A Pricing Mechanism for Task Oriented Resource Allocation in Cloud Robotics

Wang, Lujia; Liu, Ming; Meng, Max Q.-H.

doi:10.1007/978-3-319-22168-7_1

Cited by 15 publications

(13 citation statements)

References 55 publications

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“…The performance of a system in terms of SLA depends on ratio of the number of successfully executed tasks and the number of total assigned tasks [158]. In [111], [155], [156], QoS is enhanced by minimizing the SLA violations. • Availability: The accessibility, usability, scalability of the resources while executing the applications defines the availability of resources in multi-agent cloud robotics.…”

Section: B Performance Metricsmentioning

confidence: 99%

“…For example, a bandwidth allocation strategy using Stackelberg game is described in [154] and the performance of the strategy is evaluated through simulations. In addition, [155] allocates bandwidth in multi-agent cloud robotics with a task-oriented pricing mechanism. Experimental results accumulated from testbed and simulations show that the time and cost increase with number of network nodes.…”

Section: E Mechanismmentioning

confidence: 99%

“…In literature, considering the static workload, [10], [76], [111], [140], [142], [143], [154], [155] prefer reserved arrangements of resources. There are pros and cons in each resource discovery mechanism.…”

Section: A Resource Poolingmentioning

confidence: 99%

“…2) Dynamic scheduling: According to this approach, task scheduling is conducted randomly without any prior time or cost estimation. In literature, [110], [112], [141], [155]- [157], [165] schedule the tasks dynamically.…”

Section: Task Schedulingmentioning

confidence: 99%

See 3 more Smart Citations

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

Afrin

Jin

Rahman

et al. 2021

IEEE Commun. Surv. Tutorials

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Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0, agriculture, healthcare, and disaster management. These applications are composed of latency-sensitive, data-heavy, and compute-intensive tasks. The robots, however, are constrained in the computational power and storage capacity. The concept of multi-agent cloud robotics enables robotto-robot cooperation and creates a complementary environment for the robots in executing large-scale applications with the capability to utilize the edge and cloud resources. However, in such a collaborative environment, the optimal resource allocation for robotic tasks is challenging to achieve. Heterogeneous energy consumption rates and application of execution costs associated with the robots and computing instances make it even more complex. In addition, the data transmission delay between local robots, edge nodes, and cloud data centres adversely affects the real-time interactions and impedes service performance guarantee. Taking all these issues into account, this paper comprehensively surveys the state-of-the-art on resource allocation and service provisioning in multi-agent cloud robotics. The paper presents the application domains of multi-agent cloud robotics through explicit comparison with the contemporary computing paradigms and identifies the specific research challenges. A complete taxonomy on resource allocation is presented for the first time, together with the discussion of resource pooling, computation offloading, and task scheduling for efficient service provisioning. Furthermore, we highlight the research gaps from the learned lessons, and present future directions deemed beneficial to further advance this emerging field.

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Section: B Performance Metricsmentioning

confidence: 99%

Section: E Mechanismmentioning

confidence: 99%

Section: A Resource Poolingmentioning

confidence: 99%

Section: Task Schedulingmentioning

confidence: 99%

See 2 more Smart Citations

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

Afrin

Jin

Rahman

et al. 2021

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…One of the crucial determinants of performance of cloud-based robotic systems is making a decision to upload a task to the cloud versus processing it using local resources. Recent works have proposed coordination mechanisms including auctions [100], sequential games [101] and belief propagation techniques [102] for real-time resource allocation and retrieval in cloud-based mobile robotic systems. However, the performance of these techniques is susceptible to the dynamic changes in the network topology as well as delays during the coordination process.…”

Section: Open Issues Challenges and Future Directionsmentioning

confidence: 99%

A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications

2018

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Cloud robotics has recently emerged as a collaborative technology between cloud computing and service robotics enabled through progress in wireless networking, large scale storage and communication technologies, and the ubiquitous presence of Internet resources over recent years. Cloud computing empowers robots by offering them faster and more powerful computational capabilities through massively parallel computation and higher data storage facilities. It also offers access to open-source, big datasets and software, cooperative learning capabilities through knowledge sharing, and human knowledge through crowdsourcing. The recent progress in cloud robotics has led to active research in this area spanning from the development of cloud robotics architectures to its varied applications in different domains. In this survey paper, we review the recent works in the area of cloud robotics technologies as well as its applications. We draw insights about the current trends in cloud robotics and discuss the challenges and limitations in the current literature, open research questions and future research directions.

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