Adam J. Oliner

VLDB 2026 Research / reviewers in the wild / expert

dblp:29/5231 · DBLP profile ↗

← Back

17ranked-venue papers

12as first author

0since 2021 · last 2014

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 11 · 9 first-authorSecurity and privacy · 5 · 5 first-authorDatabases, data management, data science and information retrieval · 3 · 1 first-authorArtificial intelligence and machine learning · 2 · 1 first-authorComputer networks · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 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		Energy-efficient computing · 35% Distributed systems · 29% High-performance computing · 20%
Network and information security 1 paper		Malware analysis · 100%
Human-computer interaction and pervasive computing 2 papers		Ubiquitous computing and smart environments · 84% User interface design and tools · 16%
Databases, data mining, and information retrieval 1 paper		Data mining · 100%

Topics — the 12 heaviest of 16, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Malware analysis mobile malware	0.2	1	2014	The company you keep: mobile malware infection rates and inexpensive risk indicators · WWW 2014
Energy-efficient computing mobile device energy management	0.2	1	2013	Carat: collaborative energy diagnosis for mobile devices · SenSys 2013
Distributed systems system log analysis	0.1	2	2008	Alert Detection in System Logs · ICDM 2008 Critical event prediction for proactive management in large-scale computer clusters · KDD 2003
Data mining anomaly detection	0.1	1	2008	Alert Detection in System Logs · ICDM 2008
Data mining › anomaly detection › sequential anomaly detection log anomaly detection	0.1	1	2008	Alert Detection in System Logs · ICDM 2008
Ubiquitous computing and smart environments › pervasive sensing collaborative sensing	0.0	1	2013	Carat: collaborative energy diagnosis for mobile devices · SenSys 2013
Ubiquitous computing and smart environments mobile sensing	0.0	1	2013	Carat: collaborative energy diagnosis for mobile devices · SenSys 2013
Cloud and datacenter computing › cluster resource management and scheduling cluster resource management	0.0	1	2003	Critical event prediction for proactive management in large-scale computer clusters · KDD 2003
Distributed systems › fault tolerance proactive fault prediction	0.0	1	2003	Critical event prediction for proactive management in large-scale computer clusters · KDD 2003
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

energy modeling · 0.3collaborative black-box diagnosis · 0.3survey · 0.2statistical analysis · 0.2interaction log analysis · 0.2unsupervised learning · 0.2information entropy · 0.2time series analysis · 0.0rule-based classification · 0.0probabilistic networks · 0.0bayesian network · 0.0system architecture design · 0.0performance scaling studies · 0.0

