Applications In Heterogeneous Environments Research Articles

Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data

Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods.

SemLinkWS: Collaboration Social Network of Web Services to Aid Service Discovery

Service-oriented computing (SOC) has come into view as a new computing paradigm that makes systems development and business process management more flexible and agile. It has recourse to services functioning as a fundamental compound in order to assist the construction of rapid and easy composition of distributed applications in heterogeneous environments. The rapid development of web service technologies has dramatically increased the number of available web services including those available on social networks. Thus, many studies have been carried out in the discovery mechanisms. The majority of proposed approaches in the literature for web services discovery based on the description of web services themselves and neglect their interaction with each other. Through the overlapping between social computing (illustrated by social networks) and service oriented computing (illustrated by web services), social web services are produced that are different from regular web services. In fact, they are a member of a social network and have an idea about other services in the same network. The first purpose of this paper is to justify the need of the use of an extension of WSDL-S to facilitate web service discovery, and the second purpose is to build a social network of web services to take advantage of the interactions that occur between services. However, the effort of developing social web services can partly undermine the utility of their implementation.

Contexts Enhance Accuracy: On Modeling Context Aware Deep Factorization Machine for Web API QoS Prediction

Service-oriented computing (SOC) promises a world of cooperating services loosely connected, constructing agile Web applications in heterogeneous environments conveniently. Web application interface (API) as an emerging technique attracts more and more enterprises and organizations to publish their deep computing functionalities and big data on the Internet, Web API has become the backbone to promote the development of SOC, thus forming the prosperous Web API economy. However, the number of available Web APIs on the Internet is massive and growing constantly, which causes the Web API overload problem. Quality of service (QoS) as an indicator is able to well differentiate the quality of Web APIs and has been widely applied for high quality Web API selection. Since testing QoS for massive Web APIs is resource-consuming, and the QoS performance depends on contextual information such as network and location, hence accurate QoS prediction has become very crucial for personalized Web API recommendation and high quality Web application construction. To address the above issue, this paper presents a context aware deep factorization machine model (CADFM for short) for accurate Web API QoS prediction. Specifically, we first carry out detailed data analysis using real-world QoS dataset and discover a positive relationship between QoS and contextual information, which motivates us to incorporate beneficial contexts for enhancing QoS prediction accuracy. Then, we treat QoS prediction as a regression problem and propose a context aware CADFM framework that integrates the contextual information via embedding technique. Particularly, we adopt MF and MLP for high-order and nonlinear interaction modeling, so as to learn the complex interaction between users and Web APIs accurately. Finally, the experimental results on real-world QoS dataset demonstrate that CADFM outperforms the classic and the state-of-the-art baselines, thereby generating the most accurate QoS predictions and increasing the revenue of Web APIs recommendation.

Exploring Analytical Model to Performance Optimization for Mobile Application Using End-to-End Network Slicing in Cloud-Based Vehicular Networks

Today, many large usages of cloud-based vehicular networks and applications have rapidly increased. This rapid increase causes the requirement of systems to be reliable to share their resources without delay in order to ensure a better quality of service (QoS) for mobile users. Hence, network slicing is considered one of the key concepts to enhance QoS in 5G networks. At present, new architectures attempt to provide support for end-to-end server quality mechanisms. A key mechanism of network slicing supported by such modern architectures is able to either handover to better network or migrate services closer to the users as they move around. This can be done by advanced handover and server localization techniques. These sorts of advanced handover and server localization help to maintain the QoS for mobile application in heterogeneous environments. In order to obtain QoS measurements and get the network conditions in a specific area, a cloud-based vehicular network slicing management framework is proposed using an analytical modeling approach. The analytical model results obtained considering real scenarios from a Middlesex University VANET testbed. Using this framework, the mobile users will make a decision on which situation is better suited to obtain the service based on the latencies as well as queuing capacities of the networks.

A genetic algorithm for service flow management with budget constraint in heterogeneous computing

Heterogeneous computing supply various and scalable resources for many applications requirements. Its structure is based on interconnecting machines with several processing capacity spread over networks. The scientific bioinformatics and many other applications demand service flow processing in which services have dependencies execution. The environments of this computing are suitable for huge computational needs that contains diverse groups of services. Managing and mapping services of service flow to the suitable candidates who provides the service is classified as NP-complete problem. The managing such interdependent services on heterogeneous environments also takes the Quality of Service (QoS) requirements from users into account. This paper firstly proposes a model of service flow management with service cost quality requirement in heterogeneous computing. After that a service flow mapping algorithm named genetic to reduce the consumed cost of an application in heterogeneous environments is proposed. This algorithm gives a robust search technique that allow a soft cost solution to be derived from a huge search space of solutions by inheriting the evolution concepts. The obtained results from the applied experiments prove that genetic can save more than fifteen percent from the cost and also outperforms the compared algorithms in the metric of speedup and SLR.

