가상 네트워크의 자원 할당, 가격 결정 및 고장 관리

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. 서승우.네트워크 가상화는 물리적 네트워크의 공유 자원들을 복수 개의 가상 네트워크들에 동적으로 할당할 수 있게 해주는 기술이다. 자원 할당의 유연성과 가상 네트워크들 사이의 독립성 때문에, 네트워크 가상화는 네트워크 테스트베드를 설계하기 위한 기반 기술로써 주로 활용되어 왔을 뿐만 아니라, 인터넷의 다양화를 지원하기 위한 비용 효율 높은 해결책으로써 여겨지기 시작했다. 서비스에 따라 계층화된 인터넷을 설계하기 위한 하나의 수단으로써, 네트워크 가상화는 여전히 해결해야 할 많은 도전 과제들을 가지고 있다. 이 학위 논문은 가상 네트워크 환경에서 중요한 몇 가지 새로운 연구 주제들을 제시하고, 그에 대한 효과적인 해법들을 제안한다. 첫 번째로, 가상 네트워크의 다양한 QoS 요구사항을 만족시킬 수 있는 네트워크 최적 분할 방법을 제안한다. QoS와 대역폭 제한 조건을 고려하여 가상 네트워크 분할 문제를 최적화 문제로 모형화하고, 문제의 구조적 복잡성을 해결하기 위해 최단 경로 라우팅에 기반한 휴리스틱을 제안한다. 실제 인터넷 환경을 고려한 대규모 실험을 통해, 제안한 휴리스틱의 효율성과 확장성을 입증한다. 다음으로, 가상 네트워크에서 차등 접속 서비스를 위한 경제성 분석 모델을 제시한다. 먼저 사용자 가입 변동 모형이 한 값으로 수렴하기 위한 충분 조건을 유도하고, 이러한 조건 하에서 인터넷 서비스 제공자의 수익을 최대화할 수 있는 최적의 가격 결정 방법 및 대역폭 분할 방법을 찾는다. 수치 실험을 통해, 적절한 가격 결정과 대역폭 분할이 이루어진다는 가정 하에서 차등화 서비스가 단일 서비스보다 더 높은 수익성을 나타낼 수 있음을 증명한다. 마지막으로, 가상 네트워크 간 트래픽 전환을 통한 빠르고 효과적인 고장 회복 기술을 개발한다. 가상 네트워크의 구조적 특성을 활용한 고장 회복 기술을 이용하면, 모든 링크에 대한 백업 경로가 항상 존재하도록 미리 토폴로지를 설계해야 할 필요가 없고, 각 라우터에서 그 경로들에 대한 계산을 미리 해 놓을 필요가 없다. 그럼에도 불구하고, 제안한 고장 회복 방법은 기존의 기술들과 같은 좋은 성능을 보인다. 이 학위 논문은 가상 네트워크를 기반으로 하는 인터넷 환경에서 발생할 수 있는 중요한 문제들을 다루고자 한다. 이 논문에서 제안하는 분석 모델 및 실험 결과들은 현재 인터넷의 한계를 극복하고, 미래 인터넷 아키텍처를 설계하기 위한 유용한 지침을 제공할 것이다.Network virtualization is an emerging technology that enables the dynamic partitioning of a shared physical network infrastructure into multiple virtual networks. Because of its flexibility in resource allocation and independency among virtual networks, the network virtualization technology has not only been mainly deployed to build a testbed network, but also has come to be regarded as a cost-effective solution for diversifying the Internet. As a means of building the multi-layered Internet, network virtualization still faces a number of challenging issues that need to be addressed. This dissertation deals with several important research topics and provides effective solutions in network virtualization environment. First, I focus on the optimal partitioning of finite substrate resources for satisfying the diverse QoS requirements of virtual networks. I formulate virtual network partitioning problem as a mixed integer multi-commodity flow problem. Then, to tackle the structural complexity of the problem, I propose a simple heuristic based on shortest path routing algorithm. By conducting large-scale network experiments, I verify the efficiency and scalability of the heuristic. Next, I propose an economic model for tiered access service in virtual networks in order to remedy the deficiency of the existing tiered service schemes. I first derive a sufficient condition for stability of