Year	Publication	Venue	Position
2014	How carat affects user behavior: implications for mobile battery awareness applications abstract Mobile devices have limited battery life, and numerous battery management applications are available that aim to improve it. This paper examines a large-scale mobile battery awareness application, called Carat, to see how it changes user behavior with long-term use. We conducted a survey of current Carat Android users and analyzed their interaction logs. The results show that long-term Carat users save more battery, charge their devices less often, learn to manage their battery with less help from Carat, have a better understanding of how Carat works, and may enjoy competing against other users. Based on these findings, we propose a set of guidelines for mobile battery awareness applications: battery awareness applications should make the reasoning behind their recommendations understandable to the user, be tailored to retain long-term users, take the audience into account when formulating feedback, and distinguish third-party and system applications. Kumaripaba Athukorala, Eemil Lagerspetz, Maria von Kügelgen, Antti Jylhä, Adam J. Oliner, Sasu Tarkoma, Giulio Jacucci	CHI	5
2014	The company you keep: mobile malware infection rates and inexpensive risk indicators abstract There is little information from independent sources in the public domain about mobile malware infection rates. The only previous independent estimate (0.0009%) [11], was based on indirect measurements obtained from domain-name resolution traces. In this paper, we present the first independent study of malware infection rates and associated risk factors using data collected directly from over 55,000 Android devices. We find that the malware infection rates in Android devices estimated using two malware datasets (0.28% and 0.26%), though small, are significantly higher than the previous independent estimate. Based on the hypothesis that some application stores have a greater density of malicious applications and that advertising within applications and cross-promotional deals may act as infection vectors, we investigate whether the set of applications used on a device can serve as an indicator for infection of that device. Our analysis indicates that, while not an accurate indicator of infection by itself, the application set does serve as an inexpensive method for identifying the pool of devices on which more expensive monitoring and analysis mechanisms should be deployed. Using our two malware datasets we show that this indicator performs up to about five times better at identifying infected devices than the baseline of random checks. Such indicators can be used, for example, in the search for new or previously undetected malware. It is therefore a technique that can complement standard malware scanning. Our analysis also demonstrates a marginally significant difference in battery use between infected and clean devices. Hien Thi Thu Truong, Eemil Lagerspetz, Petteri Nurmi, Adam J. Oliner, Sasu Tarkoma, N. Asokan, Sourav Bhattacharya	WWW	4
2013	Carat: collaborative energy diagnosis for mobile devices abstract We aim to detect and diagnose energy anomalies, abnormally heavy battery use. This paper describes a collaborative black-box method, and an implementation called Carat, for diagnosing anomalies on mobile devices. A client app sends intermittent, coarse-grained measurements to a server, which correlates higher expected energy use with client properties like the running apps, device model, and operating system. The analysis quantifies the error and confidence associated with a diagnosis, suggests actions the user could take to improve battery life, and projects the amount of improvement. During a deployment to a community of more than 500,000 devices, Carat diagnosed thousands of energy anomalies in the wild. Carat detected all synthetically injected anomalies, produced no known instances of false positives, projected the battery impact of anomalies with 95% accuracy, and, on average, increased a user's battery life by 11% after 10 days (compared with 1.9% for the control group). Adam J. Oliner, Anand Padmanabha Iyer, Ion Stoica, Eemil Lagerspetz, Sasu Tarkoma	SenSys	1
2011	Online detection of multi-component interactions in production systems abstract We present an online, scalable method for inferring the interactions among the components of large production systems. We validate our approach on more than 1.3 billion lines of log files from eight unmodified production systems, showing that our approach efficiently identifies important relationships among components, handles very large systems with many simultaneous signals in real time, and produces information that is useful to system administrators. Adam J. Oliner, Alex Aiken	DSN	1
2010	Using correlated surprise to infer shared influence abstract We propose a method for identifying the sources of problems in complex production systems where, due to the prohibitive costs of instrumentation, the data available for analysis may be noisy or incomplete. In particular, we may not have complete knowledge of all components and their interactions. We define influences as a class of component interactions that includes direct communication and resource contention. Our method infers the influences among components in a system by looking for pairs of components with time-correlated anomalous behavior. We summarize the strength and directionality of shared influences using a Structure-of-Influence Graph (SIG). This paper explains how to construct a SIG and use it to isolate system misbehavior, and presents both simulations and in-depth case studies with two autonomous vehicles and a 9024-node production supercomputer. Adam J. Oliner, Ashutosh V. Kulkarni, Alex Aiken	DSN	1
