Cost-Aware Ant Colony Optimization for Resource Allocation in Cloud Infrastructure

2020 ◽  
Vol 13 (3) ◽  
pp. 326-335
Author(s):  
Punit Gupta ◽  
Ujjwal Goyal ◽  
Vaishali Verma
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Fitness Function ◽  
Low Cost ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Ant Colony ◽  
Cloud Infrastructure ◽  
Network Cost

Background: Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm & Round Robin improve the performance but these are not cost efficient at the same time. Objective: In early proven task scheduling algorithms network cost are not included but in this proposed ACO network overhead or cost is taken into consideration which thus improves the efficiency of the algorithm as compared to the previous algorithm. Proposed algorithm aims to improve in term of cost and execution time and reduces network cost. Methods: The proposed task scheduling algorithm in cloud uses ACO with network cost and execution cost as a fitness function. This work tries to improve the existing ACO that will give improved result in terms of performance and execution cost for cloud architecture. Our study includes a comparison between various other algorithms with our proposed ACO model. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The network cost and user requests measures the performance of the proposed model. Conclusion: The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost, network cost).

Optimized Task Scheduling and Resource Allocation in Cloud Computing Using PSO based Fitness Function

2013 ◽  
Vol 12 (23) ◽  
pp. 7090-7095 ◽  
Author(s):  
Zhongxue Yang ◽  
Xiaolin Qin ◽  
Wenrui Li ◽  
Yingjie Yang
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Fitness Function

Task Scheduling and Resource Allocation of Cloud Computing Based on QoS

2014 ◽  
Vol 915-916 ◽  
pp. 1382-1385 ◽  
Author(s):  
Bai Lin Pan ◽  
Yan Ping Wang ◽  
Han Xi Li ◽  
Jie Qian
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Computing Power ◽  
Power Scheduling ◽  
Cloud Computing System ◽  
User Demand

With the enlargement of the scope of cloud computing application, the number of users and types also increases accordingly, the special demand for cloud computing resources has also improved. Cloud computing task scheduling and resource allocation are key technologies, mainly responsible for assigning user jobs to the appropriate resources to perform. But the existing scheduling algorithm is not fully consider the user demand for resources is different, and not well provided for different users to meet the requirements of its resources. As the demand for quality of service based on cloud computing and cloud computing original scheduling algorithm, the computing power scheduling algorithm is proposed based on the QoS constraints to research the cloud computing task scheduling and resource allocation problems, improving the overall efficiency of cloud computing system.

Learning-Based Task Scheduling Using Big Bang Big Crunch for Cloud Computing Environment

2020 ◽  
Vol 13 (2) ◽  
pp. 137-146 ◽  
Author(s):  
Pradeep Singh Rawat ◽  
Priti Dimri ◽  
Punit Gupta
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Low Cost ◽  
Big Bang ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Big Crunch ◽  
Resource Cost

: Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm and Round Robin improve the performance but these are not cost efficient at the same time. : Scheduling issue and resource cost resolve using improved meta-heuristic approaches. In this work, a cost aware algorithm improved using Big-Bang Big-Crunch based task mapping is proposed which reduces the execution time and cost paid for the resources at the time of execution. The cost aware meta-heuristic technique used. Results show that the proposed algorithm provides better cost efficiency than the existing genetic algorithm. The proposed Big-Bang Big-Crunch based resource allocation technique evaluated against the Genetic approach. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The population size and user requests measures the performance of the proposed model. : The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost).

scholarly journals Resource Allocation in Cloud Computing for Energy Efficiency

2019 ◽  
Vol 8 (6) ◽  
pp. 1170-1174
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Power Consumption ◽  
High Performance ◽  
High Reliability ◽  
Low Cost ◽  
Work Load ◽  
Cloud Infrastructure ◽  
Multiple Resources ◽  
Resource Requirements

Cloud computing is a paradigm in which we have virtualized computer systems that deliver services, processing, storage, network, and other fundamental computing resources. Cloud computing enables low cost, device location independence, high reliability, scalability and sustainability. This paper describes the present state of cloud computing research by examining literature, identifying current study trends. We have analyzed the resource allocation method and concluded. It typically designs for high performance that supports the peak resource requirements. After several analyses the power consumption of data center and cloud systems as increased almost several times. There is a lack of research that addresses challenges of managing multiple resources with objective of allocating enough resources for each work load to optimizing power consumption. These papers survey various types of resource allocation algorithms that improve the cloud Infrastructure.

scholarly journals AVAILABILITY EVALUATION OF DIFFERENT PLANNING AND SCHEDULING ALGORITHMS IN HYBRID CLOUD SYSTEM

2020 ◽  
Vol 3 (4) ◽  
pp. 47-59
Author(s):  
Ahmed A. Hamed ◽  
Rabah A. Ahmed
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Hybrid Cloud ◽  
Computing Environment ◽  
Planning And Scheduling ◽  
Cloud Computing Environment ◽  
Individual Level ◽  
The Individual

The importance of hybrid cloud computing has become a reality in recent years for large and medium enterprises and even at the individual level, which increases the need for many improvements in its availability level. One of the most important things that affects availability is the task scheduling process. Task scheduling is subject to many scheduling algorithms and these algorithms differ in terms of performance and purpose, the most important aspects being improved by using an appropriate scheduling algorithm is the total execution time(makespane) and also the success rate and downtime live migration. Because working on a cloud computing environment is costly and complex, we have simulated a hybrid cloud environment using reliable and accurate simulation and used Directed acyclic graph(DAG) as a workflow application. In this paper we will compare scheduling and planning algorithms for cloud computing environment by implementing a framework using (workflowsim) based on (cloudsim) simulator in order to choose the best algorithm to verify the possibility of improving availability.

scholarly journals Optimization model for QoS based task scheduling in cloud computing environment

2020 ◽  
Vol 18 (2) ◽  
pp. 1081
Author(s):  
Sirisha Potluri ◽  
Katta Subba Rao
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Optimization Model ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Cloud Services ◽  
Allocation Model ◽  
Task Scheduling Algorithm ◽  
And Task

Shortest job first task scheduling algorithm allocates task based on the length of the task, i.e the task that will have small execution time will be scheduled first and the longer tasks will be executed later based on system availability. Min- Min algorithm will schedule short tasks parallel and long tasks will follow them. Short tasks will be executed until the system is free to schedule and execute longer tasks. Task Particle optimization model can be used for allocating the tasks in the network of cloud computing network by applying Quality of Service (QoS) to satisfy user’s needs. The tasks are categorized into different groups. Every one group contains the tasks with attributes (types of users and tasks, size and latency of the task). Once the task is allocated to a particular group, scheduler starts assigning these tasks to accessible services. The proposed optimization model includes Resource and load balancing Optimization, Non-linear objective function, Resource allocation model, Queuing Cost Model, Cloud cost estimation model and Task Particle optimization model for task scheduling in cloud computing environement. The main objectives identified are as follows. To propose an efficient task scheduling algorithm which maps the tasks to resources by using a dynamic load based distributed queue for dependent tasks so as to reduce cost, execution and tardiness time and to improve resource utilization and fault tolerance. To develop a multi-objective optimization based VM consolidation technique by considering the precedence of tasks, load balancing and fault tolerance and to aim for efficient resource allocation and performance of data center operations. To achieve a better migration performance model to efficiently model the requirements of memory, networking and task scheduling. To propose a QoS based resource allocation model using fitness function to optimize execution cost, execution time, energy consumption and task rejection ratio and to increase the throughput. QoS parameters such as reliability, availability, degree of imbalance, performance and SLA violation and response time for cloud services can be used to deliver better cloud services.

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