Taiwan UniCloud: A Cloud Testbed with Collaborative Cloud Services

This study proposes a novel efficient resource allocation mechanism for federated clouds, which takes the communication overhead into consideration, to improve system throughput and reduce resource repacking overhead in the auto-scaling mechanism. In general, when the amount of service requests increases, more and more resources are allocated to satisfy these requests. However, single cloud cannot provide unlimited services with limited physical resources; therefore, the federation of multiple clouds may be one possible solution. In the federated cloud environment, when the workload changes, the resource allocation mechanism could adopt vertical/horizontal scaling fashions to repack the required resource into virtual machines. In the vertical scaling approach, the resource allocation mechanism allocates more resources into virtual machines for improving virtual machine's capability. In the horizontal scaling approach, the resource allocation mechanism allocates more virtual machines for enhancing the virtual cluster's capability. However, frequent resource repacking may reduce the system performance. Therefore, in order to improve system throughput and reduce repacking overhead, the proposed mechanism captures the execution pattern of applications by the profiling system and the resource status by the monitoring system, and then allocates resources for configuring the virtual cluster. Performance for NAS Parallel Benchmarks is evaluated. Experimental results show that the authors' approach could reduce repacking overhead and improve system throughput by comparing two previous works.

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“…Figure 1 shows our system architecture (Chung, et al, 2014). The underlying architecture consists of different independent clouds.…”

Section: Resultsmentioning

confidence: 99%

“…Our work refers to Taiwan UniCloud (Chung, et al, 2014) which consists of SSCloud (virtual machine leasing service), SSBOX (personal cloud storage), SSDB (SQL to NoSQL database service), and SSBDS (big data service). The objective of UniCloud is to provide a cloud platform for education and research purposes among academic communities.…”

Section: Related Workmentioning

confidence: 99%

Efficient Resource Allocation Mechanism for Federated Clouds

Huang

Lee

Lai

2015

International Journal of Grid and High Performance Computing

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“…A summary of large‐scale testbeds is provided in Table . The Taiwan UniCloud testbed is a community‐driven hybrid cloud platform for academics in Taiwan. The main goal of UniCloud is to leverage resources in multiple clouds among different organizations to cope with sudden changes in demand.…”

Section: Related Workmentioning

confidence: 99%

Efficient resource management in the cloud: From simulation to experimental validation using a low‐cost Raspberry Pi testbed

et al. 2018

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Within the context of cloud computing, efficient resource management is of great importance as it can result in higher scalability and significant energy and cost reductions over time. Because of the high complexity and costs of cloud environments, however, newly developed resource allocation strategies are often only validated by means of simulations, for example, by using CloudSim or custom-developed simulation tools.This article describes a general approach for the validation of cloud resource allocation strategies, illustrating the importance of experimental validation on physical testbeds. Furthermore, the design and implementation of Raspberry Pi as a Service (RPiaaS), a low-cost embedded testbed built using Raspberry Pi nodes, is presented. RPiaaS aims to facilitate the step from simulations toward experimental evaluations on larger cloud testbeds and is designed using a microservice architecture, where experiments and all required management services are running inside containers.The performance of the RPiaaS testbed is evaluated using several benchmark experiments. The obtained results not only illustrate that the overhead of both using containers and running the required RPiaaS services is minimal but also provide useful insights for scaling up experiments between the Raspberry Pi testbed and a larger more traditional cloud testbed.The introduced validation approach is then illustrated using a case study focusing on the allocation of hierarchically structured tenant data. The results obtained through simulations are compared to the experimental results. The RPiaaS testbed proved to be a very useful tool for the initial experimental validation before moving the experiments to a large-scale testbed.

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