies to handle constraints on interservice dependence and conflict. This is an important factor that has been largely ignored by existing algorithms that might lead to the generation of infeasible composite services. Experimental results demonstrate the effectiveness of our GAs for handling the QoS-based web service selection problem with constraints on inter-service dependence and conflict, as well as their better scalability than the existing integer programming-based method for large scale web service selection problems. The major outcomes from the second problem has resulted in two GAs; a random-key GA and a cooperative coevolutionary GA (CCGA). Experiments demonstrate the good scalability of the two algorithms. In particular, the CCGA scales well as the number of composite services involved in a problem increases, while no other algorithms demonstrate this ability. The findings from the third problem result in a novel GA for composite service partitioning for decentralised execution. Compared with existing heuristic algorithms, the new GA is more suitable for a large-scale composite web service program partitioning problems. In addition, the GA outperforms existing heuristic algorithms, generating a better deployment topology for a composite web service for decentralised execution. These effective and scalable GAs can be integrated into QoS-based management tools to facilitate the delivery of feasible, reliable and high quality composite web services

A hybrid genetic algorithm for the optimal constrained web service selection problem in web service composition

Web service composition is an important problem in web service based systems. It is about how to build a new value-added web service using existing web services. A web service may have many implementations, all of which have the same functionality, but may have different QoS values. Thus, a significant research problem in web service composition is how to select a web service implementation for each of the web services such that the composite web service gives the best overall performance. This is so-called optimal web service selection problem. There may be mutual constraints between some web service implementations. Sometimes when an implementation is selected for one web service, a particular implementation for another web service must be selected. This is so called dependency constraint. Sometimes when an implementation for one web service is selected, a set of implementations for another web service must be excluded in the web service composition. This is so called conflict constraint. Thus, the optimal web service selection is a typical constrained ombinatorial optimization problem from the computational point of view. This paper proposes a new hybrid genetic algorithm for the optimal web service selection problem. The hybrid genetic algorithm has been implemented and evaluated. The evaluation results have shown that the hybrid genetic algorithm outperforms other two existing genetic algorithms when the number of web services and the number of constraints are large

QoS-oriented sesource allocation and scheduling of multiple composite web services in a hybrid cloud using a random-key genetic algorithm

In cloud computing resource allocation and scheduling of multiple composite web services is an important challenge. This is especially so in a hybrid cloud where there may be some free resources available from private clouds but some fee-paying resources from public clouds. Meeting this challenge involves two classical computational problems. One is assigning resources to each of the tasks in the composite web service. The other is scheduling the allocated resources when each resource may be used by more than one task and may be needed at different points of time. In addition, we must consider Quality-of-Service issues, such as execution time and running costs. Existing approaches to resource allocation and scheduling in public clouds and grid computing are not applicable to this new problem. This paper presents a random-key genetic algorithm that solves new resource allocation and scheduling problem. Experimental results demonstrate the effectiveness and scalability of the algorithm

QoS-Based Web Service Scheduling Accommodating Inter-Service Dependencies Using Minimal-Conflict Hill-Climbing Repair Genetic Algorithm

In the filed of semantic grid, QoS-based Web service scheduling for workflow optimization is an important problem.However, in semantic and service rich environment like semantic grid, the emergence of context constraints on Web services is very common making the scheduling consider not only quality properties of Web services, but also inter service dependencies which are formed due to the context constraints imposed on Web services. In this paper, we present a repair genetic algorithm, namely minimal-conflict hill-climbing repair genetic algorithm, to address scheduling optimization problems in workflow applications in the presence of domain constraints and inter service dependencies. Experimental results demonstrate the scalability and effectiveness of the genetic algorithm

Four Figurative Artists from Ontario

In Web service based systems, new value-added Web services\ud can be constructed by integrating existing Web services.\ud A Web service may have many implementations,\ud which are functionally identical, but have different Quality\ud of Service (QoS) attributes, such as response time, price,\ud reputation, reliability, availability and so on. Thus, a significant\ud research problem in Web service composition is\ud how to select an implementation for each of the component\ud Web services so that the overall QoS of the composite\ud Web service is optimal. This is so called QoS-aware Web service composition problem. In some composite Web services\ud there are some dependencies and conflicts between the Web service implementations. However, existing approaches cannot handle the constraints. This paper tackles the QoS-aware Web service composition problem with inter service dependencies and conflicts using a penalty-based genetic algorithm (GA). Experimental results demonstrate the effectiveness and the scalability of the penalty-based GA