Scalability Evaluation Research Articles

Scalable Evaluation of Trajectory Queries over Imprecise Location Data

Trajectory queries, which retrieve nearby objects for every point of a given route, can be used to identify alerts of potential threats along a vessel route, or monitor the adjacent rescuers to a travel path. However, the locations of these objects (e.g., threats, succours) may not be precisely obtained due to hardware limitations of measuring devices, as well as complex natures of the surroundings. For such data, we consider a common model, where the possible locations of an object are bounded by a closed region, called “imprecise region”. Ignoring or coarsely wrapping imprecision can render low query qualities, and cause undesirable consequences such as missing alerts of threats and poor response rescue time. Also, the query is quite time-consuming, since all points on the trajectory are considered. In this paper, we study how to efficiently evaluate trajectory queries over imprecise objects, by proposing a novel concept, u-bisector, which is an extension of bisector specified for imprecise data. Based on the u-bisector, we provide an efficient and versatile solution which supports different shapes of commonly-used imprecise regions (e.g., rectangles, circles, and line segments). Extensive experiments on real datasets show that our proposal achieves better efficiency, quality, and scalability than its competitors.

랜드마크 윈도우 기반의 빈발 패턴 마이닝 기법의 분석 및 성능평가

ABSTRACT With the development of online service, recent forms of databas es have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment o f marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artific ial intelligence. Landmark window-based frequent pattern mining , one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, inste ad of all the data. In this paper, we analyze and evaluate the tec hniques of the well-known landmark window-based frequent patter n mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns a s soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reaso n, such algorithms are also called online mining approaches. We evaluat e and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms’ total runtime and average processing time per transaction. In addition, to compare the ef ficiency of storage structures between them, their maximum memo ry usage is also evaluated. Lastly, we show how stably the two alg orithms conduct their mining works with respect to the database s that feature gradually increasing items. With respect to the evaluat ion results of mining time and transaction processing, hMiner h as higher speed than that of Lossy counting. Since hMiner stores candidat e frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a l attice manner; thus, it has to search for multiple nodes in ord er to access the candidate frequent patterns. On the other hand, hMiner show s worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash’s bucket s, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner pres ents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is

EVALUATING PAAS SCALABILITY AND IMPROVING PERFORMANCE USING SCALABILITY IMPROVEMENT SYSTEMS

Cloud computing has almost changed the way of obtaining resources and managing platform as a service. With these improvements, challenges like scalability testing, performance testing is emerging very fast. This paper focuses on the evaluation of scalability of PaaS as well along with that propose a graphical model where SIS (Scalability Improvement System)is proposed in which cache are implemented at the both interfaces of PaaS to enhance the scalability in terms of providing service to the user.

Scalable Evaluation of Polarization Energy and Associated Forces in Polarizable Molecular Dynamics: II.Towards Massively Parallel Computations using Smooth Particle Mesh Ewald.

In this paper, we present a scalable and efficient implementation of point dipole-based polarizable force fields for molecular dynamics (MD) simulations with periodic boundary conditions (PBC). The Smooth Particle-Mesh Ewald technique is combined with two optimal iterative strategies, namely, a preconditioned conjugate gradient solver and a Jacobi solver in conjunction with the Direct Inversion in the Iterative Subspace for convergence acceleration, to solve the polarization equations. We show that both solvers exhibit very good parallel performances and overall very competitive timings in an energy-force computation needed to perform a MD step. Various tests on large systems are provided in the context of the polarizable AMOEBA force field as implemented in the newly developed Tinker-HP package which is the first implementation for a polarizable model making large scale experiments for massively parallel PBC point dipole models possible. We show that using a large number of cores offers a significant acceleration of the overall process involving the iterative methods within the context of spme and a noticeable improvement of the memory management giving access to very large systems (hundreds of thousands of atoms) as the algorithm naturally distributes the data on different cores. Coupled with advanced MD techniques, gains ranging from 2 to 3 orders of magnitude in time are now possible compared to non-optimized, sequential implementations giving new directions for polarizable molecular dynamics in periodic boundary conditions using massively parallel implementations.