2010	A query language for understanding component interactions in production systems abstract When something unexpected happens in a large production system, administrators must first perform a search to isolate which components and component interactions are likely to be involved. The system may consist of thousands of interacting subsystems, the logging instrumentation may be noisy or incomplete, and the problem description may be vague, so this search is often the most difficult part of understanding the system’s behavior. To facilitate the search process, we present a query language and a method for computing these queries that makes minimal assumptions about the available data. We evaluate our method on nearly 1.22 billion lines of system logs from four supercomputers, two autonomous vehicles, and a server cluster. Adam J. Oliner, Alex Aiken	ICS	1
2010	Community Epidemic Detection Using Time-Correlated Anomalies Adam J. Oliner, Ashutosh V. Kulkarni, Alex Aiken	RAID	1
2008	Bad Words: Finding Faults in Spirit's Syslogs abstract Accurate fault detection is a key element of resilient computing. Syslogs provide key information regarding faults, and are found on nearly all computing systems. Discovering new fault types requires expert human effort, however, as no previous algorithm has been shown to localize faults in time and space with an operationally acceptable false positive rate. We present experiments on three weeks of syslogs from Sandia's 512-node "Spirit" Linux cluster, showing one algorithm that localizes 50% of faults with 75% precision, corresponding to an excellent false positive rate of 0.05%. The salient characteristics of this algorithm are (1) calculation of nodewise information entropy, and (2) encoding of word position. The key observation is that similar computers correctly executing similar work should produce similar logs. Jon Stearley, Adam J. Oliner	CCGRID	2
2008	Alert Detection in System Logs abstract We present Nodeinfo, an unsupervised algorithm for anomaly detection in system logs. We demonstrate Nodeinfo's effectiveness on data from four of the world's most powerful supercomputers: using logs representing over 746 million processor-hours, in which anomalous events called alerts were manually tagged for scoring, we aim to automatically identify the regions of the log containing those alerts. We formalize the alert detection task in these terms, describe how Nodeinfo uses the information entropy of message terms to identify alerts, and present an online version of this algorithm, which is now in production use. This is the first work to investigate alert detection on (several) publicly-available supercomputer system logs, thereby providing a reproducible performance baseline. Adam J. Oliner, Alex Aiken, Jon Stearley	ICDM	1
2007	What Supercomputers Say: A Study of Five System Logs abstract If we hope to automatically detect and diagnose failures in large-scale computer systems, we must study real deployed systems and the data they generate. Progress has been hampered by the inaccessibility of empirical data. This paper addresses that dearth by examining system logs from five supercomputers, with the aim of providing useful insight and direction for future research into the use of such logs. We present details about the systems, methods of log collection, and how alerts were identified; propose a simpler and more effective filtering algorithm; and define operational context to encompass the crucial information that we found to be currently missing from most logs. The machines we consider (and the number of processors) are: Blue Gene/L (131072), Red Storm (10880), Thunderbird (9024), Spirit (1028), and Liberty (512). This is the first study of raw system logs from multiple supercomputers. Adam J. Oliner, Jon Stearley	DSN	1
2006	Cooperative checkpointing: a robust approach to large-scale systems reliability abstract Cooperative checkpointing increases the performance and robustness of a system by allowing checkpoints requested by applications to be dynamically skipped at runtime. A robust system must be more than merely resilient to failures; it must be adaptable and flexible in the face of new and evolving challenges. A simulation-based experimental analysis using both probabilistic and harvested failure distributions reveals that cooperative checkpointing enables an application to make progress under a wide variety of failure distributions that periodic checkpointing lacks the flexibility to handle. Cooperative checkpointing can be easily implemented on top of existing application-initiated checkpointing mechanisms and may be used to enhance other reliability techniques like QoS guarantees and fault-aware job scheduling. The simulations also support a number of theoretical predictions related to cooperative checkpointing, including the non-competitiveness of periodic checkpointing. Adam J. Oliner, Larry Rudolph, Ramendra K. Sahoo	ICS	1
2006	Cooperative checkpointing theory abstract Cooperative checkpointing uses global knowledge of the state and health of the machine to improve performance and reliability by dynamically deciding when to skip checkpoint requests made by applications. Using results from cooperative checkpointing theory, this paper proves that periodic checkpointing is not expected to be competitive with the offline optimal. By leveraging probabilistic information about the future, cooperative checkpointing gives flexible algorithms that are optimally competitive. The results prove that simulating periodic checkpointing; by performing only every dth checkpoint, is not competitive with the offline optimal in the worst case; a simple modification gives a provably competitive algorithm. Calculations using failure traces from a prototype of IBM's Blue Gene/L show an application using cooperative checkpointing may make progress 4 times faster than one using periodic checkpointing, under realistic conditions. We contribute an approach to providing large-scale system reliability through cooperative checkpointing and techniques for analyzing the approach Adam J. Oliner, Larry Rudolph, Ramendra K. Sahoo	IPDPS	1
