CloudSimSDN: Modeling and Simulation of Software-Defined Cloud Data Centers

Son, Jungmin; Dastjerdi, Amir Vahid; Calheiros, Rodrigo N.; Ji, Xiaohui; Yoon, Young; Buyya, Rajkumar

doi:10.1109/ccgrid.2015.87

Cited by 103 publications

(80 citation statements)

References 17 publications

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“…Furthermore, the centralized controller is responsible for dynamically managing resources in data centers. Based on [49], the authors prove that CloudSimSDN simulator provides much features and facilities with better or at least the same performance as Mininet simulation environment. Also, it gives the ability for the modifications and extensions.…”

Section: Simulation Environment and Experiments Set Upmentioning

confidence: 99%

“…CloudSim is a Java based simulation framework that enables simulation, experimentation, and modeling for cloud based networks [48]. CloudSimSDN is a Java based simulation framework that is built on top of CloudSim [49]. This framework is developed mainly for simulation purposes of SDN and cloud based systems.…”

Section: Simulation Environment and Experiments Set Upmentioning

confidence: 99%

See 1 more Smart Citation

Development of Intelligent Core Network for Tactile Internet and Future Smart Systems

Ateya

Muthanna

Gudkova

et al. 2018

JSAN

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Abstract:One of the main design aspects of the Tactile Internet system is the 1 ms end-to-end latency, which is considered as being the main challenge with the system realization. Forced by recent development and capabilities of the fifth generation (5G) cellular system, the Tactile Internet will become a real. One way to overcome the 1 ms latency is to employ a centralized controller in the core of the network with a global knowledge of the system, together with the concept of network function virtualization (NFV). This is the idea behind the software defined networking (SDN). This paper introduces a Tactile Internet system structure, which employs SDN in the core of the cellular network and mobile edge computing (MEC) in multi-levels. The work is mainly concerned with the structure of the core network. The system is simulated over a reliable environment and introduces a round trip latency of orders of 1 ms. This can be interpreted by the reduction of intermediate nodes that are involved in the communication process.

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Section: Simulation Environment and Experiments Set Upmentioning

confidence: 99%

Section: Simulation Environment and Experiments Set Upmentioning

confidence: 99%

Development of Intelligent Core Network for Tactile Internet and Future Smart Systems

Ateya

Muthanna

Gudkova

et al. 2018

JSAN

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show abstract

“…The results of our extensive simulation experiments, carried out on CloudSimSDN [11], depict that significant performance improvements in terms of user QoE, QoS satisfaction, average waiting time, and service provider profit have been achieved by the proposed system compared to the state-of-the-art works.…”

mentioning

confidence: 98%

“…The simulation environment of the Quality-of-Experience (QoE) and Profit-aware Resource Allocation framework in CRAN was designed using the CloudSimSDN [11] simulation toolkit. The simulated CRAN environment consists of one BBU pool with five BBUs.…”

Section: Simulation Environmentmentioning

confidence: 99%

Tradeoff between User Quality-Of-Experience and Service Provider Profit in 5G Cloud Radio Access Network

et al. 2017

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In recent years, the Cloud Radio Access Network (CRAN) has become a promising solution for increasing network capacity in terms of high data rates and low latencies for fifth-generation (5G) cellular networks. In CRAN, the traditional base stations (BSs) are decoupled into remote radio heads (RRHs) and base band units (BBUs) that are respectively responsible for radio and baseband functionalities. The RRHs are geographically proximated whereas the the BBUs are pooled in a centralized cloud named BBU pool. This virtualized architecture facilitates the system to offer high computation and communication loads from the impetuous rise of mobile devices and applications. Heterogeneous service requests from the devices to different RRHs are now sent to the BBUs to process centrally. Meeting the baseband processing of heterogeneous requests while keeping their Quality-of-Service (QoS) requirements with the limited computational resources as well as enhancing service provider profit is a challenging multi-constraint problem. In this work, a multi-objective non-linear programming solution to the Quality-of-Experience (QoE) and Profit-aware Resource Allocation problem is developed which makes a trade-off in between the two. Two computationally viable scheduling algorithms, named First Fit Satisfaction and First Fit Profit algorithms, are developed to focus on maximization of user QoE and profit, respectively, while keeping the minimum requirement level for the other one. The simulation environment is built on a relevant simulation toolkit. The experimental results demonstrate that the proposed system outperforms state-of-the-art works well across the requests QoS, average waiting time, user QoE, and service provider profit.

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“…Son et. al presented CloudSimSDN [8], a simulation framework for software-defined cloud infrastructures. This framework incorporates a graphical interface to design the data-center topology.…”

Section: Listing 4 Data-center Configuration In Simcan 4 Evaluationmentioning

confidence: 99%

MAGICIAN: Model-based design for optimizing the configuration of data-centers

Cañizares

Núñez

Lara

2017

International Conferences on Software Engineering and Knowledge Engineering

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Designing data-centers that provide an acceptable costperformance ratio is challenging. Generally, a wide spectrum of components must be previously analyzed, such as the kind of applications to be executed in the data-center, computing/storage requirements and the network topology, among others. Since each one of these components has a direct impact on the overall system performance, the design process is complex and difficult, which usually requires the intervention of an expert.We propose a model-based approach to design datacenters. For this purpose, we have created a meta-model that describes the structure of data-center models. Then, a set of expert rules can be used to detect sub-optimal configurations, and (in some cases) correct the design. Datacenter models can be simulated, to assess their performance and scalability, for which we use a code generator into the SIMCAN tool. We have implemented our approach as an Eclipse plugin, and illustrate the usefulness of some expert rules by showing the efficiency and scalability gains of the optimized model with respect to the original one.

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CloudSimSDN: Modeling and Simulation of Software-Defined Cloud Data Centers

Cited by 103 publications

References 17 publications

Development of Intelligent Core Network for Tactile Internet and Future Smart Systems

Development of Intelligent Core Network for Tactile Internet and Future Smart Systems

Tradeoff between User Quality-Of-Experience and Service Provider Profit in 5G Cloud Radio Access Network

MAGICIAN: Model-based design for optimizing the configuration of data-centers

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