user subscription dynamics, and find the optimal pricing and capacity partitioning by addressing the revenue maximization problem of the tiered access service in a network virtualization environment. Numerical results show that the tiered service can be more profitable than the non-tiered service under proper pricing and capacity partitioning conditions. Last, I develop a fast and effective failure recovery mechanism through inter-virtual network traffic switching in virtual networks. The proposed failure recovery mechanism neither has topological constraints for the existence of backup paths, nor requires the pre-computation of them, but nevertheless guarantees as fast recovery as the existing failure recovery methods. This dissertation aims to address important issues in the virtual network-based Internet. I believe that the analysis and results in this dissertation will provide useful guidelines to improve the Internet.1 Introduction 1.1 Background and Motivation 1.2 Contributions and Outline of the Dissertation 2 Effective Partitioning for Service Level Differentiation in Virtual Networks 2.1 Introduction 2.2 Related Work 2.3 Model and Assumption 2.3.1 Business Model 2.3.2 Network Model 2.3.3 Traffic Demands 2.3.4 QoS Metric 2.4 Formulation 2.4.1 Objective 2.4.2 Substrate Partitioning Problem 2.4.3 Decomposition 2.5 Heuristic 2.6 Evaluation 2.6.1 Small Network Experiment 2.6.2 Large Network Experiment 2.7 Summary 3 Optimal Pricing and Capacity Partitioning for Tiered Access Service in Virtual Networks 3.1 Introduction 3.2 Motivating Example 3.3 A Tiered Service Model 3.3.1 Network Virtualization Environment 3.3.2 Effective Access Rate 3.3.3 Valuation Parameter and User Utility 3.3.4 User Subscription and the ISP Revenue 3.4 Non-tiered Service Analysis 3.4.1 User Subscription Dynamics 3.4.2 Optimal Pricing for Maximizing the ISP Revenue 3.5 Tiered Service Analysis 3.5.1 User Subscription Dynamics 3.5.2 Convergence of the User Subscription Dynamics 3.5.3 Optimal Pricing for Maximizing the ISP Revenue 3.6 Numerical Results 3.6.1 Non-tiered Service Example 3.6.2 Tiered Service Example 3.7 Related Work and Discussion 3.8 Summary 4 Inter-Virtual Network Traffic Switching for Fast Failure Recovery 4.1 Introduction 4.2 Background 4.3 Preliminaries 4.3.1 Virtual Network Model 4.3.2 Design Goals 4.3.3 Business Models and Switching Policy Agreement 4.3.4 Other Considerations 4.4 Failure Recovery based on Traffic Switching 4.4.1 Inter-VN Traffic Switching 4.4.2 Failure Recovery Process 4.5 Numerical Analysis 4.5.1 Delay 4.5.2 Congestion probability 4.6 Summary 5 Conclusion A Proofs of Lemmas A.1 Proof of Lemma 2 A.2 Proof of Lemma 3Docto

