PurposeThis paper follows a systematic literature review (SLR) method covering the published studies until March 2021. The authors have extracted the related studies from different online databases ...utilizing quality-assessment-criteria. In order to review high-quality studies, 32 papers have been chosen through the paper selection process. The selected papers have been categorized into three main groups, decision-making methods (17 papers), meta-heuristic methods (8 papers) and fuzzy-based methods (7 papers). The existing methods in each group have been examined based on important qualitative parameters, namely, time, cost, scalability, efficiency, availability and reliability.Design/methodology/approachCloud computing is known as one of the superior technologies to perform large-scale and complex computing. With the growing tendency of network service users to utilize cloud computing, web service providers are encouraged to provide services with various functional and non-functional features and supply them in a service pool. In this regard, choosing the most appropriate services to fulfill users' requirements becomes a challenging problem. Since the problem of service selection in a cloud environment is known as a nondeterministic polynomial time (NP)-hard problem, many efforts have been made in recent years. Therefore, this paper aims to study and assess the existing service selection approaches in cloud computing.FindingsThe obtained results indicate that in decision-making methods, the assignment of proper weights to the criteria has a high impact on service ranking accuracy. Also, since service selection in cloud computing is known as an NP-hard problem, utilizing meta-heuristic algorithms to solve this problem offers interesting advantages compared to other approaches in discovering better solutions with less computational effort and moving quickly toward very good solutions. On the other hand, since fuzzy-based service selection approaches offer search results visually and cover quality of service (QoS) requirements of users, this kind of method is able to facilitate enhanced user experience.Research limitations/implicationsAlthough the current paper aimed to provide a comprehensive study, there were some limitations. Since the authors have applied some filters to select the studies, some effective works may have been ignored. Generally, this paper has focused on journal papers and some effective works published in conferences. Moreover, the works published in non-English formats have been excluded. To discover relevant studies, the authors have chosen Google Scholar as a popular electronic database. Although Google Scholar can offer the most valid approaches, some suitable papers may not be observed during the process of article selection.Practical implicationsThe outcome of the current paper will be useful and valuable for scholars, and it can be a roadmap to help future researchers enrich and improve their innovations. By assessing the recent efforts in service selection in cloud computing and offering an up-to-date comparison of the discussed works, this paper can be a solid foundation for understanding the different aspects of service selection.Originality/valueAlthough service selection approaches have essential impacts on cloud computing, there is still a lack of a detailed and comprehensive study about reviewing and assessing existing mechanisms in this field. Therefore, the current paper adopts a systematic method to cover this gap. The obtained results in this paper can help the researchers interested in the field of service selection. Generally, the authors have aimed to specify existing challenges, characterize the efficient efforts and suggest some directions for upcoming studies.
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in ...many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.
•Providing an outline of the challenges available in the problem domain associated with the coloring of the graph.•Providing a synopsis of insights and implementation of optimization techniques in ...the graph coloring, as well as describing trends in applications of the meta-heuristic algorithms for GCP.•Highlighting the significance of meta-heuristic optimization algorithms, and several advantages they provide to tackle challenges encountered in the graph coloring.•Organizing related open issues and a few insights to solve current problems of reviewed algorithms.
Typically, Graph Coloring Problem (GCP) is one of the key features for graph stamping in graph theory. The general approach is to paint at least edges, vertices, or the surface of the graph with some colors. In the simplest case, a kind of coloring is preferable in which two vertices are not adjacent to the same color. Similarly, the two edges in the same joint should not have the same color. In addition, the same goes for the surface color of the graph. This is one of the NP-hard issues well studied in graph theory. Therefore, many different meta-heuristic techniques are presented to solve the problem and provide high performance. Seemingly, regardless of the importance of the nature-stimulated meta-heuristic methods to solve the GCP, there is not any inclusive report and detailed review about overviewing and investigating the crucial problems of the field. As a result, the present study introduces a wide-ranging reporting of nature- stimulated meta-heuristic methods, which are used throughout the graph coloring. The literature review contains a classification of significant techniques. This study mainly aims at emphasizing the optimization algorithms to handle the GCP problems. Furthermore, the advantages and disadvantages of the meta-heuristic algorithms in solving the GCP and their key issues are examined to offer more advanced meta-heuristic techniques in the future.
In recent years, the attractiveness of Peer-to-Peer (P2P) networks has been grown rapidly due to the easiness of use. The P2P system is a decentralized relationship model in which every party has the ...analogous abilities and either party can start a relationship session. In these networks, due to the high number of users, the resource discovery process becomes one of the important parts of the P2P networks. But, in many previously proposed methods, there is a common problem that is called load balancing. If the balance of workload is inefficient, it reduces the resource utilization. Therefore, in this article, we propose the Inverted Ant Colony Optimization (IACO) algorithm, a variety of the basic Ant Colony Optimization (ACO) algorithm, to improve load balancing among the peers. In the proposed method, the effect of pheromone on the selected paths by ants is inverted. In this approach, ants start to traverse the graph from the requester peer and each ant chooses the best peer for moving. Then, requirements and pheromone amount are updated. Finally, we simulate the method and evaluate its performance in comparison to the ACO algorithm in different terms. The obtained results show that the performance of the IACO is better than the ACO algorithm in terms of load balancing, waiting time and resource utilization.
