Despite significant infrastructure improvements, cloud computing still faces numerous challenges in terms of load balancing. Several techniques have been applied in the literature to improve load balancing efficiency. Recent research manifested that load balancing techniques based on metaheuristics provide better solutions for proper scheduling and allocation of resources in the cloud. However, most of the existing approaches consider only a single or few QoS metrics and ignore many important factors. The performance efficiency of these approaches is further enhanced by merging with machine learning techniques. These approaches combine the relative benefits of load balancing algorithm backed up by powerful machine learning models such as Support Vector Machines (SVM). In the cloud, data exists in huge volume and variety that requires extensive computations for its accessibility, and hence performance efficiency is a major concern. To address such concerns, we propose a load balancing algorithm, namely, Data Files Type Formatting (DFTF) that utilizes a modified version of Cat Swarm Optimization (CSO) along with SVM. First, the proposed system classifies data in the cloud from diverse sources into various types, such as text, images, video, and audio using one to many types of SVM classifiers. Then, the data is input to the modified load balancing algorithm CSO that efficiently distributes the load on VMs. Simulation results compared to existing approaches showed an improved performance in terms of throughput (7%), the response time (8.2%), migration time (13%), energy consumption (8.5%), optimization time (9.7%), overhead time (6.2%), SLA violation (8.9%), and average execution time (9%). These results outperformed some of the existing baselines used in this research such as CBSMKC, FSALB, PSO-BOOST, IACSO-SVM, CSO-DA, and GA-ACO.
Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These approaches often adopt multi-objective QoS metrics, such as reduced SLA violations, reduced makespan, high throughput, low overload, low energy consumption, high optimization, minimum migrations, and higher response time. The cloud applications are generally computation-intensive and can grow exponentially in memory with the increase in size if no proper effective and efficient load balancing technique is adopted resulting in poor quality solutions. To provide a better load balancing solution in cloud computing, with extensive data, a new hybrid model is being proposed that performs classification on the number of files present in the cloud using file type formatting. The classification is performed using Support Vector Machine (SVM) considering various file formats such as audio, video, text maps, and images in the cloud. The resultant data class provides high classification accuracy which is further fed into a metaheuristic algorithm namely Ant Colony Optimization (ACO) using File Type Formatting FTF for better load balancing in the cloud. Frequently used QoS metrics, such as SLA violations, migration time, throughput time, overhead time, and optimization time are evaluated in the cloud environment and comparative analysis is performed with recent metaheuristics, such as Ant Colony Optimization-Particle Swarm Optimization (ACOPS), Chaotic Particle Swarm Optimization (CPSO), Q-learning Modified Particle Swarm Optimization (QMPSO), Cat Swarm Optimization (CSO) and D-ACOELB. The proposed algorithm outperforms them and provides good performance with scalability and robustness.
Summary The performance of backhaul networks is the major concern for internet service providers, cloud service providers, and data centers for redundancy and reliability. With the integration of new data communication technologies, the system should be more smart and intelligent to handle data traffic coming from new networks like 5G. This new 5G standard provides connectivity among devices where all the traffic is handled by backbone and backhaul networks. The devices and nodes need multiple interfaces, but the TCP/IP standard uses a single path per connection for data communication. This limitation leads to the degradation of network performance and communication failures. There is a need to enhance data throughput and performance by using available paths and interfaces. Multipath Transport Control Protocol (MTCP) is one of the advanced extension of the TCP/IP standard for using available interfaces. This paper proposed an MTCP based infrastructure for 5G networks to handle all the coming data from these networks and provide fast reliable data handling. The proposed infrastructure is evaluated in simulation to check its performance with existing standards. The results indicate that the proposed infrastructure is minimizing the convergence time in case of any failure in the main path.
Quality can never be an accident and therefore, software engineers are paying immense attention to produce quality software product. Source code readability is one of those important factors that play a vital role in producing quality software. The code readability is an internal quality attribute that directly affects the future maintenance of the software and reusability of same code in similar other projects. Literature shows that readability does not just rely on programmer's ability to write tidy code but it also depends on programming language's syntax. Syntax is the most visible part of any programming language that directly influence the readability of its code. If readability is a major factor for a given project, the programmers should know about the language that they shall choose to achieve the required level of quality. For this we compare the readability of three most popular high-level programming languages; Java, C#, and C++. We propose a comprehensive framework for readability comparison among these languages. The comparison has been performed on the basis of certain readability parameters that are referenced in the literature. We have also implemented an analysis tool and performed extensive experiments that produced interesting results. Furthermore, to judge the effectiveness of these results, we have performed statistical analysis using SPSS (Statistical Package for Social Sciences) tool. We have chosen the Spearman's correlation ad Mann Whitney's T-test for the same. The results show that among all three languages, Java has the most readable code. Programmers should use Java in the projects that have code readability as a significant quality requirement.
Human beings reflect nomadic behaviour as they keep on travelling place to place whole day for personal or organizational purposes. The inception of modern networking technologies and the advent of wide range of applications in terms of services and resources have facilitated the users in many ways. The advancements in numerous areas such as embedded systems, WN (Wireless Networks), mobile and context-aware computing, anticipated pervasive computing dominated the human communication at large.Pervasive computing refers to the environment where information is accessible anywhere and anytime while existing system is invisible to the user. On the other hand, the invisibility of pervasive computing is also a problem in its adoption as users are unaware when and what devices collect their personal data and how it is being used. It has caused new security chaos as the more information about user is collected the more privacy and security concerns it raises, thus, the pervasive computing applications became key concern for user. This paper is aimed at analyzing the security and protection issues that arise while traveling from place to place connected with wireless mobile networks. The paper reviews many existing systems that offer possible security to pervasive users. An easy, precise and relative analysis and evaluation of surveyed pervasive systems are presented and some future directions are highlighted. Fig. 1. The advance computing capability of mobile devices made it possible to communicate where ever and whenever required. This computing capacity available in most of the daily use devices is characterized as pervasive computing. Pervasive computing is often referred as mobile computing or nomadic computing. 1. T echnology has changed the formation of communication world. According to the needs of society and industry, the modern day has witnessed bursting advancements in the applications of communication technology. The functionalities of mobile devices are increasing day-by-day and today technology has shrunken the world as illustrated in This is an open access article published by Mehran University Research Journal of Engineering and Technology, Jamshoro under the CC by 4.0 International License.
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