Retraction Note: A Hybrid Approach for Task Scheduling Using the Cuckoo and Harmony Search in Cloud Computing Environment

Cloud computing opens a new dimension and becomes a foundation technology for developing and integrating Web applications offered as cloud services. However, the number of these services is expected to grow in the future, then, it is required for providers to overcome this situation by being able to offer cloud services with several qualities. That is why the cloud services selection is becoming a big challenge. In this paper, we design an optimization approach based on a meta-heuristic by using Cuckoo Search Algorithm (CSA) with a combination with the Lévy flight behavior to solve the optimal service selection optimization problem in the cloud computing environment considering QoS constraints. The proposed algorithm consists of three phases, initialization phase of the initial population then the evaluation of this population and the last phase of the search for relevant services using Lévy flight. A simulation design demonstrates that a strong selection using the CSA can be achieved in the cloud environment.KeywordsCloud computingService selectionQoSCuckoo Search Algorithm

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date: September 30 th 2022

Block Matching (BM) is considered to be the most efficient technique for Motion Estimation (ME) in Video Compression. Many algorithms have been developed for BM which includes classical as well as metaheuristic methods. Harmony Search (HS), Differential Evolution (DE), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) are the metaheuristic methods used for this purpose. This paper explores a way to formulate a BM algorithm by using Cuckoo Search — a metaheuristic optimization algorithm which is mainly inspired by the breeding behavior of some cuckoo species which shows an obligate brood parasitism. Levy flight behavior of some fruit flies and birds is also incorporated in Cuckoo Search.

Due to the large migration of applications from traditional computing environments to cloud systems, task scheduling has become a well-known research topic. Further, assigning tasks is an essential step in the server consolidation that enables clients’ needs to be mapped to suitable resources, such as Physical Machines (PMs) or Virtual Machines (VMs). The difficulty lies in using the available shared computing resources for various tasks without degrading the quality of service (QoS) or increasing the carbon footprint. In addition, the cloud provider needs to use a good task strategy based on the requirements of the client and the IT resources capacity. The best resource utilization and quickest task execution completion times are essential metrics for a cloud provider to maintain the optimum performance of the cloud system. The appropriate solution distributes tasks to the available virtual machine while applying optimization techniques such as the artificial bee colony (ABC), the cuckoo search (CS) algorithm, and particle swarm optimization (PSO). This paper will analyze the algorithms mentioned above based on energy consumption and resource utilization.KeywordsServer consolidationCloud computingResource efficiencyMetaheuristic algorithmsEnergy consumptionPerformance parametersCloudSim

In this paper, we present a management system for distributed software development in cloud computing environments, called DDMan. Cloud computing environments provide more flexibility than conventional computing environments. In particular, the platform as a service (PaaS) in cloud computing environments provides benefits for users in a variety of aspects, such as application design, development testing, software deployment, team collaboration, web service integration, database integration, and scalability. Although the process of distributed software development evolves and changes by a lot in the past decade, there are still some user needs remaining unsatisfied. For managing distributed software development in cloud computing systems, we present the DDMan system, which extends the WebSD model of distributed software development management. In DDMan, the interactions among the roles in the software development are precisely defined. In addition to the conventional web-based interface, the DDMan system also supports the interface for the Amazon cloud environment, which is an approach to a fully automatic service of distributed software development management in cloud computing environments.

This project establishes a Chinese wall security policy model in the environment of cloud computing. In 1988 Brewer and Nash proposed a very nice commercial security policy in British financial world. Though the policy was well accepted, but the model was incorrect. A decade later, Dr. Lin provided a model in 2003 that meets Brewer & Nash’s Policy. One of the important components in Cloud computing is data center. In order for any company to store data in the center, a trustable security policy model is a must; Chinese wall security policy model will provide this assurance. The heart of the Chinese Wall Security Policy Model is the concept of Conflict of Interest (COI). The concept can be modeled by an anti-reflexive, symmetric and transitive binary relation. In this project, by extending Dr. Lin’s Model, we explore the security issues in the environment of cloud computing and develop a small system of the Chinese Wall Security Model.