2006	Evaluating cooperative checkpointing for supercomputing systems abstract Cooperative checkpointing, in which the system dynamically skips checkpoints requested by applications at runtime, can exploit system-level information to improve performance and reliability in the face of failures. We evaluate the applicability of cooperative checkpointing to large-scale systems through simulation studies considering real workloads, failure logs, and different network topologies. We consider two cooperative checkpointing algorithms: work-based cooperative checkpointing uses a heuristic based on the amount of unsaved work and risk-based cooperative checkpointing leverages failure event prediction. Our results demonstrate that, compared to periodic checkpointing, risk-based checkpointing with event prediction accuracy as low as 10% is able to significantly improve system utilization and reduce average bounded slowdown by a factor of 9, without losing any additional work to failures. Similarly, work-based checkpointing conferred tremendous performance benefits in the face of large checkpoint overheads Adam J. Oliner, Ramendra K. Sahoo	IPDPS	1
2005	Probabilistic QoS Guarantees for Supercomputing Systems abstract Supercomputing systems must be able to reliably and efficiently complete their assigned workloads, even in the presence of failures. This paper proposes a system that allows the system and users to negotiate a mutually desirable risk strategy; in order to accomplish this, the system makes probabilistic guarantees on quality of service (QoS), of the form, "Job j can be completed by deadline d with probability p". In order to make such guarantees, the system uses event prediction (forecasting) in conjunction with fault-aware job scheduling and cooperative checkpointing strategies. Using job logs and failure traces from actual high performance computing systems, we employ trace-based simulations to assess the effects of the prediction accuracy (a) and user risk strategy (U) on a variety of performance metrics. Compared to a system that does not use event prediction, a high forecasting accuracy resulted in QoS and utilization improvements of as much as 6%, along with an 89% reduction in the amount of lost work. Therefore, our results show that a system that makes probabilistic QoS guarantees using a market-based scheduling approach can increase both system performance and reliability. Adam J. Oliner, Larry Rudolph, Ramendra K. Sahoo, José E. Moreira, Manish Gupta 0002	DSN	1
2004	Fault-Aware Job Scheduling for BlueGene/L Systems abstract Summary form only given. Large-scale systems like BlueGene/L are susceptible to a number of software and hardware failures that can affect system performance. We evaluate the effectiveness of a previously developed job scheduling algorithm for BlueGene/L in the presence of faults. We have developed two new job-scheduling algorithms considering failures while scheduling the jobs. We have also evaluated the impact of these algorithms on average bounded slowdown, average response time and system utilization, considering different levels of proactive failure prediction and prevention techniques reported in the literature. Our simulation studies show that the use of these new algorithms with even trivial fault prediction confidence or accuracy levels (as low as 10%) can significantly improve the performance of the BlueGene/L system. Adam J. Oliner, Ramendra K. Sahoo, José E. Moreira, Manish Gupta 0002, Anand Sivasubramaniam	IPDPS	1
2003	Critical event prediction for proactive management in large-scale computer clusters abstract As the complexity of distributed computing systems increases, systems management tasks require significantly higher levels of automation; examples include diagnosis and prediction based on real-time streams of computer events, setting alarms, and performing continuous monitoring. The core of autonomic computing, a recently proposed initiative towards next-generation IT-systems capable of 'self-healing', is the ability to analyze data in real-time and to predict potential problems. The goal is to avoid catastrophic failures through prompt execution of remedial actions.This paper describes an attempt to build a proactive prediction and control system for large clusters. We collected event logs containing various system reliability, availability and serviceability (RAS) events, and system activity reports (SARs) from a 350-node cluster system for a period of one year. The 'raw' system health measurements contain a great deal of redundant event data, which is either repetitive in nature or misaligned with respect to time. We applied a filtering technique and modeled the data into a set of primary and derived variables. These variables used probabilistic networks for establishing event correlations through prediction algorithms. We also evaluated the role of time-series methods, rule-based classification algorithms and Bayesian network models in event prediction.Based on historical data, our results suggest that it is feasible to predict system performance parameters (SARs) with a high degree of accuracy using time-series models. Rule-based classification techniques can be used to extract machine-event signatures to predict critical events with up to 70% accuracy. Ramendra K. Sahoo, Adam J. Oliner, Irina Rish, Manish Gupta 0002, José E. Moreira, Sheng Ma, Ricardo Vilalta, Anand Sivasubramaniam	KDD	2
2002	An overview of the BlueGene/L Supercomputer abstract This 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	64