Quantum-dot cellular automata (QCA) nanotechnology is a practical suggestion for replacing present silicon-based technologies. It provides many benefits, such as low power usage, high velocity, and ...an extreme density of logic functions on a chip. In contrast, designing circuits with no waste of information (reversible circuits) may further reduce energy losses. The Feynman gate has been recognized as one of the most famous QCA-based gates for this purpose. Since reversible gates are significant, this paper develops a new optimized reversible double Feynman gate that uses efficient arithmetic elements as its key structural blocks. Additionally, we used several modeling principles to make it consistent and more robust against noise. Moreover, we examined the suggested model and compared it to the previous models regarding the complexity, clocking, number of cells, and latency. Furthermore, we applied QCADesigner to monitor the outline and performance of the proposed gate. The results show an acceptable improvement via the designed double Feynman gate in comparison to the existing designs. Finally, the temperature and cost analysis indicated the efficiency of the proposed nan-scale gate.
Summary
Cloud computing has appeared as a technology allowing a company to employ computing resources such as applications, software, and hardware to calculate over the Internet. Scholars have paid ...great attention to cloud computing because of its cutting‐edge availability, cost decrement, and boundless applications. A cloud database is a data storage site on the web where the optimal path is spotted to access the needed database. So, placing the ideal path to a database is crucial. The cloud database defined the scheduling problem to choose the perfect route. Cloud database path scheduling is a multifaceted procedure consisting of congestion control, routing list, and network flow distribution. It has a postponement in searching for the needed source route from the cloud database. Offering numerous infinite resources with the growing database workload is an NP‐Hard optimization problem where the query request needs optimal schedules to respond to the required services. So, we have used a hybrid cuckoo search (CS) and genetic algorithm (GA), motivated by a social bird's phenomenon, to solve this problem. Integrating genetic operators has dramatically enhanced the balance between the capability of searching and utilization.
Combining the CS and GA for solving database route scheduling problems in cloud computing
Reducing the completion time of a database and the consumed cost of creating a query plan in the cloud database route.
Some new technologies such as Quantum-dot Cellular Automata (QCA) is suggested to solve the physical limits of the Complementary Metal-Oxide Semiconductor (CMOS) technology. The QCA as one of the ...novel technologies at nanoscale has potential applications in future computers. This technology has some advantages such as minimal size, high speed, low latency, and low power consumption. As a result, it is used for creating all varieties of memory. Counter circuits as one of the important circuits in the digital systems are composed of some latches, which are connected to each other in series and actually they count input pulses in the circuit. On the other hand, the reversible computations are very important because of their ability in reducing energy in nanometer circuits. Improving the energy efficiency, increasing the speed of nanometer circuits, increasing the portability of system, making smaller components of the circuit in a nuclear size and reducing the power consumption are considered as the usage of reversible logic. Therefore, this paper aims to design a two-bit reversible counter that is optimized on the basis of QCA using an improved reversible gate. The proposed reversible structure of 2-bit counter can be increased to 3-bit, 4-bit and more. The advantages of the proposed design have been shown using QCADesigner in terms of the delay in comparison with previous circuits.
Profound attention to MapReduce framework has been caught by many different areas. It is presently a practical model for data-intensive applications due to its simple interface of programming, high ...scalability, and ability to withstand the subjection to flaws. Also, it is capable of processing a high proportion of data in distributed computing environments (DCE). MapReduce, on numerous occasions, has proved to be applicable to a wide range of domains. However, despite the significance of the techniques, applications, and mechanisms of MapReduce, there is no comprehensive study at hand in this regard. Thus, this paper not only analyzes the MapReduce applications and implementations in general, but it also provides a discussion of the differences between varied implementations of MapReduce as well as some guidelines for planning future research.
Today, Complementary Metal-Oxide-Semiconductor (CMOS) technology faces critical challenges, such as power consumption and current leakage at the nanoscale. Therefore, Atomic Silicon Dangling Bond ...(ASDB) technology has been proposed as one of the best candidates to replace CMOS technology; due to its high-speed switching and low power consumption. Among the most important issues in ASDB nanotechnology, output stability and robustness against possible faults may be focused. This paper first introduces a novel P-shaped pattern in ASDB, for designing stable and robust primitive logic gates, including AND, NAND, OR, NOR and XOR. Then, two combinational circuits, half-subtractor and full-subtractor, are proposed by the proposed ASDB gates. The simulation results show high output stability as well as adequate robustness, against various defects obtained by the proposed designs; on average, they have improvements of more than 56% and 62%, against DB omission defects and extra cell deposition defects; respectively. Also, the results of the investigations show that the proposed circuits have been improved by 65%, 21% and 2%, in terms of occupied area, energy and occurrence, respectively; compared to the previous works.
Offloading is one of the critical enablers of the Internet of Things (IoT) as it helps overcome the resource limitations of individual objects. Offering enough computational power for IoT ...applications at the edge has become a severe problem. An intelligent edge is a potential approach for pushing intelligence to network edges, which has played the role of intelligent decision-making in many elements of edge, notably task offloading. By leveraging the edge, IoT devices with limited battery capacity can offload a portion of the tasks that can dramatically reduce latency and improve battery life. Because IoT devices have limited battery capacity, employing deep learning approaches in such devices results in higher energy consumption. As a result, several studies used energy harvester modules, which are not available to IoT devices in real-world scenarios because many IoT devices lack such modules. This article proposes the offloading problem by leveraging the Markov decision process. Furthermore, we built a lightweight version of the reinforcement learning technique to decrease complexity and deployed it in IoT devices. Then, we used a convolutional neural network to accelerate learning and put it on the edge platform. Throughout the entire working duration of the system, these two methods collaborate to provide the optimal offloading strategy. Also, transfer learning was used to initialize Q-table values to improve the system's efficiency. The results showed that the proposed method outperforms five benchmarks in terms of delay by 3.3%, IoT device efficiency by 3.2%, energy use by 4.2%, and task failure rate by 2.9% on average.
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