Hybrid of Ant Colony Optimization and Gravitational Emulation Based Load Balancing Strategy in Cloud Computing

نویسندگان

  • Jyoti Yadav
  • Sanjay Tyagi
چکیده

1M.Tech. Scholar, Department of Computer Science & Applications, Kurukshetra University, Haryana, India 2Assistant Professor, Department of Computer Science & Applications, Kurukshetra University, Haryana, India -------------------------------------------------------------------------***-----------------------------------------------------------------------AbstractThe distributed architecture of cloud computing set up the resources distributively for delivering the services to cloud consumers. In this paper, a novel hybrid ACO and gravitation emulation based strategy considering load balancing has been implemented for solving the load balancing issue in cloud environment efficiently. Moreover, this hybrid ACO-GELS algorithm uses the physics concept of gravitational attraction between objects. GELS algorithm is powerful for local search in searching space. CloudSim has been used as a simulation tool for proposed hybrid load balancing strategy. The proposed ACO-GELS algorithm has been compared with the existing GA-GELS algorithm. It has been compared on the basis of three important factors of load balancing: resource utilization, makespan and load balancing level.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Cloud Task Scheduling for Load Balancing based on Intelligent Strategy

Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. With the increasing demand and benefits of cloud computing infrastructure, different computing can be performed on cloud environment. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization prob...

متن کامل

Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization

How to distribute and coordinate tasks in cloud computing is a challenging issue, in order to get optimal resource utilization and avoid overload. In this paper, we present a novel approach on load balancing via ant colony optimization (ACO), for balancing the workload in a cloud computing platform dynamically. Two strategies, forward-backward ant mechanism and max-min rules, are introduced to ...

متن کامل

Improved Ant Colony Load Balancing Algorithm in Cloud Computing

Cloud computing mainly deals with networking, software, data access and storage services that may not require end-user knowledge of the physical location and configuration of the system that is delivering the services. In the cloud storage, load balancing is a key issue. Load balancing is one of the main challenge in cloud computing which is required to distribute the dynamic workload across mu...

متن کامل

Cloud Load Balancing Based on Ant Colony Optimization Algorithm

Cloud Computing is growing rapidly and clients are demanding more services with better results. Therefore, load balancing as well as task scheduling in the Cloud has become a very interesting and important research area. In this paper, we present proposed approach for improving parameters in ant colonies function. The suggested definition of a new computational paradigm, is called Ant Colony Op...

متن کامل

A Hybrid Heuristic Scheduling Algorithm in Cloud Computing

In cloud computing tasks scheduling problem is NP-hard, furthermore it does onerous for attaining an optimum resolution. Extremely quick optimization algorithms are used to proximate the optimum resolution, like ACO (ant colony optimization) algorithm. In cloud computing, in consideration to solve the problem of task scheduling, a period ACO (PACO)-based arranging algorithmic rule has been used...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017