ITree: Efficiently Discovering High-Coverage Configurations Using Interaction Trees

Modern software systems are increasingly configurable. While this has many benefits, it also makes some software engineering tasks,such as software testing, much harder. This is because, in theory,unique errors could be hiding in any configuration, and, therefore,every configuration may need to undergo expensive testing. As this is generally infeasible, developers need cost-effective technique for selecting which specific configurations they will test. One popular selection approach is combinatorial interaction testing (CIT), where the developer selects a strength t and then computes a covering array (a set of configurations) in which all t-way combinations of configuration option settings appear at least once. In prior work, we demonstrated several limitations of the CIT approach. In particular, we found that a given system's effective configuration space - the minimal set of configurations needed to achieve a specific goal - could comprise only a tiny subset of the system's full configuration space. We also found that effective configuration space may not be well approximated by t-way covering arrays. Based on these insights we have developed an algorithm called interaction tree discovery (iTree). iTree is an iterative learning algorithm that efficiently searches for a small set of configurations that closely approximates a system's effective configuration space. On each iteration iTree tests the system on a small sample of carefully chosen configurations, monitors the system's behaviors, and then applies machine learning techniques to discover which combinations of option settings are potentially responsible for any newly observed behaviors. This information is used in the next iteration to pick a new sample of configurations that are likely to reveal further new behaviors. In prior work, we presented an initial version of iTree and performed an initial evaluation with promising results. This paper presents an improved iTree algorithm in greater detail. The key improvements are based on our use of composite proto-interactions - a construct that improves iTree's ability to correctly learn key configuration option combinations, which in turn significantly improves iTree's running time, without sacrificing effectiveness. Finally, the paper presents a detailed evaluation of the improved iTree algorithm by comparing the coverage it achieves versus that of covering arrays and randomly generated configuration sets, including a significantly expanded scalability evaluation with the ~ 1M-LOC MySQL. Our results strongly suggest that the improved iTree algorithm is highly scalable and can identify a high-coverage test set of configurations more effectively than existing methods.

Experimental Evaluation of Scalability and Reliability of a Feedback-Based UPC-Parameters Renegotiation Mechanism

Experimental Evaluation of Scalability and Reliability of a Feedback-Based UPC-Parameters Renegotiation Mechanism

Experimental Evaluation of Scalability and Reliability of a Feedback-Based UPC-Parameters Renegotiation Mechanism

Experimental Evaluation of Scalability and Reliability of a Feedback-Based UPC-Parameters Renegotiation Mechanism

Scalability evaluation of an FPGA-based multi-core architecture with hardware-enforced domain partitioning

There is a trend towards to dense integration of embedded systems for cost, weight, and power savings. Integration of multiple critical software functions in a single embedded platform requires domain partitioning. Groups of independent software functions exist in isolated domains to maintain individual functional correctness, even in presence of errors. Software solutions such as Real-Time Operating Systems (RTOS) with time and space partitioning are state-of-the-art segregation approaches. As an alternative to these existing solutions, we present a robust, reliable, and efficient architecture with segregation support for safety- and security-critical embedded systems. Our solution hosts different software functions on a platform with as few hardware components as possible: the System-on-a-Chip (SoC) approach.The proposed architecture instantiates multiple self-contained soft processor systems on a single chip. The architecture offers hardware-enforced segregation and is completely transparent to software applications. We demonstrate this aspect by running multiple segregated instances of unmodified off-the-shelf Linux systems from a shared memory device. Since our architecture targets reconfigurable platforms, it is also flexible and can be tailored to application-specific needs at design time.Segregation is achieved with a hierarchical connection of memory busses by secure bus bridges. The bridges perform caching, prefetching, and burst accesses to efficiently avoid temporal conflicts on shared resources. Hence, our secure bridges allow to use soft processors for critical designs.We implement several prototypes and evaluate them by using novel bus observers for characterization of bus-centric architectures. Finally, we show the effectiveness of our implemented optimizations.

