Matei Ripeanu

Giggle: A Framework for Constructing Scalable Replica Location Services

In wide area computing systems, it is often desirable to create remote read-only copies (replicas) of files. Replication can be used to reduce access latency, improve data locality, and/or increase robustness, scalability and performance for distributed applications. We define a replica location service (RLS) as a system that maintains and provides access to information about the physical locations of copies. An RLS typically functions as one component of a data grid architecture. This paper makes the following contributions. First, we characterize RLS requirements. Next, we describe a parameterized architectural framework, which we name Giggle (for GIGa-scale Global Location Engine), within which a wide range of RLSs can be defined. We define several concrete instantiations of this framework with different performance characteristics. Finally, we present initial performance results for an RLS prototype, demonstrating that RLS systems can be constructed that meet performance goals.

Deconstructing the Kazaa network

Internet traffic is experiencing a shift from Web traffic to file swapping traffic. Today a significant part of Internet traffic is generated by peer-to-peer applications, mostly by the popular Kazaa application. Yet, to date, few studies analyze Kazaa traffic, thus leaving the bulk of Internet traffic in dark. We present a large-scale investigation of Kazaa traffic based on logs collected at a large Israeli ISP, which capture roughly a quarter of all traffic between Israel and US.

Small-world file-sharing communities

Web caches, content distribution networks, peer-to-peer file sharing networks, distributed file systems, and data grids all have in common that they involve a community of users who generate requests for shared data. In each case, overall system performance can be improved significantly if we can first identify and then exploit interesting structure within a communitys access patterns. To this end, we propose a novel perspective on file sharing that considers the relationships that form among users based on the files in which they are interested. We propose a new structure that captures common user interests in data - the data-sharing graph - and justify its utility with studies on three data-distribution systems: a high-energy physics collaboration, the Web, and the Kazaa peer-to-peer network. We find small-world patterns in the data-sharing graphs of all three communities. We analyze these graphs and propose some probable causes for these emergent small-world patterns. The significance of small-world patterns is twofold: it provides a rigorous support to intuition and, perhaps most importantly, it suggests ways to design mechanisms that exploit these naturally emerging patterns.

Mapping the Gnutella Network: Macroscopic Properties of Large-Scale Peer-to-Peer Systems

Despite recent excitement generated by the peer-to-peer (P2P) paradigm and the surprisingly rapid deployment of some P2P applications, there are few quantitative evaluations of P2P systems behavior. The open architecture, achieved scale, and self-organizing structure of the Gnutella network make it an interesting P2P architecture to study. Like most other P2P applications, Gnutella builds, at the application level, a virtual network with its own routing mechanisms. The topology of this overlay network and the routing mechanisms used have a significant influence on application properties such as performance, reliability, and scalability. We describe techniques to discover and analyze the Gnutellas overlay network topology and evaluate generated network traffic. Our major findings are: (1) although Gnutella is not a pure power-law network, its current configuration has the benefits and drawbacks of a power-law structure, (2) we estimate the aggregated volume of generated traffic, and (3) the Gnutella virtual network topology does not match well the underlying Internet topology, hence leading to ineffective use of the physical networking infrastructure. We believe that our findings as well as our measurement and analysis techniques have broad applicability to P2P systems and provide useful insights into P2P system design tradeoffs.

Supporting Efficient Execution in Heterogeneous Distributed Computing Environments with Cactus and Globus

Improvements in the performance of processors and networks make it both feasible and interesting to treat collections of workstations, servers, clusters, and supercomputers as integrated computational resources, or Grids. However, the highly heterogeneous and dynamic nature of such Grids can make application development di.cult. Here we describe an architecture and prototype implementation for a Grid-enabled computational framework based on Cactus, the MPICH-G2 Grid-enabled message-passing library, and a variety of specialized features to support e.cient execution in Grid environments. We have used this framework to perform record-setting computations in numerical relativity, running across four supercomputers and achieving scaling of 88% (1140 CPU’s) and 63% (1500 CPUs). The problem size we were able to compute was about five times larger than any other previous run. Further, we introduce and demonstrate adaptive methods that automatically adjust computational parameters during run time, to increase dramatically the efficiency of a distributed Grid simulation, without modification of the application and without any knowledge of the underlying network connecting the distributed computers.

