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The focal aim of the project was to assess the economic anxiety (EA) and the performance of small and medium enterprises (SMEs) during partial and full lockdowns in Kuwait. The challenges facing the SMEs during COVID-19 and the potential solutions were also explored. The call for this vital investigation was due to the global economic fallout and the shocking drop within the marketplace caused by the COVID-19 pandemic. A descriptive approach was used for online survey design to collect datasets from 147 SMEs spanning all governorates of Kuwait in the period between March and June 2021. It included sociodemographic data, economic anxiety perception, potential challenges and solutions to SMEs, and SMEs’ performance. The data analysis using SPSS 25 showed that 78.2% of the SMEs were affected directly by the COVID-19 pandemic, and about 83% were affected negatively by the COVID-19 pandemic. In comparison, only 12.2% experienced a positive impact, mainly medical, technology, social media, food supplies, and delivery or logistics industries. With great concerns of SMEs for all dimensions related to economic anxiety (with an average of around 3.95), the greatest concerns were the financial and cash flow, followed by labor shortage (an average between 4.51 and 5.00). The results also showed that most of the performance indicators for the SMEs were low (with an average of less than or equal to 2.5), and more than 66% of them worked fewer hours during the pandemic; the number of operating hours was dropped dramatically. More than 74% of the SMEs used technology in more than 20% of their activities, representing an increase in using technologies of about 44%, and about 25.2% used social networks in more than 80% of their activities. The performance of SMEs is also found to be significantly and positively correlated with the economic anxiety levels, with a correlation coefficient of 0.186. The findings revealed significant and crucial outcomes for policymaking, decision-makers, and governmental agencies to build recovery plans and proper actions needed to manage the consequences caused by the disaster against the economic and other developments within the context of SMEs. Overall, there is a clear need to find ways and customize operations to adapt to the new work modes that require social distancing, online operations, and site management. In addition, new alternative modes of SMEs work follow to compensate for the lower working hours from the office and increased online working from home.

In data grid environments data-intensive applications require large amounts of data to execute. Data transfer is a primary cause of job execution delay. In this paper we study smart scheduling integrated with replica management optimization to improve system performance. We study the use of Genetic Algorithm (GA) for the scheduling phase of data-intensive applications. The schedulers proposed incorporate information about the datasets and their replicas needed by the jobs to be scheduled, and coschedules the jobs and the datasets to the computation node guaranteeing minimum job execution time. We employ a data grid replica management framework for the optimization phase of the replica distribution. In this approach we try to achieve a double optimization effect from both the replica management and the scheduling phases, while integrating scheduling and data replication to improve the performance of the grid system. We evaluate and compare our Genetic Algorithm (GA) with a Tabu search (TS) and the de facto Max-Min based schedulers.

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