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:
8638
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 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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