Journal Information
Journal of Parallel and Distributed Computing (JPDC)
http://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/
Impact Factor:
2.296
Publisher:
Elsevier
ISSN:
0743-7315
Viewed:
21913
Tracked:
69
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 2021-03-21
Special Issues
Special Issue on Distributed Intelligence at the Edge for the Future Internet of Things
Submission Date: 2021-05-31

Currently and even more in the future, business, industry, finance and retail, healthcare, media, entertainment and many others, are and will be completely managed, coordinated, and controlled using huge amounts of data. These operations are performed by the Internet of Things (IoT) system of connected computing, digital, and mechanical devices, all of them named using unique identifiers (UIDs) and able to transfer data over a network without human intervention. To extract value from such massive data volumes, processing power offered by cloud computing is still utilized. However, streaming data to the cloud exposes some limitations related to the increased communication and data transfer, which introduces delays and consumes network bandwidth. Another limitation that cloud-based computing for IoT poses is limited network connectivity. Therefore, the adoption of cloud computing to process data generated by IoT devices may not be applicable at all to classes of applications such as those needed for real-time, low latency, and mobile applications. Therefore, it is beyond imagination to use cloud computing to collect data, store, and work out results. Therefore, there has been a move towards mobile communication and edge computing. Billions of devices have been connected to the Internet and created zettabytes of data items. The problem remains on how to extract information from collected data best. The use of Artificial Intelligence, machine learning, neural network, and data analytic techniques in edge processing resulted in a new inter-disciplinary field that enables distributed intelligence with edge devices and is known as distributed edge AI or edge intelligence. However, research on edge AI and distributed edge AI is still relatively new, and thus models, techniques, and protocols supporting intelligent management, querying and mining of large-scale amounts of data produced at the edge are required. A lot of challenges related to providing edge intelligence include training edge devices, so they can become smarter and smarter. There is also a need of the presentation of the most recent outcome of research of distributed intelligence. This need could be illustrated by a smart city that contains for instance: garages, parkings, car washing systems, traffic unloading centrals etc. – usually belonging to different companies and running different protocols. A most likely scenario is that these devices could use different AI systems to support their activities. However, all of them are parts of one interconnected smart city; different AI systems must cooperate for common goal(s). Thus, we need distributed intelligence. Examples and different AI systems working for different edges could be multiplied; they support a variety of edges. All want to make money, kick competitors from the market out, and grab their systems. Furthermore, there is an emphasis on creating better software and algorithms that can run efficiently on resource-constrained devices. Moreover, purpose-built hardware at the edge is becoming increasingly important in the field of machine learning because companies can run software much more efficiently if they use specialized chips. Another key challenge of distributed edge AI will be the continued improvement of user interfaces that are used to communicate with other humans, including text, voice, vision, and different forms of body language. These only are some of the challenges of edge intelligence. This field is expected to arise in the upcoming years and become an essential part of the next generation of the Internet of Things that expands its reach into almost every domain. Therefore, this Special Issue seeks to identify and provide high-quality research on recent advances on edge AI and distributed edge AI. We are interested in all aspects pertaining to this multidisciplinary paradigm. Topics of interest include, but are not limited to, the following: · Distributed Intelligence at the Edge · Modeling and Development of IoT applications using Edge AI · Distributed AI with/for Secure Edge Networking · Machine-Learning Algorithms for IoT Applications · Optimization, Control, And Automation in Edge Computing for IoT · Secure Intelligent IoT-Edge Systems · Secure Intelligent Coordination and Networking Between IoT, Edge, and Cloud · AI-Based Resource Allocation in IoT-Edge Systems · Trust and Privacy Management in Intelligent IoT-Edge Systems · Quality of Service and Energy Efficiency for Intelligent IoT-Edge Systems · Data Mining and Big Data Analytics for Security and Resource Management in IoT-Edge Systems · Distributed Ledger Technologies and Blockchain in IoT Environments
Last updated by Dou Sun in 2020-12-10
Special Issue on Edge / Cloud Computing Meets Artificial Intelligence
Submission Date: 2021-09-01

Recent years have witnessed the proliferation of mobile computing and Internet-of-Things (IoT), where billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Edge-Cloud Computing, a continuously emerging parallel & distributed computing paradigm, has received a tremendous amount of attention. By pushing data storage, computing, and controls closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability and energy efficiency. Owing to the recent developments in the domain of neural networks and cloud computing, Artificial Intelligence (AI) has been applied to a variety of disciplines and proved highly successful in a vast class of intelligent applications across many domains, e.g., computer vision, pattern recognition, etc. Recently, edge intelligence, aiming to facilitate the deployment of neural networks on edge computing, has received significant attention, since hierarchical architecture of end devices proposes a possible solution to meet the high computation and low-latency requirement for the training and inference of AI algorithms. However, there are many challenges existing for novel designs of edge-cloud computing architectures for AI applications, and their co-optimization. On one hand, the high resource requirements of AI applications should be accommodated on a set of less powerful edge compute resources. Therefore, efficient, parallel & distributed and resource-conserving (e.g., memory, computing, energy, time, etc.) AI algorithms should be revisited in the edge computing environments and move the main part to cloud computing. On the other hand, the system design should also support the efficient and scalable execution of AI algorithms, including specialized accelerators, efficient parallel & distributed execution mode, optimal off-loading, and scheduling strategies, etc. In this special issue, we solicit original work exclusively on ML/AI, specifically catered to deep neural networks on/for edge computing and efficient learning systems or accelerators on edge computing, addressing specific challenges in this field. The list of possible topics includes, but not limited to: Parallel & distributed computing architectures for edge-cloud based AI Edge-Cloud collaborative computing for neural networks Edge/Fog-infused cloud architectures for ML/AI applications Power-aware efficient ML/AI algorithms for edge devices Parallel & distributed neural networks for edge/fog/cloud computing Offloading & scheduling strategy for edge AI Osmotic and catalytic computing strategies for edge/fog/cloud platforms Data parallelism and model parallelism on edge/fog/cloud computing Hardware-aware ML/AI algorithms on edge/fog/cloud computing Few-shot learning on edge devices for ML/AI applications Resource scheduling for large-scale applications of edge intelligence AI/ML algorithms for small-scale low-power edge devices AI/ML algorithms for edge/fog/cloud platforms Distributed and cooperative learning with edge devices on Cloud Architecture & applications of edge AI for IoT Accelerators for edge-cloud AI Submission Guidelines ​Authors should follow the Journal of Parallel and Distributed Computing manuscript format described at the journal site: https://www.elsevier.com/journals/journal-of-parallel-and-distributed-computing/0743-7315/guide-for-authors. Manuscripts that extend research published previously (e.g., in conference or workshop proceedings) will only be considered if they include at least 30% of significantly new material; the submission of such manuscripts must be accompanied by a “Summary of Differences” letter explaining how the authors extended their previously published work. All manuscripts and any supplementary material should be submitted through Editorial Manager (EM), available at: https://www.editorialmanager.com/jpdc/default.aspx. The authors must select "VSI: Edge/Cloud Meets AI" when they reach the "Article Type" in the submission process. Important Dates Final Submission Date: 1 September 2021 Final Acceptance Date: 1 June 2022 Guest Editors Prof. Bharadwaj Veeravalli Department of ECE, Faculty of Engineering, NUS, Singapore Email: elebv@nus.edu.sg Prof. Zeng Zeng Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore Email: zengz@i2r.a-star.edu.sg Prof. Keqin Li IEEE Fellow SUNY Distinguished Professor, USA Email: lik@newpaltz.edu
Last updated by Dou Sun in 2021-03-21
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