A Dynamic Scalable Auto-Scaling Model as a Load Balancer in the Cloud Computing Environment

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

Abstract: Cloud computing has transformed user services by introducing a new model that allows for pay-per-use access to information technology services at any time and from any location. Numerous businesses are moving their operations to the cloud as a result of the flexibility offered by cloud services, and additional data centers are being set up by service providers to provide users with services. However, it is essential to ensure efficient resource utilization and cost-effective task execution. Based on load balancing, task scheduling, resource management, quality of service, and workload management, several methods to improve performance and resource use have been reported in the literature. Data centers can use load balancing in the cloud to prevent virtual machines from being overloaded or under loaded, which is a problem in and of itself in cloud computing. As a result, developers and researchers must develop and implement a suitable load balancer for distributed and parallel cloud environments. In order for researchers to develop algorithms that are more effective, this survey provides a cutting-edge review of issues and obstacles associated with existing load-balancing methods.

 Abstract—Cloud computing is an emerging technology that is still unclear to many security problems. Ensuring the security ofstored data in cloud servers is one of the most challenging issues in such environments. Accordingly, this schema presents a Hybrid Encryption algorithm that is based on RSA Small-e and Efficient RSA (HE-RSA) for improving the reliability in cloud computing environments. The main aim of this project is to use the cryptography concepts in cloud computing communications and to increase the security of encrypted data in cloud servers with the least consumption of time and cost at the both of encryption and decryption processes.In the proposed model, the number of key generation exponents has been increased in comparison to the original RSA. Moreover,a dual encryption process has been applied in this algorithm to prevent common attacks against RSA algorithm. In addition, this schema also presents the comparison between HE-RSA and original RSA in terms of security and performance. For this purpose, Key Generation, Encryption and Decryption Time in Original RSA and HE-RSA have been compared according to the different size of exponents. Moreover, some of the common attacks against RSA algorithm have been analysed to detect the resistance of the proposed algorithm against possible attacks. Finally, the effects of using HE-RSA on cloud computing environments have been reviewed in more depth and details.

Objectives: To enlighten the issues and challenges of Load Balancing Algorithm (LBA) for cloud computing environment. In this study the authors proposed a LBA for cloud computing server. Methods/Statistical Analysis: The numerous LBAs are compared with objective, merit, demerit and challenges of the LBA in terms of parameters such as performance, response time, scalability, overhead and many more. This study facilitates identification of efficient LBA for optimizes resource use, minimum response time, maximum throughput and avoidance of overload. Findings: The load balancer is permitted in most instances to provide support continuity as well as dealing with extra traffic. Consequently the useful load balancing algorithms required to find inexpensive resource usage by provisioning of resources to cloud users. The use of proposed algorithm is to improve the overhead and load imbalance factor of the system. Application: The Load Balancing (LB) is a vital feature of cloud computing environment. The economical LB algorithm assures cost effective resource usage by provisioning of resources to cloud users on demand schedule in pay-as-you-say-manner. The LB may actually provide assistance prioritizing users by using suitable scheduling requirements and also it is crucial part in cloud computing for maintaining the work load across various systems over the network and gives minimum processing time, minimum response time and ignores the workload.

The concept oft Cloud computing has significantly changed the field of parallel and distributed computing systems today. Cloud computing enables a wide range of users to access distributed, scalable, virtualized hardware and/or software infrastructure over the Internet. Load balancing is a methodology to distribute workload across multiple computers, or other resources over the network links to achieve optimal resource utilization, maximize throughput, minimum response time, and avoid overload. With recent advent of technology, resource control or load balancing in cloud computing is main challenging issue. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to utilize resources efficiently. This paper presents a review of a few load balancing algorithms or technique in cloud computing. The objective of this paper is to identify qualitative components for simulation in cloud environment and then based on these components, execution analysis of load balancing algorithms are also presented.

Resources are provided mainly in the form of services in cloud computing. In the distribute environment of cloud computing, how to find the needed services efficiently and accurately is the most urgent problem in cloud computing. In cloud computing, services are the intermediary of cloud platform, services are connected by lots of service providers and requesters and construct the complex heterogeneous network. The traditional recommendation systems only consider the functional and non-functional requirements of services but ignore the links between providers and requesters of service, which result to the service position is not accurate. Focus on the problems, this study intends to model the relationship of the cloud services participants with the format of heterogeneous information network, which intend to mine the hidden relationships between services participants in the cloud computing environment. In theoretical research, we proposed a cloud service heterogeneous network extraction and automatic maintenance model, proposed a new service recommendation system based on heterogeneous service network ranking and clustering.

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

With the rapid development of computers and the Internet, information technology has brought about great changes to our educational field. At present the ubiquitous “cloud service” has brought education into “the cloud era”. Cloud computing has found more applications in our English teaching. Integrating the technology of “cloud computing” with the theory of “multimodal discourse analysis”, this paper researches how to carry out multimodal English teaching in the cloud computing environment.

Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments

Cloud computing has become biggest buzz in the computer era these days. It runs entire operating systems on the cloud and do everything on cloud to store data off-site. Cloud computing is primarily based on grid computing, but it’s a new computational model. Cloud computing has emerged into a new opportunity to further enhance way of hosting data centre and provide services. The primary substance of cloud computing is to deal the computing power, storage, different sort of stages and services which assigned to the external users on demand through the internet. Task scheduling in cloud computing is vital role optimisation and effective dynamic resource allocation for load balancing. In cloud, the issue focused is under utilisation and over utilisation of the resources to distribute workload of multiple network links for example, when cloud clients try to access and send request to the same cloud server while the other cloud server remain idle at that moment, leads to the unbalanced of workload on cloud data centers. Thus, load balancing is to assign tasks to the individual cloud data centers of the shared system so that no single cloud data centers is overloaded or under loaded. A Hybrid approach of Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm is combined in order to get effective response time. The proposed hybrid algorithm has been experimented by using CloudSim simulator. The result shows that the hybrid load balancing algorithm improves the cloud system performance by reducing the response time compared to the Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm. Â

Cloud computing and big data: Technologies and applications

Managing Cloud Computing Environment: Gaining Customer Trust with Security and Ethical Management

In recent years, the number of users and service providers are increasing in using cloud services so the accessibility and the effective management of the required resources, irrespective of the time and place, seem to be of great importance for both sides.Improving the performance and utilization of the cloud systems are gained by the auto-scaling of the applications; this is because of the fact that, some approaches have been proposed for auto scaling.This paper seeks to checking some value, based on the learning automata, for the scalability of the web applications, which combines virtual machine clusters and the learning automata in order to provide the best possible way for the scaling up and scaling down of the virtual machines.The results of this study indicate how an increased capacity of virtual machine which have been done by the value of thresholds could effect on SLA and overhead of responding.

Privacy-preserving multi-party logistic regression in cloud computing

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.