Research on Robot Dynamic Path Planning Based on Multiple Linear Reinforcement Learning

Under the background of digital and intelligent transformation, all walks of life are facing the conflict between internal stability and rapidly changing external needs, and the conflict between stable background and dynamic foreground. To solve these conflicts and problems, it needs to take new means to adjust from multiple dimensions. Draw support from the new info and communication operation support system, it could form a digital Internet info support system with the cloud platform, which can effectively establish fast and flexible front-end utilizations and support the rapid development, agile iteration and on-demand adjustment of related businesses. The Internet backbone network needs to make the info open. The new info communication operation support system based on the Internet can enable the info resources to be transmitted as needed. The research and utilization of cloud resource visualization and controllability has gradually become the key to the development of new Internet communication operation system. On the premise of systematically combing the Internet info and communication operation support system, this paper comprehensively studies the utilization of cloud resources, and analyses the utilization scenarios of cloud resources of the new education communication operation system, in order to better ameliorate the more efficient and secure utilization of cloud resources and communication operation system inside and outside the enterprise.

Review on Mapping of Tasks to Resources in Cloud Computing

Resource allocation and scheduling algorithms are the two essential factors that determine the satisfaction of cloud users. The major cloud resources involved here are servers, storage, network, databases, software and so on based on requirements of customers. In the competitive scenario, each service provider tries to use factors like optimal configuration of resources, pricing, Quality of Service (QoS) parameters and Service Level Agreement (SLA) in order to benefit cloud users and service providers. Since, many researchers have proposed different scheduling algorithms and resource allocation strategies, it becomes a cumbersome task to conclude which ones really benefit customers and service providers. Hence, this paper analyses and presents the most relevant considerations that would help the cloud researchers in achieving their goals in terms of mapping of tasks to cloud resources.

Minimizing the Cost for Resource Allocation from Multiple Cloud Providers

Cloud computing provides streamlined tools for exceptional business efficiency. Cloud service providers typically offer two types of plans: reserved and on-demand. Restricted policies provide low-cost long-term contracting, while order contracts are expensive and ready for short periods. Cloud resources must be delivered wisely to meet current customer demands. Many current works rely on low-cost resource-reserved strategies, which may be under- or over-provisioning. Resource allocation has become a difficult issue due to unfairness causing high availability costs and cloud demand variability. That article suggests a hybrid approach to allocating cloud services to complex customer orders. The strategy was built in two stages: accommodation stages and a flexible structure. By treating each step as an optimization problem, we can reduce the overall implementation cost while maintaining service quality. Due to the uncertain nature of cloud requests, we set up a stochastic Optimization-based approach. Our technique is used to assign individual cloud resources and the results show its effectiveness. Keywords: Cloud computing, Resource allocation, Demand

Integrated Fog and Cloud Computing Issues and Challenges

Due to the exponential growth in the number of internet-of-things (IoT) devices like smartphones and smart traffic lights, the data generated by the devices and the service requirements are increasing. The biggest issue in accessing the cloud computing is that all processing is done on cloud resources. For cloud-based services, it is utmost required to send all data to cloud resources which leads to many issues and challenges. The important issues are large volume of data, low latency rate, low bandwidth. In order to resolve such issues, there is an essential need of a smart computing paradigm which works as a moderator between cloud computing and IoT devices to improve the performances of the services, maximizing utilization of computing resources, storage. This work presents an overview and description of fog computing in the context of cloud computing and internet of things (IoT) and also sheds light on the key differences between cloud computing and fog computing. This work also presents various issues and challenges in the context of fog computing with its various applications.