Cloud computing is the most advanced technology in the real world environment and provides flexible and convenient possibilities for users to utilize available services. Resource provisioning to the satisfaction of user requirements becomes the most challenging task in the heterogeneous cloud environment. Proper admission control algorithms need to be proposed for better resource provisioning with improved user satisfaction level. In this research, Knowledge-based Service Level Agreement (SLA) aware admission controlled scheduling and resource allocation are proposed which makes use of machine learning algorithms namely Support Vector Machine (SVM) and Artificial Neural Network (ANN) for better admission control. It seeks to study the knowledge of resource status information by using machine learning algorithms in the training phase. Based on these strategies, admission control would be done in the testing phase which would lead to efficient and better resource provisioning. As our proposed work we mentioned Position Balanced Parallel Particle Swarm Optimization (PBPPSO) is utilized for optimal scheduling and resource allocation which can handle large volumes of tasks in an optimal manner.

in resource-constrained networks, particularly those with limited bandwidth to manage high-volume data transmission, network congestion is a major issue, resulting in poor quality of service, including packet loss and delay throughput. Due to self-contained batteries that limit sensor node lifetime, this issue is important in wireless sensor networks (WSNs) with limitations and restrictions, such as limited processing power, memory, and transmission. By determining a path that avoids congested highways, the network can be extended. As a result, we present a WSN route determination architecture that is congestion-aware. The architecture is divided into three stages: In a top-down hierarchical structure, the first path is created. Energy-aware assisted routing for route derivation. Exponential smoothing is used to forecast congestion, but the final parameters for route determination are not taken into account. We use fuzzy logic systems to evaluate proper weights for a variety of factors, including shop count, remaining energy, buffer occupancy, and forwarding rate, as well as a bat algorithm to optimize the weight over the membership functions. Eventually proposed model shows the high throughput, low packet loss, save energy, and extending network lifetime.

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Transmission control protocol is a single path per connection in spite of that unite paths often exist between end points. When the data has been transmitted through the connection with multipath TCP and it is lost then the data is transmitted with normal TCP which is more vulnerable to the attacks. The survey made in this paper proved that most of the security attacks like IP source route, Routing Information Protocol etc can be overcome when we send the data through the option enabled with multipath TCP as the data flows in multiple paths simultaneously thus increasing the utilization of the resources by the applications that are based on the internet. It is been observed that through the study made in this paper that the throughput of the application can be improved when the connection is established through Multipath TCP compared to normal TCP.