A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing

Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located in different datacenters from the cloud computing environment, resulting in serious data transmission delays. Edge computing reduces the data transmission delays and supports the fixed storing manner for scientific workflow private datasets, but there is a bottleneck in its storage capacity. It is a challenge to combine the advantages of both edge computing and cloud computing to rationalize the data placement of scientific workflow, and optimize the data transmission time across different datacenters. Traditional data placement strategies maintain load balancing with a given number of datacenters, which results in a large data transmission time. In this study, a self-adaptive discrete particle swarm optimization algorithm with genetic algorithm operators (GA-DPSO) was proposed to optimize the data transmission time when placing data for a scientific workflow. This approach considered the characteristics of data placement combining edge computing and cloud computing. In addition, it considered the impact factors impacting transmission delay, such as the band-width between datacenters, the number of edge datacenters, and the storage capacity of edge datacenters. The crossover operator and mutation operator of the genetic algorithm were adopted to avoid the premature convergence of the traditional particle swarm optimization algorithm, which enhanced the diversity of population evolution and effectively reduced the data transmission time. The experimental results show that the data placement strategy based on GA-DPSO can effectively reduce the data transmission time during workflow execution combining edge computing and cloud computing

Stochastic Optimization: Theory and Applications

As an important branch of applied mathematics, optimization theory, especially stochastic optimization, becomes an important tool for solving multiobjective decision-making problems in random process recently. Many kinds of industrial, biological, engineering, and economic problems can be viewed as stochastic systems, for example, area of communication, gene, signal processing, geography, civil engineering, aerospace, banking, and so forth. Stochastic optimization is suitable to solve the decision-making problems in these stochastic systems

A Cremophor-Free Formulation for Tanespimycin (17-AAG) using PEO-b-PDLLA Micelles: Characterization and Pharmacokinetics in Rats

Tanespimycin (17-allylamino-17-demethoxygeldanamycin or 17-AAG) is a promising heat shock protein 90 inhibitor currently undergoing clinical trials for the treatment of cancer. Despite its selective mechanism of action on cancer cells, 17-AAG faces challenging issues due to its poor aqueous solubility, requiring formulation with Cremophor EL (CrEL) or ethanol (EtOH). Therefore, a CrEL-free formulation of 17-AAG was prepared using amphiphilic diblock micelles of poly(ethylene oxide)-b-poly(D,L-lactide) (PEO-b-PDLLA). Dynamic light scattering revealed PEO-b-PDLLA (12:6 kDa) micelles with average sizes of 257 nm and critical micelle concentrations of 350 nM, solubilizing up to 1.5 mg/mL of 17-AAG. The area under the curve (AUC) of PEO-b-PDLLA micelles was 1.3-fold that of the standard formulation. The renal clearance (CLrenal) increased and the hepatic clearance (CLhepatic) decreased with the micelle formulation, as compared to the standard vehicle. The micellar formulation showed a 1.3-fold increase in the half-life (t1/2) of the drug in serum and 1.2-fold increase in t1/2 of urine. As expected, because it circulated longer in the blood, we also observed a 1.7-fold increase in the volume of distribution (Vd) with this micelle formulation compared to the standard formulation. Overall, the new formulation of 17-AAG in PEO-b-PDLLA (12:6 kDa) micelles resulted in a favorable 150-fold increase in solubility over 17-AAG alone, while retaining similar properties to the standard formulation. Our data indicates that the nanocarrier system can retain the pharmacokinetic disposition of 17-AAG without the need for toxic agents such as CrEL and EtOH

ELUCID. VII. Using constrained hydro simulations to explore the gas component of the cosmic web

Using reconstructed initial conditions in the Sloan Digital Sky Survey (SDSS) survey volume, we carry out constrained hydrodynamic simulations in three regions representing different types of the cosmic web: the Coma cluster of galaxies; the SDSS Great Wall; and a large low-density region at z ∼ 0.05. These simulations, which include star formation and stellar feedback but no active galactic nucleus formation and feedback, are used to investigate the properties and evolution of intergalactic and intracluster media. About half of the warm-hot intergalactic gas is associated with filaments in the local cosmic web. Gas in the outskirts of massive filaments and halos can be heated significantly by accretion shocks generated by mergers of filaments and halos, respectively, and there is a tight correlation between the gas temperature and the strength of the local tidal field. The simulations also predict some discontinuities associated with shock fronts and contact edges, which can be tested using observations of the thermal Sunyaev-Zel’dovich effect and X-rays. A large fraction of the sky is covered by Lyα and O vi absorption systems, and most of the O vi systems and low-column-density H i systems are associated with filaments in the cosmic web. The constrained simulations, which follow the formation and heating history of the observed cosmic web, provide an important avenue to interpret observational data. With full information about the origin and location of the cosmic gas to be observed, such simulations can also be used to develop observational strategie