Knowledge-Based Resource Allocation for Collaborative Simulation Development in a Multi-Tenant Cloud Computing Environment

Abstract

Cloud computing technologies have enabled a new paradigm for advanced product development powered by the provision and subscription of computational services in a multi-tenant distributed simulation environment. The description of computational resources and their optimal allocation among tenants with different requirements holds the key to implementing effective software systems for such a paradigm. To address this issue, a systematic framework for monitoring, analyzing and improving system performance is proposed in this research. Specifically, a radial basis function neural network is established to transform simulation tasks with abstract descriptions into specific resource requirements in terms of their quantities and qualities. Additionally, a novel mathematical model is constructed to represent the complex resource allocation process in a multi-tenant computing environment by considering priority-based tenant satisfaction, total computational cost and multi-level load balance. To achieve optimal resource allocation, an improved multi-objective genetic algorithm is proposed based on the elitist archive and the K-means approaches. As demonstrated in a case study, the proposed framework and methods can effectively support the cloud simulation paradigm and efficiently meet tenants' computational requirements in a distributed environment.

Elastic neural network method for load prediction in cloud computing grid

“…Whenever there is a certain job on the Cloud, the load balancer sends the job request through the suggested algorithm to be clustered to which server to be sent [22]. K-means clustering is one of the simplest clustering algorithms used for unsupervised learning problems, MacQueen, 1967 introduced it, it works mainly on minimizing the distance between the cluster center and the data point.…”

A Heuristic Algorithm for Optimal Service Composition in Complex Manufacturing Networks

“…Composition Using QoS. Much research has been done on the service composition problems and other related problems in networked manufacturing such as service provider selection methods, service network models, service composition algorithms, and manufacturing resource allocation [6,9]. For example, Wu et al [10] proposed a fuzzy-based decisionmaking method to find the best service provider; Ho et al [11] made a review of multicriteria decision making approaches to the supplier evaluation and selection problem.…”

“…Service composition problem (SCP) is one of the most primary and essential problems for optimally manufacturing resources allocating [6]. SCP in CMfg has the characteristics of high heterogeneity and large scale.…”

Toward Distributed Heterogeneous Simulation Using Internet of Things

“…Limited works have focused on performance improvement using continuous system monitoring techniques. For example Peng et al [37] have proposed a neural networkbased system that transforms tasks into an abstract models. Such models can be used to efficiently manage resource allocations in a multi-tenant environment such as cloud.…”