Journal Information
Journal of Parallel and Distributed Computing
http://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/
Impact Factor:
1.32
Publisher:
ELSEVIER
ISSN:
0743-7315
Viewed:
7381
Tracked:
24

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Call For Papers
The Journal of Parallel and Distributed Computing (JPDC) is directed to researchers, scientists, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing. The goal of the journal is to publish in a timely manner original research, critical review articles, and relevant survey papers on the theory, design, implementation, evaluation, programming, and applications of parallel and/or distributed computing systems. The journal provides an effective forum for communication among researchers and practitioners from various scientific areas working in a wide variety of problem areas, sharing a fundamental common interest in improving the ability of parallel and distributed computer systems to solve increasing numbers of difficult and complex problems as quickly and as efficiently as possible.

The scope of the journal includes (but is not restricted to) the following topics as they relate to parallel and/or distributed computing:

• Theory of parallel and distributed computing
• Parallel algorithms and their implementation
• Innovative computer architectures
• Shared-memory multiprocessors
• Peer-to-peer systems
• Distributed sensor networks
• Pervasive computing
• Optical computing
• Software tools and environments
• Languages, compilers, and operating systems
• Fault-tolerant computing
• Applications and performance analysis
• Bioinformatics
• Cyber trust and security
• Parallel programming
• Grid computing
Last updated by Dou Sun in 2016-09-25
Special Issues
Special Issue on Parallel and Distributed Computing: Deep Machine Learning – A New Frontier in Computational Intelligence Research
Submission Date: 2017-03-01

Guest Editors Dr. Arun Kumar Sangaiah, VIT University, Vellore, India Email: arunkumarsangaiah@gmail.com Dr. Naveen Chilamkurti, La Trobe University, Melbourne, Australia Email: N.Chilamkurti@latrobe.edu.au Aim and Scope: Deep learning is a fast-growing research area in the field of computational intelligence (CI) concerned with the analysis and design of learning algorithms, representations of data, at multiple levels of abstraction. The current rise in interest of deep machine learning algorithms in parallel and distributed computing of large scale data sets has been promising solution across many applications. This special issue is mainly focusing on state-of-art solutions that extent the gap of parallel and distributed computing platforms, paradigms for deep machine learning algorithms, task and applications. In addition, parallel and distributed computing platforms, models that are highly employ in deep machine learning and computational intelligence research may seem overwhelming and significantly out performs in many applications including robot control, signal processing, classification etc. Subsequently, we should note that there is rapid development for deep machine learning algorithms generally incomparable in terms of predictive performance. Furthermore, exploring the computational performance of parallel and distributed approaches for concerning speedup, efficiency, scalability are key metrics for large scale data sets the industries to realize the need for deep machine learning should be considered in conjunction with computational intelligence research. CI consists of various branches that are not limited to expert systems, artificial immune system, swarm intelligence, fuzzy system, neural network, evolutionary computing and various hybrid systems, which are combinations of two or more of the branches. Besides to deal with real world complex problems especially with large-scale dimensional data, models with deep architectures are illustrating promising path for efficient and robust solutions. Hence, the main idea of this special issue is to present new frontiers of deep machine learning algorithms and the capabilities of parallel and distributed platforms, through CI approaches that are more effective in learning applications deployed in large scale settings. Moreover, this special issue is to address comprehensive nature of parallel and distributed computing and to emphasize the character of deep learning and CI in modelling, identification, optimization, prediction, forecasting, and control of dimensionality of large scale data. In this special issue, we expect the novel work focused on addressing latest research, innovative ideas, and advances in deep learning algorithms approaches and solutions in large scale data analysis. Proposed submissions should be original, unpublished, and present novel in-depth fundamental research contributions either from a methodological/application perspective in accomplishing deep learning systems for data analytics. Topics of Interest: Note that this special issue emphasizes "real world" applications. Topics include, but are not limited to the following: Theoretical results on representation of deep learning architectures Innovative deep learning architectures/algorithms for large-scale data representation and analysis Parallel implementation of deep learning models Distributed computing, graphics processing unit (GPU) and new hardware for deep learning in CI research Deep learning algorithms that efficiently handle large-scale data Optimization methods for deep learning Large scale distributed deep networks Hybridization of deep learning models with existing techniques like evolutionary computation, neural networks and fuzzy systems Novel structure and parameter handling techniques for deep architecture models Identification of issues involved with real time implementation of deep learning models Design and/or analysis of recurrent and recursive architectures for processing of sequences and more general data structures Data mining approach in deep machine learning Applications of deep learning in data representation and analysis Important Dates Submission of papers to the journal due: March 1, 2017 First round review results: June 1, 2017 Revised papers due for submission: August 1, 2017 Second round review results: October 1, 2017 Final version of accepted papers: December 1, 2017 Publication: Decided by EiC Submission Guidelines Full papers can be submitted at http://ees.elsevier.com/jpdc/default.asp?pg=login.asp (all manuscripts should follow the submission guidelines available at the web site). During the submission process, please select "SI: PDC –Deep Machine Learning" as article type. Prospective authors are encouraged to indicate their interests any time before the submission deadline.
Last updated by Dou Sun in 2016-09-25
Special Issue on Systems for Learning, Inferencing, and Discovering
Submission Date: 2017-03-20