On Optimal and Fair Service Allocation in Mobile Cloud Computing

This paper studies the optimal and fair service allocation for a variety of mobile applications (single or group and collaborative mobile applications) in mobile cloud computing. We exploit the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. We proposed a novel framework to model mobile applications as a location-time workflows (LTW) of tasks; here users mobility patterns are translated to mobile service usage patterns. We show that an optimal mapping of LTWs to tiered cloud resources considering multiple QoS goals such application delay, device power consumption and user cost/price is an NP-hard problem for both single and group-based applications. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (73% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high mobile application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing, video streaming and multimedia file sharing applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint and Manhattan models

An Emergent Architecture for Scaling Decentralized Communication Systems (DCS)

With recent technological advancements now accelerating the mobile and wireless Internet solution space, a ubiquitous computing Internet is well within the research and industrial community's design reach - a decentralized system design, which is not solely driven by static physical models and sound engineering principals, but more dynamically, perhaps sub-optimally at initial deployment and socially-influenced in its evolution. To complement today's Internet system, this thesis proposes a Decentralized Communication System (DCS) architecture with the following characteristics: flat physical topologies with numerous compute oriented and communication intensive nodes in the network with many of these nodes operating in multiple functional roles; self-organizing virtual structures formed through alternative mobility scenarios and capable of serving ad hoc networking formations; emergent operations and control with limited dependency on centralized control and management administration. Today, decentralized systems are not commercially scalable or viable for broad adoption in the same way we have to come to rely on the Internet or telephony systems. The premise in this thesis is that DCS can reach high levels of resilience, usefulness, scale that the industry has come to experience with traditional centralized systems by exploiting the following properties: (i.) network density and topological diversity; (ii.) self-organization and emergent attributes; (iii.) cooperative and dynamic infrastructure; and (iv.) node role diversity. This thesis delivers key contributions towards advancing the current state of the art in decentralized systems. First, we present the vision and a conceptual framework for DCS. Second, the thesis demonstrates that such a framework and concept architecture is feasible by prototyping a DCS platform that exhibits the above properties or minimally, demonstrates that these properties are feasible through prototyped network services. Third, this work expands on an alternative approach to network clustering using hierarchical virtual clusters (HVC) to facilitate self-organizing network structures. With increasing network complexity, decentralized systems can generally lead to unreliable and irregular service quality, especially given unpredictable node mobility and traffic dynamics. The HVC framework is an architectural strategy to address organizational disorder associated with traditional decentralized systems. The proposed HVC architecture along with the associated promotional methodology organizes distributed control and management services by leveraging alternative organizational models (e.g., peer-to-peer (P2P), centralized or tiered) in hierarchical and virtual fashion. Through simulation and analytical modeling, we demonstrate HVC efficiencies in DCS structural scalability and resilience by comparing static and dynamic HVC node configurations against traditional physical configurations based on P2P, centralized or tiered structures. Next, an emergent management architecture for DCS exploiting HVC for self-organization, introduces emergence as an operational approach to scaling DCS services for state management and policy control. In this thesis, emergence scales in hierarchical fashion using virtual clustering to create multiple tiers of local and global separation for aggregation, distribution and network control. Emergence is an architectural objective, which HVC introduces into the proposed self-management design for scaling and stability purposes. Since HVC expands the clustering model hierarchically and virtually, a clusterhead (CH) node, positioned as a proxy for a specific cluster or grouped DCS nodes, can also operate in a micro-capacity as a peer member of an organized cluster in a higher tier. As the HVC promotional process continues through the hierarchy, each tier of the hierarchy exhibits emergent behavior. With HVC as the self-organizing structural framework, a multi-tiered, emergent architecture enables the decentralized management strategy to improve scaling objectives that traditionally challenge decentralized systems. The HVC organizational concept and the emergence properties align with and the view of the human brain's neocortex layering structure of sensory storage, prediction and intelligence. It is the position in this thesis, that for DCS to scale and maintain broad stability, network control and management must strive towards an emergent or natural approach. While today's models for network control and management have proven to lack scalability and responsiveness based on pure centralized models, it is unlikely that singular organizational models can withstand the operational complexities associated with DCS. In this work, we integrate emergence and learning-based methods in a cooperative computing manner towards realizing DCS self-management. However, unlike many existing work in these areas which break down with increased network complexity and dynamics, the proposed HVC framework is utilized to offset these issues through effective separation, aggregation and asynchronous processing of both distributed state and policy. Using modeling techniques, we demonstrate that such architecture is feasible and can improve the operational robustness of DCS. The modeling emphasis focuses on demonstrating the operational advantages of an HVC-based organizational strategy for emergent management services (i.e., reachability, availability or performance). By integrating the two approaches, the DCS architecture forms a scalable system to address the challenges associated with traditional decentralized systems. The hypothesis is that the emergent management system architecture will improve the operational scaling properties of DCS-based applications and services. Additionally, we demonstrate structural flexibility of HVC as an underlying service infrastructure to build and deploy DCS applications and layered services. The modeling results demonstrate that an HVC-based emergent management and control system operationally outperforms traditional structural organizational models. In summary, this thesis brings together the above contributions towards delivering a scalable, decentralized system for Internet mobile computing and communications

