Demo Data from EDBT 2026 / reviewers in the wild / expert
Yosef Moatti
dblp:28/3824
· DBLP profile ↗
10ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 6Databases, data management, data science and information retrieval · 2 · 1 first-authorSecurity and privacy · 1Software engineering, systems software and programming languages · 1Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
4 papers |
Storage systems · 54% Cloud and datacenter computing · 35% High-performance computing · 7% | |
| Network and information security
1 paper |
Systems and software security · 50% Authentication and access control · 50% | |
| Databases, data mining, and information retrieval
1 paper |
Distributed and cloud data management · 100% |
Topics — the 9 heaviest of 11, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed and cloud data management
query offloading |
0.3 | 1 | 2017 | Too Big to Eat: Boosting Analytics Data Ingestion from Object Stores with Scoop · ICDE 2017 |
Authentication and access control › access control
least privilege |
0.3 | 1 | 2017 | WatchIT: Who Watches Your IT Guy? · SOSP 2017 |
Systems and software security
operating system security |
0.3 | 1 | 2017 | WatchIT: Who Watches Your IT Guy? · SOSP 2017 |
Storage systems
object storage |
0.3 | 1 | 2017 | Crystal: Software-Defined Storage for Multi-Tenant Object Stores · FAST 2017 |
Storage systems › storage architecture
software-defined storage |
0.3 | 1 | 2017 | Crystal: Software-Defined Storage for Multi-Tenant Object Stores · FAST 2017 |
Cloud and datacenter computing › workload isolation
container isolation |
0.1 | 1 | 2017 | WatchIT: Who Watches Your IT Guy? · SOSP 2017 |
High-performance computing › supercomputing
bluegene/l |
0.0 | 1 | 2002 | An overview of the BlueGene/L Supercomputer · SC 2002 |
High-performance computing
supercomputing |
0.0 | 1 | 2002 | An overview of the BlueGene/L Supercomputer · SC 2002 |
Integrated circuit design
system-on-chip |
0.0 | 1 | 2002 | An overview of the BlueGene/L Supercomputer · SC 2002 |
Methods — techniques the papers use, named apart from their topics
resource prediction · 0.6container isolation · 0.6active object storage layer · 0.6ETL-type actions · 0.6system architecture design · 0.0performance scaling studies · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Lamda-Flow: Automatic Pushdown of Dataflow Operators Close to the DataabstractModern data analytics infrastructures are composed of physically disaggregated compute and storage clusters. Thus, dataflow analytics engines, such as Apache Spark or Flink, are left with no choice but to transfer datasets to the compute cluster prior to their actual processing. For large data volumes, this becomes problematic, since it involves massive data transfers that exhaust network bandwidth, that waste compute cluster memory, and that may become a performance barrier. To overcome this problem, we present λFlow: a framework for automatically pushing dataflow operators (e.g., map, flatMap, filter, etc.) down onto the storage layer. The novelty of λFlow is that it manages the pushdown granularity at the operator level, which makes it a unique problem. To wit, it requires addressing several challenges, such as how to encapsulate dataflow operators and execute them on the storage cluster, and how to keep track of dependencies such that operators can be pushed down safely onto the storage layer. Our evaluation reports significant reductions in resource usage for a large variety of IO-bound jobs. For instance, λFlow was able to reduce both network bandwidth and memory requirements by 90% in Spark. Our Flink experiments also prove the extensibility of λFlow to other engines. Raúl Gracia Tinedo, Marc Sánchez Artigas, Pedro García López, Yosef Moatti, Filip Gluszak |
CCGRID | 4 |
| 2019 | Big data skipping in the cloudabstractAccording to today's best practices, cloud compute and storage services should be deployed and managed independently. However, this generates a problem for big data analytics in the cloud: potentially huge datasets need to be shipped from the storage service to the compute service to analyse the data. To address this, minimizing the amount of data sent across the network is critical to achieve good performance and low cost. Data skipping is a technique which achieves this for SQL style analytics on structured data. Oshrit Feder, Guy Khazma, Gal Lushi, Yosef Moatti, Paula Ta-Shma |
SYSTOR | 4 |
| 2019 | Software-defined object storage in multi-tenant environments
Raúl Gracia Tinedo, Josep Sampé, Gerard París, Marc Sánchez Artigas, Pedro García López, Yosef Moatti |
Future Gener. Comput. Syst. | 6 |
