Cost Aware Offloading Selection and Resource Allocation for Cloud Based Multi-Robot Systems

Sun, Yung-Nien; Zhou, Xingshe; Yang, Gang

doi:10.1587/transinf.2017edl8128

Cited by 3 publications

(7 citation statements)

References 13 publications

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“…In the literature, most of the works emphasising the cost-related issues of multi-agent cloud robotics, aim at minimizing the resource deployment cost. For example, [110], [143], [154], [155] reduce the resource deployment cost while allocating resources for the robotic applications. • User expenses: The amount paid by the robotic systems and its users to occupy the resources for executing tasks of applications is known as user expenses.…”

Section: B Performance Metricsmentioning

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%

“…Cloud service providers virtualize the computing servers and offer a variety of computing instances including virtual machines (VMs) and containers to the multi-agent robotic systems. The authors of [110]- [112] focus on allocating cloud instances for executing robotic applications. Moreover, cloud instances are dynamically configurable according to the resource requirements of the applications.…”

Section: A Resource Typementioning

confidence: 99%

“…Therefore, it is essential for service provider to improve the cooling system and generate less heat while handling the application tasks. In [110], the reduction of both energy consumption and heat generation are emphasized to meet the power usage in multi-agent cloud robotic systems. Apart from these power-related metrics, the detail analysis of residual battery lifetime of end devices and the exploitation of energy characteristics of communication medium are also required to design a power-efficient multi-agent cloud robotic system.…”

Section: B Performance Metricsmentioning

confidence: 99%

“…Identical tasks from different applications as well as from the same application can form BoT application model, which assists concurrent utilization of resources in distributed environments. In literature, [110], [112], [154]- [157], [165] use BoT for modelling and allocating resources for multiple applications.…”

Section: Application Structurementioning

confidence: 99%

See 4 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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