Main Track (MT) Session 2

Chair: Young Choon Lee

 152 An Empirical Study of Hadoop's Energy Efficiency on a HPC Cluster [abstract]Abstract: Map-Reduce programming model is commonly used for efficient scientific computations, as it executes tasks in parallel and distributed manner on large data volumes. The HPC infrastructure can effectively increase the parallelism of map-reduce tasks. However such an execution will incur high energy and data transmission costs. Here we empirically study how the energy efficiency of a map-reduce job varies with increase in parallelism and network bandwidth on a HPC cluster. We also investigate the effectiveness of power-aware systems in managing the energy consumption of different types of map-reduce jobs. We comprehend that for some jobs the energy efficiency degrades at high degree of parallelism, and for some it improves at low CPU frequency. Consequently we suggest strategies for configuring the degree of parallelism, network bandwidth and power management features in a HPC cluster for energy efficient execution of map-reduce jobs. Nidhi Tiwari, Santonu Sarkar, Umesh Bellur, Maria Indrawan-Santiago 167 Optimal Run Length for Discrete-Event Distributed Cluster-Based Simulations [abstract]Abstract: In scientific simulations the results generated usually come from a stochastic process. New solutions with the aim of improving these simulations have been proposed, but the problem is how to compare these solutions since the results are not deterministic. Consequently how to guarantee that the output results are statistically trusted. In this work we apply a statistical approach in order to define the transient and steady state in discrete event distributed simulation. We used linear regression and batch method to find the optimal simulation size. As contributions of our work we can enumerate: we have applied and adapted the simple statistical approach in order to define the optimal simulation length; we propose the approximate approach to normal distribution instead of generate replications sufficiently large; and the method can be used in other kind of non-terminating science simulations where the data either have a normal distribution or can be approximated by a normal distribution. Francisco Borges, Albert Gutierrez-Milla, Remo Suppi, Emilio Luque 173 A CUDA Based Solution to the Multidimensional Knapsack Problem Using the Ant Colony Optimization [abstract]Abstract: The Multidimensional Knapsack Problem (MKP) is a generalization of the basic Knapsack Problem, with two or more constraints. It is an important optimization problem with many real-life applications. It is an NP-hard problem and finding optimal solutions for MKP may be intractable. In this paper we use a metaheuristic algorithm based on ant colony optimization (ACO). Since several steps of the algorithm can be carried out concurrently, we propose a parallel implementation under the GPGPU paradigm (General Purpose Graphics Processing Units) using CUDA. To use the algorithm presented in this paper, it is necessary to balance the number of ants, number of rounds used, and whether local search is used or not, depending on the quality of the solution desired. In other words, there is a compromise between time and quality of solution. We obtained very promising experimental results and we compared our implementation with those in the literature. The results obtained show that ant colony optimization is a viable approach to solve MKP efficiently, even for large instances, with the parallel approach. Henrique Fingler, Edson Cáceres, Henrique Mongelli, Siang Song 174 Comparison of High Level FPGA Hardware Design for Solving Tri-Diagonal Linear Systems [abstract]Abstract: Reconfigurable computing devices can increase the performance of compute intensive algorithms by implementing application specific co-processor architectures. The power cost for this performance gain is often an order of magnitude less than that of modern CPUs and GPUs. Exploiting the potential of reconfigurable devices such as Field-Programmable Gate Arrays (FPGAs) is typically a complex and tedious hardware engineering task. Re- cently the major FPGA vendors (Altera, and Xilinx) have released their own high-level design tools, which have great potential for rapid development of FPGA based custom accelerators. In this paper, we will evaluate Altera’s OpenCL Software Development Kit, and Xilinx’s Vivado High Level Sythesis tool. These tools will be compared for their per- formance, logic utilisation, and ease of development for the test case of a tri-diagonal linear system solver. David Warne, Neil Kelson, Ross Hayward 181 Blood Flow Arterial Network Simulation with the Implicit Parallelism Library SkelGIS [abstract]Abstract: Implicit parallelism computing is an active research domain of computer science. Most implicit parallelism solutions to solve partial differential equations, and scientific simulations, are based on the specificity of numerical methods, where the user has to call specific functions which embed parallelism. This paper presents the implicit parallel library SkelGIS which allows the user to freely write its numerical method in a sequential programming style in C++. This library relies on four concepts which are applied, in this paper, to the specific case of network simulations. SkelGIS is evaluated on a blood flow simulation in arterial networks. Benchmarks are first performed to compare the performance and the coding difficulty of two implementations of the simulation, one using SkelGIS, and one using OpenMP. Finally, the scalability of the SkelGIS implementation, on a cluster, is studied up to 1024 cores. Hélène Coullon, Jose-Maria Fullana, Pierre-Yves Lagrée, Sébastien Limet, Xiaofei Wang

