Resources In Cloud Environment Research Articles

Achieving Agility through Auto-Scaling: Strategies for Dynamic Resource Allocation in Cloud Computing

Abstract: Auto-scaling is a crucial aspect of cloud computing, allowing for the efficient allocation of computational resources in response to immediate demand. This article delves into the concept of auto-scaling, its key components, and the strategies used to effectively manage resources in cloud environments. This study emphasizes the importance of auto-scaling in the cloud computing landscape by exploring its benefits, including cost efficiency, performance optimization, high availability, and scalability [1]. The article explores the various factors to consider when implementing scaling policies, such as selecting the right approach for scaling, whether it be predictive or reactive and the availability of auto-scaling services provided by major cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure [2, 3]. In addition, the paper addresses the challenges and complexities related to configuring auto-scaling systems, cost management, and latency in resource provisioning [4]. The article also showcases case studies that illustrate the successful implementation of auto-scaling in different industries, along with valuable insights and recommended approaches [5]. Lastly, this paper delves into future trends and research directions in auto-scaling techniques, integration with emerging technologies, and potential research areas [6].

A neural network framework for optimizing parallel computing in cloud servers

Energy efficiency has become a major focus in optimizing hardware resource usage for Cloud servers. One approach widely employed to enhance the execution of parallel applications is thread-level parallelism (TLP) exploitation. This technique leverages multiple threads to improve computational efficiency and performance. However, the increasing heterogeneity of resources in cloud environments and the complexity of selecting the optimal configuration for each application pose a significant challenge to cloud users due to the massive number of possible configurations and the need to effectively harness TLP in diverse hardware setups to achieve optimal energy efficiency and performance. To address this challenge, we propose TLP-Allocator, an artificial neural network (ANN) optimization strategy that uses hardware and software metrics to build and train an ANN model. It predicts worker node and thread count combinations that provide optimal energy-delay product (EDP) results. In experiments using ten well-known applications on a private cloud with heterogeneous resources, we show that TLP-Allocator predicts combinations that yield EDP values close to the best achieved by an exhaustive search. It also improves the overall EDP by 38.2% compared to state-of-the-art workloads scheduling on cloud environments.

Multiobjective Prioritized Workflow Scheduling in Cloud Computing Using Cuckoo Search Algorithm

Effective workflow scheduling in cloud computing is still a challenging problem as incoming workflows to cloud console having variable task processing capacities and dependencies as they will arise from various heterogeneous resources. Ineffective scheduling of workflows to virtual resources in cloud environment leads to violations in service level agreements and high energy consumption, which impacts the quality of service of cloud provider. Many existing authors developed workflow scheduling algorithms addressing operational costs and makespan, but still, there is a provision to improve the scheduling process in cloud paradigm as it is an nondeterministic polynomial-hard problem. Therefore, in this research, a task-prioritized multiobjective workflow scheduling algorithm was developed by using cuckoo search algorithm to precisely map incoming workflows onto corresponding virtual resources. Extensive simulations were carried out on workflowsim using randomly generated workflows from simulator. For evaluating the efficacy of our proposed approach, we compared our proposed scheduling algorithm with existing approaches, i.e., Max–Min, first come first serve, minimum completion time, Min–Min, resource allocation security with efficient task scheduling in cloud computing-hybrid machine learning, and Round Robin. Our proposed approach is outperformed by minimizing energy consumption by 15% and reducing service level agreement violations by 22%.

