<span>Despite the increasing use of cloud computing technology because it offers unique features to serve its customers perfectly, exploiting the full potential is very difficult due to the many problems and challenges. Therefore, scheduling resources are one of these challenges. Researchers are still finding it difficult to determine which of the scheduling algorithms are appropriate and effective and that helps increases the performance of the system to accomplish these tasks. This paper provides a broad and detailed examination of resource scheduling algorithms in the environment of a cloud computing environment and highlights the advantages and disadvantages of some algorithms to help researchers in selecting the best algorithms to schedule a particular workload to get a satisfy a quality of service, guarantee good utilization of the cloud resources also minimizing the make-span.</span>

Quay cranes scheduling at container terminals is a fertile area of study that is attracting researchers as well as practitioners in different parts of the world, especially in OR and artificial intelligence (AI). This process efficiency may affect the accomplishment and the competitive merits. As such, four local search algorithms (LSs) are utilized in the current work. These are hill climbing (HC), simulated annealing (SA), tabu search (TS), and iterated local search (ILS). The results obtained demonstrated that none of these LSs succeeded to achieve good results on all instances. This is because different QCSP instances have different characteristics with NP-hardness nature. Therefore, it is difficult to define which LS can yield the best outcomes for all instances. Consequently, appropriate LS selection should be governed by the type of problem and search status. The current work proposes to achieve this, the self-adaptation heuristic (self-H). The self-H is composed of two separate stages: The upper (LS-controller) and the lower (QCSP-solver). The LS-controller embeds an adaptive selection mechanism to adaptively select which LS is to be adopted by the QCSP-solver to solve the given problem. The results revealed that the self-H outperformed others as it attained better results over most instances and competitive results.

<p>Over the last decade there has been an increase in number of E-mails or comments to a company via social media sites, to satisfy their customers, the company must take in to consideration these messages and comments and know whether the customers are satisfied with what the company offers or not. Several techniques have been proposed to analyze the sentiment of the comment writer. Dealing with the Arabic language is faced with many challenges, such as it is a morphologically rich language and how to return the word to its original root. In this paper the challenges of dealing with the Arabic language were reviewed and a framework was also established to analyze the comments in Arabic and classify it into positive, negative or neutral sentiment. The framework was trained and tested and then the con-clusions were drawn based on its work.</p>

<span>Recent years have witnessed a great interest in scientific applications with large data and processing-intensive, so cloud computing is used which provides the resources needed to implement and run these applications. One of the challenges in the management of scientific workflow applications is scheduling them to solve many combinatorial optimization problems, including reducing execution time, cost, resource utilization, and energy <span>consumption. Due to the fact that the iterated local search algorithm (ILS) has been successfully applied to solve many combinatorial optimization problems, this paper investigates the performance of ILS in solving the scientific workflow scheduling problem which is a highly constrained problem. The main components that are different from one problem to others are the ILS parameters, local search, and perturbation, which must be</span> <span>carefully designed. The performance of the standard ILS has been examined and compared with the latest technology. The experimental results show that the proposed algorithm (ILS) obtained good results compared to the best-known results in the literature. This is due to the ILS being an adaptable metaheuristic, which can be simply adapted to different search situations and instances.</span></span>

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