Irregular applications occur in many subject matters. While inherently parallel, they exhibit highly variable execution performance at a local level due to unpredictable memory access patterns and/or network transfers, divergent control structures, and data imbalances. Moreover, they often require fine-grain synchronization and communication on large-data structures such as graphs, trees, unstructrured grids, tables, sparse matrices, deep nets, and their combinations (such as, for example, attributed graphs). They have a significant degree of latent parallelism, which however is difficult to exploit due to their complex behavior. Current high performance architectures rely on data locality and regular computation to reduce access latencies, and often do not cope well with the requirements of these applications. Furthermore, irregular applications are difficult to scale on current supercomputing machines, due to their limits in fine-grained synchronization and small data transfers. Irregular applications pertain both to well established and emerging fields, such as machine learning, social network analysis, bioinformatics, semantic graph databases, Computer Aided Design (CAD), and computer security. Many of these application areas also process massive sets of unstructured data, which keep growing exponentially. Addressing the issues of irregular applications on current and future architectures will become critical to solve the challenges in science and data analysis of the next few years. This special issue seeks to explore solutions for supporting efficient execution of irregular applications in the form of new features at the level of the micro- and system-architecture, network, languages and libraries, runtimes, compilers, analysis, algorithms. Topics of interest, of both theoretical and practical significance, include but are not limited to: - Micro- and System-architectures, including multi- and many-core designs, heterogeneous processors, accelerators (GPUs, vector processors, Automata processor), reconfigurable (coarse grained reconfigurable and FPGA designs) and custom processors - Network architectures and interconnect (including high-radix networks, optical interconnects) - Novel memory architectures and designs (including processors-in memory) - Impact of new computing paradigms on irregular workloads (including neuromorphic processors and quantum computing) - Modeling, simulation and evaluation of novel architectures with irregular workloads - Innovative algorithmic techniques - Combinatorial algorithms (graph algorithms, sparse linear algebra, etc.) - Impact of irregularity on machine learning approaches - Parallelization techniques and data structures for irregular workloads - Data structures combining regular and irregular computations (e.g., attributed graphs) - Approaches for managing massive unstructured datasets (including streaming data) - Languages and programming models for irregular workloads - Library and runtime support for irregular workloads - Compiler and analysis techniques for irregular workloads - High performance data analytics applications, including graph databases
Last updated by Dou Sun in 2016-09-08
Special Issue on Advanced Algorithms and Applications for IoT Cloud Computing Convergence
Submission Date: 2017-03-30

