The rebel of global networked resource is Cloud computing and it shared the data to the users easily. With the widespread availability of network technologies, the user requests increase day by day. Nowadays, the foremost complication in Cloud technology is task scheduling. The cargo position and arrangement of the tasks are the two important parameters in the Cloud domain, which can provide the Quality of Service (QoS). In this paper, we formulated the optimal minimization of makespan and energy consumption in task scheduling using Local Pollination-based Gray Wolf Optimizer (LPGWO) algorithm. In the hybrid concept, Gray Wolf Optimizer (GWO) algorithm and Flower Pollination Algorithm (FPA) are combined and used. In the presence of GWO, the best searching factor is used to increase the convergence speed and FPA is used to distribute the data to the next packet of candidate solution using local pollination concept. Chaotic mapping and OBL are used to provide a suitable initial candidate for task solutions. Therefore, the experiments delivered better task scheduling results in low and high heterogeneities of physical machines. Ultimately, the comparison with the simulation results had shown the minimum convergence speed of makespan and energy consumption.

Nowadays, Cloud computing is a new computing model in the field of information technology and research. Generally, the cloud environment aims in providing the resource that depends upon the user’s necessity. The major problem caused by cloud computing is task scheduling. Nevertheless, the previous scheduling methods concentrate only on the resource needs, memory, implementation time and cost. In this paper, we introduced an optimal task-scheduling algorithm of the local pollination-based moth search algorithm (LPMSA), which is the hybridization of moth search algorithm (MSA) and flower pollination algorithm (FPA). The proposed LPMSA chooses an optimal solution for proper task scheduling in the cloud. Moreover, the exploitation capacity of MSA is improved by using the local search of the FPA algorithm. In this work, we use 2-fold simulation processes that are implemented under the platform of JAVA. The proposed LPMSA for task-scheduling performance is evaluated using low and high heterogeneous machines with uniform and non-uniform parameters. The experimental analysis demonstrates that the proposed LPMSA approach is well suitable for cloud task scheduling thereby reducing the makespan and energy consumption during proper task scheduling.

Nature has a huge role to maintain the stability in the environment. But, natural disasters damage the environment, affect the life cycle and decline the lifetime of living beings. Nature is destroyed by different disasters namely earthquake, fire, flood, landslide, air pollution and so on. Among these, forest fire is one of the foremost dangerous natural disasters which cause several serious issues like biodiversity loss, global warming, fuel wood loss and air pollution in the environment. Therefore, prediction of fire occurring in forest plays a crucial role to save lots of the environment. Thus, researchers focused on different technologies with different methodologies for predicting the fire that occurred in forest, as early as possible. Moreover, smoke is the focal point for fire, some of the researchers pay their attention on detecting the smoke of the forest using different technologies. Therefore, this paper gives a summary for effectively detecting the smoke and fire in forest.

No abstract

No abstract