Hybrid Evolutionary Algorithm based Task Scheduling Mechanism for Resource Allocation in Cloud Environment

WITHDRAWN: Experimental performance analysis of load balancing of tasks using honey bee inspired algorithm for resource allocation in cloud environment

A checkpointed league championship algorithm-based cloud scheduling scheme with secure fault tolerance responsiveness

Cloud computing and big data: Technologies and applications

The problem of scheduling of tasks in distributed, heterogeneous, and multiprocessing computing environment like grid and cloud computing is considered as one of the most important issue from research perspective. As the performance of such kind of systems is highly depends upon the way, how tasks are allocated among the multiple processing units for their efficient execution. The underlying objective of any task scheduling mechanism is to minimize the overall makespan for the execution of given set of jobs/tasks and computing machines. Scheduling of tasks in cloud computing falls in the class of NP-hard optimization problem. As a result, many meta-heuristic algorithms have been applied and tested to solve this problem but still lot of scope is there for the better strategies. The characteristic of the good algorithm is that it must be adaptable to the dynamic environment. Through this paper, we are proposing task scheduling mechanism based on particle swarm optimization (PSO) in which opposition-based learning technique is used to avoid premature convergence and to accelerate the convergence of standard PSO and compared same with the well-established task scheduling strategies based on PSO, mPSO (modified PSO), genetic algorithm GA, max–min, minimum completion time and minimum execution time. The results obtained for the various class of experiments clearly establish that the proposed opposition-based learning inspired particle swarm optimization based scheduling strategy performs better in comparison to its peers which are taken into the consideration.

High energy consumption is one of the key issues of cloud computing systems. Incoming jobs in cloud computing environments have the nature of randomness, and compute nodes have to be powered on all the time to await incoming tasks. This results in a great waste of energy. An energy-saving task scheduling algorithm based on the vacation queuing model for cloud computing systems is proposed in this paper. First, we use the vacation queuing model with exhaustive service to model the task schedule of a heterogeneous cloud computing system. Next, based on the busy period and busy cycle under steady state, we analyze the expectations of task sojourn time and energy consumption of compute nodes in the heterogeneous cloud computing system. Subsequently, we propose a task scheduling algorithm based on similar tasks to reduce the energy consumption. Simulation results show that the proposed algorithm can reduce the energy consumption of the cloud computing system effectively while meeting the task performance.

With the widespread application of cloud computing technology, the energy consumption problem of data centers is becoming increasingly severe. This research is dedicated to presenting a highly efficient and smart solution for optimizing energy usage in cloud computing systems, especially since effectively managing energy consumption through resource allocation and task scheduling has now become a critical challenge in the field. In view of this, the study first constructs a resource allocation model based on redundant resource recovery to dynamically identify and recover redundant resources. Second, a task scheduling optimization method based on dual deep reinforcement learning is established, which utilizes directed acyclic graphs to model task dependencies and optimizes task execution order and resource allocation strategies through reinforcement learning agents. In the testing of the resource allocation optimization model, when the number of tasks is 500, the redundant resource recovery rate is 87.0%, and the computational complexity is 16.7 s. The response time in the regional resource environment is 2.28 s, and the load adaptability is 95.8%. In the task scheduling method test, the proposed method has a memory occupancy of 85 MB and a scheduling delay of 0.13 s in a high-bandwidth environment of 300 Mbps. The results show that the proposed method outperforms traditional scheduling strategies in terms of resource utilization and energy consumption, and contributes to achieving the goal of green cloud computing. The research aims to provide an efficient and intelligent solution for energy optimization in cloud computing systems.

Energy efficient task scheduling based on deep reinforcement learning in cloud environment: A specialized review

The cloud computing is the new technique used most today for computation and storage. Edge computing is also a new computing domain that gives computation of tasks and storage of data nearer to the data sources. Various applications of edge computing are IOT, healthcare, retail, manufacturing etc. Virtualization technologies have enabled cloud platforms to provide various services such as virtual machines to the users. Task scheduling is the major issues faced in both cloud and edge domains. Since the usage of machines is high, it is difficult to assign tasks manually. So, an efficient and optimized algorithm is required in the cloud as well as the edge environment. Here, a task scheduling is implemented based on Heuristic Optimization Technique to examine the performance measures using cloudsim. Task scheduling and optimization techniques dynamically allocate resources in the cloud environment. Similarly, in edge computing, scheduling the task is performed with different optimization techniques such as random fit, and max fit algorithms. These task scheduling methods are allocated to the VM based on the resources that are available at the VM. These algorithms are implemented, and it increased the VM utilization by 4.82%. The processing time taken is reduced by 2.67% and the percentage of failed tasks is reduced by 3.68%. The experiment result shows the importance of the scheduling in the cloud and edge environment, in terms of processing time, failed task and increase in VM utilization in edge devices.

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user's tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one.

Recently, the development of cloud computing has received considerable attention. For cloud service providers, packing VMs onto a small number of servers is an effective way to reduce energy costs, so as to improve the efficiency of the data center. However allocating too many VMs on a physical machine may cause some hot spots which violate the SLA of applications. Load balancing of the entire system is hence needed to guarantee the SLA. In this paper, we present a simulated-annealing load balancing algorithm for solving the resource allocation and scheduling problem in a cloud computing environment. Experimental results show that this method is able to achieve load balancing, and performs better than the round robin and basic simulated-annealing algorithms.

