Multi criteria biased randomized method for resource allocation in distributed systems: Application in a volunteer computing system

Panadero, Javier; Armas, Jésica de; Serra, Xavier; Marquès, Joan Manuel

doi:10.1016/j.future.2017.11.039

Cited by 17 publications

(4 citation statements)

References 26 publications

(29 reference statements)

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“…However, these corporate efforts are seriously affected by some security concerns like privacy. Panadero et al [21] developed "the multicriteria biased randomized method (MBRM)" to pick out nodes assuring a minimal quality of service for the users. This method is founded on a lexicographic ordering multicriteria strategy.…”

Section: Related Workmentioning

confidence: 99%

An infrastructure-assisted job scheduling and task coordination in volunteer computing-based VANET

Waheed

Shah

Khan

et al. 2021

Complex Intell. Syst.

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Vehicular networks as the key enablers in Intelligent Transportation Systems (ITS) and the Internet of Things (IoT) are key components of smart sustainable cities. Vehicles as a significant component of smart cities have emerging in-vehicle applications that can assist in good governance for sustainable smart cities. Most of these applications are delay sensitive and demand high computational capabilities that are provided by emerging technologies. Utilizing the distributed computational resources of vehicles with the help of volunteer computing is an efficient method to fulfill the high computational requirements of vehicles itself and the other components of smart cities. Vehicle as a resource is an emerging concept that must be considered to address the future challenges of sustainable smart cities. In this paper, an infrastructure-assisted job scheduling and task coordination mechanism in volunteer computing-based VANET called RSU-based VCBV is proposed, which enhances the architecture of VANET to utilize the surplus resources of vehicles for task execution. We propose job scheduling and task coordination algorithms for different volunteer models. Further, we design and implement an adaptive task replication method to seek fault tolerance by avoiding task failures due to locations of vehicles. We propose a task replication algorithm called location-based task replication algorithm. Extensive simulations validate the performance of our proposed volunteer models while comparing average task execution time and weight ratios with existing work.

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Section: Related Workmentioning

confidence: 99%

An infrastructure-assisted job scheduling and task coordination in volunteer computing-based VANET

Waheed

Shah

Khan

et al. 2021

Complex Intell. Syst.

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“…K-means algorithm is the most typical algorithm in the partition method, and it is also the most used algorithm today. It makes the elements in the data set move between different class clusters through multiple iterations until they reach the appropriate class cluster [11][12]. K in the k-means algorithm is the number of class clusters formed by the target.…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

Resource Allocation of Distributed System Based on Self-organizing Clustering Algorithm

2020

DPS

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With the development of network physical system, the distributed resource allocation strategy combined with physical system has been paid more and more attention, and many valuable research results have been obtained. This paper mainly studies the resource allocation of distributed system based on self-organizing clustering algorithm. This paper first introduces K-means clustering algorithm, and then proposes an intelligent scheduling algorithm based on K-means clustering algorithm. Through the simulation experiment, we can know that the resource allocation algorithm proposed in this paper is effective.

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“…al. [41,42] proposes the Multi Criteria Biased Randomized (MCBR) method, a selection method for large-scale systems that use unreliable nodes. MCBR method is based on a multicriteria optimization strategy.…”

Section: Service/node Placementmentioning

confidence: 99%

PiCasso: Enabling information-centric multi-tenancy at the edge of community mesh networks

et al. 2019

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Edge computing is radically shaping the way Internet services are run by enabling computations to be available close to the users -thus mitigating the latency and performance challenges faced in today's Internet infrastructure. Emerging markets, rural and remote communities are further away from the cloud and edge computing has indeed become an essential panacea. Many solutions have been recently proposed to facilitate efficient service delivery in edge data centers. However, we argue that those solutions cannot fully support the operations in Community Mesh Networks (CMNs) since the network connection may be less reliable and exhibit variable performance. In this paper, we propose to leverage lightweight virtualisation, Information-Centric Networking (ICN), and service deployment algorithms to overcome these limitations. The proposal is implemented in the PiCasso system, which utilises innetwork caching and name based routing of ICN, combined with our HANET (HArdware and NETwork Resources) service deployment heuristic, to optimise the forwarding path of service delivery in a network zone. We analyse the data collected from the Guifi.net Sants network zone, to develop a smart heuristic for the service deployment in that zone. Through a real deployment in Guifi.net, we show that HANET improves the response time up to 53% and 28.7% for stateless and stateful services respectively. PiCasso achieves 43% traffic reduction on service delivery in our real deployment, compared to the traditional host-centric communication. The overall effect of our ICN platform is that most content and service delivery requests can be satisfied very close to the client device, many times just one hop away, decoupling QoS from intra-network traffic and origin server load.

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Multi criteria biased randomized method for resource allocation in distributed systems: Application in a volunteer computing system

Cited by 17 publications

References 26 publications

An infrastructure-assisted job scheduling and task coordination in volunteer computing-based VANET

An infrastructure-assisted job scheduling and task coordination in volunteer computing-based VANET

Resource Allocation of Distributed System Based on Self-organizing Clustering Algorithm

PiCasso: Enabling information-centric multi-tenancy at the edge of community mesh networks

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