QR-tree: An efficient and scalable method for evaluation of continuous range queries

In this paper, we explore the problem of the scalable evaluation of continuous range queries (CRQs) over moving objects, each of which continually retrieves the moving objects that are currently within a given query region of interest. Most existing methods assume that moving objects continually communicate with the server to report their current locations and the server continuously updates the results of queries. However, such an assumption degrades the system performance, because the communication cost is huge and the server workload is increased when the number of moving objects and queries is enormous. In this paper, we propose a novel query indexing structure, referred to as the Query Region tree (QR-tree), which allows the server to cooperate with moving objects efficiently by leveraging the available computational resources of the moving objects to improve the overall system performance. In addition, we present another version of the QR-tree, called the Bit-vector Query Region tree (BQR-tree), for the evaluation of CRQs that specify additional non-spatial selections. The BQR-tree stores a summary of the non-spatial information specified by CRQs in the form of bit-vectors. Through a series of comprehensive simulations, we verify the efficiency of the QR-tree and the BQR-tree in terms of the communication cost and server workload.

Remote service discovery and binding architecture for soft real-time QoS in indoor location-based service

Indoor location-based service (LBS) is generally distinguished from web services that have no physical location and user context. In particular, various resources have dynamic and frequent mobility in indoor environments. In addition, an indoor LBS includes numerous service lookups being requested concurrently and frequently from several locations, even through a network infrastructure requiring high scalability in indoor environments. The traditional centralized LBS approach needs to maintain a geographical map of the entire building or complex in its central server, which can cause low scalability and traffic congestion. This paper presents a self-organizing and fully distributed indoor LBS platform with regional cooperation among devices. A service lookup algorithm based on the proposed distributed architecture searches for the shortest physical path to the nearest service resource. A continuous service binding mechanism guarantees a probabilistic real-time QoS regardless of dynamic and frequent mobility in a soft real-time system such as an indoor LBS. Performance evaluation of the proposed algorithm and platform is compared to the traditional centralized architecture in the experimental evaluation of scalability and real test bed environments.

Evaluation of Scalability and Bandwidth Efficiency of Multipoint to Multipoint Hierarchy for Fast Recovery in MPLS Networks

Multi-Point to Multi-Point Traffic Engineering (MP2MP-TE) leads to an important scalability in Multi Protocol Label Switching-Traffic Engineering (MPLS-TE) networks. This paper emphasizes on the support of Fast-reroute (FRR) in MPLS-TE networks by using MP2MP bypass TE-tunnels. Hence, one MP2MP bypass TE-tunnel can be used to protect several primary TE-tunnels. During failure, the primary TE-tunnel is encapsulated into the MP2MP bypass TE-tunnel which calls for defining a new type of MPLS hierarchy, i.e. the multipoint to multipoint hierarchy. In this paper we present a simulation study that evaluates several fast rerouting scenarios depending on the number of leaves of a MP2MP bypass TE-tunnel and on the number of primary TE-tunnels that can be encapsulated into one MP2MP bypass TE-tunnel. In particular, the scalability/bandwidth efficiency tradeoff between these schemes is analyzed and valuable comparisons with the existing approaches are presented.

Indexing Volumetric Shapes with Matching and Packing.

We describe a novel algorithm for bulk-loading an index with high-dimensional data and apply it to the problem of volumetric shape matching. Our matching and packing algorithm is a general approach for packing data according to a similarity metric. First an approximate k-nearest neighbor graph is constructed using vantage-point initialization, an improvement to previous work that decreases construction time while improving the quality of approximation. Then graph matching is iteratively performed to pack related items closely together. The end result is a dense index with good performance. We define a new query specification for shape matching that uses minimum and maximum shape constraints to explicitly specify the spatial requirements of the desired shape. This specification provides a natural language for performing volumetric shape matching and is readily supported by the geometry-based similarity search (GSS) tree, an indexing structure that maintains explicit representations of volumetric shape. We describe our implementation of a GSS tree for volumetric shape matching and provide a comprehensive evaluation of parameter sensitivity, performance, and scalability. Compared to previous bulk-loading algorithms, we find that matching and packing can construct a GSS-tree index in the same amount of time that is denser, flatter, and better performing, with an observed average performance improvement of 2X.

