Intelligent processing with smart devices and informative communications in everyday tasks brings an effective platform for the internet of things (IOT). Internet of things is seeking its own way to be the universal solution for all the real-life scenarios. Even though many theoretical studies pave the basic requirement for the internet of things, still the evidence-based learning (EBL) is lacking to deal with the application of the internet of things. As a contribution of this chapter, the basic requirements to study about internet of things with its deployment architecture for mostly enhanced applications are analyzed. This shows researchers how to initiate their research focus with the utilization of internet of things.
Cloud computing helps to share data and provide many resources to users. Users pay only for those resources as much they used. Rapid increase in load to these cloud framework cannot be predicted. Load balancing is one of the issues in cloud computing that distributes the workload to the nodes in such a way no node is overloaded or under - loaded. Load balancing is a main challenge in cloud environment. In this work, scheduling algorithm is applied for load balancing by considering the cost of task execution and make span. This scheduling algorithm efficiently maps task to available nodes in cloud and it is beneficial to user and service provider. Load balancing segregates assignments of tasks among all available virtual machines from datacenters. Assignment of tasks to virtual machines can be done with minimum delay. To enhance the make span, resource utilization, our proposed framework utilizes AFSS-SHC load balancing strategy. A metaheuristics swarm intelligence algorithm which is NP-hard have been suggested to balance load across devices. The algorithms taken into account are-HEFT,PSO and PSO-HC. The proposed methodology AFSS-SHC optimized the task scheduling. Random tasks have been taken for this purpose and simulated to show that the proposed methodology works efficiently to reduce the make span of tasks to reduce the cost.
Dynamic cloud computing technique enables resources to be assigned to different clients based on the current demand of each client turning the cloud to a limitless computational platform with limitless storage space which improves the performance of cloud services. To achieve best resource allocation in dynamic hosting frameworks, cloud service providers should provision resources intelligently to all clients. Cloud computing empowers consumers to access online resources using the internet, from anywhere at any time without considering the underlying hardware, technical management, and maintenance problems of the original resources. In this chapter, the authors present a detail study of various resource allocation and other scheduling challenges as well as cloud simulation frameworks tools like CloudSim and ICanCloud.
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