Akihiro Nakao

GENI: A federated testbed for innovative network experiments

GENI, the Global Environment for Networking Innovation, is a distributed virtual laboratory for transformative, at-scale experiments in network science, services, and security. Designed in response to concerns over Internet ossification, GENI is enabling a wide variety of experiments in a range of areas, including clean-slate networking, protocol design and evaluation, distributed service offerings, social network integration, content management, and in-network service deployment. Recently, GENI has been leading an effort to explore the potential of its underlying technologies, SDN and GENI racks, in support of university campus network management and applications. With the concurrent deployment of these technologies on regional and national R&E backbones, this will result in a revolutionary new national-scale distributed architecture, bringing to the entire network the shared, deeply programmable environment that the cloud has brought to the datacenter. This deeply programmable environment will support the GENI research mission and as well as enabling research in a wide variety of application areas.

Software-Defined Networking

Software-Defined Networking (SDN) is considered promising to simplify network management and enable research innovations based on the decomposition of the control and data planes. In this paper, we review SDN-related technologies. In particular, we try to cover three main parts of SDN: applications, the control plane, and the data plane anticipating that our efforts will help researchers set appropriate and meaningful directions for future SDN research.

CoreLab: an emerging network testbed employing hosted virtual machine monitor

Network testbeds for developing, deploying, and experimenting with new network services have evolved as recent rapid progress in virtualization technology. This paper proposes a new network testbed that enhances PlanetLab and is based on a hosted virtual machine monitor (VMM) as a virtual execution environment (VEE) for network services to run on. This paper reports our experiences in developing such a prototype network testbed employing Kernel-based Virtual Machine (KVM) as a hosted VMM. The paper examines the performance and scalability of the prototype to see whether or not it fulfills what we believe to be the requirements for a new network testbed.

Cloud Rack: Enhanced virtual topology migration approach with Open vSwitch

Cloud computing has emerged to provide infrastructure for cost-effectively hosting applications with dynamically changing demands for computational resources. Dynamic resource allocation in cloud computing is made possible through virtualization technologies. In addition, live migration of services hosted in virtual machines (VMs) among multiple data centers is expected to add more flexibility in elastic resource allocation. Our recent work, Mobitopolo, has shown the proof of concept of an infrastructure to enable live migration of VMs with logical connections intact among them. However, since Mobitopolo makes more of portability, it suffers from network performance. In this paper, we propose alternative design and implementation of logical topology migration using KVM and Open vSwitch (OVS) to realize an infrastructure for applications demanding for much network performance.

User-assisted in-network caching in information-centric networking

In information-centric networking, in-network caching has the potential to improve network efficiency and content distribution performance by satisfying user requests with cached content rather than downloading the requested content from remote sources. In this respect, users who request, download, and keep the content may be able to contribute to in-network caching by sharing their downloaded content with other users in the same network domain (i.e., user-assisted in-network caching). In this paper, we examine various aspects of user-assisted in-network caching in the hopes of efficiently utilizing user resources to achieve in-network caching. Through simulations, we first show that user-assisted in-network caching has attractive features, such as self-scalable caching, a near-optimal cache hit ratio (that can be achieved when the content is fully cached by the in-network caching) based on stable caching, and performance improvements over in-network caching. We then examine the caching strategy of user-assisted in-network caching. We examine three caching strategies based on a centralized server that maintains all content availability information and informs each user of what to cache. We also examine three caching strategies based on each users content availability information. We first show that the caching strategy affects the distribution of upload overhead across users and the number of cache hits in each segment. One interesting observation is that, even with a small storage space (i.e., 0.1% of the content size per user), the centralized and distributed approaches improve the cache hit ratio by 50% and 45%, respectively. With an overall view of caching information, the centralized approach can achieve a higher cache hit ratio than the distributed approach. Based on this observation, we discuss a distributed approach with a larger view of caching information than the distributed approach and, through simulations, confirm that a larger view leads to a higher cache hit ratio. Another interesting observation is that the random distributed strategy yields comparable performance to more complex strategies.