Main Track (MT) Session 4

Chair: Y. Cui

 222 GPU Optimization of Pseudo Random Number Generators for Random Ordinary Differential Equations [abstract]Abstract: Solving differential equations with stochastic terms involves a massive use of pseudo random numbers. We present an application for the simulation of wireframe buildings under stochastic earthquake excitation. The inherent potential for vectorization of the application is used to its full extent on GPU accelerator hardware. A representative set of pseudo random number generators for uniformly and normally distributed pseudo random numbers has been implemented, optimized, and benchmarked. The resulting optimized variants outperform standard library implementations on GPUs. The techniques and improvements shown in this contribution using the Kanai-Tajimi model can be generalized to other random differential equations or stochastic models as well as other accelerators. Christoph Riesinger, Tobias Neckel, Florian Rupp, Alfredo Parra Hinojosa, Hans-Joachim Bungartz 229 Design and Implementation of Hybrid and Native Communication Devices for Java HPC [abstract]Abstract: MPJ Express is a messaging system that allows computational scientists to write and execute parallel Java applications on High Performance Computing (HPC) hardware. The software is capable of executing in two modes namely cluster and multicore modes. In the cluster mode, parallel applications execute in a typical cluster environment where multiple processing elements communicate with one another using a fast interconnect like Gigabit Ethernet or other proprietary networks like Myrinet and Infiniband. In this context, the MPJ Express library provides communication devices for Ethernet and Myrinet. In the multicore mode, the parallel Java application executes on a single system comprising of shared memory or multicore processors. In this paper, we extend the MPJ Express software to provide two new communication devices namely the native and hybrid device. The goal of the native communication device is to interface the MPJ Express software with native—typically written in C—MPI libraries. In this setting the bulk of messaging logic is offloaded to the underlying MPI library. This is attractive because MPJ Express can exploit latest features, like support for new interconnects and efficient collective communication algorithms of the native MPI library. The second device, called the hybrid device, is developed to allow efficient execution of parallel Java applications on clusters of shared memory or multicore processors. In this setting the MPJ Express runtime system runs a single multithreaded process on each node of the cluster—the number of threads in each process is equivalent to processing elements within a node. Our performance evaluation reveals that the native device allows MPJ Express to achieve comparable performance to native MPI libraries—for latency and bandwidth of point-to-point and collective communications—which is a significant gain in performance compared to existing communication devices. The hybrid communication device—without any modifications at application level—also helps parallel applications achieve better speedups and scalability. We witnessed comparative performance for various benchmarks—including NAS Parallel Benchmarks—with hybrid device as compared to the existing Ethernet communication device on a cluster of shared memory/multicore processors. Bibrak Qamar, Ansar Javed, Mohsan Jameel, Aamir Shafi, Bryan Carpenter 231 Deploying a Large Petascale System: the Blue Waters Experience [abstract]Abstract: Deployment of a large parallel system is typically a very complex process, involving several steps of preparation, delivery, installation, testing and acceptance. Despite the availability of various petascale machines currently, the steps and lessons from their deployment are rarely described in the literature. This paper presents the experiences observed during the deployment of Blue Waters, the largest supercomputer ever built by Cray and one of the most powerful machines currently available for open science. The presentation is focused on the final deployment steps, where the system was intensively tested and accepted by NCSA. After a brief introduction of the Blue Waters architecture, a detailed description of the set of acceptance tests employed is provided, including many of the obtained results. This is followed by the major lessons learned during the process. Those experiences and lessons should be useful to guide similarly complex deployments in the future. Celso Mendes, Brett Bode, Gregory Bauer, Jeremy Enos, Cristina Beldica, William Kramer 248 FPGA-based acceleration of detecting statistical epistasis in GWAS [abstract]Abstract: Genotype-by-genotype interactions (epistasis) are believed to be a significant source of unexplained genetic variation causing complex chronic diseases but have been ignored in genome-wide association studies (GWAS) due to the computational burden of analysis. In this work we show how to benefit from FPGA technology for highly parallel creation of contingency tables in a systolic chain with a subsequent statistical test. We present the implementation for the FPGA-based hardware platform RIVYERA S6-LX150 containing 128 Xilinx Spartan6-LX150 FPGAs. For performance evaluation we compare against the method iLOCi. iLOCi claims to outperform other available tools in terms of accuracy. However, analysis of a dataset from the Wellcome Trust Case Control Consortium (WTCCC) with about 500,000 SNPs and 5,000 samples still takes about 19 hours on a MacPro workstation with two Intel Xeon quad-core CPUs, while our FPGA-based implementation requires only 4 minutes. Lars Wienbrandt, Jan Christian Kässens, Jorge González-Domínguez, Bertil Schmidt, David Ellinghaus, Manfred Schimmler

