Cloudy GSA for load scheduling in cloud computing

The load scheduling is a paramount concern in the cloud-based computing due to the involvement of conflicting parameters and fluctuating demands of users. Though the problem of load scheduling comes in the category of NP-hard problem, it is utmost essential to design a load scheduling approach to tackle the challenges in cloud computing. These challenges could be elevated by using metaheuristic approaches that offer an optimal solution to an NP-hard problem. In this research, authors propose a binary self-adaptive JAYA-based load scheduling algorithm to solve the dynamically independent load scheduling problem in cloud-based computing. The proposed algorithm is compared with the lately invented metaheuristic-based task scheduling algorithms such as bird swarm optimization (BSO), modified particle swarm optimization (MPSO), and the standard JAYA. The proposed algorithm is evaluated using a real-world dataset for the various QoS scheduling parameters to advocate the effectiveness of the algorithm. The simulated results show notable improvements over other compared algorithms for makespan, average resource utilization, and load-balancing.

In the last few decades, a novel branch of intelligent computation algorithms has been inspired by a swarm intelligence theory which imitates the behavior of animals. These algorithms are successfully applied to solve many kinds of simple and complex problems in several fields such as optimization problems, pattern recognition, image processing, features section, and task scheduling in cloud and parallel computing. Cloud computing has not only become the preferred environment for several companies, but also helps others to overcome many server-related issues by utilizing characteristics such as reliability, flexibility, high scalability, and security. Therefore, many intelligent computation algorithms are used to improve this environment. In this chapter, an overview of swarm intelligence for solving the scheduling problems of tasks in cloud computing is presented, including particle swarm optimization, cat optimization algorithm, artificial bee colony, lion optimization algorithm, whale optimization algorithm, bat algorithm, gray wolf optimizer, cuckoo search algorithm, hybrid swarm algorithms, and multi-objective swarm optimization. All these algorithms are described and presented with their achievements in solving task scheduling issues in cloud computing.

Cloud computing has spread fast because of its high performance distributed computing. It offers services and access to shared resources to internet users through service providers. Efficient performance of task scheduling in clouds is one of the most important research issues which needs to be focused on. Various task scheduling algorithms for cloud based on metaheuristic techniques have been examined and showed high performance in reasonable time such as scheduling algorithms based on Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). In this paper, we propose a new task-scheduling algorithm based on Lion Optimization Algorithm (LOA), for cloud computing. LOA is a nature-inspired population-based algorithm for obtaining global optimization over a search space. It was proposed by Maziar Yazdani and Fariborz Jolai in 2015. It is a metaheuristic algorithm inspired by the special lifestyle of lions and their cooperative characteristics. The proposed task scheduling algorithm is compared with scheduling algorithms based on Genetic Algorithm and Particle Swarm Optimization. The results demonstrate the high performance of the proposed algorithm, when compared with the other algorithms.

WITHDRAWN: Efficient Secure Aware Scheduling Model for Enhancing Security and Workflow Model in Cloud Computing

Cloud computing and big data: Technologies and applications

Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues

<p>Cloud computing is a model for delivering, hosting and accessing shared pool of resources and services over the internet in on-demand, self-service, dynamically scalable and metered manner. Scheduling access to cloud resources is a topic of interest to both researchers and IT community. Several approaches have been proposed from the traditional methods to those that are exhaustive in nature. However, Cloud task scheduling is an NP-hard optimization problem, and can break down deterministic or exhaustive approaches with the increase in the number of variables to be optimized. Recently there is has been attempt to use meta-heuristic algorithm for scheduling in cloud computing. These include Genetic Algorithms (GA), Particle Swamp Optimization (PSO), Ant Colony Optimization Algorithm (ACO) and other nature inspired algorithms. The algorithms offer NP-hard problems global solutions acceptable in time frame proportional to the number of variables to be optimized. We use ACO algorithm for scheduling in cloud computing environment. Load balancing is added into the algorithm to prevent the algorithm from falling into local minima. A comparison with (43) shows that the proposed algorithm achieved 51.95% in makespan. However, the compared work is better than this work in the average running time by 86.50%.</p>

