Evaluation and efficiency comparison of evolutionary algorithms for service placement optimization in fog architectures

Guerrero, Carlos; Lera, Isaac; Juiz, Carlos

doi:10.1016/j.future.2019.02.056

Cited by 95 publications

(60 citation statements)

References 68 publications

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“…The authors in [17] presented a fog-based solution that offers computation and storage services for IoT applications. It aims to reduce the network latency of these applications by placing services into fog nodes close to the IoT devices.…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Optimization of Task-to-Node Assignment in Opportunistic Fog RAN

2020

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The opportunistic fog radio access network (OF-RAN) is a promising RAN architecture proposed for next-generation cellular networks. OF-RAN extends the current fog RAN (F-RAN) architecture by introducing virtual fog access points (v-FAPs) that can be formed on-demand by a set of resourceful end-user devices available in an opportunistic manner. These devices in the v-FAP collaboratively serve as the service nodes to a resource-limited local client by performing resource-demanding processing tasks on its behalf. Hence, appropriately assigning the client task to the service nodes of the v-FAP, i.e., task-to-node assignment (TNA), is a fundamental problem in OF-RAN. This paper formulates and solves the TNA as a multi-objective optimization problem, with the goals of minimizing energy and latency of the v-FAP, while maximizing fairness (or load balancing) amongst its service nodes by minimizing their maximum load.

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Section: Related Workmentioning

confidence: 99%

Multi-Objective Optimization of Task-to-Node Assignment in Opportunistic Fog RAN

2020

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“…Three evolutionary algorithms for service embedding in the Fog are proposed by Guerrero et al [15], and three objectives were drawn: minimizing latency; minimizing free resources; and optimizing service spread (even distribution of services). These proposals suffer from scalability and modularity problems, like any other basic genetic algorithm [16].…”

Section: Related Workmentioning

confidence: 99%

“…From the reviewed literature, some observations arise. First, simulation seems to be the main method for evaluation [13], [15], [19], [21]- [23], which is due to the complex scenario comprised by the Cloud to IoT continuum. Mathematical solutions are also broadly used, following ILP or MILP models [13], [17], [19], [22]; even though these models perform well in offline studies, they suffer from time constraints when applied to online scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…Mathematical solutions are also broadly used, following ILP or MILP models [13], [17], [19], [22]; even though these models perform well in offline studies, they suffer from time constraints when applied to online scenarios. Although the embedding problem has been previously addressed, SCs resiliency has been mostly overlooked [13]- [15], [17], [18]. The works considering resiliency can be grouped into link failures [19], [20], and node failures via resource allocation [21] or replication [22], [23].…”

Section: Related Workmentioning

confidence: 99%

“…In order to keep the results from the first level, the acceptance vector resulting from the first optimization level is fixed and used as a parameter for the second optimization level. Constraints depicted from (15) to (19) are kept at this stage to guarantee feasibility.…”

Section: Volume 4 2016mentioning

confidence: 99%

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Resilient Service Chains through Smart Replication

et al. 2020

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The Internet of Things paradigm enables a new set of smart end-user applications. The Cloud-Fog-Mist-Internet of Things infrastructure provides communication, compute, and storage support for these applications. However, this complex, heterogeneous, and distributed landscape requires orchestration and management mechanisms in order to guarantee their proper functioning. One particular factor to manage is the capacity to provide service resilience even in the presence of failures in components of the substrate infrastructure. This research proposes a set of mechanisms to formalize, orchestrate, and embed a batch of service requests for chained Virtual Functions to fulfill the specific requirements of applications while enhancing their availability and ultimately their resilience. In detail, this work introduces a formal grammar to describe customized Service Chains, allowing the definition of replicas for different Virtual Functions, and an Integer Linear Programming model for Virtual Function embedding that prioritizes the use of nodes with higher availability. Additionally, an alternative heuristic is presented to handle more complex scenarios by taking advantage of the multi-tier scenario comprising the Cloud-Fog-Mist-Internet of Things. Simulation results for the embedding mechanisms show that it is possible to increase the resilience of chained Virtual Functions, while balancing the load of the infrastructure nodes.

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DMAP: A decentralized matching game theory based optimized Internet of Things application placement in fog computing environment

Sharma

Thangaraj

2022

Concurrency and Computation

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Summary Internet of Things (IoT) devices have become part of our daily life. IoT applications are used in vast domains such as smart healthcare, smart cities, smart transportation, Industry 4.0, and so forth. However, many IoT applications come under the ultra‐reliable and low‐latency communications category; minimal execution time is crucial for such applications. Limitations such as network reliability and the cloud's multi‐hop distance to the IoT devices can affect providing efficient solutions for IoT applications. Fog computing has emerged as an important paradigm that extends cloud computing by delivering cloud‐like services nearer to the end‐users. Placement of IoT applications onto the appropriate fog nodes has an important influence on the overall execution time of applications and energy consumption of fog nodes. Efficiently deploying IoT applications to fog nodes is difficult due to two factors: fog nodes have varying processing capacity and are geographically located in different places from IoT devices. Hence, this article proposes a decentralized bi‐objective optimization application placement policy, that is, DMAP, to minimize IoT applications' overall execution time and energy consumption of fog nodes. The matching game methodology is used for mapping applications to fog nodes. The performance of DMAP is verified using large‐scale simulation experiments. Experimental results show significant improvement in overall execution time, energy consumption, and scalability compared to the existing solutions.

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Evaluation and efficiency comparison of evolutionary algorithms for service placement optimization in fog architectures

Cited by 95 publications

References 68 publications

Multi-Objective Optimization of Task-to-Node Assignment in Opportunistic Fog RAN

Multi-Objective Optimization of Task-to-Node Assignment in Opportunistic Fog RAN

Resilient Service Chains through Smart Replication

DMAP: A decentralized matching game theory based optimized Internet of Things application placement in fog computing environment

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