Main Track (MT) Session 7

Chair: Maria Indrawan-Santiago

 321 Evolving Agent-based Models using Complexification Approach [abstract]Abstract: This paper focuses on parameter search for multi-agent based models using evolutionary algorithms. Large numbers and variable dimensions of parameters require a search method which can efficiently handle a high dimensional search space. We are proposing the use of complexification as it emulates the natural way of evolution by starting with a small constrained search space and expanding it as the evolution progresses. We examined the effects of this method on an EA by evolving parameters for two multi-agent based models. Michael Wagner, Wentong Cai, Michael Harold Lees, Heiko Aydt 356 Discrete modeling and simulation of business processes using event logs. [abstract]Abstract: An approach to business process modelling for short term KPI prediction, based on event logs and values of environment variables, is proposed. Ready-for-simulation process model is built semi-automatically, expert only inputs desired environment variables, which are used as features during the learning process. Process workflow is extracted as a Petri Net model using a combination of process mining algorithms. Dependencies between features and process variables are formalized using decision and regression trees techniques. Experiments were conducted to predict KPIs of real companies. Ivan Khodyrev, Svetlana Popova 376 Modeling and Simulation Framework for Development of Interactive Virtual Environments [abstract]Abstract: The article presents a framework for interactive virtual environments’ development for simulation and modeling of complex systems. The framework uses system’s structural model as a core concept for composition and control of simulation-based scientific experiments not in terms of technological processes or workflows but in terms of domain-specific objects and their interconnection within the investigated system. The proposed framework enables integration and management of resources available within a cloud computing environment in order to support automatic simulation management and to provide the user with an interactive visual domain-specific interface to the system. Konstantin Knyazkov, Sergey Kovalchuk 34 Using interactive 3D game play to make complex medical knowledge more accessible [abstract]Abstract: This research outlines a new approach, that takes complex medical, nutritional & activity data and presents it to the diabetic patient in the form of a mobile app/game that uses interactive 3D computer graphics & game play to make this complex information more accessible. The pilot randomized control study results indicate that the Diabetes Visualizer’s use of interactive 3D game play increased the participants understanding of the condition, and its day-to-day management. More importantly the Diabetes Visualizer app stimulated participants interest in, and desire to engage in the task of diabetes management. Dale Patterson

