Load balancing optimization is categorized as NP-hard problem, playing an important role in enhancing the cloud utilization. Different methods have been proposed for achieving the system load balancing in cloud environment. VM migration is one of these techniques, proposed to improve the VMs' functionality. Despite of the advantageous of VM migration, there are still some drawbacks which urged researchers to improve VM migration methods. In this paper we propose a new load balancing technique, using Endocrine algorithm which is inspired from regulation behavior of human's hormone system. Our proposed algorithm achieves system load balancing by applying selforganizing method between overloaded VMs. This technique is structured based on communications between VMs. It helps the overloaded VMs to transfer their extra tasks to another under-loaded VM by applying the enhanced feed backing approach using Particle Swarm Optimization (PSO). To evaluate our proposed algorithm, we expanded the cloud simulation tool (Cloudsim) which is developed by University of Melbourne. The simulation result proves that our proposed load balancing approach significantly decreases the timespan compared to traditional load balancing techniques. Moreover it increases the Quality Of Service (QOS) as it minimizes the VMs' downtime.
Abstract-In the development of large ad-hoc Wireless Sensor and Actuator Agent Networks (SANETS), a multitude of disparate problems are faced. In order for these networks to function, software must be able to effectively manage: unreliable dynamic distributed communication, the power constraints of un-wired devices, failure of hardware devices in hostile environments and the remote allocation of distributed processing tasks throughout the network. The solutions to these problems must be solved in a highly scalable manner. The paper describes the process of analysis of the requirements and presents a design of a serviceoriented software infrastructure (middleware) solution for scalable ad-hoc networks, in a context of a system made of mobile sensors and actuators.
In Cloud Computing, designing an efficient workflow scheduling algorithm is considered as a main goal. Load balancing is one of the most sophisticated methodologies, which can optimize workflow scheduling by distributing the load evenly among available resources. A well-designed load balancing algorithm has significant impact on performance and output in Cloud Computing. Therefore, designing robust load balancing techniques to manage the networks' load has always been a priority. Researchers have proposed and examined different load balancing methods; there is, however, a large knowledge gap in adopting an efficient load balancing algorithm in the Cloud system. This paper describes how a generalized spring tensor, an evolutionary algorithm with mathematical apparatus, can be utilized for a more efficient and effective load management in Cloud Computing. Considering the fluctuation and magnitude of the load, a novel application of workflow scheduling is investigated in the context of various mathematical patterns. The preliminary results of the research show that defining the dependency ratio between workflow tasks in Cloud Computing, results in better resource management, maximized performance and minimized response time while dealing with customer's requests.
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