Due to the exponential rise in the usage of the internet and smart devices, there is a demand for enhanced network efficiency and user satisfaction in a cloud computing environment. Moreover, moving to the cloud systems, it mainly focuses on storage, computation and resources. Due to copious growth, there exist more challenges as well. Among those, resource allocation in cloud computing is the main study, which is essential to determine the QoS and improved performance concerning reliability, confidentiality, trust, security, user satisfaction, profits, etc. This paper plans to prepare a detailed review on trust-based resource allocation in the collaborative cloud. The cloud industry has been assessed in terms of trust-based and other important factors to produce a road plan for resource allocation. Many papers are reviewed here and give a substantial evaluation of cloud resources and their resource allocation models using machine learning and optimization models. First, this survey provides an elaborated study concerning the various cloud resources considering the performance and QoS. Eventually, it extends the research based on trust-based approaches, with the intention of motivating the researchers to focus on trust-based resource allocation on collaborative cloud computing (CCC) atmosphere.
Collaborative cloud computing utilizes information technology to successfully provide service over the network and serve the end users with tremendously stronger computational capability and enormous memory space at lower costs. Moreover, providing highly trustworthy service is the most fundamental task even on this platform. So far, only some contributions are there that meet the requirements of trust computing in this scenario. This proposal estimates the Quality of Service and trust by analyzing the system behavior using a new trust computing model. This is handled using Neural Network model. Further, a parallel resource matching framework is introduced using the concept of MapReduce concept, thereby the resource allocation is performed without any conflicts. Particularly, the resource allocation is performed precisely by optimization logic, where an Improved Grey Wolf Optimizer is introduced to do the same. In fact, the proposed algorithm is the enhanced version of traditional Grey Wolf Optimizer. Finally, the performance of the projected model is compared over other state-of-the-art models concerning different performance measures.
The cloud computing is arising as a popular computing paradigm, as it is good in offering its users an on-demand scalable resource based services over the internet. In the peak hours, a single cloud is not at all efficient in serving an application; therefore the collaborative cloud model has been introduced. The collaborative cloud computing (CCC) make use of the globally-scattered distributed cloud resources of the diverse organizations collectively in a co-operative manner to provide the required service to the user. The allocation as well as the management of the resources is being a challenging task in the CCC, due to the heterogeneity of the resources. On the other hand, the assurance of the Quality of Service (QoS) and reliability of these resources is challenging. Further, it would be efficient if the resources are provided based on the system behavior. In this research work, a novel trust computing model is developed, which predicts both the QoS and Trust via analyzing the system behavior. The proposed model encloses three major phases: trust- QoS behavior estimation, resource matching and resource allocation. Initially, the QoS as well as Trust behavior of the system is estimated via a Neural Network (NN) model. Subsequently, the resource allocation is performed using the parallel resource matching framework, which is based on the concept of Map-Reduce. More particularly, the precious resource allocation is achieved by an optimization logic called Improved Grey Wolf Optimizer (IGWO). Here, the improvement of GWO emphasis the consideration of both the best and worst fitness.
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