A Task-Based Greedy Scheduling Algorithm for Minimizing Energy of MapReduce Jobs

MapReduce and its open source implementation, Hadoop, have gained widespread adoption for parallel processing of big data jobs. Since the number of such big data jobs is also rapidly rising, reducing their energy consumption is increasingly more important to reduce environmental impact as well as operational costs. Prior work by Mashayekhy et al. (IEEE Trans. Parallel Distributed Syst. 26, 2720–2733, 2016), has tackled the problem of energy-aware scheduling of a single MapReduce job but we provide a far more efficient heuristic in this paper. We first model the problem as an Integer Linear Program to find the optimal solution using ILP solvers. Then we present a task-based greedy scheduling algorithm, TGSAVE, to select a slot for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without significant performance loss while satisfying the service level agreement (SLA). We perform several experiments on a Hadoop cluster to measure characteristics of tasks for nine different applications to evaluate our proposed algorithm. The results show that the total energy consumption of MapReduce jobs obtained by TGSAVE is up to 35% less than that achieved by EMRSA proposed in Mashayekhy et al. (IEEE Trans. Parallel Distributed Syst. 26, 2720–2733, 2016), its closest rival, for same workloads. Besides, TGSAVE is capable of finding a solution in same order of time for up to 74% tighter deadlines than the tightest deadline that EMRSA can find a feasible one. On average, TGSAVE solution is approximately 1.4% far from the optimal solution, and it can meet deadlines as tight as 12%, on average, above the energy-oblivious minimum makespan in the benchmarks we examined.

Greedy-Ant: Ant Colony System-Inspired Workflow Scheduling for Heterogeneous Computing

The last decades have seen a considerable progress on workflow scheduling in heterogeneous computing environments. However, existing methods still need to be improved on the performance in the makespan-based metrics. This paper proposes a novel workflow scheduling algorithm named Greedy-Ant to minimize total execution time of an application in heterogeneous environments. First, the ant colony system is applied to scheduling from a new standpoint by guiding ants to explore task priorities and simultaneously assign tasks to machines. Second, forward/backward dependence is defined to indicate the global significance of each node, based on which, a new heuristic factor is proposed to help ants search for task sequences. Finally, a greedy machine allocating strategy is presented. Experimental results demonstrate that Greedy-Ant outperforms the state of the art up to 18% in the metric of speedup.

An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge

Land surface albedo has been recognized by the Global Terrestrial Observing System (GTOS) as an essential climate variable crucial for accurate modeling and monitoring of the Earth's radiative budget. While global climate studies can leverage albedo datasets from MODIS, VIIRS, and other coarse-resolution sensors, many applications in heterogeneous environments can benefit from higher-resolution albedo products derived from Landsat. We previously developed a “MODIS-concurrent” approach for the 30-meter albedo estimation which relied on combining post-2000 Landsat data with MODIS Bidirectional Reflectance Distribution Function (BRDF) information. Here we present a “pre-MODIS era” approach to extend 30-m surface albedo generation in time back to the 1980s, through an a priori anisotropy Look-Up Table (LUT) built up from the high quality MCD43A BRDF estimates over representative homogenous regions. Each entry in the LUT reflects a unique combination of land cover, seasonality, terrain information, disturbance age and type, and Landsat optical spectral bands. An initial conceptual LUT was created for the Pacific Northwest (PNW) of the United States and provides BRDF shapes estimated from MODIS observations for undisturbed and disturbed surface types (including recovery trajectories of burned areas and non-fire disturbances). By accepting the assumption of a generally invariant BRDF shape for similar land surface structures as a priori information, spectral white-sky and black-sky albedos are derived through albedo-to-nadir reflectance ratios as a bridge between the Landsat and MODIS scale. A further narrow-to-broadband conversion based on radiative transfer simulations is adopted to produce broadband albedos at visible, near infrared, and shortwave regimes. We evaluate the accuracy of resultant Landsat albedo using available field measurements at forested AmeriFlux stations in the PNW region, and examine the consistency of the surface albedo generated by this approach respectively with that from the “concurrent” approach and the coincident MODIS operational surface albedo products. Using the tower measurements as reference, the derived Landsat 30-m snow-free shortwave broadband albedo yields an absolute accuracy of 0.02 with a root mean square error less than 0.016 and a bias of no more than 0.007. A further cross-comparison over individual scenes shows that the retrieved white sky shortwave albedo from the “pre-MODIS era” LUT approach is highly consistent (R2=0.988, the scene-averaged low RMSE=0.009 and bias=−0.005) with that generated by the earlier “concurrent” approach. The Landsat albedo also exhibits more detailed landscape texture and a wider dynamic range of albedo values than the coincident 500-m MODIS operational products (MCD43A3), especially in the heterogeneous regions. Collectively, the “pre-MODIS” LUT and “concurrent” approaches provide a practical way to retrieve long-term Landsat albedo from the historic Landsat archives as far back as the 1980s, as well as the current Landsat-8 mission, and thus support investigations into the evolution of the albedo of terrestrial biomes at fine resolution.