In China, with the continuous advance of cloud computing technologies such as virtualization technology and big data processing technology, small and medium-sized enterprises are constantly updating and improving their management information system. In recent years, computer network technology and information technology have been gradually applied to enterprise management. With the development of enterprises, a large number of heterogeneous software services deployed on different servers are accumulated in enterprises. Cloud computing can not only reduce the cost of informatization construction of small and medium-sized enterprises, strengthen the integration of information resources, promote resource sharing, but also promote enterprise management and service innovation. In view of the development of social economy and the advance of the industry, if the enterprise wants to be better based on the market, it must realize the scientific management mode, and constantly improve the efficiency and quality of work. Through the cloud computing model, this paper introduces the system construction of the management information system for small and medium-sized enterprises in the cloud computing environment and the implementation scheme of enterprises in the cloud computing environment.

The cloud computing is an Internet-based resource sharing system in which virtualized resources are provided over the Internet. Cloud computing refers to a class of systems and applications that employ distributed resources for use in various applications; these computing resources are utilized over a network to facilitate the execution of tasks. However, cloud computing resources are heterogeneous and dynamic, connecting a broad range of resources. Thus, there are a large numbers of application and data center in the cloud computing environment. Therefore, the security issues of authentication and communication in application services and data center need to be considered in the cloud computing environment.

In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.

The uniaxial compressive strength of weak rocks (UCSWR) is among the essential parameters involved for the design of underground excavations, surface and underground mines, foundations in/on rock masses, and oil wells as an input factor of some analytical and empirical methods such as RMR and RMI. The direct standard approaches are difficult, expensive, and time-consuming, especially with highly fractured, highly porous, weak, and homogeneous rocks. Numerous endeavors have been made to develop indirect approaches of predicting UCSWR. In this research work, a new intelligence method, namely relevance vector regression (RVR), improved by the cuckoo search (CS) and harmony search (HS) algorithms is introduced to forecast UCSWR. The HS and CS algorithms are combined with RVR to determine the optimal values for the RVR controlling factors. The optimized models (RVR-HS and RVR-CS) are employed to the available data given in the open-source literature. In these models, the bulk density, Brazilian tensile strength test, point load index test, and ultrasonic test are used as the inputs, while UCSWR is the output parameter. The performances of the suggested predictive models are tested according to two performance indices, i.e. mean square error and determination coefficient. The results obtained show that RVR optimized by the HS model can be successfully utilized for estimation of UCSWR with R2 = 0.9903 and MSE = 0.0031203.

Assisting elderly individuals in indoor environments is crucial, as they often encounter mobility and safety challenges. Autonomous Unmanned Aerial Vehicles (UAVs) navigation offers a transformative solution, significantly enhancing safety, efficiency, and quality of life. These UAVs can perform tasks such as monitoring, delivering essential items, and responding to emergencies, providing invaluable support and promoting greater independence for elderly individuals. To achieve effective assistance, autonomous UAV navigation relies on robust path planning with obstacle avoidance to ensure optimal performance. A newly developed path planning, based on a new hybrid Water Cycle Algorithm (WCA), stands out by addressing the challenge of avoiding obstacles while adhering to non-holonomic constraints and conserving energy in complex, cluttered indoor environments. In Population-Based Algorithms (PBAs), the initial population plays a crucial role, as it greatly impacts the algorithm’s efficiency in exploring the search space and its rate of convergence. The new developed hybrid algorithm, called WCA-HS, is based on using the Harmony Search (HS) algorithm to identify the optimal initial population for the WCA. The results indicate that the hybrid WCA-HS outperforms classical WCA and other metaheuristic algorithms, such as HS, Firefly Algorithm (FA), Cuckoo Search (CS), Genetic Algorithm (GA), Differential Evolution (DE), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC), over 20 independent runs, highlighting the effectiveness of the population initialization technique. Additionally, the developed indoor path planning system was evaluated in highly cluttered and low-light environments, showcasing its robustness and real-world applicability, making it highly effective for assisting elderly individuals in complex indoor environments.