SLA Aware Task-Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm

Task scheduling in Cloud Computing paradigm poses new challenges for cloud provider as heterogeneous, diversified tasks arrived on to cloud console. To schedule these type of tasks efficiently on to virtual resources in cloud paradigm, an effective scheduler is needed, which precisely maps tasks to virtual machines by considering priorities of both tasks and VMs. Existing scheduling algorithms failed to map tasks precisely to virtual resources due to high dynamic nature in cloud environment which leads to increase of makespan and SLA violations will be increased. In this paper, authors proposed a task-scheduling mechanism, which considers task priorities and VMs. To model this scheduling paradigm we have chosen whale optimization through which our scheduler will take decisions for scheduling tasks precisely onto virtual resources in cloud environment. Entire simulation was carried out on CloudSim. Initially we have chosen random generated workload to run simulation and after that, we have considered a real-time workload named as BigDataBench and ran our simulation. Finally, we compared our proposed work with classical baseline mechanisms. From simulations we observed that proposed whale scheduler improved makespan for PSO, ACO, GA, and W-schedulers by 20.07%, 17.55%, 19.9%, and 6.35%, respectively, and 17.3%, 17.86%, 17.64%, and 5.93%, respectively, for BigDataBench workloads. SLA violations improved over PSO, ACO, GA, and W-Scheduler by 56.76%, 42.17%, 35.29%, and 24.53%, respectively, and 63.42%, 23.33%, 55.51%, and 40.1%, respectively, for BigDataBench workloads. From extensive simulation results, our proposed scheduler using whale optimization approach minimizes makespan and SLA violations to a great extent.

MBRSDTC: Design of a multimodal bioinspired model to improve resource scheduling efficiency with differential task‐level constraints

AbstractScheduling of resources in cloud environments requires design of multiple pattern analysis models that include but are not limited to, inter‐task dependency pattern analysis, make‐span pattern analysis, virtual machine (VM or resource) based capacity analysis, deadline analysis, and VM‐to‐task compatibility analysis. Existing scheduling models are either highly complex, or do not integrate comprehensive analysis modules for efficient scheduling of resources to tasks. Moreover, some of these models showcase limited scalability when applied to large‐scale deployment scenarios. To overcome these issues, this text proposes design of a multimodal bioinspired model to improve resource‐scheduling efficiency with differential task‐level constraints. The proposed model initially collects multimodal information sets about tasks and underlying resources in order to augment analysis efficiency for different task types. These information sets are initially processed by a Grey Wolf Optimization (GWO)‐based scheduling model that assists in grouping tasks based on their make‐span, deadline and dependency levels. The grouped tasks are then scheduled via an incremental learning Elephant Herding Optimization (EHO) model that assists in assigning grouped tasks to capacity‐tuned resources (or VMs). Due to integration of these optimization methods, the proposed model is capable of improving the efficiency of resource scheduling by 8.5%, while reducing computational complexity by 4.3%, while improving the deadline hit ratio by 5.9%, and lowering energy consumption by 1.5% when compared with standard machine learning based scheduling techniques. Due to which the proposed model is capable of deployment for a wide variety of real‐time scheduling scenarios.

Perspicacious Fuzzy Clustering Franchised by Grasshopper Optimization Dexterity for Robust Resource Scheduling with Effective Load Balancing in Cloud Precedent

Utilizing cloud resources become promising in encroachment of internet technology, countenancing everyone to use resources for little or no cost. It will be very important to have task scheduling for sharing resources in cloud environment. To maintain effective resource usage, cloud technology equally divides workload among shareable resources when it receives task requests. Machine learning and metaheuristic algorithms afford dynamic component for equitable task distribution in cloud paradigm. The current state-of-art unsupervised models-based load balancing arbitrarily selects centroid locations and struggles to achieve incorrect task requests. Using an optimization technique that takes inspiration from behavioral science, study aims to build well-balanced clustering model-based task scheduling system. In order to efficiently schedule tasks among virtual servers in cloud environment, this proposed work styles aids of perspicacious fuzzy and Grass Hopper algorithms. The results showed that PFC-GOD upsurges cloud resources usage while lowering make-span, execution time, and high balance load scheduling.

Efficient Workflow Scheduling algorithm in cloud computing using Whale Optimization

Task Scheduling is a tremendous challenge in cloud computing as tasks are varied in terms of their processing capacity and interdependent on each other i.e. workflows. Therefore, it is difficult to map workflows to suitable virtual resources in cloud environment. Many authors developed various schedulers, which addresses makespan and Quality of service metrics. Authors have not addressed parameters makespan in combination with migration time, which also affects performance of cloud computing environment. In this manuscript, a new workflow-scheduling mechanism is proposed, which takes priorities of tasks and schedules tasks effectively on to corresponding virtual resources. Whale optimization algorithm as the methodology to model this algorithm. Extensive simulations carried out on workflowsim simulator. It was evaluated aganist existing PSO, CS, ACO, GA algorithms. Finally, from simulation results, it was identified that makespan, migration time and energy consumption were minimized to a good extent.

