Laurent Bindschaedler is a researcher at EPFL, Switzerland. His interests lie in big data, cloud computing, blockchain, and security. He has been obsessed about systems design and software architecture since he was 12. Entrepreneur in his spare time, he also enjoys chess and rock climbing.

The best way to predict the future is to invent it.Steve Jobs



  • L. Bindschaedler, J. Malicevic, N. Schiper, A. Goel, and W. Zwaenepoel, “Rock You like a Hurricane: Taming Skew in Large Scale Analytics,” in Proceedings of the thirteenth eurosys conference, 2018.
    title = {Rock {Y}ou like a {H}urricane: {T}aming {S}kew in {L}arge {S}cale {A}nalytics},
    author = {Bindschaedler, Laurent and Malicevic, Jasmina and Schiper, Nicolas and Goel, Ashvin and Zwaenepoel, Willy},
    affiliation = {EPFL},
    year = {2018},
    booktitle={Proceedings of the Thirteenth EuroSys Conference},
    details = {},
    documenturl = {},
    location = {Porto, Portugal},
    abstract = {Current cluster computing frameworks suffer from load imbalance and limited parallelism due to skewed data distributions, processing times, and machine speeds. We observe that the underlying cause for these issues in current systems is that they partition work statically. Hurricane is a high-performance large-scale data analytics system that successfully tames skew in novel ways. Hurricane performs adaptive work partitioning based on load observed by nodes at runtime. Overloaded nodes can spawn clones of their tasks at any point during their execution, with each clone processing a subset of the original data. This allows the system to adapt to load imbalance and dynamically adjust task parallelism to gracefully handle skew. We support this design by spreading data across all nodes and allowing nodes to retrieve data in a decentralized way. The result is that Hurricane automatically balances load across tasks, ensuring fast completion times. We evaluate Hurricane’s performance on typical analytics workloads and show that it significantly outperforms state- of-the-art systems for both uniform and skewed datasets, because it ensures good CPU and storage utilization in all cases.},


  • [PDF] [DOI] A. Roy, L. Bindschaedler, J. Malicevic, and W. Zwaenepoel, “Chaos: Scale-out Graph Processing from Secondary Storage,” in 25th Symposium on Operating Systems Principles, 2015.
    abstract = {Chaos scales graph processing from secondary storage to
    multiple machines in a cluster. Earlier systems that
    process graphs from secondary storage are restricted to a
    single machine, and therefore limited by the bandwidth
    and capacity of the storage system on a single machine.
    Chaos is limited only by the aggregate bandwidth and
    capacity of all storage devices in the entire cluster.
    Chaos builds on the streaming partitions introduced by
    X-Stream in order to achieve sequential access to
    storage, but parallelizes the execution of streaming
    partitions. Chaos is novel in three ways. First, Chaos
    partitions for sequential storage access, rather than for
    locality and load balance, re- sulting in much lower
    pre-processing times. Second, Chaos distributes graph
    data uniformly randomly across the clus- ter and does not
    attempt to achieve locality, based on the observation
    that in a small cluster network bandwidth far outstrips
    storage bandwidth. Third, Chaos uses work steal- ing to
    allow multiple machines to work on a single partition,
    thereby achieving load balance at runtime. In terms of
    performance scaling, on 32 machines Chaos takes on
    average only 1.66 times longer to process a graph 32
    times larger than on a single machine. In terms of
    capacity scaling, Chaos is capable of handling a graph
    with 1 trillion edges representing 16 TB of input data, a
    new milestone for graph processing capacity on a small
    commodity cluster.},
    affiliation = {EPFL},
    author = {Roy, Amitabha and Bindschaedler, Laurent and Malicevic,
    Jasmina and Zwaenepoel, Willy},
    booktitle = {25{t}h {S}ymposium on {O}perating {S}ystems {P}rinciples},
    details = {},
    documenturl = {},
    doi = {10.1145/2815400.2815408},
    isbn = {978-1-4503-3834-9},
    keywords = {Graph Processing; Storage; Distributed Systems},
    location = {Monterey, California, USA},
    oai-id = {},
    oai-set = {conf},
    review = {REVIEWED},
    status = {ACCEPTED},
    submitter = {233074; 233074; 233074; 233074},
    title = {Chaos: {S}cale-out {G}raph {P}rocessing from {S}econdary {S}torage},
    unit = {LABOS},
    year = 2015


  • [PDF] L. Bindschaedler and A. Roy, “Benchmarking X-Stream and Graphchi,” , 2014.
    title = {Benchmarking {X}-{S}tream and {G}raphchi},
    affiliation = {EPFL},
    author = {Bindschaedler, Laurent and Roy, Amitabha},
    unit = {LABOS},
    year = 2014


