A Dispersed Computing Architecture for Resource-Centric Computation and Communication

Schurgot, Mary R.; Wang, Michael; Conway, Adrian E.; Greenwald, Lloyd; Lebling, P. David

doi:10.1109/mcom.2019.1800776

Cited by 24 publications

(8 citation statements)

References 14 publications

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“…e dispersed computing paradigm was proposed by the Defense Advanced Research Projects Agency (DARPA) in 2016 [39]. e basic idea of DCOMP is to realize the code and data on-demand movement, which enable the secure and collective execution of tasks on geographically dispersed, ubiquitous, and heterogeneous computing platforms [40,41]. DCOMP is aimed at breaking the traditional "end to end" design principle of the network and adopting a "forwarding and computing hop by hop" manner, which renders the network not only a data transmission channel but also a data processing platform.…”

Section: Background and Motivationmentioning

confidence: 99%

A Trusted and Privacy-Preserved Dispersed Computing Scheme for the Internet of Mobile Things

Zhao

Zhao³

et al. 2022

Security and Communication Networks

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Existing computing paradigms of the Internet of Mobile Things (IoMT) and social networks cannot effectively serve users due to their limited computing ability, dynamic mobile networks, weak connectivity, and high energy consumption and investment costs. Dispersed computing (DCOMP) is a promising way to solve the above issues. However, DCOMP is emergent, and few studies apply DCOMP to IoMT and social networks. Moreover, security problems also arise when introducing DCOMP to IoMT and social networks. In this paper, we propose a novel reference architecture to realize DCOMP in IoMT or social networks. We also propose two models—a trusted application discovery and acquisition model and a security domain-based computing offloading model—to enhance the security of the proposed architecture. The key idea of the first model is to use blockchain to construct a trusted application storage and acquisition system. This system guarantees that the tasks offloaded to dispersed devices are trusted. In the second model, we design two algorithms to offload tasks to appropriate dispersed devices to protect users’ privacy as much as possible. The experimental results prove the effectiveness of the proposed scheme.

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Section: Background and Motivationmentioning

confidence: 99%

A Trusted and Privacy-Preserved Dispersed Computing Scheme for the Internet of Mobile Things

Zhao

Zhao³

et al. 2022

Security and Communication Networks

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“…Meanwhile, the matching relationship is established, and the accepted tasks are removed from set L unmatched (line 5-9); Otherwise, the new task request is added to the preference list L j , and L j is reordered (line10-14). Then, update the preference list L τ of rejected tasks by deleting the current top-ranked NCP j (line [15][16]. The above process is the key to NCP's independent evaluation and decision-making.…”

Section: Task-ncp Bilateral Matching Algorithmmentioning

confidence: 99%

“…Driven by the popularity of IoT devices, dispersed computing [16], [17] as a new computing paradigm is eyecatching. Based on the mutual aid idea of "one for all and all for one", dispersed computing can adequately leverage the idle computation resources of surrounding available devices.…”

Section: Introductionmentioning

confidence: 99%

Resolving Multitask Competition for Constrained Resources in Dispersed Computing: A Bilateral Matching Game

Zhang

Cai

et al. 2021

IEEE Internet Things J.

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With the explosive emergence of computationintensive and latency-sensitive applications, data processing could be envisioned to perform closer to the data source. Similar to edge and fog computing, dispersed computing is considered as a complementary computing paradigm, which can excavate potential computation resources in the network to further bring the computation to users, and serve as a supplement for sharing computing pressure when the edge is overloaded. In this paper, we first make full use of idle and geographically dispersed computation resources via task offloading, contributing to conserve energy for mobile devices. Specially, a dispersed computing offloading framework concerning the interests of users and network computation points is proposed. We further transform the initial problem into a multi-objective optimization problem subject to latency and resource constraints. To tackle such a complex problem, an energy-saving bilateral matching algorithm is designed to obtain the optimal task offloading strategy. The simulation results demonstrate that our proposed algorithm can outperform the benchmark schemes in terms of user fairness and can achieve a relatively balanced energy cost ratio. Furthermore, comparative experiments with edge computing are implemented in Amber Response and Disaster Relief scenarios respectively to reveal the advantages of the proposed framework.

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“…DARPA entrusted Raytheon BBN Technologies (the premier research and development centers of Raytheon), Vencore Inc., BAE Systems Inc., and LGS Innovations to implement the DCOMP plan. The purpose of the DCOMP project is to improve the ability of task data calculation by using local computing resources, improve the reliability of the field network, distinguish available computing resources, specify the computing tasks of data in order of importance, and try to redesign and innovate the network protocol of the traditional Internet architecture, so as to significantly reduce the delay and bandwidth consumption and improve the performance of applications in a complex and uncertain battlefield environment [5].…”

Section: Introductionmentioning

confidence: 99%

Dispersed Computing for Tactical Edge in Future Wars: Vision, Architecture, and Challenges

Yang

Sun

et al. 2021

Wireless Communications and Mobile Computing

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In the future, the tactical edge is far away from the command center, the resources of communication and computing are limited, and the battlefield situation is changing rapidly, which leads to the weak connection and fast changes of network topology in a harsh and complex battlefield environment. Thus, to meet the needs of communication and computing to build a new generation of computing architecture for real-time sharing and service collaboration of tactical edge resources to win the future war, the dispersed computing (DCOMP) seeks a new solution to satisfy the requirements of fast and efficient sensing, transmission, integrating, scheduling, and processing of various information in the tactical edge. Through the research of a traditional computing paradigm of mobile cloud computing (MCC), fog computing (FC), mobile edge computing (MEC), mobile ad hoc network (MANET), etc., it can be found that these computations have difficulty in meeting the high changing and complex battlefield environment and we propose a novel architecture of DCOMP to build a scalable, extensible, and robust decision-making system, to realize powerful and secure communication, computing, storage, and information processing capabilities for the tactical edge. We illustrate the fundamental principles of building a network model, channel allocation, and forwarding control mechanism of the network architecture for DCOMP called DANET and then design a new architecture, programming model, task awareness, and computing scheduling for DCOMP. Finally, we discuss the main requirements and challenges of DCOMP in future wars.

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A Dispersed Computing Architecture for Resource-Centric Computation and Communication

Cited by 24 publications

References 14 publications

A Trusted and Privacy-Preserved Dispersed Computing Scheme for the Internet of Mobile Things

A Trusted and Privacy-Preserved Dispersed Computing Scheme for the Internet of Mobile Things

Resolving Multitask Competition for Constrained Resources in Dispersed Computing: A Bilateral Matching Game

Dispersed Computing for Tactical Edge in Future Wars: Vision, Architecture, and Challenges

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