A distributed quality of service-enabled load balancing approach for cloud environment

PurposeDuring high demand for the virtualized resources in cloud environment, efficient task scheduling achieves the desired performance criteria by balancing the load in the system.Design/methodology/approachIt 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.FindingsThe 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.Originality/value90%

Optimization of Resources in Cloud Environment using Nature-Inspired Algorithm

Optimization of Resources in Cloud Environment using Nature-Inspired Algorithm

Market-oriented online bi-objective service scheduling for pleasingly parallel jobs with variable resources in cloud environments

In this paper, we study the market-oriented online bi-objective service scheduling problem for pleasingly parallel jobs with variable resources in cloud environments, from the perspective of SaaS (Software-as-as-Service) providers who provide job-execution services. The main process of scheduling SaaS services in clouds is: a SaaS provider purchases cloud instances from IaaS providers to schedule end users’ jobs and charges users accordingly. This problem has several particular features, such as the job-oriented end users, the pleasingly parallel jobs with soft deadline constraints, the online settings, and the variable numbers of resources. For maximizing both the revenue and the user satisfaction rate, we design an online algorithm for SaaS providers to optimally purchase IaaS instances and schedule pleasingly parallel jobs. The proposed algorithm can achieve competitive objectives in polynomial run-time. The theoretical analysis and simulations based on real-world Google job traces as well as synthetic datasets validate the effectiveness and efficiency of our algorithm.

Scheduling Workflows With Composite Tasks: A Nested Particle Swarm Optimization Approach

Scientific cloud workflows enable the access to distributed computing resources in cloud environments for executing scientific computing applications. In the literature, most workflow scheduling models assume that each workflow task is mapped to only one service instance. But in computation and data-intensive applications, it is common that the computation resources provided by a single service instance are insufficient for some complicated tasks which contain several closely correlated sub-tasks. To manage such complicated workflows, this article devises a novel workflow model with composite tasks (cWFS). The model views a complicated task as a composite task and allows mapping multiple service instances to a composite task. The data transmission among sub-tasks of a composite task can also be addressed by the proposed model. To solve cWFS problem, we devise a nested particle swarm optimization (N-PSO) that utilizes two kinds of populations, i.e., the outer population and inner population. Since N-PSO is a bit time-consuming, we further devise a Fast version of N-PSO (FN-PSO), which can save more than 60 percent of running time compared with N-PSO. The proposed approaches are evaluated on five real-world workflow types. The experimental results verify that the proposed approaches can solve the new workflow model effectively.

Scheduling mechanisms in the cloud environment: a methodological analysis

PurposeToday, with the rapid growth of cloud computing (CC), there exist several users that require to execute their tasks by the available resources to obtain the best performance, reduce response time and use resources. However, despite the significance of the scheduling issue in CC, as far as the authors know, there is not any systematic and inclusive paper about studying and analyzing the recent methods. This paper aims to review the current mechanisms and techniques, which can be addressed in this area.Design/methodology/approachThe central purpose of this paper refers to offering a complete study of the state-of-the-art planning algorithms in the cloud and also instructions for future research. Besides, this paper offers a methodological analysis of the scheduling mechanisms in the cloud environment.FindingsThe central role of this paper is to present a summary of the present issues related to scheduling in the cloud environment, providing a structure of some popular techniques in cloud scheduling scope and defining key areas for the development of cloud scheduling techniques in the future research.Research limitations/implicationsIn this paper, scheduling mechanisms are classified into two main categories include deterministic and non-deterministic algorithms; however, it can also be classified into different categories. In addition, the selection of all related papers could not be ensured. It is possible that some appropriate and related papers were removed in the search process.Practical implicationsAccording to the results of this paper, the requirement for more suitable algorithms exists to allocate tasks for resources in cloud environments. In addition, some principal rules in cloud scheduling should be re-evaluated to achieve maximum productivity and minimize wasted expense and effort. In this direction, to stay away from overloading and under loading of components and resources, the proposed method should execute workloads in an adaptable and scalable way. As the number of users increased in cloud environments, the number of tasks in the cloud that needed to be scheduled proportionally increased. Thus, an efficient mechanism is needed for scheduling tasks in these environments.Originality/valueThe general information gathered in this study makes the researchers acquainted with the state-of-the-art scheduling area of the cloud. Entirely, the answers to the research questions summarized the main objective of scheduling, current challenges, mechanisms and methods in the cloud systems. The authors hope that the results of this paper lead researchers to present more efficient scheduling techniques in cloud systems.

