Suprio Ray

Realistic Mobility for Mobile Ad Hoc Network Simulation

In order to conduct meaningful performance analysis of routing algorithms for Mobile Ad Hoc Networks (MANETs), it is essential that the mobility model on which the simulation is based reflects realistic mobility behavior. However, current mobility models for MANET simulation are either unrealistic or are tailor-made for particular scenarios. We introduce GEMM, a tool for generating mobility models that are both realistic and heterogeneous. These models are capable of simulating complex and dynamic mobility patterns representative of real-world situations. We present simulation results using AODV, OLSR and ZRP, three MANET routing algorithms and show that mobility-model changes have a significant impact on their performance.

Software engineering meets control theory

The software engineering community has proposed numerous approaches for making software self-adaptive. These approaches take inspiration from machine learning and control theory, constructing software that monitors and modifies its own behavior to meet goals. Control theory, in particular, has received considerable attention as it represents a general methodology for creating adaptive systems. Control-theoretical software implementations, however, tend to be ad hoc. While such solutions often work in practice, it is difficult to understand and reason about the desired properties and behavior of the resulting adaptive software and its controller. This paper discusses a control design process for software systems which enables automatic analysis and synthesis of a controller that is guaranteed to have the desired properties and behavior. The paper documents the process and illustrates its use in an example that walks through all necessary steps for self-adaptive controller synthesis.

Jackpine: A benchmark to evaluate spatial database performance

The volume of spatial data generated and consumed is rising exponentially and new applications are emerging as the costs of storage, processing power and network bandwidth continue to decline. Database support for spatial operations is fast becoming a necessity rather than a niche feature provided by a few products. However, the spatial functionality offered by current commercial and open-source relational databases differs significantly in terms of available features, true geodetic support, spatial functions and indexing. Benchmarks play a crucial role in evaluating the functionality and performance of a particular database, both for application users and developers, and for the database developers themselves. In contrast to transaction processing, however, there is no standard, widely used benchmark for spatial database operations. In this paper, we present a spatial database benchmark called Jackpine. Our benchmark is portable (it can support any database with a JDBC driver implementation) and includes both micro benchmarks and macro workload scenarios. The micro benchmark component tests basic spatial operations in isolation; it consists of queries based on the Dimensionally Extended 9-intersection model of topological relations and queries based on spatial analysis functions. Each macro workload includes a series of queries that are based on a common spatial data application. These macro scenarios include map search and browsing, geocoding, reverse geocoding, flood risk analysis, land information management and toxic spill analysis. We use Jackpine to evaluate the spatial features in 2 open source databases and 1 commercial offering.

Realistic mobility for MANET simulation

We accept this thesis as conforming to the required standard Abstract In order to conduct meaningful performance analysis of routing algorithms in the context of Mobile Ad Hoc Networks (MANET), it is essential that the underlying mobility model on which the simulation is based reflects realistic mobility behavior. However, current mobility models for MANET simulation are either unrealistic or are tailor-made for particular scenarios. Furthermore, none of the existing mobility models support heterogeneous mobility behavior among different mobile nodes in the simulation. This thesis introduces GEMM, a tool for generating mobility models that are both realistic and heterogeneous. These models are capable of simulating complex and dynamic mobility patterns representative of real-world situations. The input to GEMM is a set of model descriptions and the output is a mobility scenario that can be used by either the Glomosim or NS2 network simulator. Simulation results are presented using AODV, OLSR and ZRP, three previously published MANET routing algorithms. These results illustrate that mobility-model changes have a significant impact on their performance. The results underscore the importance of using realistic mobility scenarios in MANET simulation and demonstrate the ability of GEMM to generate such mobility scenarios.

A parallel spatial data analysis infrastructure for the cloud

Spatial data analysis applications are emerging from a wide range of domains such as building information management, environmental assessments and medical imaging. Time-consuming computational geometry algorithms make these applications slow, even for medium-sized datasets. At the same time, there is a rapid expansion in available processing cores, through multicore machines and Cloud computing. The confluence of these trends demands effective parallelization of spatial query processing. Unfortunately, traditional parallel spatial databases are ill-equipped to deal with the performance heterogeneity that is common in the Cloud. We introduce Niharika, a parallel spatial data analysis infrastructure that exploits all available cores in a heterogeneous cluster. Niharika first uses a declustering technique that creates balanced spatial partitions. Then, Niharika adapts to performance heterogeneity and processing skew in the spatial dataset using dynamic load-balancing. We evaluate Niharika with three load-balancing algorithms and two different spatial datasets (both from TIGER) using Amazon EC2 instances. Niharika adapts to the performance heterogeneity in the EC2 nodes, thereby achieving excellent speedups (e.g., 63.6X using 64 cores on 16 4-core EC2 nodes, in the best case) and outperforming an approach that does not adapt.

Speeding up Spatial Database Query Execution using GPUs

Abstract Spatial databases are used in a wide variety of real-world applications, such as land surveying, urban planning, and environmental assessments, as well as geospatial Web services. As uses of spatial databases become more widespread, there is a growing need for good performance of spatial applications. In spatial workloads, queries tend to be computationally-intensive due to the complex processing of geometric relationships. Furthermore, a significant fraction of spatial query execution time is spent on CPU stalls due to memory accesses, caused by the ever-increasing processor-memory speed gap. With the advent of massively-parallel graphics-processing hardware (GPUs) and frameworks like CUDA, opportunities for speeding up spatial processing have emerged. In addition to massive parallelism, GPUs can also better hide the memory latency.We aim to speed up spatial query execution using CUDA and recent GPU cards. One of the main challenges in using GPUs is the transfer time from main memory to GPU memory. We implement a set of six typical spatial queries and achieve a baseline speedup (without the transfer cost) of 62-318x over the CPU counterparts. We show that the transfer cost can be amortized over the execution of each individual query. For simpler spatial queries, the transfer time is a significant fraction of the query execution time, but we still achieve a 6-10x speedup. For more complex spatial queries, the transfer time becomes negligible compared to the processing time, and we obtain a 62-240x speedup.