Main Track (MT) Session 8

Chair: Michela Taufer

 115 The influence of network topology on reverse-engineering of gene-regulatory networks [abstract]Abstract: Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern computational biology investigations into gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene expression time-course data. Common mathematical formalisms used to represent such models capture both the relative weight or strength of a regulator gene and the type of the regulator (activator, repressor) with a single model parameter. The goal of this study is to quantify the role this parameter plays in terms of the computational performance of the reverse-engineering process and the predictive power of the inferred GRN models. We carried out three sets of computational experiments on a GRN system consisting of 22 genes. While more comprehensive studies of this kind are ultimately required, this computational study demonstrates that models with similar training (reverse-engineering) error that have been inferred under varying degrees of a priori known topology information, exhibit considerably different predictive performance. This study was performed with a newly developed multiscale modeling and simulation tool called MultiGrain/MAPPER. Alexandru Mizeranschi, Noel Kennedy, Paul Thompson, Huiru Zheng, Werner Dubitzky 188 Maximizing the Cumulative Influence through a Social Network when Repeat Activation Exists [abstract]Abstract: We study the problem of employing social networks for propagate influence when repeat activation is involved. While influence maximization has been extensively studied as the fundamental solution, it neglects the reality that a user may purchase a product/service repeatedly, incurring cumulative sales of the product/service. In this paper, we explore a new problem of cumulative influence maximization that brings the influence maximization a step closer to real-world viral marketing applications. In our problem setting, repeat activation exists and we aim to find a set of initial users, through which the maximal cumulative influence can be stimulated in a given time period. To describe the repeat activation behavior, we adopt the voter model to reflect the variation of activations over time. Under the voter model, we formulate the maximization problem and present an effective algorithm. We test and compare our method with heuristic algorithms on real-world data sets. Experimental results demonstrate the utility of the proposed method. Chuan Zhou, Peng Zhang, Wenyu Zang, Li Guo 320 Mining Large-scale Knowledge about Events from Web Text [abstract]Abstract: This paper addresses the problem of automatic acquisition of semantic relations between events. Since most of the previous researches rely on annotated corpus, the main challenge is the need for more generic methods to identify related event pairs and to extract event-arguments (particularly the predicate, subject and object). Motivated by this background, we develop a three-phased approach that acquires causality from the Web. Firstly, we use explicit connective markers (such as “because”) as linguistic cues to discover causal related events. Then, we extract the event-arguments based on local dependency parse trees of event expressions. In the final phase, we propose a statistical model to measure the potential causal relations. The present results of our empirical evaluation on a large-scale Chinese Web corpus have shown that (a) the use of local dependency tree extensively improves both the accuracy and recall of event-arguments extraction task; (b) our measure which is an improvement on PMI has a better performance. Yanan Cao, Peng Zhang, Jing Guo, Li Guo 200 Discovering Multiple Diffusion Source Nodes in Social Networks [abstract]Abstract: Social networks have greatly amplified spread of information across different communities. However, we recently observe that various malicious information, such as computer virus and rumors, are broadly spread via social networks. To restrict these malicious information, it is critical to develop effective method to discover the diffusion source nodes in social networks. Many pioneer works have explored the source node identification problem, but they all based on an ideal assumption that there is only a single source node, neglecting the fact that malicious information are often diffused from multiple sources to intentionally avoid network audit. In this paper, we present a multi-source locating method based on a given snapshot of partially and sparsely observed infected nodes in the network. Specifically, we first present a reverse propagation method to detect recovered and unobserved infected nodes in the network, and then we use community cluster algorithms to change the multi-source locating problem into a bunch of single source locating problems. At the last step, we identify the nodes having the largest likelihood estimations as the source node on the infected clusters. Experiments on three different types of complex networks show the performance of the proposed method. Wenyu Zang, Peng Zhang, Chuan Zhou, Li Guo 293 The Origin of Control in Complex Networks [abstract]Abstract: Recent work at the borders of network science and control theory have begun to investigate the control of complex systems by studying their underlying network representations. A majority of the work in this nascent field has looked at the number of controls required in order to fully control a network. In this talk I will present research that provides a ready breakdown of this number into categories that are both easy to observe in real world networks as well as instructive in understanding the underlying functional reasons for why the controls must exist. This breakdown is able to shed light on several observations made in the previous literature regarding controllability of networks. This decomposition produces a mechanism to cluster networks into classes that are consistent with their large scale architecture and purpose. Finally, we observe that synthetic models of formation generate networks with control breakdowns substantially different from what is observed in real world networks. Justin Ruths

