Adaptive Network Resource Reallocation for Hot-Spot Avoidance on SDN-Based Cluster System

Shimizu, Masaharu; Watashiba, Yasuhiro; Date, Susumu; Shimojo, Shinsuke

doi:10.1109/cloudcom.2016.0105

Cited by 4 publications

(8 citation statements)

References 4 publications

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“…The available bandwidth is 100 Mbps. The cluster scheme in [37] is introduced for comparison purposes. Figure 9 shows that the communication overhead of the proposed cluster scheme is lower than that of the scheme in [37].…”

Section: Simulation Of Time Overhead and Energy Consumptionmentioning

confidence: 99%

“…The cluster scheme in [37] is introduced for comparison purposes. Figure 9 shows that the communication overhead of the proposed cluster scheme is lower than that of the scheme in [37]. This is because the proposed ant colony optimization enables big data analysis and provide an optimized cluster controller for SDN.…”

Section: Simulation Of Time Overhead and Energy Consumptionmentioning

confidence: 99%

“…The network size varies from 200 to 500. Additionally, the comparison of the energy consumption is performed between the proposed cluster scheme and the scheme in [37]. As shown in Fig.…”

Section: Simulation Of Time Overhead and Energy Consumptionmentioning

confidence: 99%

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Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks

Dong

Ota

et al. 2018

IEEE Trans. Netw. Serv. Manage.

179

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In software-defined networks (SDN), the abstracted control plane is its symbolic characteristic, whose core component is the software-based controller. The control plane is logically centralized, but the controllers can be physically distributed and composed of multiple nodes. To meet the service management requirements of large-scale network scenarios, the control plane is usually implemented in the form of distributed controller clusters. Cluster management technology monitors all types of events and must maintain a consistent global network status, which usually leads to big data in SDNs. Simultaneously, the cluster security is an open issue because of the programmable and dynamic features of SDNs. To address the above challenges, this paper proposes a big data analysis-based secure cluster management architecture for the optimized control plane. A security authentication scheme is proposed for cluster management. Moreover, we propose an ant colony optimization approach that enables big data analysis scheme and the implementation system that optimizes the control plane. Simulations and comparisons show the feasibility and efficiency of the proposed scheme. The proposed scheme is significant in improving the security and efficiency SDN control plane.

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Section: Simulation Of Time Overhead and Energy Consumptionmentioning

confidence: 99%

Section: Simulation Of Time Overhead and Energy Consumptionmentioning

confidence: 99%

See 1 more Smart Citation

Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks

Dong

Ota

et al. 2018

IEEE Trans. Netw. Serv. Manage.

179

View full text Add to dashboard Cite

show abstract

“…The proposed scheme uses central resource management architecture to obtain the global information and finds out a sub-optimal solution with proportional fairness for resource allocation. An SDN-enhanced Job Management System [21] was developed as a networkaware resource management system (RMS) that offers a framework whereby administrators prescribe their own resource provisioning strategy using the information on both computing and network resources in a cluster system [22]. Furthermore, reference [22] explored an adaptive network resource allocation method with which network resources can be reallocated to jobs in execution, in response to the change of process placement occurred at job dispatch and termination events.…”

Section: A Maintaining the Integrity Of The Specificationsmentioning

confidence: 99%

“…An SDN-enhanced Job Management System [21] was developed as a networkaware resource management system (RMS) that offers a framework whereby administrators prescribe their own resource provisioning strategy using the information on both computing and network resources in a cluster system [22]. Furthermore, reference [22] explored an adaptive network resource allocation method with which network resources can be reallocated to jobs in execution, in response to the change of process placement occurred at job dispatch and termination events. In reference [23], a minimum-control-latency optimised algorithm based on greedy controlling pattern design was developed.…”

Section: A Maintaining the Integrity Of The Specificationsmentioning

confidence: 99%

A Game-Theoretic Based QoS-Aware Capacity Management for Real-Time EdgeIoT Applications

Aliyu

Chen

et al. 2017

2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)

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Abstract-More and more real-time IoT applications such as smart cities or autonomous vehicles require big data analytics with reduced latencies. However, data streams produced from distributed sensing devices may not suffice to be processed traditionally in the remote cloud due to: (i) longer Wide area network (WAN) latencies and (ii) limited resources held by a single Cloud. To solve this problem, a novel Software-defined network (SDN) based InterCloud architecture is presented for mobile edge computing environments, known as EdgeIoT. An adaptive resource capacity management approach is proposed to employ a policy-based QoS control framework using principles in coalition games with externalities. To optimise resource capacity policy, the proposed QoS management technique solves, adaptively, a lexicographic ordering bi-criteria coalition structure generation (CSG) problem. It is an onerous task to guarantee in a deterministic way that a real-time EdgeIoT application satisfies low latency requirement specified in service level agreements (SLA). CloudSim 4.0 toolkit is used to simulate an SDN-based InterCloud scenario, and the empirical results suggest that the proposed approach can adapt, from an operational perspective, to ensure low latency QoS for real-time EdgeIoT application instances.

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Dynamic Traffic Control of Staging Traffic on the Interconnect of the HPC Cluster System

et al. 2020

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High-performance computing (HPC) cluster systems sometimes adopt a two-layered file system composed of local and global file systems to achieve both capacity and performance in storage. In such a cluster system, the input data of an application needs to be staged from the global storage into the local storage, and the output data needs to be staged from the local storage out to the global storage. This staging operation must be efficiently and quickly performed to gain higher job throughput because an inefficient staging operation prevents waiting job requests from being executed. In particular, in the case of the cluster system with the oversubscribed interconnect shared by the storage and the computing nodes, the inter-node communication and this staging operation traffic collides, which may degrade the job throughput. In this research, we focus on the traffic collision of the inter-node communication and the staging traffic to improve job throughput, targeting the cluster system with the oversubscribed interconnect where these two types of traffic flow. In other words, whether the dynamic control of the traffic flow derived from the staging operation leads to the improvement in the job throughput or not is investigated. For the investigation, we present a traffic collision avoidance method to dynamically configure a set of data paths for each type of the traffic only while the staging operation is conducted. The evaluation in this paper shows that the proposed method avoids a traffic collision and accelerates the staging operation by 22.0% on our cluster system. Also, this evaluation indicates the overhead of the application incurred by the proposed method is negligible. Furthermore, 8.7% of the job execution time is reduced by the proposed method.

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Adaptive Network Resource Reallocation for Hot-Spot Avoidance on SDN-Based Cluster System

Cited by 4 publications

References 4 publications

Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks

Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks

A Game-Theoretic Based QoS-Aware Capacity Management for Real-Time EdgeIoT Applications

Dynamic Traffic Control of Staging Traffic on the Interconnect of the HPC Cluster System

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