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During high demand for the virtualized resources in cloud environment, efficient task scheduling achieves the desired performance criteria by balancing the load in the system.

It is a task scheduling approach used for load balancing in cloud environment. Task scheduling in such an environment is used for the task execution on a suitable resource by considering some parameters and constraints to achieve performance.

The presented mechanism is an extension of the previous proposed work quality of service (QoS)-enabled join minimum loaded queue (JMLQ) (Sharma et al., 2019c). The proposed approach has been tested in the CloudSim simulator, and the results show that the proposed approach achieves better results in comparison to QoS-enabled JMLQ and its other variants in the cloud environment.

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Determining the roles of multiple CSPs is important because it affects job costs and time off. The primary objective of this work is to ensure an efficient and complex distribution of resources in cloud-based computing. Workflow study of various algorithms such as ant colony optimization (ACO), differential evolution algorithm, genetic algorithm, particle swarm optimization (PSO), hybridization of the above algorithms (ADGP). For research, CSP’s tools are put all over the world.

The main objective of this study is to effectively introduce cloud-based computing in CSPs. The algorithm minimizes resource response time and overall workflow tasks. It seeks to improve load balancing by modifying the algorithm to support load balancing. In the proposed multipurpose scheduling methods, the ADGP algorithm performs better than any other proposed algorithm during the resource response. This algorithm was found to be superior to the selected 200 sources and thousands of tasks. It reduces resource response time by copying service nodes through several sites. As this algorithm moves faster to the best solution, the response time of the resource is reduced compared to other algorithms.

Hybrid ACOs perform best when it comes to resource management when workloads are uniformly spread across multiple virtual machines. However, hybrids PSOs are better suited to choosing the best options to minimize costs. Overall, an optimal cloud-based scheduling solution can be successfully simulated using CloudSim in CSP to share resources between end-users to support consumers and users effectively.

Hybrid ACOs perform best when it comes to resource management when workloads are uniformly spread across multiple virtual machines. However, hybrids PSOs are better suited to choosing the best options to minimize costs. Overall, an optimal cloud-based scheduling solution can be successfully simulated using CloudSim in CSP to share resources between end-users to support consumers and users effectively.

Cloud computing provides a dynamic, heterogeneous and elastic environment by offering accessible ‘cloud services’ to end-users. The tasks involved in making cloud services available, such as matchmaking, selection and composition, are essential and closely related to each other. Integration of these tasks is critical for optimal composition and performance of the cloud service platform. More efficient solutions could be developed by considering cloud service tasks collectively, but the research and academic community have so far only considered these tasks individually. The purpose of this paper is to propose an integrated QoS-based approach for cloud service matchmaking, selection and composition using the Semantic Web.

In this paper, the authors propose a new approach using the Semantic Web and quality of service (QoS) model to perform cloud service matchmaking, selection and composition, to fulfil the requirements of an end user. In the Semantic Web, the authors develop cloud ontologies to provide semantic descriptions to the service provider and requester, so as to automate the cloud service tasks. This paper considers QoS parameters, such as availability, throughput, response time and cost, for quality assurance and enhanced user satisfaction.

This paper focus on the development of an integrated framework and approach for cloud service life cycle phases, such as discovery, selection and composition using QoS, to enhance user satisfaction and the Semantic Web, to achieve automation. To evaluate performance and usefulness, this paper uses a scenario based on a Healthcare Decision-Making System (HDMS). Results derived through the experiment prove that the proposed prototype performs well for the defined set of cloud-services tasks.

As a novel concept, our proposed integrated framework and approach for cloud service matchmaking, selection and composition based on the Semantic Web and QoS characterisitcs (availability, response time, throughput and cost), as part of the service level agreement (SLA) will help the end user to match, select and filter cloud services and integrate cloud-service providers into a multi-cloud environment.

Current industrial scenario is largely dependent on cloud computing paradigms. On-demand services provided by cloud data centre are paid as per use. Hence, it is very important to make use of the allocated resources to the maximum. The resource utilization is highly dependent on the allocation of resources to the incoming request. The allocation of requests is done with respect to the physical machines present in the datacenter. While allocating the tasks to these physical machines, it needs to be allocated in such a way that no physical machine is underutilized or over loaded. To make sure of this, optimal load balancing is very important.

The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks. The major focus of the proposed work is to optimize the load balancing in a datacenter. When optimization happens, none of the physical machine is neither overloaded nor under-utilized, hence resulting in efficient utilization of the resources.

The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load (RR) ant colony optimization (ACO), artificial bee colony (ABC) with respect to the selected parameters response time, virtual machine migrations, host shut down and energy consumption. All the four parameters gave a positive result when the algorithm is simulated.

The contribution of this paper is towards the domain of cloud load balancing. The paper is proposing a novel approach to optimize the cloud load balancing process. The results obtained show that response time, virtual machine migrations, host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study. The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.

Cloud computing is becoming increasingly popular as it facilitates convenient, ubiquitous, on-demand network access to a shared pool of configurable computing resources and applications that can be quickly retrieved and released. Despite its numerous merits, it faces setbacks in data security and privacy. Data encryption is one of the most popular solutions for data security in the cloud. Various encryption algorithms have been implemented to address security concerns. These algorithms have been reviewed along with the Jumbling Salting algorithm and its applications. The framework for using Jumbling Salting to encrypt text files in the cloud environment (CloudJS) has been thoroughly studied and improvised. The purpose of this paper is to implement the CloudJS algorithm, to discuss its performance and compare the obtained results with existing cloud encryption schemes.