Game theory-based optimization of distributed idle computing resources in cloud environments

With the rapid cloud technological advancement and economic growth, more and more organizations have purchased cloud resources for daily business operations besides building their own IT infrastructures. Thus, it is very important to understand the economy of cloud computing. In this paper, we mainly focus on examining the private idle computing resources owned by various organizations who are willing to form a network of ad hoc cloud provider and sell the services to cloud users. In such a case, the organizations cannot only meet their own demands, but also sell their idle computing resources in the form of ad hoc cloud. Naturally, the organizations, as provider, aim at maximizing their own profit through adjusting business costs and sale prices. Due to the uncertainty of the amount of idle computing resources, dynamic pricing is challenging. We approach the problem from the perspective of game theory and formulate it as a non-cooperative game among multiple organizations, i.e., the game player. For each player, a utility function is used to represent its profits. The players choose request strategies and sales service strategies to maximize the utility function. This paper has proved that there exists Nash equilibrium for this game problem. We proposed an iterative proximal algorithm (IPA) for calculating the Nash equilibrium. After analyzing the convergence of the IPA, we found that the algorithm converges to the Nash equilibrium solution when reasonable conditions are satisfied and conforms to the theoretical proof. Experimental results show that our proposed algorithm can quickly converge to a stable state, and by calculating the appropriate service (resource) request strategies and selling service strategies for all organizations, organizations' profit are increased compared to without IPA algorithm.

Predicting Virtual Machine Resource Consumption Based on Optimized Grey Model

In order to relatively change the computing specifications of virtual machines (VMs for short) for the changing resource consumption of VMs, and reduce the impact of the changes, this paper proposes the optimized grey GM(1, 1) model to predict the resource consumption of VMs. Because VM resources may fluctuate greatly in a short time, the initial data are smoothed first, and then modeled, the model edge value is optimized according to the characteristics of cloud environment. Experiments show that the accuracy and stability of the prediction model can be improved by sorting out the initial data and optimizing the model. The prediction value is helpful to design the allocation scheme of VM resources, improve the utilization rate of physical resources in cloud environment.

Fault Tolerance Based Load Balancing Approach for Web Resources in Cloud Environment

Cloud computing consists group of heterogeneous resources scattered around the world connected through the network. Since high performance computing is strongly interlinked with geographically distributed service to interact with each other in wide area network, Cloud computing makes the architecture consistent, low-cost, and well-suited with concurrent services. This paper presents a fault tolerance load balancing technique based on resource load and fault index value. The proposed technique works in two phases: resource selection and task execution. The resource selection phase selects the suitable resource for task execution. A resource with least resource load and fault index value is selected for task execution. Further task execution phase sets checkpoints at various intervals for saving the task state periodically. The checkpoints are set at various intervals based on resource fault index. When a task is executed on a resource, fault index value of selected resource is updated accordingly. This reduces the checkpoint overhead by avoiding unnecessary placements of checkpoints. The proposed model is validated on CloudSim and provides improved performance in terms of response time, makespan, throughput and checkpoint overhead in comparison to other state-of-the-art methods.

Modified drosophila optimization algorithm for managing re-sources in cloud environment

Optimizing a problem is common among the researchers in all the fields. The worst case of the optimization problem is that when it is not solved by putting lots of efforts and human capital is spoiled in dealing with the problem. So, to search for the optimal solution of a problem is becoming a tedious job for the scholars. Many algorithms have been applied to solve these long-standing complex problems. In this paper, Drosophila Food search optimization (DFO) Algorithm is applied, which explores its vision foraging behavior in the global optimization process. The objective behind the use of DFOA is to achieve fast computation, maximizing resource utilization and minimizing makespan. The survey of our work presents the state-of-the-art in recent research.