Adaptive Composition of Distributed Pervasive Applications in Heterogeneous Environments

Complex pervasive applications need to be distributed for two main reasons: due to the typical resource restrictions of mobile devices, and to use local services to interact with the immediate environment. To set up such an application, the distributed components require spontaneous composition. Since dynamics in the environment and device failures may imply the unavailability of components and devices at any time, finding, maintaining, and adapting such a composition is a nontrivial task. Moreover, the speed of such a configuration process directly influences the user since in the event of a configuration, the user has to wait. In this article, we introduce configuration algorithms for homogeneous and heterogeneous environments. We discuss a comprehensive approach to pervasive application configuration that adapts to the characteristics of the environment: It chooses the most efficient configuration method for the given environment to minimize the configuration latency. Moreover, we propose a new scheme for caching and reusing partial application configurations. This scheme reduces the configuration latency even further such that a configuration can be executed without notable disturbance of the user.

AN EFFECTIVE DISTRIBUTED MODEL FOR XMLISED POWER SYSTEM DATA GENERATION

One of the outcomes of the continuous research on the evolution of distributed computing is the Web services. The aim of this paper is to represent Power System data effectively in XML in order to improve the interoperability and to develop an enhanced distributed model for unique XMLised Power System Data generation for solving various Power System applications in heterogeneous environment. Power System industries are now increasingly becoming privatized and hence the system data is becoming increasingly distributed, with more constrained and complex operational and control requirements. Because of the complex physical connectivity of the power systems, all levels of industry like generation, transmission, distribution and market need proper operational and equipmental data. As expected, the data to be shared between different power system applications is huge and hence it is vital to have an efficient and reliable data generation model to reduce more human efforts and to have the data in a secure and compatible form. The developed JAX-RPC-based model has the capability to generate the data dynamically in XML, fetching the power system data from various sources such as database, text file, etc. The standards such as XML and SOAP enable software design based on loose coupling which reduces restriction and eliminates similarity requirement between coordinating applications.

A nomadic access mechanism for enabling dynamic video surveillance over IEEE 802.15.4 networks

IEEE 802.15.4 networking technology is designed to be the common standard for integrating WSN applications in heterogeneous environments. However, applications considering mobile nodes along with strict temporal requirements, such as those required for video transmission, are an unexplored field for this technology. These applications involve different challenges and issues that the direct employment of the IEEE 802.15.4 standard does not resolve. Therefore, in this paper a cross-layer mechanism consisting of application and medium access arbitration is presented, enabling the efficient connection and operation of mobile nodes together with the transmission of video flows. The proposed mechanism is evaluated via simulation and its feasibility checked by means of a first prototype. The study of power consumption is also taken into account and so are the quality of service parameters and the human quality perception of the received video stream. The results obtained are presented and further discussed.

A Modified Fast Approximated Power Iteration Subspace Tracking Method for Space‐Time Adaptive Processing

We propose a subspace‐tracking‐based space‐time adaptive processing technique for airborne radar applications. By applying a modified approximated power iteration subspace tracing algorithm, the principal subspace in which the clutter‐plus‐interference reside is estimated. Therefore, the moving targets are detected by projecting the data on the minor subspace which is orthogonal to the principal subspace. The proposed approach overcomes the shortcomings of the existing methods and has satisfactory performance. Simulation results confirm that the performance improvement is achieved at very small secondary sample support, a feature that is particularly attractive for applications in heterogeneous environments.

