De-centralised dynamic task scheduling using hill climbing algorithm in cloud computing environments

Cloud computing and big data: Technologies and applications

The user group encountered in cloud computing is large, and the amount of tasks and data to be processed is also large. How to schedule tasks effectively becomes an important problem that can be solved in cloud computing. A binary fitness genetic algorithm (DFGA) is proposed, which is aimed at the programming model of cloud computing. Not only can the algorithm find a plan result with a short overall task completion time, but the short-term performance of the plan result is relatively short. The algorithm was compared with the adaptive genetic algorithm (AGA) through simulation experiments in a short time. The experimental results show that the algorithm is superior to the adaptive genetic algorithm and is an effective task planning algorithm in the cloud computing environment. Genetic algorithm is a global heuristic algorithm used to solve optimization problems. It is adaptive, learning, and parallel. Genetic algorithms have great advantages, especially when dealing with a large number of tasks. Tasks are assigned to multiple processors for processing simultaneously. At the same time, genetic algorithms are also extensible, which can be easily combined with other algorithms to absorb the advantages of other algorithms to make up for their own shortcomings. At present, many authors of scientific research have used the advantages of genetic algorithms to apply them to task scheduling problems, and the scheduling results obtained are superior to traditional scheduling solutions. This paper studies the task scheduling algorithm based on improved genetic algorithm in cloud computing environment. This article uses CloudSim as the object, and through data analysis, compared with HEFT, the SLR of the HEFTD algorithm is shortened by 10.55%, 8.99%, 16.99%, 19.79%, and 9.89%, that is, HEFTD in different CCR. When the CCR is 1 and 2, the algorithm performance is greatly improved.

In order to solve the problems of unbalanced load, slow convergence speed and low utilization of virtual machine resources existing in the previous task scheduling optimization strategies, this paper proposes a task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing. Firstly, based on the principle of cloud computing task scheduling, a scheduling model using improved ant colony algorithm is proposed to avoid the optimization strategy falling into local optimization. Then, task scheduling satisfaction function is constructed by combining the three objectives of the shortest waiting time, the degree of resource load balance and the cost of task completion to search the optimal solution of task scheduling. Finally, the reward and punishment coefficient is introduced to optimize the pheromone updating rules of ant colony algorithm, which speeds up the solution speed. Besides, we use dynamic update of volatility coefficient to optimize overall performance of this strategy, and introduce virtual machine load weight coefficient in the process of local pheromone updating, so as to ensure the load balance of virtual machine. The feasibility of our algorithm is analyzed and demonstrated by experiments with Cloudsim. The experimental results show that the proposed algorithm has the fastest convergence speed, the shortest completion time, the most balanced load and the highest utilization rate of virtual machine resources compared with other methods. Therefore, our proposed task scheduling optimization strategy has the best performance.

In view of the large-scale and complex development trend of task scheduling in the current cloud computing environment, the traditional task priority scheduling algorithm is difficult to ensure the system load balance. A cloud computing task scheduling algorithm based on dynamic priority and scheduling decision is proposed(TS-DP). The algorithm calculates the task dynamic priority according to the task value and task urgency. At the same time, imitating the behavior of fireflies, the task scheduling decision variables are obtained by attraction (the user's expected earliest completion time), fluorescence brightness (task processing time constraint) and load balance, and then the tasks are scheduled to the executable computing node corresponding to the maximum decision variable value according to the task priority. Experimental results show that the algorithm can effectively improve the resource scheduling performance of cloud computing, shorten the completion time of the total task, and achieve good load balancing, especially when the number of tasks and the scale of nodes are large.

In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.

