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Background As the 2016 Global Strategy on Human Resources for Health: Workforce 2030 (GSHRH) outlines, health systems can only function with health workforce (HWF). Bangladesh is committed to achieving universal health coverage (UHC) hence a comprehensive understanding of the existing HWF was deemed necessary informing policy and funding decisions to the health system. Methods The health labour market analysis (HLMA) framework for UHC cited in the GSHRH was adopted to analyse the supply, need and demand of all health workers in Bangladesh. Government’s information systems provided data to document the public sector HWF. A national-level assessment (2019) based on a country representative sample of 133 geographical units, served to estimate the composition and distribution of the private sector HWF. Descriptive statistics served to characterize the formal and informal HWF. Results The density of doctors, nurses and midwives in Bangladesh was only 9.9 per 10 000 population, well below the indicative sustainable development goals index threshold of 44.5 outlined in the GSHRH. Considering all HWFs in Bangladesh, the estimated total density was 49 per 10 000 population. However, one-third of all HWFs did not hold recognized roles and their competencies were unknown, taking only qualified and recognized HWFs into account results in an estimated density 33.2. With an estimate 75 nurses per 100 doctors in Bangladesh, the second area, where policy attention appears to be warranted is on the competencies and skill-mix. Thirdly, an estimated 82% of all HWFs work in the private sector necessitates adequate oversight for patient safety. Finally, a high proportion of unfilled positions in the public sector, especially in rural areas where 67% of the population lives, account only 11% of doctors and nurses. Conclusion Bangladesh is making progress on many of the milestones of the GSHRH, notably, the establishment of the HWF unit and reporting through the national health workforce accounts. However, particular investment on strengthening the intersectoral HWF coordination across sectors; regulation for assurance of patient safety and adequate oversight of the private sector; establishing accreditation mechanisms for training institutions; and halving inequalities in access to a qualified HWF are important towards advancing UHC in Bangladesh.

Resource allocation is one of the major concern in cloud computing model. When several problems exists in rendering a useful resource allocator. In this research , a self adaptive resource allocation frame work based on machine learning is proposed for modelling and analysing the problem of multi-dimensional cloud resource. This novel self-adaptive resource allocation architecture consists of three stages, QoS prediction model, Improved Bat Algorithm (IBA) and Energy Efficient Model (EEM). The first one, the QoS prediction model, which depends on the same scale of system's past events data, can attain a comparable accuracy with regard to QoS prediction. Secondly, an Energy Efficient Model, which is based on Modified Clonal Selection Algorithm (MCSA) is introduced for minimizing the energy depletion. Thirdly, a runtime decision-making algorithm that depends on improved bat algorithm can rapidly decide on a suitable function for resource allocation

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