A Model to support the decision process for migration to cloud computing.

Cloud computing is an emerging paradigm for provisioning computing and IT services. Migration from traditional systems setting up to cloud computing is a strategic organisational decision that can affect organisations’ performance, productivity, and growth as well as competitiveness. Organisations wishing to migrate their legacy systems to the cloud often need to go through a difficult and complicated decision-making process. This can be due to multiple factors including restructuring IT resources, the still evolving nature of the cloud environment, and the continuous expansion of the cloud services, configurations and providers. This research explores the factors that would influence decision making for migration to the cloud, its impact on IT management, and the main tasks that organisations should consider to ensure successful migration projects. The sequential exploratory strategy is followed for the exploration. This strategy is implemented through the utilisation of a two-stage survey for collecting the primary data. The analysis of the two-stage survey as well as the literature identified eleven determinants that increase the complexity in the decisions to migrate to the cloud. In the literature some of those determinants were realised, accordingly, there have been many proposed methods for supporting migration to the cloud. However, no systematic decision making process exists that clearly identifies the main steps and explicitly describes the tasks to be performed within each step. This research aims to fill this need by proposing a model to support the decision process for migrating to cloud. The model provides a structure which covers the whole process of migration decisions. It guides decision makers through a step-by-step approach aiding organisations with their decision making. The model was evaluated by exploring the views of a group of the cloud practitioners on it. The analysis of the views demonstrated a high level of acceptance by the practitioners with regard to the structure, tasks, and issues addressed by the model. The model offers an encouraging preliminary structure for developing a cloud Knowledge-Based Decision Support System

A Decision Process Model to Support Migration to Cloud Computing

Migration to cloud computing is a strategic organisational decision that can affect performance, productivity, growth, as well as increase competitiveness. The decision to migrate is usually complicated and dynamic due to the immaturity and the still evolving nature of the cloud environment. Although there have been many proposed methods for supporting the migration, no systematic decision process exists that clearly identifies the main steps and explicitly describes the tasks to be performed within each step. In this paper, a decision-making process model, based on a two-stage survey, is proposed. The model guides decision makers through a step-by-step approach, aiding them with their decisions for cloud migration. It offers a preliminary structure for developing a cloud knowledge-based decision support system. The model was evaluated by a group of cloud practitioners. The analysis demonstrates a high level of acceptance with regard to the structure, tasks involved and issues addressed by it

A Review of the Current Level of Support to Aid Decisions for Migrating to Cloud Computing

© 2016 Copyright held by the owner/author(s). Cloud computing provides an innovative delivery model that enables enterprises to reduce operational costs and improve flexibility and scalability. Organisations wishing to migrate their legacy systems to the cloud often need to go through a difficult and complicated decision-making process. This can be due to multiple factors including restructuring IT resources, the still evolving nature of the cloud environment, and the continuous expansion of the services offered. These have increased the requirement for tools and techniques to help the decision-making process for migration. Although significant contributions have been made in this area, there are still many aspects which require further support. This paper evaluates the existing level of support to aid the decision-making process. It examines the complexity of decisions, evaluates the current state of Decision Support Systems in respect of migrating to the cloud, and analyses three models that proposed support for the migration processes. This paper identifies the need for a coherent approach for supporting the whole decision-making process. Further, it explores possible new approaches for addressing the complex issues involved in decision-making for migrating to the cloud