The massive growth of cloud computing leads to huge amounts of energy consumption and release of carbon footprints as data centers are housed by a large number of servers. Consequently, the cloud service providers are looking for eco-friendly solutions to reduce energy consumption and carbon emissions. As a result, task scheduling has drawn attention, in which efficient resource utilization and minimum energy consumption take into great consideration. This is an exigent issue, especially for the heterogeneous environment. In this work, we put forward an energy-efficient task scheduling algorithm (ETSA) to address the demerits associated with task consolidation and scheduling. The proposed algorithm ETSA takes into account the completion time and total utilization of a task on the resources, and follows a normalization procedure to make a scheduling decision. We evaluate the proposed algorithm ETSA to measure energy efficiency and makespan in the heterogeneous environment. The experimental results are compared with recent algorithms, namely random, round robin, dynamic cloud list scheduling, energy-aware task consolidation, energy-conscious task consolidation and MaxUtil. The proposed algorithm ETSA provides an elegant trade-off between energy efficiency and makespan than the existing algorithms.

Cloud computing is the delivery of various services via the Internet. Data storage, databases, networking, software etc are examples of these resources. Cloud is growing daily and encounters numerous challenges. Among the challenges one of them is scheduling. There are many types of scheduling, one of them is task scheduling. In cloud computing, task scheduling is a issue that reduces system performance. The task scheduling issue is one of the critical issues in the cloud computing because cloud performance is heavily reliant on it. An effective algorithm is needed to increase system performance. In this paper, we attempt to study the performance of a task scheduling algorithm in a cloud data centre.

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.

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

Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a hybrid heuristic algorithm based energy-efficient cloud computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the chaotic based particle swarm optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the hybrid heuristic workflow scheduling algorithm (HHWS) and distributed dynamic VM management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods.

With the rapid development of processing and storage technologies and the success of the Internet, computing resources have become cheaper, more powerful and more ubiquitously available than ever before. This technological trend has enabled the realization of a new computing model, called cloud computing. In cloud, scheduling is an important application. In cloud environments, load balancing task scheduling is an important problem that directly affects resource utilization. Undoubtedly, load balancing scheduling is a serious aspect that should be considered because of its significant impact on both the back end and the front end of the cloud research industry. Good resource utilization is achieved whenever an effective load balance is achieved in the cloud. But, load balancing in cloud computing is an NP-hard optimization problem. In order to accomplish this problem, a novel load balancing task scheduling algorithm in cloud using Adaptive Dragonfly algorithm (ADA) is proposed. The ADA is a combination of dragonfly algorithm and firefly algorithm. Moreover, to attain the better performance, multi-objective function is developed based on three parameters namely, completion time, processing costs and load. Finally, the performance of proposed methodology is evaluated in terms of different metrics namely, execution cost and execution time. The experimental results demonstrate that a proposed approach accomplishes better load balancing result compared to other approaches.

This study introduces a mobile application designed to streamline and supervise institutional operations. The application facilitates seamless coordination for cleaning staff and supervisors. At our university, cleaners used to mark their tasks on paper to prove completion, but this method did not allow for tracking cleaning durations and details. To address these issues, we developed an application that offers a new approach to institutional digital transformation for monitoring cleaning tasks. The application is programmed in Flutter, with a Firebase database, and allows for the recording of task start and completion times, providing managers with a transparent overview. Cleaners access and complete tasks via the user interface, while managers monitor activities, track progress, and generate daily reports through the management interface. A key feature of the application is its ability to archive historical data, allowing managers to access past records and gain insights into operational trends. Additionally, the capability to export reports in PDF format offers accessibility and ease of sharing. The evaluation of the application was conducted by examining data from the Cleaning Services Monitoring Unit under the University's Administrative and Financial Affairs Department. The examined data comprises 280 days and includes 33,587 records. By meticulously recording task start and completion times, the application provides managers with a transparent overview for analysis and evaluation. According to the data from the Cleaning Services Monitoring Unit, the application has increased efficiency by 23% and reduced task completion times by 28%. As a result, cleaners have been able to complete an additional task each day. This demonstrates that the application enhances functionality and makes operations more efficient.

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.

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.

PurposeSea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions.Design/methodology/approachCloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance.FindingsThe performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively.Originality/valueTo the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.