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Register a new userIP over P2P: Enabling Self-configuring Virtual IP Networks for Grid Computing
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Added by
P. Oscar Boykin
Publication date
2006
fields
Informatics Engineering
and research's language is
English
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Peer-to-peer (P2P) networks have mostly focused on task oriented networking, where networks are constructed for single applications, i.e. file-sharing, DNS caching, etc. In this work, we introduce IPOP, a system for creating virtual IP networks on top of a P2P overlay. IPOP enables seamless access to Grid resources spanning multiple domains by aggregating them into a virtual IP network that is completely isolated from the physical network. The virtual IP network provided by IPOP supports deployment of existing IP-based protocols over a robust, self-configuring P2P overlay. We present implementation details as well as experimental measurement results taken from LAN, WAN, and Planet-Lab tests.
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Given some of the recent advances in Distributed Hash Table (DHT) based Peer-To-Peer (P2P) systems we ask the following questions: Are there applications where unstructured queries are still necessary (i.e., the underlying queries do not efficiently map onto any structured framework), and are there unstructured P2P systems that can deliver the high bandwidth and computing performance necessary to support such applications. Toward this end, we consider an image search application which supports queries based on image similarity metrics, such as color histogram intersection, and discuss why in this setting, standard DHT approaches are not directly applicable. We then study the feasibility of implementing such an image search system on two different unstructured P2P systems: power-law topology with percolation search, and an optimized super-node topology using structured broadcasts. We examine the average and maximum values for node bandwidth, storage and processing requirements in the percolation and super-node models, and show that current high-end computers and high-speed links have sufficient resources to enable deployments of large-scale complex image search systems.
P2P overlays provide a framework for building distributed applications consisting of few to many resources with features including self-configuration, scalability, and resilience to node failures. Such systems have been successfully adopted in large-scale services for content delivery networks, file sharing, and data storage. In small-scale systems, they can be useful to address privacy concerns and for network applications that lack dedicated servers. The bootstrap problem, finding an existing peer in the overlay, remains a challenge to enabling these services for small-scale P2P systems. In large networks, the solution to the bootstrap problem has been the use of dedicated services, though creating and maintaining these systems requires expertise and resources, which constrain their usefulness and make them unappealing for small-scale systems. This paper surveys and summarizes requirements that allow peers potentially constrained by network connectivity to bootstrap small-scale overlays through the use of existing public overlays. In order to support bootstrapping, a public overlay must support the following requirements: a method for reflection in order to obtain publicly reachable addresses, so peers behind network address translators and firewalls can receive incoming connection requests; communication relaying to share public addresses and communicate when direct communication is not feasible; and rendezvous for discovering remote peers, when the overlay lacks stable membership. After presenting a survey of various public overlays, we identify two overlays that match the requirements: XMPP overlays, such as Google Talk and Live Journal Talk, and Brunet, a structured overlay based upon Symphony. We present qualitative experiences with prototypes that demonstrate the ability to bootstrap small-scale private structured overlays from public Brunet or XMPP infrastructures.
We describe a proposal for an extensible, component-based software architecture for natural language engineering applications. Our model leverages existing linguistic resource description and discovery mechanisms based on extended Dublin Core metadata. In addition, the application design is flexible, allowing disparate components to be combined to suit the overall application functionality. An application specification language provides abstraction from the programming environment and allows ease of interface with computational grids via a broker.
Two-stream networks have achieved great success in video recognition. A two-stream network combines a spatial stream of RGB frames and a temporal stream of Optical Flow to make predictions. However, the temporal redundancy of RGB frames as well as the high-cost of optical flow computation creates challenges for both the performance and efficiency. Recent works instead use modern compressed video modalities as an alternative to the RGB spatial stream and improve the inference speed by orders of magnitudes. Previous works create one stream for each modality which are combined with an additional temporal stream through late fusion. This is redundant since some modalities like motion vectors already contain temporal information. Based on this observation, we propose a compressed domain two-stream network IP TSN for compressed video recognition, where the two streams are represented by the two types of frames (I and P frames) in compressed videos, without needing a separate temporal stream. With this goal, we propose to fully exploit the motion information of P-stream through generalized distillation from optical flow, which largely improves the efficiency and accuracy. Our P-stream runs 60 times faster than using optical flow while achieving higher accuracy. Our full IP TSN, evaluated over public action recognition benchmarks (UCF101, HMDB51 and a subset of Kinetics), outperforms other compressed domain methods by large margins while improving the total inference speed by 20%.
In modern distributed computing systems, unpredictable and unreliable infrastructures result in high variability of computing resources. Meanwhile, there is significantly increasing demand for timely and event-driven services with deadline constraints. Motivated by measurements over Amazon EC2 clusters, we consider a two-state Markov model for variability of computing speed in cloud networks. In this model, each worker can be either in a good state or a bad state in terms of the computation speed, and the transition between these states is modeled as a Markov chain which is unknown to the scheduler. We then consider a Coded Computing framework, in which the data is possibly encoded and stored at the worker nodes in order to provide robustness against nodes that may be in a bad state. With timely computation requests submitted to the system with computation deadlines, our goal is to design the optimal computation-load allocation scheme and the optimal data encoding scheme that maximize the timely computation throughput (i.e, the average number of computation tasks that are accomplished before their deadline). Our main result is the development of a dynamic computation strategy called Lagrange Estimate-and Allocate (LEA) strategy, which achieves the optimal timely computation throughput. It is shown that compared to the static allocation strategy, LEA increases the timely computation throughput by 1.4X - 17.5X in various scenarios via simulations and by 1.27X - 6.5X in experiments over Amazon EC2 clusters
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