Recently, cloud computing have been witnessing high deployment rate of large scale scientific and business applications, this is due to the on-demand provisioning of shared pool of computational resources like networks, storage, and servers, it offers. Each of these applications is made up of various tasks whose execution determine the overall performance of the application. Task scheduling problem on cloud is an NP-hard problem, and thus task scheduling constitute one of the crucial aspects of resources management system in cloud computing, which ensures the attainment of the general user Quality of Service (QoS) performance in terms of response time, total execution time(makespan), throughput among others. In addition, appropriate task scheduling is effective in reducing the operational cost of cloud service providers in terms of energy consumption and resource utilization. This paper focuses on task scheduling problem using a novel Chaotic Symbiotic Organisms Search (CSOS) algorithm to minimize makespan and cost. The main idea is to prevent the premature convergence of SOS at early stages of optimization process by implementing a chaotic map, which enlarges the search space and provides diversity. The performance of the proposed CSOS algorithm is evaluated by extensive simulation using CloudSim toolkit simulation framework and compared with SOS and PSO. Simulation results reveal significant improvement in performance by the proposed CSOS in reducing cost and makespan, in task scheduling.

Cloud computing has transformed IT infrastructure by offering scalable, pay-as-you-go resources. However, efficiently allocating cloud resources under dynamic workloads while minimizing operational costs and maintaining service-level agreements (SLAs) remains a critical challenge. This paper presents a hybrid AI-driven framework for cost-efficient resource allocation in cloud environments. The proposed model integrates Long Short-Term Memory (LSTM) networks for workload forecasting, reinforcement learning (RL) for dynamic decision-making, heuristic scheduling (inspired by ACO and GA) for optimal task assignments, and economic pricing using the ERA model. A real-world data set from Google Cluster Trace is used to validate the framework. Simulation results show a forecasted CPU utilization of 10.79% with an estimated cost of ₹1.08 and one SLA violation, demonstrating the potential of hybrid AI models for real-time and cost-aware resource provisioning.

Cloud computing (CC) has many benefits, so its use has spread rapidly, particularly in the business sector. An important consideration in the acceptance of CC is whether the CC system is dependable, and it can differ among industry and service type. However, little research has considered the effect of dependability (composed of availability, reliability, security, maintainability) on CC acceptance. Especially, group comparisons between high IT-intensive (Hi-ITi) and low IT-intensive (Lo-ITi) industries have not been reported, nor have comparisons between software-as-a-service (SaaS) and platform-as-a-service (PaaS)/infrastructure-as-a-service (IaaS). This study aims to explore how the dependability of CC affects users’ intent to accept it, with focus on how this intent is affected by intensity of IT use (by industry) and by the type of CC service used. To validate the proposed model, this study applied structural equation modeling and conducted multi-group analysis. A total of 230 business managers in South Korea represent the sample for our study. For the full dataset, the three dependability attributes (availability, reliability, security) do not affect the usefulness of CC, but do affect the ease of use of CC. The usefulness of CC is a determinant for positive intention to accept CC, whereas the ease of use of CC is not. Maintainability is the strongest determinant of CC adoption for the full dataset, and for all individual groups, except those that use SaaS. For Hi-ITi and Lo-ITi industries, results show that managers show no differences in their perceptions of the effect of dependability attributes (availability, reliability, security) on the usefulness and the ease of CC. The absence of such a difference in managers’ perception also applies to the relationship between two core variables of TAM (i.e., perceived usefulness, perceived ease of use) and behavioral intention to accept CC. For SaaS and PaaS/IaaS, managers have different perceptions of security on the usefulness of CC, and the effect of the usefulness of CC on the intention to accept CC. The findings can provide academic researchers and industry practitioners with a differentiated and in-depth perspective on the understanding and the spread of CC.

Cloud computing allows execution and deployment of different types of applications such as interactive databases or web-based services which require distinctive types of resources. These applications lease cloud resources for a considerably long period and usually occupy various resources to maintain a high quality of service (QoS) factor. On the other hand, general big data batch processing workloads are less QoS-sensitive and require massively parallel cloud resources for short period. Despite the elasticity feature of cloud computing, fine-scale characteristics of cloud-based applications may cause temporal low resource utilization in the cloud computing systems, while process-intensive highly utilized workload suffers from performance issues. Therefore, ability of utilization efficient scheduling of heterogeneous workload is one challenging issue for cloud owners. In this paper, addressing the heterogeneity issue impact on low utilization of cloud computing system, conjunct resource allocation scheme of cloud applications and processing jobs is presented to enhance the cloud utilization. The main idea behind this paper is to apply processing jobs and cloud applications jointly in a preemptive way. However, utilization efficient resource allocation requires exact modeling of workloads. So, first, a novel methodology to model the processing jobs and other cloud applications is proposed. Such jobs are modeled as a collection of parallel and sequential tasks in a Markovian process. This enables us to analyze and calculate the efficient resources required to serve the tasks. The next step makes use of the proposed model to develop a preemptive scheduling algorithm for the processing jobs in order to improve resource utilization and its associated costs in the cloud computing system. Accordingly, a preemption-based resource allocation architecture is proposed to effectively and efficiently utilize the idle reserved resources for the processing jobs in the cloud paradigms. Then, performance metrics such as service time for the processing jobs are investigated. The accuracy of the proposed analytical model and scheduling analysis is verified through simulations and experimental results. The simulation and experimental results also shed light on the achievable QoS level for the preemptively allocated processing jobs.

EATS: Energy-Aware Tasks Scheduling in Cloud Computing Systems