A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing

Abstract: For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing, we propose a resource cost model that defines the demand of tasks on resources with more details. This model reflects the relationship between the user's resource costs and the budget costs. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan and the user's budget costs as constraints of the optimization problem, achieving multi-objective optimization of both performance and cost. An improved ant colony algorithm has been proposed to solve this problem. Two constraint functions were used to evaluate and provide feedback regarding the performance and budget cost. These two constraint functions made the algorithm adjust the quality of the solution in a timely manner based on feedback in order to achieve the optimal solution. Some simulation experiments were designed to evaluate this method's performance using four metrics: 1) the makespan; 2) cost; 3) deadline violation rate; and 4) resource utilization. Experimental results show that based on these four metrics, a multi-objective optimization method is better than other similar methods, especially as it increased 56.6% in the best case scenario

An Improved Ant Colony Algorithm for New energy Industry Resource Allocation in Cloud Environment

The new energy industry development is affected by many factors. Among them, the resources utilization ratio is a major reason for the low productivity of enterprises. As the core problem of cloud computing, the resource allocation problem has been widely concerned by the people, and the resource allocation problem of the new energy industry as the key to energy innovation and transformation should be more paid attention to. In multi-resource cloud computing scenarios, requests made by users often involve multiple types of resources. Traditional resource allocation algorithms have a single optimization object, typically time efficiency. In order to achieve cluster load balancing, utilization of system resources and improvement of system work efficiency, this paper proposes a new cloud computing allocation algorithm based on improved ant colony algorithm. According to the limit conditions of cloud computing environment and computing resources, this paper finds the shortest response time of all resource nodes and gets a set of best available nodes. This method can meet the quality requirements of cloud computing, and the task completion time of the improved algorithm is shorter, the number of algorithm iterations is less, and the load balancing effect is better. Through MATLAB simulation experiments, the effectiveness of the proposed method is verified

Multi-objective Optimization of Grid Computing for Performance, Energy and Cost

In this paper, new multi-objective optimization algorithm is proposed. It optimizes the execution time, the energy consumption and the cost of booked nodes in the grid architecture at the same time. The proposed algorithm selects the best frequencies depends on a new optimization function that optimized these three objectives, while giving equivalent trade-off for each one. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption of the message passing parallel iterative method executed over grid. DVFS is also reduced the computing power of each processor executing the parallel applications. Therefore, the performance of these applications is decreased and so on the payed cost for the booking nodes is increased.  However, the proposed multi-objective algorithm gives the minimum energy consumption and minimum cost with maximum performance at the same time. The proposed algorithm is evaluated on the SimGrid/SMPI simulator while running the parallel iterative Jacobi method. The experiments show that it reduces on average the energy consumption by up to 19.7 %, while limiting the performance and cost degradations to 3.2 % and 5.2 % respectively

Learning Optimal Time Series Combination and Pre-Processing by Smart Joins

In industrial applications of data science and machine learning, most of the steps of a typical pipeline focus on optimizing measures of model fitness to the available data. Data preprocessing, instead, is often ad-hoc, and not based on the optimization of quantitative measures. This paper proposes the use of optimization in the preprocessing step, specifically studying a time series joining methodology, and introduces an error function to measure the adequateness of the joining. Experiments show how the method allows monitoring preprocessing errors for different time slices, indicating when a retraining of the preprocessing may be needed. Thus, this contribution helps quantifying the implications of data preprocessing on the result of data analysis and machine learning methods. The methodology is applied to two case studies: synthetic simulation data with controlled distortions, and a real scenario of an industrial process.This research has been partially funded by the 3KIA project (ELKARTEK, Basque Government)

Role of Evolutionary Algorithms in Construction Projects Scheduling

Due to the increase in the stakeholders and their objectives the construction projects have significantly been affected by the ongoing demands leading to increase in complexity of scheduling problems, research in the field of Multi-Objective Optimization (MOO) have increased significantly. Through their population-based search methodologies, Evolutionary Algorithms drove attention to their efficiency in addressing scheduling problems involving two or three objectives. Genetic Algorithms (GA) particularly have been used in most of the construction optimization problems due to their ability to provide near-optimal Pareto solutions in a reasonable amount of time for almost all objectives. However, when optimizing more than three objectives, the efficiency of such algorithms degrades and trade-offs among conflicting objectives must be made to obtain an optimal Pareto Frontier. To address that, this paper aims to provide a comparative analysis on four evolutionary algorithms (Genetic algorithms – Memetic algorithms – Particle Swarm – Ant colony) in the field of construction scheduling optimization, gaps are addressed, and recommendations are proposed for future research development