The paper uses six research questions to analyze the performance of CloudJS algorithm in the cloud environment. The research questions are about measuring encryption time and throughput, decryption time and throughput, the ratio of cipher to the plain text of CloudJS algorithm with respect to other Cloud algorithms like AES and DES. For this purpose, the algorithm has been implemented using dockers-containers in the Linux environment.

It was found that CloudJS performs well in terms of encryption time, decryption time and throughput. It is marginally better than AES and undoubtedly better than DES in these parameters. The performance of the algorithm is not affected by a number of CPU cores, RAM size and Line size of text files. It performs decently well in all scenarios and all resultant values fall in the desired range.

CloudJS can be tested with cloud simulation platforms (CloudSim) and cloud service providers (AWS, Google Cloud). It can also be tested with other file types. In the future, CloudJS algorithm can also be implemented in images and other files.

To the best of the knowledge, this is the first attempt to implement and analysis of a custom encryption algorithm (CloudJS) in the cloud environment using dockers-containers.

Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.

VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.

The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.

User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

Cloud Computing has become a more promising technology with potential opportunities, through reducing the high cost of running the traditional business applications and by leading to new business models. Nonetheless, this technology is fraught with many challenges. From a Software as a Service (SaaS) provider perspective, deployment choices are one of the major perplexing issues in determining the degree to which the application owners’ objectives are met while considering their customers’ targets. The purpose of this paper is to present a new model that allows the service owner to optimize the resources selection based on defined metrics when responding to many customers’ with various priorities.

More than 65 academic papers have been collected, a short list of the most related 35 papers have been reviewed, in addition to assessing the functionality of major cloud systems. A potential set of techniques has been investigated to determine the most appropriate ones. Moreover, a new model has been built and a study of different simulation platforms has been conducted.

The findings demonstrate that serving many SaaS customer requests, with different agreements and expected outcomes, would have mutual influence that impact the overall provider objectives. Furthermore, this paper investigates how tagging those customers with various priorities, with reflection of their importance to the provider, permits controlling and aligning the selection of computing resources as per the current objectives and defined priorities.

This study provides researchers with a useful literature, which can assist them in relevant subject. Additionally, it uses a value-based approach and particle swarm technique to model and solve the optimization of the computing resource selection, considering different business objectives for both stakeholders, providers and customers. This study derives priority of a number of factors, by which service providers can make strong and adaptive decisions.

The paper includes implications on how the SaaS service provider can make decisions to select the needed virtual machines type driven by his own preferences.

This paper rests on the usage of Particle Swarm Optimization technique to optimize the business value of the service provider, as well as the usage of value-based approach. This will help model that value in order to combine the total profit of the provider and the customer satisfaction, based on the agreed budget and processing time requested by the customer. Another additional approach has been charted by using the customer severity factor that allows the provider to reflect the customer importance while making the placement decision.

Cloud computing services gained huge attention in recent years and many organizations started moving their business data traditional server to the cloud storage providers. However, increased data storage introduces challenges like inefficient usage of resources in the cloud storage, in order to meet the demands of users and maintain the service level agreement with the clients, the cloud server has to allocate the physical machine to the virtual machines as requested, but the random resource allocations procedures lead to inefficient utilization of resources.

This thesis focuses on resource allocation for reasonable utilization of resources. The overall framework comprises of cloudlets, broker, cloud information system, virtual machines, virtual machine manager, and data center. Existing first fit and best fit algorithms consider the minimization of the number of bins but do not consider leftover bins.

The proposed algorithm effectively utilizes the resources compared to first, best and worst fit algorithms. The effect of this utilization efficiency can be seen in metrics where central processing unit (CPU), bandwidth (BW), random access memory (RAM) and power consumption outperformed very well than other algorithms by saving 15 kHz of CPU, 92.6kbps of BW, 6GB of RAM and saved 3kW of power compared to first and best fit algorithms.

The proposed multi-objective bin packing algorithm is better for packing VMs on physical servers in order to better utilize different parameters such as memory availability, CPU speed, power and bandwidth availability in the physical machine.

The concept of business process (BP) as a service is a new solution in enterprises for the purpose of using specific BPs. BPs represent combinations of software services that must be properly executed by the resources provided by a company’s information technology infrastructure. As the policy requirements are different in each enterprise, processes are constantly evolving and demanding new resources in terms of computation and storage. To support more agility and flexibility, it is common today for enterprises to outsource their processes to clouds and, more recently, to cloud federation environment. Ensuring the optimal allocation of cloud resources to process service during the execution of workflows in accordance with user policy requirements is a major concern. Given the diversity of resources available in a cloud federation environment and the ongoing process changes required based on policies, reallocating cloud resources for service processing may lead to high computational costs and increased overheads in communication costs.

This paper presents a new adaptive resource allocation approach that uses a novel algorithm extending the natural-based intelligent water drops (IWD) algorithm that optimizes the resource allocation of workflows on the cloud federation which can estimate and optimize final deployment costs. The proposed algorithm is implemented and embedded within the WokflowSim simulation toolkit and tested in different simulated cloud environments with different workflow models.

The algorithm showed noticeable enhancements over the classical workflow deployment algorithms taking into account the challenges of data transfer. This paper made a comparison between the proposed IWD-based workflow deployment (IWFD) algorithm with other proposed algorithms. IWFD presented considerable improvements in the makespan, cost and data transfer in most situations in the cloud federation environment.

An extension for WorkflowSim to support the implementation of BPs in a federation cloud space regarding BP policy. Optimize workflow execution performance in Federated clouds by means of IWFD algorithm.

Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.

Cloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.

The energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.

This paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.