Main Track (MT) Session 11

Chair: Dieter Kranzlmuller

 360 Workflow as a Service in the Cloud: Architecture and Scheduling Algorithms [abstract]Abstract: With more and more workflow systems adopting cloud as their execution environment, it becomes increasingly challenging on how to efficiently manage various workflows, virtual machines (VMs) and workflow execution on VM instances. To make the system scalable and easy-to-extend, we design a Workflow as a Service (WFaaS) architecture with independent services. A core part of the architecture is how to efficiently respond continuous workflow requests from users and schedule their executions in the cloud. Based on different targets, we propose four heuristic workflow scheduling algorithms for the WFaaS architecture, and analyze the differences and best usages of the algorithms in terms of performance, cost and the price/performance ratio via experimental studies. Jianwu Wang, Prakashan Korambath, Ilkay Altintas, Jim Davis, Daniel Crawl 36 Large Eddy Simulation of Flow in Realistic Human Upper Airways with Obstructive Sleep Apnea [abstract]Abstract: Obstructive sleep apnea (OSA) is a common type of sleep disorder characterized by abnormal repetitive cessation in breathing during sleep caused by partial or complete narrowing of pharynx in the upper airway. The upper airway surgery is commonly performed for this disorder, however the success rate is limited because the lack of the thorough understanding of the primary mechanism associated with OSA. The computational fluid dynamics (CFD) simulation with Large Eddy Simulation approach is conducted to investigate a patient-specific upper airway flow with severe OSA. Both pre and post-surgical upper airway models are simulated to reveal the effect of the surgical treatment. Only the inhaled breathing is conducted with six periods (about 15 second) unsteady flow. Compared with the results before and after treatment, it is illustrated that there exists a significant pressure and shear stress dropping region near the soft palate before treatment; and after the treatment the flow resistance in the upper airway is decreased and the wall shear stress value is significantly reduced. Mingzhen Lu, Yang Liu, Jingying Ye, Haiyan Luo 86 Experiments on a Parallel Nonlinear Jacobi-Davidson Algorithm [abstract]Abstract: The Jacobi-Davidson (JD) algorithm is very well suited for the computation of a few eigenpairs of large sparse complex symmetric nonlinear eigenvalue problems. The performance of JD crucially depends on the treatment of the so-called correction equation, in particular the preconditioner, and the initial vector. Depending on the choice of the spectral shift and the accuracy of the solution, the convergence of JD can vary from linear to cubic. We investigate parallel preconditioners for the Krylov space method used to solve the correction equation. We apply our nonlinear Jacobi-Davidson (NLJD) method to quadratic eigenvalue problems that originate from the time-harmonic Maxwell equation for the modeling and simulation of resonating electromagnetic structures. Yoichi Matsuo, Hua Guo, Peter Arbenz 184 Improving Collaborative Recommendation via Location-based User-Item Subgroup [abstract]Abstract: Collaborative filter has been widely and successfully applied in recommendation system. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. Some previous studies have explored that there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items and subgroup analysis can get better accuracy. While, we find that geographical information of user have impacts on user group preference for items. Hence, In this paper, we propose a Bayesian generative model to describe the generative process of user-item subgroup preference under considering users' geographical information. Experimental results show the superiority of the proposed model. Zhi Qiao, Peng Zhang, Yanan Cao, Chuan Zhou, Li Guo 90 Optimizing Shared-Memory Hyperheuristics on top of Parameterized Metaheuristics [abstract]Abstract: This paper studies the auto-tuning of shared-memory hyperheuristics developed on top of a unified shared-memory metaheuristic scheme. A theoretical model of the execution time of the unified scheme is empirically adapted for particular metaheuristics and hyperheuristics through experimentation. The model is used to decide at running time the number of threads to obtain a reduced execution time. The number of threads is different for the different basic functions in the scheme, and depends on the problem to be solved, the metaheuristic scheme, the implementation of the basic functions and the computational system where the problem is solved. The applicability of the proposal is shown with a problem of minimization of electricity consumption in exploitation of wells. Experimental results show that satisfactory execution times can be achieved with auto-tuning techniques based on theoretical-empirical models of the execution time. José Matías Cutillas Lozano, Domingo Gimenez