Масштабируемый метод оценки управления доверием на основе распределенных систем онлайн мониторинга

Abstract . Collaborative systems are growing in use and popularity. The need to boost the methods concerning the interoperability is growing as well; therefore, trustworthy interactions of the different systems are a priority. The decision regarding with whom and how to interact with other users or applications depends on each system. We focus on providing trust verdicts by evaluating the behaviors of different agents, using distributed on-line network monitoring. This will provide trust management systems information regarding a trustee experience, for those trust management systems based on soft trust. In this work, we propose a scalable evaluation method for any on-line network monitoring system, by using an auxiliary model, an extended finite state automaton (EFSA), and as well as other known methods to reduce the time complexity of the evaluation algorithm. Keywords: trust management; on-line network monitoring; scalable evaluation; 1. Introduction Internet applications have become one of the most popular ways to socially interact, make commerce, and create collaborative work; making them a daily part of our living. With time, the collaborative aspects supported by Internet have evolved bringing new tools, methodologies and concepts. These systems keep growing in use and in popularity. The need to boost the interoperability methods related to them is growing as well; making thus trustworthy interactions of the different systems a priority. These concepts of trust have been brought to computer science. The systems need to interact with users and with other applications. The decision regarding with whom and how to interact with other users or applications depend on each application or system. There are many definitions of trust in the literature, but the one we adopt

Scalable evaluation of platelet aggregation by the degree of blood migration

Platelet aggregation plays a key role in vascular thrombosis. Antiplatelet drug therapy is commonly used for the prevention of abnormal platelet aggregation. So, measuring platelet aggregation function is critically important in clinical field. Here, we introduce a scalable evaluation method of platelet aggregation measured with the degree of blood migration through microchannel in a microfluidic chip. Unlike conventional methods that require expertise with system physics to operate devices, our approach is using microfluidics system, which requires only a syringe vacuum. The scalable migration factors, migration distance and touchdown time, are capable of distinguishing various antiplatelet drug effects under microfluidics and would be effective for the quick and easy evaluation of quantitative platelet aggregation.

A real‐time capable coherent data cache for multicores

SUMMARYIn multicore systems, the concurrent access to shared data generates a bottleneck for the system performance. Cache coherence techniques have been introduced to enable fast access while preserving the data coherence, but these coherence protocols are critical in hard real‐time systems. Because the frequent inter‐cache communication leads to unpredictable interferences between the cores, the system's timing behaviour is hard to analyse. In this paper, we propose a new, hard real‐time capable strategy for multicore systems called on‐demand coherent cache (ODC2). The technique is based on marginal hardware extensions compared with noncoherent caches and the use of common synchronisation techniques. ODC2 provides coherent accesses to cached shared data as well as caching of private data. Because the presented strategy does not induce interferences between local caches, ODC2 is capable for hard real‐time systems. We present an evaluation of performance and scalability of ODC2 compared with two standard coherence protocols using a bus‐based multicore system. Copyright © 2013 John Wiley & Sons, Ltd.

Scalability Analysis of KVM-Based Private Cloud For Iaas

One of the cloud technology cores is virtualization. Virtual Machine Manager (VMM), which is also called hypervisor, is said to have good scalability if it provides services to many virtual machines with a fair management of resources to maintain optimal performance of virtual machines’. Scalability evaluation of virtualization technology needs to be done so that cloud developers can choose the appropriate hypervisor according to the scenario of cloud usage. This study was conducted to determine the scalability of KVM in a cloud with OpenStack platform. Three scalability metrics were used (overhead, linearity, and isolation) to measure the scalability of different machine resources: CPU, network, and disk. The results showed that KVM exhibits good scalability in CPU and network. KVM is suitable for a scenario in which isolation between its CPU and harddisk is needed. KVM is suggested not to be used in a scenario where harddisk is accessed intensively.

