Cloud resource mapping through crow search inspired metaheuristic load balancing technique

2021 ◽  
Vol 93 ◽  
pp. 107221
Author(s):  
Harvinder Singh ◽  
Sanjay Tyagi ◽  
Pardeep Kumar
Keyword(s):  
Load Balancing ◽  
Resource Mapping ◽  
Cloud Resource

Elastic Optical Network Service-Oriented Architecture (SOA) Used for Cloud Computing and Its Resource Mapping Optimization Scheme

2020 ◽  
Vol 15 (4) ◽  
pp. 442-449
Author(s):  
Xun Xia ◽  
Ling Chen
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Resource Utilization ◽  
Service Oriented Architecture ◽  
Optical Network ◽  
Spectrum Utilization ◽  
Mapping Algorithm ◽  
Resource Mapping ◽  
Elastic Optical Network ◽  
Service Oriented

In this study, starting from the elastic optical network, the layered and function isolated service-oriented architecture (SOA) is introduced, so as to propose an elastic optical network SOA for cloud computing, and further study the resource mapping of optical network. Linear mapping model, random routing mapping algorithm, load balancing mapping algorithm and link separation mapping algorithm are introduced respectively, and the resource utilization effect of different mapping algorithms for the proposed optical network is compared. During the experiment, firstly, the elastic optical network is tested. It is found that the node utilization and spectrum utilization of the underlying optical fiber level network are significantly improved. Within the average service time of 0.312 s∼0.416 s, the corresponding node utilization and spectrum utilization are 90% and 80% respectively. In the resource mapping experiment, load balancing algorithm and link separation algorithm can effectively improve the mapping success rate of services. Among them, the link separation mapping algorithm can improve the spectrum resource utilization of optical network by 15.6%. The elastic optical network SOA proposed in this study is helpful to improve the use of network resources.

scholarly journals An efficient Task Scheduling and Load Balancing in Cloud Computing using KD-Tree Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 2426-2433
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Heuristic Algorithms ◽  
Research Work ◽  
Tree Algorithm ◽  
Cloud Resource Management ◽  
High Resource ◽  
Cloud Resource ◽  
And Task

Cloud Computing provides the sharing ability and access for available cloud host and various distributed environments, namely Load Balancing (LB), virtualization technologies and scheduling techniques. The satisfaction of both users and cloud providers are the major issues for effective LB and task scheduling algorithms in cloud resource management, where the requirements namely high resource utilization, low monetary costs and minimum makespan. Many researchers tried to develop various heuristic and meta-heuristic algorithms to attain the aforementioned user requirements. But, when the number of tasks grows exponentially, these algorithms failed to achieve LB, lower running time, and it faces the high time complexity. In this research work, a KD-Tree algorithm is developed to address the issues of heuristic algorithms and provide efficient LB by partitioning the environments into several tasks. According to the deadline of task execution, the remaining tasks are adjusted dynamically by the proposed KD-tree algorithm in the virtual environment. The experiments are conducted to evaluate the efficiency of KD-Tree algorithm with existing heuristic techniques by using makespan, energy consumption and task migrations. When the number of tasks is 20, the proposed KD-Tree algorithm achieved 71.33% makespan and 5% task migrations.

scholarly journals Load Balancing and Thermal-Aware in Geo-Distributed Cloud Data Centers Based on Vlans

2018 ◽  
Vol 6 (3) ◽  
pp. 113-117 ◽  
Author(s):  
Mustafa I. Khaleel
Keyword(s):  
Load Balancing ◽  
Geographical Location ◽  
Energy Aware ◽  
Cloud Data ◽  
Electricity Cost ◽  
Geographically Dispersed ◽  
Cloud Resource ◽  
Cloud Servers ◽  
Distributed Cloud ◽  
Cloud Resources

Power consumption in datacenters has become an emerging concern for the cloud providers. This poses enormous challenges for the programmers to motivate new paradigms to enhance the efficiency of cloud resources through designing innovative energy-aware algorithms. However, balancing the weights over geographically dispersed datacenters has been shown to be essential in decreasing the temperature consumption per datacenter. In this paper, we have formulated a load balancing paradigm to exploit the idea of scheduling scientific workflows over distributed cloud resources to make system outcome more efficient. The proposed heuristic works based on three constraints. First, initiating cloud resource locality for tenants and calculating the shortest distance in order to direct module applications to the closet resources and conserving more bandwidth cost. Second, selecting the most temperature aware datacenters based on geographical climate to maintain electricity cost for the providers. Third, running multiple datacenters within the same geographical location instead of housing the entire workloads in a single datacenter. This allows providers to take a tremendous advantage of sustaining the system from degradation or even unpredictable failure which in turn will frustrate the tenants. Furthermore, applications are formulated as Directed Acyclic Graph (DAG)-structured workflow. For the underlying cloud hardware, our model groups the cloud servers to communicate as if they were in the same physical location. Additionally, both modes, on-demand and reservation, are supported in our algorithm. Finally, the simulation showed that our method was able to enhance the utilization rates about 67% compared to the baseline model.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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