Latency-Aware Task Assignment and Scheduling in Collaborative Cloud Robotic Systems

Li, Shenghui; Zheng, Zhiheng; Chen, Wuhui; Zheng, Zibin; Wang, Junbo

doi:10.1109/cloud.2018.00016

Cited by 20 publications

(49 citation statements)

References 13 publications

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“…Based on the characteristics of optimization function such as convex (minimization) or concave (maximization) and the relation among the constraints, the choice of optimization method (linear programming, integer programming, non-linear programming, combinatorial, stochastic) is made in multi-agent cloud robotic system. For example, [142] linearizes a mixed-integer non-linear problem into an integer linear programming (ILP) model and reduces the computation and communication time during robotic application execution. Similarly, [142] allocates resources with minimal latency.…”

Section: E Mechanismmentioning

confidence: 99%

“…For example, [142] linearizes a mixed-integer non-linear problem into an integer linear programming (ILP) model and reduces the computation and communication time during robotic application execution. Similarly, [142] allocates resources with minimal latency. However, for complicated optimization problem, it is not always feasible to find the solution satisfying all the constraints, especially when the resource allocation is required to conduct in real-time.…”

Section: E Mechanismmentioning

confidence: 99%

See 1 more Smart Citation

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: E Mechanismmentioning

confidence: 99%

Section: E Mechanismmentioning

confidence: 99%

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

“…Resolver el problema de localización es esencial para cualquier vehículo autónomo, para poder desempeñar de forma precisa el resto de las tareas necesarias, como la planificación y el control de movimientos. En primer lugar, la planificación de las tareas de inspección se puede llevar a cabo empleando técnicas de búsquedas en grafos [12], ampliamente conocidas y que proporcionan muy buenos resultados. Estas técnicas se basan en encontrar los mejores caminos entre las zonas, en este caso de inspección, que se deben visitar minimizando algunos factores como la distancia, la energía necesaria para realizar los movimientos, la maniobrabilidad del camino que une dichas zonas o cualquier otro criterio que permita establecer un coste.…”

Section: Vehículos De Inspección Autónomosunclassified

Revisión de tecnologías habilitadoras para el control biológico y de plagas en el sector hortofrutícola

Martin¹,

Llopis²,

Rubio³

et al. 2021

Xlii Jornadas De Automática : Libro De Actas

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La monitorización continua tanto de plagas que afectan a los cultivos hortofrutícolas en agricultura intensiva de invernadero, como de la lucha biológica, utilizada en este tipo de cultivos para combatir las plagas de una forma sostenible con el medio ambiente son esenciales en este sector. Actualmente, en la mayoría de los casos este control se realiza de forma manual (no siempre sin dificultad), lo que requiere a los agricultores de una supervisión continua para poder detectar, en el menor tiempo posible, cualquier brote que afecte a sus cultivos. En este trabajo se presenta una revisión de tecnologías habilitadoras para permitir una monitorización continua, sostenible y de forma autónoma en este sector estratégico en España. Estas tecnologías se integran en una propuesta de sistema de ayuda a la decisión para el control de plagas y control biológico.

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“…In static task assignment, [33] studied static algorithm allocation for a multi-robot system without considering cloud infrastructure and communication times. As far as we know, the only works that have addressed a similar problem are [31], and [32]. [32] solved the allocation problem by minimizing memory and time for a single robotic cloud system.…”

Section: Introductionmentioning

confidence: 99%

“…[32] solved the allocation problem by minimizing memory and time for a single robotic cloud system. [31] approached the assignment problem incompletely by considering only the minimum time and ignoring the memory parameter. Their method is also incomplete because it minimizes the total execution time without fully considering communications, as shown in [32].…”

Section: Introductionmentioning

confidence: 99%

Optimal Algorithm Allocation for Robotic Network Cloud Systems

Alirezazadeh,

Correia,

Alexandre

2021

Preprint

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Cloud robotics enables robots to benefit from the massive storage and computational power of the cloud, overcoming the capacity limitations of cooperative robots. When the decision is made to use a robotic network cloud system to execute a task or a set of tasks, the main goal will be to use the cheapest, in terms of smallest memory, and fastest, in terms of shortest execution time, robots. Previous studies have mainly focused on minimizing the cost of the robots in retrieving resources by knowing the resource allocation in advance. When a task arrives in the system, it can be assigned to any processing unit, one of the robots, an eventual fog node or the cloud, and the question is where a task should be processed to optimize performance. Here, we develop a method for a robotic network cloud system to determine where each algorithm should be allocated for the system to achieve optimal performance, regardless of which robot initiates the request. We can find the minimum required memory for the robots and the optimal way to allocate the algorithms with the shortest time to complete each task. We show how our proposed method works, and we experimentaly compare its performance with a state-of-the-art method, using real world data, showing the improvements that can be obtained.

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Latency-Aware Task Assignment and Scheduling in Collaborative Cloud Robotic Systems

Cited by 20 publications

References 13 publications

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

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

Revisión de tecnologías habilitadoras para el control biológico y de plagas en el sector hortofrutícola

Optimal Algorithm Allocation for Robotic Network Cloud Systems

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