Distributed Schemes for Routing Table Management in Next Generation Routers

Current generation routers are being replaced by scalable next generation routers in order to satisfy the growing demand of Internet traffic in core networks. The next generation routers, on the hardware side, are provided with a very large switching capacity, high speed interfaces and additional computing and memory resources. There is a need to re-design the router software in a distributed way in order to efficiently exploit advanced hardware features of the next generation routers. One of the key functions of a router is to manage routes obtained from different routing protocols. The current centralized architecture of the routing table manager will likely face difficulties to handle the growth of routing tables and path computation requests, particularly for high traffic-density networks. This paper proposes two distributed IGP route management schemes for routers. The first scheme enhances the scalability at the control card level. The second one uses the parallel processing of the switch fabric in order to increase the handled number of routes by adding more line cards. Scalability evaluation is done for the two distributed schemes and a centralized architecture in terms of CPU cycles used and required memory.

Facilitating representation and retrieval of structured cases: Principles and toolkit

Case-Based reasoning (CBR) applications are increasingly used for problems involving structured data, especially in process-oriented domains. Available CBR frameworks rely primarily on flat case representations, leaving developers of process-oriented CBR systems without general-purpose tools aimed at their needs. This paper discusses needs to support structure-based retrieval for CBR and how they have been addressed in the development of the Structure Access Interface (SAI), a toolkit for representation and retrieval of structured cases which is now available for general use. Integrating SAI into a CBR project eliminates the need for developing a storage scheme for graph data structures and facilitates the development of retrieval algorithms by (1) providing useful code for common retrieval tasks and (2) delineating the tasks which may require domain-specific implementations. SAI maintains flexibility through its customizability yet carries solutions for many common tasks for CBR developers, as well as illuminating some general principles for addressing common representation and retrieval needs for process-oriented CBR tasks and the relationship between CBR needs for structured retrieval and needs and methods in graph oriented database research. The paper closes with an evaluation of SAI's scalability and demonstration of the value of enabling developers to select retrieval methods suited to their tasks. It closes with a discussion of SAI's relationship to the growing body of work on graph databases.

A systematic literature review of service choreography adaptation

A service choreography is a distributed service composition in which services interact without a centralized control. Adequate adaptation strategies are required to face complex and ever-changing business processes, given the collaborative nature of choreographies. Choreographies should also be able to adapt to changes in its non-functional requirements, such as response time, and especially for large-scale choreographies, adaptation strategies need to be automated and scale well. However, the body of knowledge regarding choreography adaptation approaches has not yet been consolidated and systematically evaluated. By means of a systematic literature review, in which we examined seven scientific paper sources, we identified and analyzed the state-of-the-art in choreography adaptation. We found 24 relevant primary studies and grouped them into six categories: model-based, measurement-based, multi-agent-based, formal method-based, semantic reasoning-based, and proxy layer-based. We analyzed (i) how each strategy deals with different types of requirements, (ii) what their required degree of human intervention is, (iii) how the different studies considered scalability, (iv) what implementations are currently available, and (v) which choreography languages are employed. From the selected studies, we extracted key examples of choreography adaptation usage and analyzed the terminology they adopted with respect to dynamic adaptation. We found out that more attention has been devoted to functional requirements and automated adaptation; only one work performs scalability evaluation; and most studies present some sort of implementation and use a specific choreography notation.

Quality assessment of multidimensional video scalability

Scalability is a powerful concept for adaptive video content delivery to many end users having heterogeneous and dynamic characteristics of networks and devices. In order to maximize users' quality of experience by selecting appropriate combinations of multiple scalability parameters, it is crucial to understand and model the relationship between multidimensional scalability and perceived quality. In this article, we address the latest advances in subjective and objective quality evaluation of multidimensional video scalability for optimal content distribution, present their applications, and discuss future trends and challenges.