京都大学 · 情報科学
Rui Kang教授の研究室は、ネットワークスライシングや仮想ネットワーク関数(VNF)の最適配置を柱とした、サービスの継続性と信頼性を高めるためのインフラ最適化技術を研究しています。特に、仮想マシンの稼働スケジュールを考慮した時間的連続性の最大化や、障害に強いバックアップ配置戦略の構築に注力しており、Kubernetes環境と連携する高度なスケジューリング手法の開発も進めています。また、教育分野においても、回路理論の理解を深めるための応用的学習手法の開発や、多様性教育における物語ベースの学びの有効性を検証する教育的アプローチも展開しています。
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This paper proposes an optimization model to derive the virtual network function (VNF) allocation of time slots in sequence aiming to maximize the continuous available time of service function chains (SFCs) in a network. The proposed model suppresses service interruptions otherwise created by the unavailability of virtual machines (VMs) and the reallocation of VNFs. The proposed model computes VNF allocation in a series of time slots based on a VM availability schedule, which provides informatio
As an important consideration in the design of plate-fin heat exchangers, the selection of plate-fin surfaces is associated with the estimation of the fin performance in many cases. The fin performance of offset strip fin (OSF) and plain fin is numerically investigated with well-validated 3D models in the present study. The comparative analysis shows that the conventional fin efficiency and fin effectiveness concepts provide an incomplete assessment of the fin performance of the fins, and lead t
Kirchhoff’s circuit laws are important basic concepts in many basic disciplines, such as electricity and circuitry. However, in the usual teaching process, teachers often teach this knowledge using some simple examples of analysis and calculation but without deep discussions. As a result, students could not have a deep understanding about these concepts or not apply the concepts on actual problems flexibly. Therefore, passive learning habits are formed. The lack of self-exploration awareness and
The reliability of virtual network can be increased by allocating virtual network functions (VNF) to suitable locations. The VNF placement problems are formulated as optimization models with different objectives. The models are solved by optimization software and heuristic algorithms. The allocation results obtained by the models are used to allocate the VNFs to nodes. Since different users have different objectives, it is necessary to allocate different groups of VNFs by using different models.
A suitable virtual network function (VNF) placement considering a node availability schedule extends service continuous serviceable time by suppressing service interruptions caused by function reallocation and node unavailabilities. However, function placement cannot avoid service interruptions caused by node unavailabilities. This paper proposes a primary and backup VNF placement model to avoid service interruptions caused by node unavailabilities by using backup functions. The considered backu
This study explores the outcomes of a potentially powerful multicultural pedagogy---the use of multicultural narratives in teacher education. Participants were 22 preservice teachers enrolled in a required, semester-long, foundation course at a liberal arts public university in middle Georgia. Data sources include responses to ten classroom- or school-based multicultural narratives and reflection papers. The results indicate that narrative analysis helped preservice teachers facilitate transfer
This paper proposes an optimization model to derive a virtual network function (VNF) allocation for time slots in sequence aiming to maximize the continuous available time of service function chains (SFCs) in a network. The proposed model suppresses service interruptions due to the unavailability of nodes and the reallocation of VNFs. Compared with conventional models, the proposed model computes the VNF allocation in a series of time slots based on the network node availability schedule, which
The availability schedule provides information on whether each network node is available at each time slot. The service interruptions caused by node unavailability marked in availability schedule can be suppressed if the functions are allocated according to the availability schedule. However, the given availability schedule may have gaps with the actual one and influence the VNF allocation. This paper proposes a robust optimization model to allocate virtual network functions (VNFs) in service fu
Diversity in network function virtualization is to use a group of thin replicas to provide the network services under the required processing ability. Redundancy is to provide a certain number of replicas against function failures and improve network reliability. Kubernetes is a system to deploy and manage virtual network functions automatically. Existing tools in Kubernetes do not provide a resource type to provide required functions jointly considering VNF diversity and redundancy. This paper
This paper proposes an optimization model to derive the resilient virtual network function allocation in service function chains aiming to reduce the recovery time during the migrations from the primary functions to backup functions. We consider k-fault-tolerance assurance and satisfy the service requirements under different error patterns in this model. The allocation provided by the proposed model ensures that the processing ability satisfies the requirements even though there are k failed nod
A virtual network function allocation model to maximize continuous available time of service function chains was introduced in our previous work. The performance of this model needs to be evaluated on network devices. It is time-consuming and costly to deploy functions with real network devices. Existing simulation tools require powerful computation capability, which limits the usable cases. We implement a network service header based service function chain application which can be cooperated wi
Service function chain (SFC) allocation problems have been studied in previous works. Models with different objectives decide the allocations of functions in chains. Currently, the allocation strategies cannot be applied in Kubernetes automatically so that the performance of these models cannot be evaluated. There is a lack of existing tools which can connect the allocation models and function deployments for SFCs. We implement an open virtual network based SFC-compatible network plugin for Kube
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