Internet of Things (IoT) applications are considered to be a major source of big data obtained from a more connected dynamic and real life world and is evolving at a rapid pace. The realization of the IoT vision brings Information and Communication Technology (ICT) closer to many aspects of the real-world life instead of the virtual life through advanced theories, algorithms and applications. Technology of real-world IoT based on cloud computing has rapidly emerged as a novel industry and life paradigm. These topics will be the most comprehensive field focused on the various aspects of advances in computer engineering technologies, applications, and services. In cloud computing environments that include mobile infrastructures, the most important and final goal is to provide users more secure and richer Internet of Things services. Tremendous advances in algorithms of sensing, processing, communication and actuating core technologies are leading to new intelligent IoT services in our life such as smart cities, smart healthcare, smart grids, and others to improve all aspects of life. There might be many issues to realize it and provide intelligence IoT services based on the advanced applied algorithms and application technologies with much effort and enormous attention. The advanced applied algorithms and application technologies of this research area poses challenges such as context information fusion, security, reliability, autonomous and intelligent connecting, trust application and framework for real-world life. Advanced algorithms and applications for IoT based on the cloud computing research contributions that present new technologies, concepts, or analysis, reports on experiences and experiments of implementation and application of theories, and tutorials on new trends, are required in this research fields. For the aforementioned reasons, the special issue intends to give the detailed state-of-the-art of issues and solution guidelines for the future paradigm of technologies and applications for IoT cloud computing convergence. In addition, it will provide a completing panorama of the current research efforts that are inherent to topics of high interests in the new theoretical algorithms and applications for IoT service on cloud computing environment. This special issue solicits innovative ideas and solutions in all aspects around the Advanced Algorithms and Applications for IoT based Cloud Computing. The general scope of this issue covers the theory, design and modeling, prototyping, programming and implementation of IoT service systems and applications. The following is a non-exhaustive list of topics in focus of this special issue: - Advanced IoT services algorithms, technologies and applications on cloud computing - Interoperable and Interactive middleware for IoT on cloud computing - Semantic technologies, applications and frameworks for IoT on cloud computing - Infrastructure for computing service capabilities for IoT on cloud computing - Real-time algorithms, technologies and applications with real-world IoT on cloud computing - Advanced mathematic theory and technology IoT on cloud computingAdvanced algorithms IoT of live, virtual and construction on cloud computing - Related theory and technologies between web service and IoT in cloud computing - Advanced security, privacy, authentication, trust and verification scheme for IoT on cloud computing - Cloud-based IoT mobility management and QoE/QoS enhancement - Advanced theory and technologies for High Performance and Communications with IoT - Innovative applications and communication protocols for the combination of IoT and Cloud in various fields (e.g., health, multimedia, vehicular systems, smart cities)
Last updated by Dou Sun in 2016-12-16
Special Issue on Quality of Service in Smart Cities
Submission Date: 2017-03-31