| 2017 | Crystal: Software-Defined Storage for Multi-Tenant Object Stores
Raúl Gracia Tinedo, Josep Sampé, Edgar Zamora-Gómez, Marc Sánchez Artigas, Pedro García López, Yosef Moatti, Eran Rom |
FAST | 6 |
| 2017 | Too Big to Eat: Boosting Analytics Data Ingestion from Object Stores with ScoopabstractExtracting value from data stored in object stores,such as OpenStack Swift and Amazon S3, can be problematicin common scenarios where analytics frameworks and objectstores run in physically disaggregated clusters. One of the mainproblems is that analytics frameworks must ingest large amountsof data from the object store prior to the actual computation;this incurs a significant resources and performance overhead. Toovercome this problem, we present Scoop. Scoop enables analyticsframeworks to benefit from the computational resources of objectstores to optimize the execution of analytics jobs. Scoop achievesthis by enabling the addition of ETL-type actions to the dataupload path and by offloading querying functions to the objectstore through a rich and extensible active object storage layer. Asa proof-of-concept, Scoop enables Apache Spark SQL selectionsand projections to be executed close to the data in OpenStackSwift for accelerating analytics workloads of a smart energy gridcompany (GridPocket). Our experiments in a 63-machine clusterwith real IoT data and SQL queries from GridPocket show thatScoop exhibits query execution times up to 30x faster than thetraditional “ingest-then-compute” approach. Yosef Moatti, Eran Rom, Raúl Gracia Tinedo, Dalit Naor, Doron Chen, Josep Sampé, Marc Sánchez Artigas, Pedro García López, Filip Gluszak, Eric Deschdt, Francesco Pace, Daniele Venzano, Pietro Michiardi |
ICDE | 1 |
| 2017 | WatchIT: Who Watches Your IT Guy?abstractSystem administrators have unlimited access to system resources. As the Snowden case highlighted, these permissions can be exploited to steal valuable personal, classified, or commercial data. This problem is exacerbated when a third party administers the system. For example, a bank outsourcing its IT would not want to allow administrators access to the actual data. We propose WatchIT: a strategy that constrains IT personnel's view of the system and monitors their actions. To this end, we introduce the abstraction of perforated containers -- while regular Linux containers are too restrictive to be used by system administrators, by "punching holes" in them, we strike a balance between information security and required administrative needs. Following the principle of least privilege, our system predicts which system resources should be accessible for handling each IT issue, creates a perforated container with the corresponding isolation, and deploys it as needed for fixing the problem. Noam Shalev, Idit Keidar, Yaron Weinsberg, Yosef Moatti, Elad Ben-Yehuda |
SOSP | 4 |
| 2012 | VM Placement Strategies for Cloud ScenariosabstractThe problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP) that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSP technique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver. Nicolò Maria Calcavecchia, Ofer Biran, Erez Hadad, Yosef Moatti |
IEEE CLOUD | 4 |
| 2011 | Guaranteeing High Availability Goals for Virtual Machine PlacementabstractThe placement of virtual machines (VMs) on a cluster of hosts under multiple constraints, including administrative (security, regulations) resource-oriented (capacity, energy), and QoS-oriented (performance) is a highly complex task. We define a new high-availability property for a VM; when a VM is marked as k-resilient, as long as there are up to k host failures, it should be guaranteed that it can be relocated to a non-failed host without relocating other VMs. Together with Hardware Predictive Failure Analysis and live migration, which enable VMs to be evacuated from a host before it fails, this property allows the continuous running of YMs on the cluster despite host failures. The complexity of the constraints associated with k-resiliency, which are naturally expressed by Second Order logic statements, prevented their integration into the placement computation until now. We present a novel algorithm which enables this integration by transforming the k-resiliency constraints to rules consumable by a generic Constraint Programming engine, prove that it guarantees the required resiliency and describe the implementation. We provide some preliminary results and compare our high availability support with naive solutions. Eyal Bin, Ofer Biran, Odellia Boni, Erez Hadad, Elliot K. Kolodner, Yosef Moatti, Dean H. Lorenz |
ICDCS | 6 |
