Efficient Task Scheduling in Cloud Computing using Multi-objective Hybrid Ant Colony Optimization Algorithm for Energy Efficiency (original) (raw)
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IEEE Access, 2015
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 multiobjective 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. INDEX TERMS Cloud computing, ant colony, task scheduling, deadline, cost constraint.
Energy-aware Task Scheduling using Ant-colony Optimization in Cloud
Task scheduling in a distributed environment like cloud has always been a prime problem to tackle with. Many algorithms have been developed to efficiently schedule the resources to the tasks and thereby increasing the profit for the cloud providers. Due to efficient task scheduling algorithms, the resources or servers are mostly kept busy and running the tasks of the clients and consuming a lot of power. Due to the increase in demand and the profit they get, the cloud providers tend to increase their infrastructure and so they deploy a lot many datacenters. The cost of establishment of datacenters is huge in terms of power. Further, the increased use of datacenters has reached to an extent where the cost of maintenance of these datacenters surpassed the total hardware acquisition cost. There arises a need for energy conservation. The project proposes an idea of saving energy while scheduling tasks to the virtual machines to run them. It is primarily based on the fact that different hosts in the cloud have different CPU utilization which directly affects their total power consumption. It uses ant-colony optimization to find the hosts that consume least power based on their CPU utilization and schedule the tasks and finally compares the performance of the algorithm with existing techniques. Through experiments, it is found out that the proposed method conserves the energy consumption in the hosts up to 22%.
Cost Effective Ant Colony Optimization in Cloud Computing
International Journal of Innovative Technology and Exploring Engineering, 2019
Cloud computing is a term for a wide range of developments possibilities. It is rapidly growing paradigm in software technology that offers different services. Cloud computing has come of age, since Amazon's rollouted the first of its kind of cloud services in 2006. It stores the tremendous amount of data that are being processed every day. Cloud computing is a reliable computing base for data-intensive jobs. Cloud computing provide computing resources as a service. It is on-demand availability of computing resources without direct interaction of user. A major focus area of cloud computing is task scheduling. Task scheduling is one among the many important issues to be dealt with. It means to optimize overall system capabilities and to allocate the right resources. Task scheduling referred to NP-hard problem. The proposed algorithm is Cost Effective ACO for task scheduling, which calculates execution cost of CPU, bandwidth, memory etc. The suggested algorithm is compared with Cl...
Cluster Computing
Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users’ jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called SMO_ACO, for addressing the scheduling problem. The proposed SMO_ACO algorithm combines Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads....
Human-centric Computing and Information Sciences, 2017
Since cloud computing provides computing resources on a pay per use basis, a task scheduling algorithm directly affects the cost for users. In this paper, we propose a novel cloud task scheduling algorithm based on ant colony optimization that allocates tasks of cloud users to virtual machines in cloud computing environments in an efficient manner. To enhance the performance of the task scheduler in cloud computing environments with ant colony optimization, we adapt diversification and reinforcement strategies with slave ants. The proposed algorithm solves the global optimization problem with slave ants by avoiding long paths whose pheromones are wrongly accumulated by leading ants.
Cloud Task Scheduling Based on Ant Colony Optimization
The International Arab Journal of Information Technology, 2015
Cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper a cloud task scheduling policy based on ant colony optimization algorithm compared with different scheduling algorithms FCFS and round-robin, has been presented. The main goal of these algorithms is minimizing the makespan of a given tasks set. Ant colony optimization is random optimization search approach that will be used for allocating the incoming jobs to the virtual machines. Algorithms have been simulated using Cloudsim toolkit package. Experimental results showed that cloud task scheduling based on ant colony optimization outperformed FCFS and round-robin algorithms.
Ant Colony Optimization for Efficient Resource Allocation in Cloud Computing
—Resource scheduling and energy consumption is an important issue of Cloud Computing. The intention of optimization for scheduling resources is an important issue to be considered in scheduling of different resources among heterogeneous users. The resources are placed in a distributed location in cloud and the major task is to distribute the resources effectively such that the processing time and energy is reduced. In this paper, Ant Colony optimization technique is proposed to optimize the resources in an efficient manner. ACO is used to choose one among the different alternative paths to determine the processing order of each resource. The search space is reduced to provide a better solution. Travelling Salesman Problem(TSP) is the application that is used here to find the shortest path to the destination. This reduces the delay in allocating resources to the user by providing a global search technique. The energy conservation which is the main objective of Green Computing, can also be achieved using this technique.
An Ant Algorithm for Cloud Task Scheduling
Proceedings of the 1st International Workshop on Cloud Computing and Information Security, 2013
CITATIONS 9 READS 211 4 authors, including: Some of the authors of this publication are also working on these related projects:
Cloud Computing Initiative Using Modified Ant Colony Framework
2009
Scheduling of diversified service requests in distributed computing is a critical design issue. Cloud is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. It is not only the clusters and grid but also it comprises of next generation data centers. The paper proposes an initial heuristic algorithm to apply modified ant colony optimization approach for the diversified service allocation and scheduling mechanism in cloud paradigm. The proposed optimization method is aimed to minimize the scheduling throughput to service all the diversified requests according to the different resource allocator available under cloud computing environment.
Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach
2015 International Conference on Cloud Computing Research and Innovation (ICCCRI), 2015
Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both the execution time and execution costs. In solving the problem of optimizing the execution costs while meeting deadline constraints, we developed an efficient approach based on ant colony system (ACS). For scheduling T tasks on R resources, an ant in ACS represents a solution with T dimensions, with each dimension being a task and the value of each dimension being an integer ranges in [1, R] to indicate scheduling the task on which resource. With such solution encoding, the ant in ACS constructs a solution in T steps, with each step optimally selecting one resource from the R resources, according to both the pheromone and heuristic information. Therefore, the solution encoding is very simple and straight to reflect the mapping relation of tasks and resources. Moreover, the solution construct process is very natural to find optimal solution based on the encoding scheme. We have conducted extensive experiments based on workflows with various scales and various cloud resources. We compare the results with those of particle swarm optimization (PSO) and dynamic objective genetic algorithm (DOGA) approaches. The experimental results show that ACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions.