According to the advances in users’service requirements,physical hardware accessibility,and speed of resource delivery,Cloud Computing(CC)is an essential technology to be used in many fields.Moreover,the Internet of ...According to the advances in users’service requirements,physical hardware accessibility,and speed of resource delivery,Cloud Computing(CC)is an essential technology to be used in many fields.Moreover,the Internet of Things(IoT)is employed for more communication flexibility and richness that are required to obtain fruitful services.A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents.This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number of IoT devices with low resources.Different agents are integrated based on relevant features of behavioral interaction using classification techniques to balance the workload.Aload balancing algorithm is developed to serve users’requests to improve the solution of workload problems with an efficient distribution.The activity task from IoT devices has been classified by feature selection methods in the preparatory phase to optimize the scalability ofCC.Then,the server’s availability is checked and the classified task is assigned to its suitable server in the main phase to enhance the cloud environment performance.Multi-agent load balancing framework is succeeded to cope with the importance of using large-scale requirements of CC and(low resources and large number)of IoT.展开更多
Aiming at the load imbalance and poor scalability in single-tier Web server clusters, an efficient load balancing ap- proach is proposed for constructing an N-hierarchical （multi-tier） Web server cluster. In each la...Aiming at the load imbalance and poor scalability in single-tier Web server clusters, an efficient load balancing ap- proach is proposed for constructing an N-hierarchical （multi-tier） Web server cluster. In each layer, multiple load balancers are set to receive the user requests simultaneously, and different load bal- ancing algorithms are used to construct the high-scalable Web cluster system. At the same time, an improved load balancing al- gorithm is proposed, which can dynamically calculate weights according to the utilization of the server resources, and reasonably distribute the loads for each server according to the load status of the servers. The experimental results show that the proposed ap- proach can greatly decrease the load imbalance among the Web servers and reduce the response time of the entire Web cluster system.展开更多
Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resour...Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resources to the maximum.The resource utilization is highly dependent on the allocation of resources to the incoming request.The allocation of requests is done with respect to the physical machines present in the datacenter.While allocating the tasks to these physical machines,it needs to be allocated in such a way that no physical machine is underutilized or over loaded.To make sure of this,optimal load balancing is very important.Design/methodology/approach-The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks.The major focus of the proposed work is to optimize the load balancing in a datacenter.When optimization happens,none of the physical machine is neither overloaded nor under-utilized,hence resulting in efficient utilization of the resources.Findings-The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load(RR)ant colony optimization(ACO),artificial bee colony(ABC)with respect to the selected parameters response time,virtual machine migrations,host shut down and energy consumption.All the four parameters gave a positive result when the algorithm is simulated.Originality/value-The contribution of this paper is towards the domain of cloud load balancing.The paper is proposing a novel approach to optimize the cloud load balancing process.The results obtained show that response time,virtual machine migrations,host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study.The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.展开更多
文摘According to the advances in users’service requirements,physical hardware accessibility,and speed of resource delivery,Cloud Computing(CC)is an essential technology to be used in many fields.Moreover,the Internet of Things(IoT)is employed for more communication flexibility and richness that are required to obtain fruitful services.A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents.This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number of IoT devices with low resources.Different agents are integrated based on relevant features of behavioral interaction using classification techniques to balance the workload.Aload balancing algorithm is developed to serve users’requests to improve the solution of workload problems with an efficient distribution.The activity task from IoT devices has been classified by feature selection methods in the preparatory phase to optimize the scalability ofCC.Then,the server’s availability is checked and the classified task is assigned to its suitable server in the main phase to enhance the cloud environment performance.Multi-agent load balancing framework is succeeded to cope with the importance of using large-scale requirements of CC and(low resources and large number)of IoT.
基金Supported by the National Natural Science Foundation of China(61073063,61173029,61272182 and 61173030)the Ocean Public Welfare Scientific Research Project of State Oceanic Administration of China(201105033)National Digital Ocean Key Laboratory Open Fund Projects(KLDO201306)
文摘Aiming at the load imbalance and poor scalability in single-tier Web server clusters, an efficient load balancing ap- proach is proposed for constructing an N-hierarchical （multi-tier） Web server cluster. In each layer, multiple load balancers are set to receive the user requests simultaneously, and different load bal- ancing algorithms are used to construct the high-scalable Web cluster system. At the same time, an improved load balancing al- gorithm is proposed, which can dynamically calculate weights according to the utilization of the server resources, and reasonably distribute the loads for each server according to the load status of the servers. The experimental results show that the proposed ap- proach can greatly decrease the load imbalance among the Web servers and reduce the response time of the entire Web cluster system.
文摘Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resources to the maximum.The resource utilization is highly dependent on the allocation of resources to the incoming request.The allocation of requests is done with respect to the physical machines present in the datacenter.While allocating the tasks to these physical machines,it needs to be allocated in such a way that no physical machine is underutilized or over loaded.To make sure of this,optimal load balancing is very important.Design/methodology/approach-The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks.The major focus of the proposed work is to optimize the load balancing in a datacenter.When optimization happens,none of the physical machine is neither overloaded nor under-utilized,hence resulting in efficient utilization of the resources.Findings-The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load(RR)ant colony optimization(ACO),artificial bee colony(ABC)with respect to the selected parameters response time,virtual machine migrations,host shut down and energy consumption.All the four parameters gave a positive result when the algorithm is simulated.Originality/value-The contribution of this paper is towards the domain of cloud load balancing.The paper is proposing a novel approach to optimize the cloud load balancing process.The results obtained show that response time,virtual machine migrations,host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study.The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.