CONIC: Content-Oriented Network with Indexed Caching

In this paper, we present Content-Oriented Network with Indexed Caching (CONIC), a deployable and self-scaling architecture to exploit spare storage and bandwidth from end-systems to eliminate redundant traffic and enable efficient and fast access to content. Our trace-driven simulation indicates that CONIC can reduce 25% to 50% traffic volume and can cumulatively halve the latency of content access in the real-world network environment. Also, our prototype implementation verifies the deployability of the CONIC architecture.

DDoS defense as a network service

While distributed denial-of-service (DDoS) threats have been raising concerns for many years, a widely-acceptable solution is still absent. In this paper, we advocate a novel and promising solution for DDoS defense by using powerful cloud infrastructures as new battlefields. To explore this idea, we design and implement a cloud-based attack defense system called CLAD, which is running on cloud infrastructures as a network service to protect Web servers.

OverCourt: DDoS mitigation through credit-based traffic segregation and path migration

Distributed Denial of Service (DDoS) attacks have become one of the most serious threats to the Internet. To mitigate DDoS attacks, much progress has been made in developing currency-based solutions, where a sender is required to spend resources such as computational cost, bandwidth, prior knowledge, and human actions to purchase her legitimacy before sending packets. In this paper, we propose an innovative overlay-based DDoS mitigation architecture by introducing a credit-based accounting mechanism, where a sender can send packets based on her credit points earned by her legitimate communication behaviors instead of expending resources in advance. Since the credit points given to a sender is designed to be measured based on her history of communication patterns, a well-behaving sender can gain her credit points while an ill-behaving one will lose her credit points. We propose an architecture of such a credit-based system, named OverCourt, where a well-behaving client may dynamically migrate to a protected channel when her credit points exceed a threshold while an ill-behaving client will be blocked after her credit points have been exhausted. The analysis and simulation results show that OverCourt can mitigate DDoS attacks under various DDoS attack scenarios.

Best-Effort Network Layer Packet Reordering in Support of Multipath Overlay Packet Dispersion

Simultaneous use of multiple disjoint Internet paths holds promise for exploiting available bandwidth as well as reacting more quickly to Internet path faults. While routing overlay networks have provided a method for accessing these paths, out-of-order packet delivery, which results from dispersion of packets across paths of varying latency, severely degrades the performance of highly optimized transport protocols such as TCP, which assume largely in-order delivery. While traditional approaches perform reordering at the transport or application layers, such approaches have the disadvantage of entangling the complexity of reordering with their own operation, as well as requiring reimplementation for each protocol or application. Herein, we address these issues by proposing the modularization of packet reordering functionality as an optional, best-effort, network layer service for routing overlay networks. We empirically demonstrate the feasibility of this approach by showing that it can realize performance and reliability gains even using unmodified TCP in the face of highly multipath environments and heavy packet reordering.

A Resource-Efficient Method for Crawling Swarm Information in Multiple BitTorrent Networks

Bit Torrent is one of the most popular P2P file sharing applications in the world. Each Bit Torrent network is called a swarm and millions of peers may join multiple swarms. Due to swarms large network size and complexity, many resources (PC servers, the Internet connection, etc.) are required for measuring all the swarms in the world. For this reason, the existing work is forced to measure only a part of the entire set of swarms, thus, ends up understanding only a part of it. In this paper, we propose a resource-efficient method for crawling multiple Bit Torrent swarms by only a limited amount of resources such as a single PC server. In the proposed method, our crawler avoids collecting redundant information of swarms without pressing WAN access links nor expending much processing resources. We also use a number of techniques to efficiently crawl all the participating peers of multiple swarms. We crawl over 4.3 million unique .torrent files, small files that store metadata used in Bit Torrent, and 48,000 tracker addresses. We can crawl 4.3 million swarms within an hour. We obtain 24 swarm snapshots and 10 million unique peers in a day.