With rapid urbanization, it is predicted that by the year 2050, two-thirds of the estimated global population of 9.5 billion will be residing in cities. This will place huge demands on the core city systems including transport, energy, education, environment, communication, water, healthcare, citizen services, waste management, housing and livelihoods. These large scale, distributed and heterogeneous systems will have to be managed effectively, efficiently and economically in order to ensure sustainable development and high quality of life. The vision of Smart Cities addresses the above challenges by using advances in information and communication technologies to instrument urban city systems, interconnect them and make them more intelligent. Instrumentation allows the monitoring, collection and storage of data from distributed users, networks, infrastructures and environments, and extraction of actionable information from it; interconnection enables the sharing of data and information among distributed systems, services, applications and communities; and intelligence supports better decision-making. Quality of Service (QoS) in the context of distributed Smart City systems can refer to quality of data collected, the quality of information extracted or the quality of decision-making. It can refer to the quality of protection of distributed data and information and related issues of security, privacy and trust in distributed systems. It can refer to the traditional distributed systems' quality related issues including performance, availability, reliability, scalability, interoperability, reusability, provision and management of Smart City networks and infrastructures. From an end-user perspective, it can refer to the quality of experience as perceived by the citizens, and can include quality of presentation, delivery and perception, and finally, it can refer to quality of life. This special issue on Quality of Service in Smart Cities seeks high-quality papers that address quality related issues in the context of ICT-enabled distributed Smart City systems, services and applications including, but not limited to the following areas: - Quality of Service - Quality of Data - Quality of Information - Quality of Protection - Quality of Decision-making - Quality of Experience - Quality of Presentation - Quality of Delivery - Quality of Perception - Quality of Life - Smart & Efficient Energy Management - Smart Transportation - Smart Buildings - Internet of Things for Smart Cities - E-Government Services - Emergency Management - Social Computing & Networks - Environment and Urban Monitoring - E-Health Systems - Intelligent Traffic Management
Last updated by Dou Sun in 2016-12-16
Special Issue on Tools and Techniques for End-to-End Monitoring of Quality of Service in Internet of Things Application Ecosystems
Submission Date: 2017-06-01

The Internet of Things (IoT) paradigm promises to help solve a wide range of issues that relate to our wellbeing. This paradigm is touted to benefit a wide range of application domains including (but not limited to) smart cities, smart home systems, smart agriculture, health care monitoring, and environmental monitoring (e.g. landslides, heatwave, flooding). Invariably, these application use cases produce big data generated by different types of human media (e.g. social media sources such as Twitter, Instagram, and Facebook) and digital sensors (e.g. rain gauges, weather stations, pore pressure sensors, tilt meters). Traditionally, the big data sets generated by IoT application ecosystems have been hosted and processed by traditional cloud datacenters (e.g. Amazon Web Services, Microsoft Azure). However, in recent times the traditional centralized model of cloud computing is undergoing a paradigm shift towards a decentralized model, so that these existing scheduling models can cope with the recent evolution of the smart hardware devices at the network edge such as smart gateways (e.g. Raspberry Pi 3, UDOO board, esp8266) and network function virtualisation solutions (e.g. Cisco IOx, HP OpenFlow and Middlebox Technologies). These devices on the network edge can offer computing and storage capabilities on a smaller scale often referred to as Edge datacenter to support the traditional cloud datacenter in tackling the future data processing and application management challenges that arise in the IoT application ecosystems as discussed above. Ultimately, the success of IoT applications will critically depend on the intelligence of tools and techniques that can monitor and verify the correct operation of such IoT ecosystems from end to end including the sensors, big data programming models, and the hardware resources available in the edge and cloud datacenters that form an integral part of an end-to-end IoT ecosystem. In the past 20 years a large body of research has developed frameworks and techniques to monitor the performance of hardware resources and applications in distributed system environments (grids, clusters, clouds). Monitoring tools that were popular in the grid and cluster computing era included R-GMA, Hawkeye, Network Weather Service (NWS), and Monitoring and Directory Service (MDS). These tools were concerned only with monitoring performance metrics at the hardware resource-level (CPU percentage, TCP/IP performance, available non-paged memory), and not at the application-level (e.g. event detection delay in the context of particular IoT applications). On the other hand, cluster-wide monitoring frameworks (Nagios, Ganglia - adopted by big data orchestration platforms such as YARN, Apache Hadoop, Apache Spark) provide information about hardware resource-level metrics (cluster utilisation, CPU utilisation, memory utilisation). In the public cloud computing space, monitoring frameworks and techniques (e.g. Amazon CloudWatch used by Amazon Elastic MapReduce, Azure Fabric Controller) typically monitor an entire CPU resource as a black box, and so cannot monitor application-level performance metrics specific to IoT ecosystem whereas techniques and frameworks such as Monitis and Nimsoft can monitor application-specific performance metrics (such as web server response time).
Last updated by Dou Sun in 2016-12-16
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