Main Track (MT) Session 12

Chair: Luiz DeRose

 187 The K computer Operations: Experiences and Statistics [abstract]Abstract: The K computer, released on September 29, 2012, is a large-scale parallel supercomputer system consisting of 82,944 compute nodes. We have been able to resolve a significant number of operation issues since its release. Some system software components have been fixed and improved to obtain higher stability and utilization. We achieved 94% service availability because of a low hardware failure rate and approximately 80% node utilization by careful adjustment of operation parameters. We found that the K computer is an extremely stable and high utilization system. Keiji Yamamoto, Atsuya Uno, Hitoshi Murai, Toshiyuki Tsukamoto, Fumiyoshi Shoji, Shuji Matsui, Ryuichi Sekizawa, Fumichika Sueyasu, Hiroshi Uchiyama, Mitsuo Okamoto, Nobuo Ohgushi, Katsutoshi Takashina, Daisuke Wakabayashi, Yuki Taguchi, Mitsuo Yokokawa 195 Quantum mechanics study of hexane isomers through gamma-ray and graph theory combined with C1s binding energy and nuclear magnetic spectra (NMR) [abstract]Abstract: Quantum mechanically calculated positron-electron annihilation gamma-ray spectra, C1s binding energy spectra and NMR spectra are employed to study the electronic structures of hexane and its isomers, which is assisted using graph theory. Our recent positron-electron annihilation gamma-ray spectral study of n-hexane in gas phase and core ionization (IPs) spectral studies for small alkanes and their isomers, have paved the path for the present correlation study where quantum mechanics is combined with graph theory, C1s ionization spectroscopy and nuclear magnetic resonance (NMR), to further understand the electronic structure and topology for the hexane isomers. The low-energy plane wave positron (LEPWP) model indicated that the positrophilic electrons of a molecule are dominated by the electrons in the lowest occupied valence orbital (LOVO). The most recent results using NOMO indicated that the electronic wave functions dominate the electron-positron wave functions for molecular systems. In addition to quantum mechanics, chemical graphs are also studied and are presented in the present study. Subhojyoti Chatterjee and Feng Wang 257 Dendrogram Based Algorithm for Dominated Graph Flooding [abstract]Abstract: In this paper, we are concerned with the problem of flooding undirected weighted graphs under ceiling constraints. We provide a new algorithm based on a hierarchical structure called {\em dendrogram}, which offers the significant advantage that it can be used for multiple flooding with various scenarios of the ceiling values. In addition, when exploring the graph through its dendrogram structure in order to calculate the flooding levels, independent sub-dendrograms are generated, thus offering a natural way for parallel processing. We provide an efficient implementation of our algorithm through suitable data structures and optimal organisation of the computations. Experimental results show that our algorithm outperforms well established classical algorithms, and reveal that the cost of building the dendrogram highly predominates over the total running time, thus validating both the efficiency and the hallmark of our method. Moreover, we exploit the potential parallelism exposed by the flooding procedure to design a multi-thread implementation. As the underlying parallelism is created on the fly, we use a queue to store the list of the sub-dendrograms to be explored, and then use a dynamic round-robin scheduling to assign them to the participating threads. This yields a load balanced and scalable process as shown by additional benchmark results. Our program runs in few seconds on an ordinary computer to flood graphs with more that $20$ millions of nodes. Claude Tadonki 278 HP-DAEMON: High Performance Distributed Adaptive Energy-efficient Matrix-multiplicatiON [abstract]Abstract: The demands of improving energy efficiency for high performance scientific applications arise crucially nowadays. Software-controlled hardware solutions directed by Dynamic Voltage and Frequency Scaling (DVFS) have shown their effectiveness extensively. Although DVFS is beneficial to green computing, introducing DVFS itself can incur non-negligible overhead, if there exist a large number of frequency switches issued by DVFS. In this paper, we propose a strategy to achieve the optimal energy savings for distributed matrix multiplication via algorithmically trading more computation and communication at a time adaptively with user-specified memory costs for less DVFS switches, which saves 7.5% more energy on average than a classic strategy. Moreover, we leverage a high performance communication scheme for fully exploiting network bandwidth via pipeline broadcast. Overall, the integrated approach achieves substantial energy savings (up to 51.4%) and performance gain (28.6% on average) compared to ScaLAPACK pdgemm() on a cluster with an Ethernet switch, and outperforms ScaLAPACK and DPLASMA pdgemm() respectively by 33.3% and 32.7% on average on a cluster with an Infiniband switch. Li Tan, Longxiang Chen, Zizhong Chen, Ziliang Zong, Rong Ge, Dong Li 279 Evaluating the Performance of Multi-tenant Elastic Extension Tables [abstract]Abstract: An important challenge in the design of databases that support multi-tenant applications is to provide a platform to manage large volumes of data collected from different businesses, social media networks, emails, news, online texts, documents, and other data sources. To overcome this challenge we proposed in our previous work a multi-tenant database schema called Elastic Extension Tables (EET) that combines multi-tenant relational tables and virtual relational tables in a single database schema. Using this approach, the tenants’ tables can be extended to support the requirements of individual tenants. In this paper, we discuss the potentials of using EET multi-tenant database schema, and show how it can be used for managing physical and virtual relational data. We perform several experiments to measure the feasibility and effectiveness of EET by comparing it with a commercially available multi-tenant schema mapping technique used by SalesForce.com. We report significant performance improvements obtained using EET when compared to Universal Table Schema Mapping (UTSM), making the EET schema a good candidate for the management of multi-tenant data in Software as a Service (SaaS) and Big Data applications. Haitham Yaish, Madhu Goyal, George Feuerlicht