VMFlock: virtual machine co-migration for the cloud

This paper presents VMFlockMS, a migration service optimized for cross-datacenter transfer and instantiation of groups of virtual machine (VM) images that comprise an application-level solution (e.g., a three-tier web application). We dub these groups of related VM images VMFlocks. VMFlockMS employs two main techniques: first, data deduplication within the VMFlock to be migrated and between the VMFlock and the data already present at the destination datacenter, and, second, accelerated instantiation of the application at the target datacenter after transferring only a partial set of data blocks and prioritization of the remaining data based on previously observed access patterns originating from the running VMs. VMFlockMS is designed to be deployed as a set of virtual appliances which make efficient use of the available cloud resources to locally access and deduplicate the images and data in a distributed fashion with minimal requirements imposed on the cloud API to access the VM image repository. VMFlockMS provides an incrementally scalable and high-performance migration service. Our evaluation shows that VMFlockMS can reduce the data volumes to be transferred over the network to as low as 3% of the original VMFlock size, enables the complete transfer of the VM images belonging to a VMFlock over transcontinental link up to 3.5x faster than alternative approaches, and enables booting these VM images with as little as 5% of the compressed VMFlock data available at the destination.

Locating Data in (Small-World?) Peer-to-Peer Scientific Collaborations

Data-sharing scientific collaborations have particular characteristics, potentially different from the current peer-to-peer environments. In this paper we advocate the benefits of exploiting emergent patterns in self-configuring networks specialized for scientific data-sharing collaborations. We speculate that a peer-to-peer scientific collaboration network will exhibit small-world topology, as do a large number of social networks for which the same pattern has been documented. We propose a solution for locating data in decentralized, scientific, data-sharing environments that exploits the small-worlds topology. The research challenge we raise is: what protocols should be used to allow a self-configuring peer-to-peer network to form small worlds similar to the way in which the humans that use the network do in their social interactions?

Cache replacement policies revisited: the case of P2P traffic

Peer-to-peer (P2P) file-sharing applications generate a large part if not most of todays Internet traffic. The large volume of this traffic (thus the high potential benefits of caching) and the large cache sizes required (thus nontrivial costs associated with caching) only underline that efficient cache replacement policies are important in this case. P2P file-sharing traffic has several characteristics that distinguish it from well studied Web traffic and that require a focused study of efficient cache management policies. This paper uses trace driven simulations to compare traditional cache replacement policies with new policies that try to exploit characteristics of the P2P file-sharing traffic generated by applications using the FastTrack protocol.

Design and analysis of a social botnet

Online Social Networks (OSNs) have attracted millions of active users and have become an integral part of todays web ecosystem. Unfortunately, in the wrong hands, OSNs can be used to harvest private user data, distribute malware, control botnets, perform surveillance, spread misinformation, and even influence algorithmic trading. Usually, an adversary starts off by running an infiltration campaign using hijacked or adversary-owned OSN accounts, with an objective to connect with a large number of users in the targeted OSN. In this article, we evaluate how vulnerable OSNs are to a large-scale infiltration campaign run by socialbots: bots that control OSN accounts and mimic the actions of real users. We adopted the design of a traditional web-based botnet and built a prototype of a Socialbot Network (SbN): a group of coordinated programmable socialbots. We operated our prototype on Facebook for 8weeks, and collected data about user behavior in response to a large-scale infiltration campaign. Our results show that (1) by exploiting known social behaviors of users, OSNs such as Facebook can be infiltrated with a success rate of up to 80%, (2) subject to user profile privacy settings, a successful infiltration can result in privacy breaches where even more private user data are exposed, (3) given the economics of todays underground markets, running a large-scale infiltration campaign might be profitable but is still not particularly attractive as a sustainable and independent business, (4) the security of socially-aware systems that use or integrate OSN platforms can be at risk, given the infiltration capability of an adversary in OSNs, and (5) defending against malicious socialbots raises a set of challenges that relate to web automation, online-offline identity binding, and usable security.

StoreGPU: exploiting graphics processing units to accelerate distributed storage systems

Today Graphics Processing Units (GPUs) are a largely underexploited resource on existing desktops and a possible cost-effective enhancement to high-performance systems. To date, most applications that exploit GPUs are specialized scientific applications. Little attention has been paid to harnessing these highly-parallel devices to support more generic functionality at the operating system or middleware level. This study starts from the hypothesis that generic middleware level techniques that improve distributed system reliability or performance (such as content addressing, erasure coding, or data similarity detection) can be significantly accelerated using GPU support. We take a first step towards validating this hypothesis, focusing on distributed storage systems. As a proof of concept, we design StoreGPU, a library that accelerates a number of hashing based primitives popular in distributed storage system implementations. Our evaluation shows that StoreGPU enables up to eight-fold performance gains on synthetic benchmarks as well as on a high-level application: the online similarity detection between large data files.