  • [PDF] I. Bilogrevic, M. Jadliwala, I. Lam, I. Aad, P. Ginzboorg, V. Niemi, L. Bindschaedler, and J. Hubaux, “Big Brother Knows Your Friends: on Privacy of Social Communities in Pervasive Networks,” in Proceedings of the 10th International Conference on Pervasive Computing, 2012.
    abstract = {Wireless network operators increasingly deploy WiFi
    hotspots and low-power, low-range base stations in order
    to satisfy users' growing demands for context-aware
    services and performance. In addition to providing better
    service, such capillary infrastructure deployment
    threatens users' privacy with respect to their social
    ties and communities, as it allows infrastructure owners
    to infer users' daily social encounters with increasing
    accuracy, much to the detriment of their privacy. Yet, to
    date, there are no evaluations of the privacy of
    communities in pervasive wireless networks. In this
    paper, we address the important issue of privacy in
    pervasive communities by experimentally evaluating the
    accuracy of an adversary-owned set of wireless sniffing
    stations in reconstructing the communities of mobile
    users. During a four-month trial, 80 participants carried
    mobile devices and were eavesdropped on by an adversarial
    wireless mesh network on a university campus. To the best
    of our knowledge, this is the first study that focuses on
    the privacy of communities in a deployed pervasive
    network and provides important empirical evidence on the
    accuracy and feasibility of community tracking in such
    affiliation = {EPFL},
    author = {Bilogrevic, Igor and Jadliwala, Murtuza and Lam, Istvan
    and Aad, Imad and Ginzboorg, Philip and Niemi, Valtteri
    and Bindschaedler, Laurent and Hubaux, Jean-Pierre},
    booktitle = {Proceedings of the 10th {I}nternational {C}onference on
    {P}ervasive {C}omputing},
    details = {},
    documenturl = {},
    keywords = {Privacy; Pervasive networks; Social communities; User-study},
    location = {Newcastle, UK},
    oai-id = {},
    oai-set = {conf},
    review = {REVIEWED},
    status = {ACCEPTED},
    submitter = {167223; 167223; 167223},
    title = {Big {B}rother {K}nows {Y}our {F}riends: on {P}rivacy of
    {S}ocial {C}ommunities in {P}ervasive {N}etworks},
    unit = {LCA LCA1},
    year = 2012
  • [PDF] L. Bindschaedler, M. Jadliwala, I. Bilogrevic, I. Aad, P. Ginzboorg, V. Niemi, and J. Hubaux, “Track Me If You Can: On the Effectiveness of Context-based Identifier Changes in Deployed Mobile Networks,” in Proceedings of the 19th Annual Network \& Distributed System Security Symposium (NDSS 2012), 2012.
    abstract = {Location privacy is a major concern in an increasingly
    connected and highly pervasive network of mobile users.
    Novel location-based applications and device-to-device
    services (on these mobile devices) are gaining
    popularity, but at the same time, these services allow
    curious service providers and eavesdroppers to track
    users and their movements. Earlier research efforts on
    location-privacy preservation, which were mostly based on
    identifier-change mechanisms in spatio-temporal
    de-correlation regions called mix-zones, show that
    coordinated identifier-change techniques are reasonably
    effective in a simulation setting, although some smart
    attacks are still possible. However, a thorough analysis
    of these mechanisms that takes into consideration
    communication patterns and mobility from a real-life
    deployment is missing from these results. In this paper,
    we evaluate in a real-life setting the effectiveness of
    standard mix-zone-based privacy protection mechanisms
    against probabilistic tracking attacks. Our exper- iments
    involved 80 volunteers carrying smartphones for 4 months
    and being constantly eavesdropped on an adversarial mesh
    network of standard wireless Access Points (APs). To the
    best of our knowledge, this is the first study that
    provides empirical evidence about the effectiveness of
    mix-zone-based privacy-preserving mechanisms against
    practical adversaries in upcoming wireless and mobile
    affiliation = {EPFL},
    author = {Bindschaedler, Laurent and Jadliwala, Murtuza and
    Bilogrevic, Igor and Aad, Imad and Ginzboorg, Philip and
    Niemi, Valtteri and Hubaux, Jean-Pierre},
    booktitle = {Proceedings of the 19th {A}nnual {N}etwork \&
    {D}istributed {S}ystem {S}ecurity {S}ymposium ({NDSS}
    details = {},
    documenturl = {},
    location = {San Diego, California, USA},
    oai-id = {},
    oai-set = {conf},
    publisher = {Internet Society},
    review = {REVIEWED},
    status = {ACCEPTED},
    submitter = {186152; 186152; 186152; 170654},
    title = {Track {M}e {I}f {Y}ou {C}an: {O}n the {E}ffectiveness of
    {C}ontext-based {I}dentifier {C}hanges in {D}eployed
    {M}obile {N}etworks},
    unit = {LCA LCA1},
    year = 2012


  • [PDF] [DOI] L. Bindschaedler, H. Knoche, and J. Huang, “Making Mobile Augmented Reality A Reality,” in Mobile HCI 2011, 2011.
    affiliation = {EPFL},
    author = {Bindschaedler, Laurent and Knoche, Hendrik and Huang, Jeffrey},
    booktitle = {Mobile {HCI} 2011},
    details = {},
    documenturl = {},
    doi = {10.1145/2037373.2037504},
    isbn = {978-1-4503-0541-9 },
    location = {Stockholm, Sweden},
    oai-id = {},
    oai-set = {conf},
    review = {REVIEWED},
    status = {PUBLISHED},
    submitter = {172898; 170654},
    title = {Making {M}obile {A}ugmented {R}eality {A} {R}eality},
    unit = {LDM},
    year = 2011