Towards an XML-based Bytecode Level Transformation Framework

Virtual machines (VMs) facilitate the deployment of applications in heterogeneous environments. Popular VMs such as Sun Microsystem's Java VM or Microsoft's Common Language Runtime (CLR) use a stack-based machine architecture to execute bytecode instructions. The focus of this paper is to explore the ability of XML to serve as a generic framework to represent bytecode instructions from different VMs. With an XML representation, supporting technologies such as XSL stylesheets and XPath expressions can be used to provide various transformations of bytecode instructions. We demonstrate the flexibility and power of this approach by showing examples of cross-compilation for various CLR bytecode instructions to the JVM as well as API mappings of the underlying runtime libraries.

Performance evaluation of a dynamic load-balancing library for cluster computing

The performance of scientific applications in heterogeneous environments has been improved with the research advances in dynamic scheduling at application and runtime system levels. This paper presents the performance evaluation of a library as a result of an integrated approach to dynamic load balancing. This approach combines the advantages of optimising data migration via novel dynamic loop-scheduling strategies with the advances in resource management and task migration capabilities offered by a recently developed parallel runtime system. The performance of the library has been investigated by its use in three scientific applications: the N-body simulations, the profiling of automatic quadrature routines and the solution to the 3D heat equation. The investigations focus on the performance degradation owing to the overhead induced by the runtime system software layer. The experimental results obtained indicate only a slight increase in the cost of load balancing owing to this overhead. The results validate the suitability of the runtime system as an implementation platform for dynamic load-balancing schemes and underscore the significance of using the integrated approach, as well as the benefits of using the library especially in cluster applications characterised by irregular and unpredictable behaviour.

Standards for talking computers [Book Review

Web Services recently emerged as the ideal choice for building distributed applications in heterogeneous environments where competing technologies (such as the Object Management Group's Corba, Microsoft's Distributed Component Object Model, or Sun's Java Remote Method Invocation) never really caught on. Web Services employ XML to facilitate communication in distributed heterogeneous systems. XML's easy-to-interpret format is perfect for building interoperable systems. In the unlikely case that an XML parser doesn't already exist in your programming environment, you can implement it in a breeze. Moreover, XML syntax is humanreadable-a highly desirable property for debugging sessions. In short, using XML simplifies computer-computer interaction so that distributed applications can more easily share data.

Hard Real-Time CORBA (HRTC) for Process Control Systems 2

Control systems for process plants are complex applications running in several interacting computers with varying degrees of integration. The construction, deployment and maintenance of the software system is a difficult problem and distributed object oriented technology offers a good way to deal with it. The open standard CORBA provides flexible middleware capable of integrating complex applications in heterogeneous environments, but was originally designed with large business applications in mind and is not perfectly suited for the construction of control systems. Even with recent advances in the real-time specification for CORBA. it is only suitable for soft real-time applications and do not deal with the tight requirements of closed control loops. In this paper, the building of a process control testbed to identify requirements for CORBA control systems, with both predictable and event-driven transports, is presented. The benefits of such technology are discussed.

Technology 1993 - Software

Significant developments during 1992, including the end of an era of prosperity for software and computer companies, are examined. The battle for dominance among operating systems, specifically Windows, OS2, and Unix-based systems, is discussed. The growth in popularity of the function points metric, a new system of notation, activity in object-oriented programming, and integration of electronic design automation applications in heterogeneous environments are addressed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Development and evaluation of continuously weighing mini-lysimeters

Micrometeorological methods for measuring evapotranspiration are based on the assumption that the water vapor flux is constant with respect to height within the first few meters above the ground, and consequently are not applicable where an area is not extensively homogeneous. An increasing emphasis on energy and water flux studies in heterogeneous terrain (e.g. urban and hilly environments) has created a need to develop alternative approaches to quantify the latent heat flux. Lysimetry is an established alternative that permits the direct measurement of evapotranspiration from the ground surface and thus can provide accurate point estimates of the latent heat flux in heterogeneous environments. To date, the continuously weighing lysimeters that have been developed and extensively tested are large (surface area generally greater than 2 m 2), stationary and expensive to construct and maintain. These instruments are also inappropriate for most geographic studies of energy and water budgets in heterogeneous environments because knowledge of the spatial variability of evapotranspiration and quantification of the horizontal transport of moisture and heat require multiple, simultaneous flux determinations. This research tests two continuously weighing, portable mini-lysimeters (<0.2 m 2) designed for practical application in heterogeneous environments. The performance of the lysimeters are compared with evaporative flux measurements obtained using eddy correlation instrumentation from an extensive homogeneous surface. The soil moisture status of the monolith and the stomatal resistance of the plants within the lysimeter are compared with simultaneous measurements from the surrounding grass field. The results show that the evaporative flux, soil moisture and stomatal resistance measurements from both lysimeters compare favorably with measurements that are representative of their surroundings.