In the evolving landscape of biomedical biometrics, where multimodal approaches are increasingly crucial for reliable user authentication, this research presents a comprehensive study. The primary focus is on the construction and performance evaluation of a robust big data prediction model within a cloud computing environment. The advent of big data and cloud computing has revolutionized the field of biomedical biometrics, offering immense potential for advanced data analysis and prediction. This research presents the development and evaluation of a robust prediction model for multimodal biometric data in biomedical applications. The proposed model incorporation of Reliable Discrete Variable Topology (RDVT) into the prediction model. RDVT introduces a topological data structure that enhances data reliability and ensures the integrity of multimodal biometric information. The construction and training of the prediction model are meticulously detailed, encompassing data preprocessing, feature extraction, clustering, classification, and model evaluation. Additionally, the integration of a fuzzy clustering algorithm enhances the model's ability to handle uncertainty and imprecision in biometric data. The advancement of multimodal biometrics in the biomedical field by introducing the Reliable Discrete Variable Topology (RDVT) and a big data prediction model based on a fuzzy clustering algorithm in a cloud computing environment. The model's performance is rigorously assessed through extensive experimentation, including accuracy, precision, recall, and F1-score measurements.

This paper concentrates on how to mine useful information from massive XML documents in cloud computing environment. The structure of the Cloud computing and the corresponding tree data model of a XML document are analyzed in advance. Afterwards, structure of the proposed XML data mining system is illustrated, which is made up of three layers, such as “Application layer”, “Data processing layer”, and “XML Data converting layer”. In the XML Data converting layer, XML data are collected from databases and documents, and then the source data can be converted to XML file effectively. In the data processing layer, the process of data selection, cleaning and standardization for XML data set is implemented, moreover, a XML data set with higher degree of structure and rich semantics are obtained. In the application layer, “the results report module”, “data query module” and “results analysis module” are included. Next, massive XML data mining algorithm is proposed. The main innovations of this algorithm lie in that 1) the structure of a XML document is represented as an unordered tree, 2) the sub-structures of a XML document are modeled as sub-trees, and XML trees are regarded as a forest which is made up of all the sub-trees. Experimental results show that the proposed method can effectively mine useful information from massive XML documents in cloud computing environment with high efficiency.

Computers play essential roles in variety of fields to make their timely work efficient, with the help of high-performance computing architecture like Distributed Computing, Grid architecture, Cluster and Cloud technology. Cloud Computing is experiencing rapid development in both Academic and Industry. A Cloud Environment is a computing model with diverse capabilities which enables users with appropriate and need based access to reciprocated pool of computing resources like networking facility, storage servers, application servers and other services. Essential part of Cloud environment is proficient allocation of resources to user with suitable task. The Cloud Computing Environment has anthology of uncountable nodes, variety of resources, with potential challenges in task scheduling and execution. This paper presents a systematic study on Cloud Computing Environment with an assortment of optimization factors and its functionality in Cloud Computing.KeywordsCloud ComputingTask schedulingOptimization factorsTask allocation

The number of digital health records made each day will likely keep increasing a lot. Medical images have important details that help doctors make correct diagnoses and start treatments early. Securing medical images in a cloud computing environment is important. Medical images contain sensitive patient information. This information must be protected to keep it private and safe. This paper systematically reviews the current literature from leading academic databases including (ReseachGate, ScienceDirect, Scopus) to examine need for Medical Images security in Cloud Computing Environments. The findings reveal that cloud systems store data online, which makes it easier to access but also more vulnerable to security threats. To protect medical images, strong security measures are needed. These measures include encryption, access control, and regular monitoring. Proper security ensures that only authorized personnel can view or modify the images. It also helps prevent data breaches and unauthorized access. Protecting medical images in the cloud is necessary to maintain patient confidentiality and trust. This paper reviews and classify the issues, challenges and needs for securing medical images in cloud computing environments which a hot area that needs to be addressed by future research.