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Introducing IBM Haifa Research Lab
May 10, 2009
Organized by TAU/CS and IBM Haifa Research Lab


Activities for healthcare and life sciences
Ohad Greenshpan

The Healthcare and Life Sciences group is unique in its focuses on an industry sector. The group was established about 10 years ago, and since then has dealt with various directions in the IT space. The more traditional directions deal with mechanisms to archive, store, manage, transfer, and access health-related data. Over the time, the research focus has evolved to other directions such as data analytics, web data management and integration, business process management, and others. In my talk I will give an overview of our group’s activities, discuss the challenges in the healthcare domain, and drill down to several interesting projects. I will conclude by giving some examples of how research is being done, trying to give a feeling of the role of a researcher in our labs, of our relationship with academia, and ways to initiate collaboration with one another.

Machine learning analysis of clinical genomic data - from HIV positive to hypertensive patients
Michal Rosen-Zvi

In my talk I will illustrate how advanced machine-learning and data mining techniques available today, along with the abundance of medical data, can provide a powerful decision support system and contribute to the emerging area of information based medicine.

In recent years there is a lot of focus on the personalized treatment approach where therapy provided to a patient can be optimized based on the individual’s personal clinical-genomic factors. In the past few years we have carried out clinical genomic analysis that falls under this broad approach. This research has been carried out as part of two different EU-funded projects.  In the EuResist project we learnt to optimize cocktails for HIV patients based on the virus genome and other factors. The main result is a freely available decision support system -

In the Hypergenes project we are learning to correlate between SNPs (single nucleotide polymorphism) and hypertension.

Constraint satisfaction: from theory to solving complex industrial problems 
Merav Aharoni

Constraint satisfaction involves finding a solution for a given set of constraints over a set of variables with finite domains. Typical problems come from the fields of artificial intelligence and operations research. They vary from riddles, such as Sudoku, to industrial applications, such as finding an optimal assignment for thousands of professionals to open positions.

We have developed two powerful engines for solving general constraint satisfaction problems at the IBM Haifa Research Lab. The systematic constraint solver, GEC, is based on the maintain-arc-consistency (MAC) algorithm that involves reducing the variable domains by removing values which cannot partake in any valid solution. The stochastic constraint solver, Stocs, is based on stochastic local-search.

In this talk, we outline these two algorithms with an emphasis on active areas of research. We will also describe a few of the applications that use these tools as their solving engine.

Static analysis of programs and models
Yishai Feldman

Many large corporations have huge complex systems in operation, and maintaining them is becoming more and more difficult.  I will describe two directions of research on the static analysis of legacy systems, aimed at building tools to assist legacy modernization.

We are building an infrastructure for static analysis of enterprise systems, based on a language-independent internal representation.  On top of that infrastructure we are building tools for program understanding and transformation.  As part of this work, we have designed a family of new program-slicing algorithms, which produce more accurate slices than state-of-the-art algorithms, especially for unstructured languages.  We are also developing flexible and reliable code-motion refactoring algorithms.  For the most fundamental refactoring, Extract Method, these do not require that extracted code be contiguous.

On a different level, System Grokker is a tool that extracts models and analyzes them to increase the level of abstraction.  System Grokker improves system understanding by allowing the representation of various system organization views, calculation of metrics, and discovery of high level abstractions and patterns.  It assists system validation by exposing problematic relationships and anti-patterns, and supports software evolution process by suggesting architectural improvements and simulating architectural changes.

Cloud computing: automating service elasticity in RESERVOIR
David Breitgand

In the first part of my talk, I will give a brief overview of RESERVOIR, a project funded by EU in the framework of the 7th Programme, led by IBM Haifa Research Labs. RESERVOIR explores novel technologies for federated cloud computing, aiming at sharing IT resources "without borders".

In the second part of my talk I will present a simple, yet powerful, methodology for application-independent diagnostic and remediation of performance hot spots in elastic multi-tier client/server applications, deployed as collections of black box Virtual Machines (VM) in an IaaS Cloud such as RESERVOIR. Our out-of-band black-box performance management system, Network Analysis for Remediating Performance Bottlenecks (NAP), listens to the TCP/IP traffic on the virtual network interfaces of the VMs comprising an application and analyzes statistical properties of this traffic.

From this analysis, which is application independent and transparent to the VMs, NAP identifies performance bottlenecks that might affect application performance and derives application resizing decisions that are most likely to alleviate performance degradation.

We prototyped our solution for the Xen hypervisor and evaluated it using the popular Trade6 benchmark that simulates a typical e-commerce application. Our results show that NAP successfully identifies performance bottlenecks in a complex multi-tier application setting, while incurring negligible performance overhead.

Muli Ben-Yehuda, David Breitgand, Michael Factor, Hillel Kolodner, Valentin Kravtsov, Dan Pelleg, "NAP, a Building Block for Remediating Performance Bottlenecks via Black Box Network Analysis", to appear in 6th IEEE International Conference on Autonomic Computing (ICAC'09), June 15-19, Barcelona, Spain.

Green storage and beyond - new challenges in today's storage systems
Dalit Naor

The landscape of storage technology is changing - performance is no longer the main objective. Storage systems today need to efficiently cope with data deluge and need to support new cost and business models for storing and preserving the data. In this talk I will review some emerging challenges in this area and will concentrate on one particular challenge - energy efficient storage systems. I will present two distinct approaches that were studied in our group at IBM Haifa Research Lab to deal with power reduction in storage systems. The first approach is based on turning off storage units while the other approach is based on reducing the power consumption by tuning the I/O workload.

For the first approach we consider large scale, distributed storage systems (a la Cloud) with built-in redundancy mechanisms. We investigate how such systems can reduce their power consumption during low-utilization time intervals by operating in a low-power mode, whereby a subset of the disks or nodes are powered down. We investigate the power savings attainable under various scenarios.

For the second approach, we must deal with the fact that real power measurements in storage are hard to come by. We developed a scalable power modeling method that estimates the power consumption of storage workloads. The modeling concept is based on identifying the major workload contributors to the power consumed by the disk arrays. Our power estimation results are highly accurate compared to real measurements conducted in a lab setting.

My talk will be based on: [1] Low Power Mode in Cloud Storage Systems, by Danny Harnik, Dalit Naor and Itai Segall, to appear in SMTPS 2009. and [2] Storage Modeling for Power Estimation, by Miriam Allalouf, Yuriy Arbitman, Michael Factor, Ronen Kat, Kalman Meth and Dalit Naor, to appear in SYSTOR 2009.

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