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:
8017
Tracked:
26

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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 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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