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Cloud computing has the potential to play contribution to the growth and competitiveness of enterprises mainly for SMEs. By adopting cloud computing services SMEs will be able to obtain the latest technology, without the need for upf computing from an SME stance. Motivations, requirements, and concerns surrounding the adoption of cloud computing are discussed. A survey of 300 SMEs conducted in the UK shows an interest in exploiting shown concerns with regards to security and vendor lock computing adoption. The findings are expected to assist SMEs in their adoption of cloud computing servic inform service providers with respect to end Keywords: Cloud computing, Cloud computing migration INTRODUCTION The rapid evolvement in the contemporary business market has made competition to be at the highest level, thereby resulting products and skills to become obsolete ABSTRACT Cloud computing has the potential to play a major role in addressing inefficiencies and make a fundamental contribution to the growth and competitiveness of enterprises mainly for SMEs. By adopting cloud computing services SMEs will be able to obtain the latest technology, without the need for upfront cost. This paper explores the perception of cloud computing from an SME stance. Motivations, requirements, and concerns surrounding the adoption of cloud computing are discussed. A survey of 300 SMEs conducted in the UK shows an interest in exploiting cloud services. However, SMEs have shown concerns with regards to security and vendor lock-in. These concerns could have influenced the speed of cloud computing adoption. The findings are expected to assist SMEs in their adoption of cloud computing servic inform service providers with respect to end-users' concerns. Cloud computing migration, Cloud computing services, SMEs The rapid evolvement in the contemporary business market has made competition to be at the highest level, thereby resulting products and skills to become obsolete BASED INVESTIGATION 1 Journal of Information Technology Management PERSPECTIVE: A SURVEYa major role in addressing inefficiencies and make a fundamental contribution to the growth and competitiveness of enterprises mainly for SMEs. By adopting cloud computing services SMEs ront cost. This paper explores the perception of cloud computing from an SME stance. Motivations, requirements, and concerns surrounding the adoption of cloud computing are cloud services. However, SMEs have in. These concerns could have influenced the speed of cloud computing adoption. The findings are expected to assist SMEs in their adoption of cloud computing services; they may also implement new strategic ideas at an even faster pace to gain a competitive advantage over their rivals within the global market. "The need to quickly respond to business demands is imperative in this new age. Waiting six to eight weeks for a new server deployment is strategy should enable SMEs to incorporate new technologies, reduce costs, develop process innovation, and enhance speed of implementation. A developed IT infrastructure can remove some of the barriers to global competition and allow SMEs to be more ent, competitive and also provide a degree of CLOUD COMPUTING FROM SMES PERSPECTIVE: A SURVEY-BASED INVESTIGATIO

Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms

Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. Despite its benefits, virtual screening generates enormous data and suffers from drawbacks such as high dimensions and imbalance. This paper tackles data imbalance and aims to improve virtual screening accuracy, especially for a minority dataset. For a dataset identified without considering the data’s imbalanced nature, most classification methods tend to have high predictive accuracy for the majority category. However, the accuracy was significantly poor for the minority category. The paper proposes a K-mean algorithm coupled with Synthetic Minority Oversampling Technique (SMOTE) to overcome the problem of imbalanced datasets. The proposed algorithm is named as KSMOTE. Using KSMOTE, minority data can be identified at high accuracy and can be detected at high precision. A large set of experiments were implemented on Apache Spark using numeric PaDEL and fingerprint descriptors. The proposed solution was compared to both no-sampling method and SMOTE on the same datasets. Experimental results showed that the proposed solution outperformed other methods

Impact of the COVID-19 pandemic on patients with paediatric cancer in low-income, middle-income and high-income countries: a multicentre, international, observational cohort study

OBJECTIVES: Paediatric cancer is a leading cause of death for children. Children in low-income and middle-income countries (LMICs) were four times more likely to die than children in high-income countries (HICs). This study aimed to test the hypothesis that the COVID-19 pandemic had affected the delivery of healthcare services worldwide, and exacerbated the disparity in paediatric cancer outcomes between LMICs and HICs. DESIGN: A multicentre, international, collaborative cohort study. SETTING: 91 hospitals and cancer centres in 39 countries providing cancer treatment to paediatric patients between March and December 2020. PARTICIPANTS: Patients were included if they were under the age of 18 years, and newly diagnosed with or undergoing active cancer treatment for Acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, Wilms' tumour, sarcoma, retinoblastoma, gliomas, medulloblastomas or neuroblastomas, in keeping with the WHO Global Initiative for Childhood Cancer. MAIN OUTCOME MEASURE: All-cause mortality at 30 days and 90 days. RESULTS: 1660 patients were recruited. 219 children had changes to their treatment due to the pandemic. Patients in LMICs were primarily affected (n=182/219, 83.1%). Relative to patients with paediatric cancer in HICs, patients with paediatric cancer in LMICs had 12.1 (95% CI 2.93 to 50.3) and 7.9 (95% CI 3.2 to 19.7) times the odds of death at 30 days and 90 days, respectively, after presentation during the COVID-19 pandemic (p<0.001). After adjusting for confounders, patients with paediatric cancer in LMICs had 15.6 (95% CI 3.7 to 65.8) times the odds of death at 30 days (p<0.001). CONCLUSIONS: The COVID-19 pandemic has affected paediatric oncology service provision. It has disproportionately affected patients in LMICs, highlighting and compounding existing disparities in healthcare systems globally that need addressing urgently. However, many patients with paediatric cancer continued to receive their normal standard of care. This speaks to the adaptability and resilience of healthcare systems and healthcare workers globally

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality