An Integrated Framework for Cloud Service Selection Based on BOM and TOPSIS

Many businesses have experienced difficulties in selecting a cloud service provider(CSP)due to the rapid advancement of cloud computing services and the proliferation of CSPs.Many independent criteria should be considered when evaluating the services provided by different CSPs.It is a case of multi-criteria decision-making(MCDM).This paper presents an integrated MCDM cloud service selection framework for determining the most appropriate service provider based on the best only method(BOM)and technique for order of preference by similarity to ideal solution(TOPSIS).To obtain the weights of criteria and the relative importance of CSPs based on each criterion,BOM performs pairwise comparisons of criteria and also for alternatives on each criterion,and TOPSIS uses these weights to rank cloud alternatives.An evaluation and validation of the proposed framework have been carried out through a use-case model to prove its efficiency and accuracy.Moreover,the developed framework was compared with the analytical hierarchical process(AHP),a popular MCDM approach,based on two perspectives:efficiency and consistency.According to the research results,the proposed framework only requires 25%of the comparisons needed for the AHP approach.Furthermore,the proposed framework has a CR of 0%,whereas AHP has 38%.Thus,the proposed framework performs better than AHPwhen it comes to computation complexity and consistency,implying that it is more efficient and trustworthy.

Investigation of SARS-CoV-2 Main Protease Potential Inhibitory Activities of Some Natural Antiviral Compounds Via Molecular Docking and Dynamics Approaches

Coronaviruses caused an outbreak pandemic disease characterized by a severe acute respiratory distress syndrome leading to the infection of more than 200 million patients and the death of more than 4 million individuals.The primary treatment is either supportive or symptomatic.Natural products have an important role in the development of various drugs.Thus,screening of natural compounds with reported antiviral activities can lead to the discovery of potential inhibitory entities against coronaviruses.In the current study,an in-silico molecular docking experiment was conducted on the effects of some of these natural antiviral phytoconstituents,(e.g.,procyanidin B2,theaflavin,quercetin,ellagic acid,caffeoylquinic acid derivatives,berginin,eudesm-1β,6α,11-triol and arbutin),on the crystal structure of SARS-CoV-2 main protease(PDB ID:6w63)using AutoDock-Vina software.Many of the docked compounds revealed good binding affinity,with procyanidin B2(–8.6 Kcal/mol)and theaflavin(–8.5 Kcal/mol)showing a better or similar binding score as the ligand(–8.5 Kcal/mol).Molecular dynamics simulations were carried out at 100 ns and revealed that procyanidin B2 forms a more stable complex with SARS-CoV-2 main protease than theaflavin.Procyanidin B2,theaflavin,and 4,5-dicaffeoylquinic acid were evaluated for toxicity by ProTox-II webserver and were non-toxic according to the predicted LD50 values and safe on different organs and pathways.Additionally,these phytoconstituents showed good ADME properties and acceptable lipophilicity,as evaluated using WLOGP.Amongst the tested compounds,procyanidin B2 showed the highest lipophilic value.It is worth mentioning that these natural inhibitiors of SARS-CoV-2 main protease are components of green and black tea that can be used as a supporting supplement for COVID patients or as potential nuclei for further drug design and development campaigns.