Effective Allocation of Resources and Task Scheduling in Cloud Environment using Social Group Optimization

Effective resource distribution to regulate load uniformly in heterogeneous cloud environments is crucial. Resource allotment which is taken after capable task scheduling is a critical worry in cloud environment. The incoming job requests are assigned to resources equally by load balancer in such a way that resources are utilized effectively. Number of cloud clients is very great in number, degree of approaching job requests is uninformed and information is tremendous in cloud application. Resources in cloud environment are constrained. Hence, it is not easy to deploy different applications with unpredictable limits and functionalities in heterogeneous cloud environment. The proposed method has two phases such as allocation of resources and scheduling of tasks. Effective resource allocation is proposed using social group optimization algorithm and scheduling of tasks using shortest-job-first scheduling algorithm for minimizing the makespan time and maximizing throughput. Experimentations are performed for accurate simulations on artificial data for heterogeneous cloud environment. Experimental results are compared with first-in, first-out and genetic algorithm-based shortest-job-first scheduling. Validity of the proposed method noticeably gives improved performance of the system in provisions of makespan time and throughput.

An approach for scaling cloud resource management

Given its current development trajectory, the complexity of cloud computing ecosystems are evolving to where traditional resource management strategies will struggle to remain fit for purpose. These strategies have to cope with ever-increasing numbers of heterogeneous resources, a proliferation of new services, and a growing user-base with diverse and specialized requirements. This growth not only significantly increases the number of parameters needed to make good decisions, it increases the time needed to take these decisions. Consequently, traditional resource management systems are increasingly prone to poor decisions making. Devolving resources management decisions to the local environment of that resource can dramatically increase the speed of decisions making; moreover, the cost of gathering global information can thus be eliminated; saving communication costs. Experimental data, provided in this paper, illustrate that extant cloud deployments can be used as effective vehicles for devolved decision making. This finding strengthens the case for the proposed paradigm shift, since it does not require a change to the architecture of existing cloud systems. This shift would result in systems in which resources decide for themselves how best they can be used. This paper takes this idea to its logical conclusion and proposes a system for supporting self-managing resources in cloud environments. It introduces the concept of coalitions, consisting of collaborating resources, formed for the purpose of service delivery. It suggests the utility of restricting the interactions between the end-user and the cloud service provider to a well-defined services interface. It shows how clouds can be considered functionally, as engines for delivering an appropriate set of resources in response to service requests. And finally, since modern applications are increasingly constructed from sophisticated workflows of complex components, it shows how combinatorial auctions can be used to effectively deliver packages of resources to support those workflows.

Privacy‐ensuring electronic health records in the cloud

SummaryDespite the evident benefits of the access to virtually unlimited computational resources in cloud environments, enterprises and researchers still face upending challenges when deploying applications that deal with sensitive information to the cloud. That is specially true for medical or tax records, for which there are strong legal restrictions to data escrow. In these cases one must be certain that a third party, such as the cloud provider, will never have access to the data. This work presents a solid access control framework that uses hybrid cryptography at client‐side and a two‐factor authentication technique to guarantee a secure key management protocol. We also demonstrate the use of homomorphic and order‐preserving encryption as a viable solution for the computation of regular searches over electronic health records in the cloud, while preserving the confidentiality of clinical data and the privacy of patients, even in the face of a semi‐honest, or “honest, but curious,” cloud provider. We introduce a trusted element, a browser extension, to prevent attacks from malicious cloud providers. The result is evaluated through a full‐featured prototype that manages health records modeled with a few OpenEHR archetypes. The prototype can be easily extended to handle any data structure modeled with OpenEHR.

Method of multiagent scheduling of resources in cloud computing environments

The paper describes a method of scheduling distributed computing resources in cloud environments for solving user tasks using a variety of software agents physically located on separate processor units connected to the cloud infrastructure and representing their interests in the process of computing. The advantages of the proposed approach are as follows: Firstly, the fast tracking of all resource changes occurring to the processing unit using agents and real time correction of the computing process taking into account these changes, which in turn makes it possible to use computing resources with a dynamically changing performance in the cloud environment (e.g., personal privately owned computers), and, secondly, a cost reduction for the cloud infrastructure because there is no need to introduce expensive dedicated nodes that perform service functions into its structure.