Skew-resistant parallel in-memory spatial join

Spatial join is a crucial operation in many spatial analysis applications in scientific and geographical information systems. Due to the compute-intensive nature of spatial predicate evaluation, spatial join queries can be slow even with a moderate sized dataset. Efficient parallelization of spatial join is therefore essential to achieve acceptable performance for many spatial applications. Technological trends, including the rising core count and increasingly large main memory, hold great promise in this regard. Previous parallel spatial join approaches tried to partition the dataset so that the number of spatial objects in each partition was as equal as possible. They also focused only on the filter step. However, when the more compute-intensive refinement step is included, significant processing skew may arise due to the uneven size of the objects. This processing skew significantly limits the achievable parallel performance of the spatial join queries, as the longest-running spatial partition determines the overall query execution time. Our solution is SPINOJA, a skew-resistant parallel in-memory spatial join infrastructure. SPINOJA introduces MOD-Quadtree declustering, which partitions the spatial dataset such that the amount of computation demanded by each partition is equalized and the processing skew is minimized. We compare three work metrics used to create the partitions and three load-balancing strategies to assign the partitions to multiple cores. SPINOJA uses an in-memory column-store to store the spatial tables. Our evaluation shows that SPINOJA outperforms in-memory implementations of previous spatial join approaches by a significant margin and a recently proposed in-memory spatial join algorithm by an order of magnitude.

Supporting Location-Based Services in a Main-Memory Database

With the proliferation of mobile devices and explosive growth of spatio-temporal data, Location-Based Services (LBS) have become an indispensable technology in our daily lives. The key characteristics of the LBS applications include a high rate of time-stamped location updates, and many concurrent historical, present and predictive queries. The commercial providers of LBS must support all three kinds of queries and address the high update rates. While they employ relational databases for this purpose, traditional databases are unable to cope with the growing demands of many LBS systems. Support for spatio-temporal indexes within these databases are limited to R-tree based approaches. Although a number of advanced spatio-temporal indexes have been proposed by the research community, only a few of them support historical queries. These indexing techniques, with support for historical queries, are unable to sustain high update and query throughput typical in LBS. Technological trends involving increasingly large main memory and core footprints offer opportunities to address some of these issues. We present several key ideas to support high performance commercial LBS by exploiting in-memory database techniques. Taking advantage of very large memory available in modern machines, our system maintains the location data and index for the past N days in memory. Older data and index are kept in disk. We propose an in-memory storage organization for high insert performance. We also introduce a novel spatio-temporal index that maintains partial temporal indexes in a versioned-grid structure. The partial temporal indexes are organized as compressed bitmaps. With extensive evaluation, we demonstrate that our system supports high insert and query throughputs and it outperforms the leading LBS system by a significant margin.

Surveying the landscape: an in-depth analysis of spatial database workloads

Spatial databases are increasingly important for a wide variety of real-world applications, such as land surveying, urban planning, cartography and location-based services. However, spatial database workload properties are not well-understood. For example, it is unknown to what degree one spatial application resembles another in terms of resource demand, or how the demand will change as more concurrent queries (i.e., more users) are added. We show that spatial workloads have a different CPU execution profile than well-studied decision support workloads, as represented by TPC-H. We present a framework to automatically classify spatial queries and characterize spatial workload mixes. We first analyze the resource consumption (i.e., computation and I/O) of a representative set of spatial queries, which are then classified into five distinct categories. Next, we create five homogeneous spatial workloads, each composed of queries from one of these classes. We then vary database-specific parameters (e.g., the buffer pool size) and workload specific parameters (e.g., the query mix), to characterize a workload in terms of CPU utilization and I/O activity trends. We study workloads simulating real-world spatial database applications and show how our framework can classify them and predict resource utilization trends under various settings. This can provide clues to the database administrator regarding which resources are heavily contended and can guide resource upgrades. We further validate our approach by applying it to a much larger dataset, and to a second DBMS.

Enhanced database support for location-based services

The ubiquity of GPS-enabled mobile devices and sensors have led to the explosive growth of time-stamped location data. Consequently Location-Based Services (LBS) has become a popular technology impacting various aspects of our lives. LBS applications are characterized by very high rate of location record updates, and many concurrent historic, present and predictive queries. Commercial LBS providers rely on relational databases to manage their data. However, traditional relational databases do not provide adequate support to meet the growing demands of many LBS systems. Moreover, existing indexing techniques that support historical queries are unable to sustain high update and query throughput as required by many LBS applications. To address this, we propose to exploit in-memory database techniques and present a few key ideas to support high performance commercial LBS. We also introduce a novel in-memory spatio-temporal index in which the spatial domain is organized as grid cells and for each grid cell partial temporal indexes are maintained for moving objects that visited the cell. The partial temporal indexes are implemented as compressed bitmaps. Using fast bitmap operations and utilizing parallelism rendered by multi-core systems, our system offers significantly better performance than traditional relational databases.