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Title: Workload Optimization In Federated Cloud Environment
Authors: S R, Shishira
Supervisors: A., Kandasamy
Keywords: Cloud Workloads;Workload chracterization;Prediction;Optimization
Issue Date: 2022
Publisher: National Institute of Technology Karnataka, Surathkal
Abstract: Cloud computing is an essential paradigm for processing, computing, storing, and com- munication bandwidth. It offers services on an on-demand basis for the user, that is, pay per use. Cloud computing consists of numerous resources, including the provision of networks, databases for storage, servers, virtual machines, and potential application. It is a widely used technique to handle large amounts of data as it provides versatility and functionality for optimization. Customers submit their request for data exchange and to store it in an existing cloud environment. The customer has a huge advantage in paying for the currently required services. In a federated cloud environment, one or more cloud service providers share their servers to handle user requests. It promotes cost savings, service utilization, and performance enhancement. Clients would bene- fit as a Service Quality Agreement exists between the two. The Cloud federation is an evolving technology through which cloud service providers cooperate to provide clients with customized services to enjoy the real benefits of Cloud Computing. The federated service provider achieve better resource usage and Quality of Service by cooperation, thereby enhancing their market prospects. Workloads are the collection of raw inputs provided to the processing arhcitecture. Based on the successful processing of workloads, efficiency can be assessed. Differ- ent workloads have distinct feature sets. The secret to making optimal configuration decisions and improving system performance is by recognizing the characteristics of workloads. Multiple requests are handled quickly under the dynamic cloud environ- ment, which contributes to the resource allocation problem.The cloud will maintain the workflow active through the proper allocation of resources, virtualization software, or repositories. However, the precise load estimation model is important for efficient management of resources. i It is hard to manage a large number of workloads in an enterprise cloud system. Workloads are the sum of data for processing that are provided to the hardware resource. Its behavior and characteristics play an important role in the efficient processing of resource requests. It is also difficult to predict the existence of workloads if they alter excessively. In this thesis, we propose a conceptual framework for efficient prediction and optimization of workloads that can be easily adapted to a system to address this problem to address this problem. Serving the request in considerably less time leads to an issue with resource allocation. In order to auto-scale the resources, it is more comfortable to have previous awareness of the incoming loads. For the better prediction of workloads in the cloud world, a novel architecture is proposed. Predicted workloads could also be configured smoothly for better use without waving off, the SLA negotiated between the provider and customers. Three essentials for the management of cloud resources are considered in the proposed Fitness Function Extraction Model, i.e. CPU, Disk, and Memory storage. This thesis proposes a BeeM-NN architecture by incorporating Workload Neural Network Algorithm and Novel Bee Mutation Optimization Algorithm into a cloud en- vironment for optimized workload prediction. The proposed model initially includes the Fitness Function Extraction Algorithm to retrieve the attribute samples from the Microsoft Azure traces. With the Novel Bee Mutation Optimization Algorithm in the cloud, the expected QoS are optimized. The developed model is tested using the feder- ated cloud service providers workload data traces and is analyzed with the benchmark methods. The result indicated that the proposed model obtained higher accuracy than the existing systems with optimum efficiency in resource and cost usage.
Appears in Collections:1. Ph.D Theses

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