QoS-Driven Job Scheduling: Multi-Tier Dependency Considerations

For a cloud service provider, delivering optimal system performance while fulfilling Quality of Service (QoS) obligations is critical for maintaining a viably profitable business. This goal is often hard to attain given the irregular nature of cloud computing jobs. These jobs expect high QoS on an on-demand fashion, that is on random arrival. To optimize the response to such client demands, cloud service providers organize the cloud computing environment as a multi-tier architecture. Each tier executes its designated tasks and passes the job to the next tier; in a fashion similar, but not identical, to the traditional job-shop environments. An optimization process must take place to schedule the appropriate tasks of the job on the resources of the tier, so as to meet the QoS expectations of the job. Existing approaches employ scheduling strategies that consider the performance optimization at the individual resource level and produce optimal single-tier driven schedules. Due to the sequential nature of the multi-tier environment, the impact of such schedules on the performance of other resources and tiers tend to be ignored, resulting in a less than optimal performance when measured at the multi-tier level. In this paper, we propose a multi-tier-oriented job scheduling and allocation technique. The scheduling and allocation process is formulated as a problem of assigning jobs to the resource queues of the cloud computing environment, where each resource of the environment employs a queue to hold the jobs assigned to it. The scheduling problem is NP-hard, as such a biologically inspired genetic algorithm is proposed. The computing resources across all tiers of the environment are virtualized in one resource by means of a single queue virtualization. A chromosome that mimics the sequencing and allocation of the tasks in the proposed virtual queue is proposed

CLOSURE: A cloud scientific workflow scheduling algorithm based on attack-defense game model

The multi-tenant coexistence service mode makes the cloud-based scientific workflow encounter the risks of being intruded. For this problem, we propose a CLoud scientific wOrkflow SchedUling algoRithm based on attack-defensE game model (CLOSURE). In the algorithm, attacks based on different operating system vulnerabilities are regarded as different “attack” strategies; and different operating system distributions in a virtual machine cluster executing the workflows are regarded as different “defense” strategies. The information of the attacker and defender is not balanced. In other words, the defender cannot obtain the information about the attacker’s strategies, while the attacker can acquire information about the defender’s strategies through a network scan. Therefore, we propose to dynamically switch the defense strategies during the workflow execution, which can weaken the network scan effects and transform the workflow security problem into an attack-defense game problem. Then, the probability distribution of the optimal mixed defense strategies can be achieved by calculating the Nash Equilibrium in the attack-defense game model. Based on this probability, diverse VMs are provisioned for workflow execution. Furthermore, a task-VM mapping algorithm based on dynamic Heterogeneous Earliest Finish Time (HEFT) is presented to accelerate the defense strategy switching and improve workflow efficiency. The experiments are conducted on both simulation and actual environment, experimental results demonstrate that compared with other algorithms, the proposed algorithm can reduce the attacker’s benefits by around 15.23%, and decrease the time costs of the algorithm by around 7.86%

Hybrid Cat Swarm Optimization and Simulated Annealing for Dynamic Task Scheduling on Cloud Computing Environment

The unpredictable number of tasks arriving at cloud datacenter and the rescaling of virtual processing elements can affect the provisioning of better Quality of Service expectations during task scheduling in cloud computing. Existing researchers have contributed several task scheduling algorithms to provide better QoS expectations but are characterized with entrapment at the local search and high dimensional breakdown due to slow convergence speed and imbalance between global and local search, resulting from lack of scalability. Dynamic task scheduling algorithms that can adjust to long-time changes and continue facilitating the provisioning of better QoS are necessary for cloud computing environment. In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. In the proposed method, the orthogonal Taguchi approach is applied to enhance the SA which is incorporated into the local search of the proposed CSMCSOSA algorithm for scalability performance. A multi-objective QoS model based on execution time and execution cost criteria is presented to evaluate the efficiency of the proposed algorithm on CloudSim tool with two different datasets. Quantitative analysis of the algorithm is carried out with metrics of execution time, execution cost, QoS and performance improvement rate percentage. Meanwhile, the scalability analysis of the proposed algorithm using Isospeed-efficiency scalability metric is also reported. The results of the experiment show that the proposed CSM-CSOSA has outperformed Multi-Objective Genetic Algorithm, Multi-Objective Ant Colony and Multi-Objective Particle Swarm Optimization by returning minimum execution time and execution cost as well as better scalability acceptance rate of 0.48110.8990 respectively. The proposed solution when implemented in real cloud computing environment could possibly meet customers QoS expectations as well as that of the service providers