In recent years, adoption of cloud computing for computational needs is growing significantly due to various factors such as no upfront cost and access to latest service. In general, cloud infrastructure providers offer a wide range of services with different pricing models, instance types and a host of value-added features. Efficient selection of cloud services constitutes significant management challenges for cloud consumer, which is tedious and involves large information processing. To overcome this, the cloud brokers provide resource provisioning options that ease the task of choosing the best services based on consumers requirements and also provide a uniform management interface to access cloud services. This paper proposes a novel cloud brokering architecture that provides an optimal deployment plan for placement of virtual resources in multiple clouds. The objective of the deployment plan is to select the best cloud services with optimal cost, taking into account various attributes defined in service measurement index (SMI) with additional physical and logical constraints. The proposed cloud brokering architecture has been modeled using mixed integer programming formulation and Benders decomposition algorithm to solve efficiently. Efficacy of the proposed algorithm has been verified by extensive numerical studies and sensitivity analysis.Keywords Cloud broker · Infrastructure as a service · Deployment plan · Service measurement index · Mixed integer programming · Optimization
Cloud services are offered independently or combining two or more services to satisfy consumer requirements. Different types of cloud service providers such as direct sellers, resellers and aggregators provide services with different level of service features and quality. The selection of best suitable services involves multi-criteria nature of services to be compared with the presence of both qualitative and quantitative factors, which make it considerably more complex. To overcome this complexity, a fuzzy hybrid multi-criteria decision making approach has been proposed, which includes both qualitative and quantitative factors. Triangular fuzzy numbers are used in all pairwise comparison matrices in the Fuzzy ANP and the criteria weights are utilized by Fuzzy TOPSIS and Fuzzy ELECTRE methods to rank the alternatives. This strategy is demonstrated with selection of cloud based collaboration tool for designers. Finally, sensitivity analysis is performed to prove the robustness of the proposed approach.
Oyster mushroom cultivation is an eco-friendly, profitable agribusiness because of its nutritious and medicinal value but it is labor-intensive, and it grows in a sensitive environment. It must use an autonomous environment control system to optimize and offer the necessary growing conditions. To optimize the temperature and humidity and consequently maximize mushroom production, a smart mushroom watering, monitoring, and controlling system using fuzzy logic controllers (FLC) have been developed. This study suggests installing an Internet of Things (IoT) node that uses DHT22 sensors, an ESP32 board, and relays to control actuators (sprinkler, cooling fan, humidifier, and heater) in a smart oyster mushroom house. The controller on sensing current temperature and humidity values using the fuzzy logic technique switches on/off the actuators automatically. The data collected at each IoT node is collected and visualized on the ThingSpeak platform. The computed response time for actuators using FLC is compared with that calculated using a simple ON-OFF method. The findings demonstrate that response time calculation using fuzzy logic is significantly accurate and faster than a simple ON-OFF approach. Additionally, the graphs created in live on ThingSpeak, demonstrate that the FLC algorithm is more robust and effective than the simple ON-OFF algorithm.
An automatic environment control systems for greenhouses are turning to be very significant because of food demand, and rise in temperature and population of the world. This article proposes to design and implement a low cost, robust and water efficient autonomous smart internet of things (IoT) system to monitor and control the temperature, and humidity of an outdoor oyster mushroom growing unit. The IoT-based control system involves DHT22 sensors, ESP32 controller and actuators (water pump and cooling fan) to facilitate the adequate amount of air for circulation to maintain temperature and water to maintain humidity inside an outdoor oyster mushroom growing unit as per its requirement. A real working prototype is developed and implemented on integrating fuzzy inference system (FIS) in ESP32 controller using Arduino C with the help of its integrated design environment. The FIS is designed to calculate the switching on/off time of water pump and cooling fan on sensing current temperature, and humidity inside oyster mushroom unit with respect to ambient temperature, and humidity respectively. The prototype provides inside temperature, humidity, ambient temperature, ambient humidity, water pump time and fan time on Thing-speak platform in real time. Furthermore, the data is used for design and simulation of Adaptive Neuro Fuzzy Inference Controller for an outdoor oyster mushroom growing unit in MATLAB/Simulink to improve the performance of the system. The practical applicability of the proposed ANFIS controller over FIS Controller and industrial PID Controller is shown by simulation findings with use of experimental data. The system reduces water use as well as an extremely extraordinary administration required for monitoring the mushroom unit. In addition, it increases robustness of the system.
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