| 2003 | Easy: Engineering High Availability QoS in wServicesabstractDeveloping and administrating distributed applications is complex. Frameworks, hiding the distribution hurdles through encapsulation were proposed, but their acceptance by the industry has been limited. The main reason is the difficulty to provide a simple interface to meet a wide range of application types. In this paper we address the functional services provided over the Web (henceforth, wServices). Examples of wServices are grid services. These typically small software components are often developed under tight budget and timeframe constraints. A wService may be deployed on different platforms and provide different QoS guarantees. With the advent of e-business, wServices become an important type of distributed applications. We claim that narrowing the view to this type of applications allows providing a simple interface. Furthermore, we show that good performance can be achieved if wService developers provide simple tuning parameters as part of a wService package. In our Easy model, platform and QoS specifics are decoupled from wService development costs. In addition we aim at increasing automation of wService deployment on various platforms and for different QoS. The focus of this paper is on performance aware high availability, achieved through wService cloning and replication of its state. In our philosophy, wService developers are aware of potential cloning and replication but not of the mechanisms that provide it. We demonstrate the feasibility of Easy through a prototype with an automatically deployed TomCat Web Container. Easy clones TomCat and replicates its states. We show that this automated process imposes only slight performance degradation compared to a manual one. Eliezer Dekel, Oleg Frenkel, Gera Goft, Yosef Moatti |
SRDS | 4 |
| 2002 | An overview of the BlueGene/L SupercomputerabstractThis paper gives an overview of the BlueGene/L Supercomputer. This is a jointly funded research partnership between IBM and the Lawrence Livermore National Laboratory as part of the United States Department of Energy ASCI Advanced Architecture Research Program. Application performance and scaling studies have recently been initiated with partners at a number of academic and government institutions,including the San Diego Supercomputer Center and the California Institute of Technology. This massively parallel system of 65,536 nodes is based on a new architecture that exploits system-on-a-chip technology to deliver target peak processing power of 360 teraFLOPS (trillion floating-point operations per second). The machine is scheduled to be operational in the 2004-2005 time frame, at price/performance and power consumption/performance targets unobtainable with conventional architectures. Narasimha R. Adiga, Gheorghe Almási 0001, George S. Almási, Yariv Aridor, Rajkishore Barik, Daniel K. Beece, Ralph Bellofatto, Gyan Bhanot, Randy Bickford, Matthias A. Blumrich, Arthur A. Bright, José R. Brunheroto, Calin Cascaval, José G. Castaños, Waiman Chan, Luis Ceze, Paul Coteus, Siddhartha Chatterjee, Dong Chen 0005, George L.-T. Chiu, Thomas M. Cipolla, Paul Crumley, K. M. Desai, Alina Deutsch, Tamar Domany, Marc Boris Dombrowa, Wilm E. Donath, Maria Eleftheriou, C. Christopher Erway, J. Esch, Blake G. Fitch, Joseph Gagliano, Alan Gara, Rahul Garg 0001, Robert S. Germain, Mark Giampapa, Balaji Gopalsamy, John A. Gunnels, Manish Gupta 0002, Fred G. Gustavson, Shawn Hall, Ruud A. Haring, David F. Heidel, Philip Heidelberger, Lorraine M. Herger, Dirk Hoenicke, R. D. Jackson, T. Jamal-Eddine, Gerard V. Kopcsay, Elie Krevat, Manish P. Kurhekar, Alphonso P. Lanzetta, Derek Lieber, L. K. Liu, M. Lu, Mark P. Mendell, A. Misra, Yosef Moatti, Lawrence S. Mok, José E. Moreira, Ben J. Nathanson, Matthew Newton, Martin Ohmacht, Adam J. Oliner, Vinayaka Pandit, R. B. Pudota, Rick A. Rand, Richard D. Regan, Bradley Rubin, Albert E. Ruehli, Silvius Vasile Rus, Ramendra K. Sahoo, Alda Sanomiya, Eugen Schenfeld, M. Sharma, Edi Shmueli, Sarabjeet Singh, Peilin Song, Vijay Srinivasan, Burkhard D. Steinmacher-Burow, Karin Strauss, Christopher W. Surovic, Richard A. Swetz, Todd Takken, R. Brett Tremaine, Mickey Tsao, Arun R. Umamaheshwaran, P. Verma, Pavlos Vranas, T. J. Christopher Ward, Michael E. Wazlowski, W. Barrett, C. Engel, B. Drehmel, B. Hilgart, D. Hill, F. Kasemkhani, David J. Krolak, Chun-Tao Li 0001, Thomas A. Liebsch, James A. Marcella, A. Muff, A. Okomo, M. Rouse, A. Schram, M. Tubbs, G. Ulsh, Charles D. Wait, J. Wittrup, Myung Bae, Kenneth A. Dockser, Lynn Kissel, Mark K. Seager, Jeffrey S. Vetter, K. Yates |
SC | 58 |