Residential demand response is one of the key enabling technologies which plays an important role in managing the load demand of prosumers. However, the load scheduling problem becomes quite challenging due to the involvement of dynamic parameters and renewable energy resources. This work has proposed a bi-level load scheduling mechanism with dynamic electricity pricing integrated with renewable energy and storage system to overcome this problem. The first level involves the formulation of load scheduling and optimization problems as optimal stopping problems with the objective of energy consumption and delay cost minimization. This problem involved the real-time electricity pricing signal, customers load scheduling priority, machine learning (ML) based forecasted load demand, and renewable & storage unit profiles, which is solved using mathematical programming with branch-and-cut & branch-and-bound algorithms. Since the first-level optimization problem is formulated as a stopping problem, the optimal time slots are obtained using a one-step lookahead rule to schedule the load with the ability to handle the uncertainties. The second level is used to further model the load scheduling problem through the dynamic electricity pricing signal. The cost minimization objective function is then solved using the genetic algorithm (GA), where the input parameters are obtained from the first-level optimization solution. Furthermore, the impact of load prioritization in terms of time factor and electricity price is also modeled to allow the end-users to control their load. Analytical and simulation results are conducted using solar-home electricity data, Ausgrid, AUS to validate the proposed model. Results show that the proposed model can handle uncertainties involved in the load scheduling process along with a cost-effective solution in terms of cost and discomfort reduction. Furthermore, the bi-level process ensures cost minimization with end-user satisfaction regarding the dynamic electricity price signal.

In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling.

Symbiotic Organism Search optimization based task scheduling in cloud computing environment

An efficient approach to the map-reduce framework and genetic algorithm based whale optimization algorithm for task scheduling in cloud computing environment

Due to the exceptional benefits of cloud computing, it has magnetized IT leaders and entrepreneurs at all levels. The cloud's popularity is attributed to various technologies like the Internet of Things (IoT), mobile computing, Fog, etc. Scheduling in cloud computing is still a challenging issue due to its NP-Hard nature. In recent years, many techniques have been proposed for optimal scheduling that can subsequently improve efficient Quality of Service (QoS). This paper has developed and analysed a novel Sailfish Optimization-based Scheduling Algorithm. SOSA is implemented on 2 data-sets; a real-world data-set from NASA workload, and a randomly generated data-set. SOSA exhibited 13.71 and 7.81 % average performance improvement in makespan compared to GA and PSO and 11.30 and 30.78 % average improvement in execution cost compared to GA and PSO.
 HIGHLIGHTS
 
 Scheduling in Cloud computing has NP -Hard Problem nature that may degrades Quality of Service to users
 A Novel Sailfish optimization based scheduling algorithm (SOSA) technique is proposed to increase Quality of Service in terms of Makespan and Execution Cost
 Simulation has been performed in CloudSim on NASA workload and synthetic dataset
 Proposed SOSA techniques proves to be more effective to provide Quality of Service and proves to be more significant as compared to GA and PSO

In order to solve the problems of uneven spatial distribution of data nodes and unclear weight relationship of virtual scheduling features in cloud computing platform, a virtual resource scheduling method based on improved ant colony algorithm is studied and designed to improve the performance of virtual resource scheduling in cloud computing platform by this method. After analyzing the information resource sequence change of the cloud computing platform, according to the STR - Tree partition graph, a simulated annealing-based algorithm is employed to classify the resource types after optimal scheduling into IO types, middle types and CPU types, and the time span and load balance are set as the measurement indexes. The simulation results show that after applying this method, the occupied resources of the main platform are 535 MB, which are much lower than the other two comparison algorithms, and the method has improved the allocation rationality, resource balance, maximum queue length and energy consumption. This result indicates that applying this virtual resource scheduling method can effectively improve the intelligent scheduling of virtual resources in the cloud computing platform.

An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment

Cloud computing turns to be a big shift from the conventional perception of the IT resources. It is a transpiring computing technology that is increasingly stabling itself as the promising future of distributed on-demand computing. The processes comprised in it are the ones that act as a vital backbone and which strengthen the entire stream of cloud computing as a whole. In specific, Task scheduling is the one such phenomena that enhances the cloud computing in terms of performance. Hence task scheduling that is considered as a predominant one amidst others is what this paper comprises all about. Maximizing the profit via assigning the whole task to the virtual machine is what the problem of scheduling deals with. Although there prevails many more ways to resolve this problem, this paper explores one such solution that consumes lesser number of resources, having lower cost and much importantly consuming lesser energy. By making a profound research regarding this approach of scheduling so as to represent the multi-objective function, both lion optimization algorithm and gravitational search algorithm are hybridized. In spite of having certain drawbacks which could be avoided although, the brighter side relies the merits of making use of both lion search and gravitational search algorithm. There could be many means of measurement for computing the performance of the algorithm. The different algorithms that aid to depict the comparable study encompasses gravitational search algorithm, genetic algorithm and lion, particle swarm optimization. The experimental results serve as the evident for depicting the bitterness of our proposed algorithm compared to the prevailing approaches. As an unexplored path may seem trivial but is effective so does the betterment of our lion approach.