From geographically dispersed data centers towards hierarchical edge computing

Internet scale data centers are generally dispersed in different geographical regions. While the main goal of deploying the geographically dispersed data centers is to provide redundancy, scalability and high availability, the geographic dispersity provides another opportunity for efficient employment of global resources, e.g., utilizing price-diversity in electricity markets or utilizing locational diversity in renewable power generation. In other words, an efficient approach for geographical load balancing (GLB) across geo-dispersed data centers not only can maximize the utilization of green energy but also can minimize the cost of electricity. However, due to the different costs and disparate environmental impacts of the renewable energy and brown energy, such a GLB approach should tap on the merits of the separation of green energy utilization maximization and brown energy cost minimization problems. To this end, the notion of green workload and green service rate, versus brown workload and brown service rate, respectively, to facilitate the separation of green energy utilization maximization and brown energy cost minimization problems is proposed. In particular, a new optimization framework to maximize the profit of running geographically dispersed data centers based on the accuracy of the G/D/1 queueing model, and taking into consideration of multiple classes of service with individual service level agreement deadline for each type of service is developed. A new information flow graph based model for geo-dispersed data centers is also developed, and based on the developed model, the achievable tradeoff between total and brown power consumption is characterized. Recently, the paradigm of edge computing has been introduced to push the computing resources away from the data centers to the edge of the network, thereby reducing the communication bandwidth requirement between the sources of data and the data centers. However, it is still desirable to investigate how and where at the edge of the network the computation resources should be provisioned. To this end, a hierarchical Mobile Edge Computing (MEC) architecture in accordance with the principles of LTE Advanced backhaul network is proposed and an auction-based profit maximization approach which effectively facilitates the resource allocation to the subscribers of the MEC network is designed. A hierarchical capacity provisioning framework for MEC that optimally budgets computing capacities at different hierarchical edge computing levels is also designed. The proposed scheme can efficiently handle the peak loads at the access point locations while coping with the resource poverty at the edge. Moreover, the code partitioning problem is extended to a scheduling problem over time and the hierarchical mobile edge network, and accordingly, a new technique that leads to the optimal code partitioning in a reasonable time even for large-sized call trees is proposed. Finally, a novel NOMA augmented edge computing model that captures the gains of uplink NOMA in MEC users\u27 energy consumption is proposed

The comparison of internet pricing scheme in multi link bottleneck multi service network

In this research we set up pricing scheme of multilink internet bottleneck for multi-service network by giving the modified models and the solution.This model is based on the local server data in Palembang.Internet Service Provider (ISP) requires the appropriate pricing schemes in order to maximize revenue and provide quality services that can satisfy the Internet users.The model established by setting the base price (α) as constants and the premium quality of service (β) as variables and constants.Then the model will be solved using Program LINGO 13.0 to obtain the optimal solution.From the results obtained shows the optimal solution that ISP can use the models to generate maximum revenue and gives options according to the user needs in accordance with the goal of ISP.The optimal solution results compared to previous work show that the larger dimension of the problem, the goals can also change according to the needs. From LINGO 13.0, the solution for four services and 3 links offered were maximized when we set up the base price (α) as constants and the premium quality of service (β) as constants for Ii = Ii-1. So ISP can use the modification scheme to achieve its goals.The realistic case to be solved by LINGO 13.0 is limited to have only four services and three links

Intelligent adaptive bandwidth provisioning for quality of service in umts core networks

Master'sMASTER OF ENGINEERIN

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network