YAFS: A Simulator for IoT Scenarios in Fog Computing

Lera, Isaac; Guerrero, Carlos; Juiz, Carlos

doi:10.1109/access.2019.2927895

Cited by 172 publications

(99 citation statements)

References 32 publications

(35 reference statements)

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“…The proposed embedding mechanisms were validated using YAFS simulator [33] given its strong support for Fog critical features and its capacity to introduce failures during the simulation [34], which allows evaluating the availability of the Service Chains. The experiments were conducted on a PC with 32GB 2400MHz DDR4 RAM and 2.80GHz Intel Core i7-7700HQ with 4 cores and 8 threads (2 threads per core) processor.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

Resilient Service Chains through Smart Replication

et al. 2020

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“…We will further consider the location of fog nodes and MEC nodes in continuous data flow problem [42]. Furthermore, we will conduct more simulations in an event-driven simulator [43], such as the YAFS [44], in our future work.…”

Section: Discussionmentioning

confidence: 99%

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Luo

Qiu

2019

Sensors

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The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization of IoT devices in continuous data-flow applications is a challenging problem. Since the anomalous nodes in the network will cause the increase of energy consumption, it is necessary to make continuous data flows bypass these nodes as much as possible. At present, the existing research work related to the performance of continuous data-flow is often optimized from system architecture design and deployment. In this paper, a mathematical programming method is proposed for the first time to optimize the runtime performance of continuous data flow applications. A lightweight anomaly detection method is proposed to evaluate the reliability of nodes. Then the node reliability is input into the optimization algorithm to estimate the task latency. The latency-aware energy consumption optimization for continuous data-flow is modeled as a mixed integer nonlinear programming problem. A block coordinate descend-based max-flow algorithm is proposed to solve this problem. Based on the real-life datasets, the numerical simulation is carried out. The simulation results show that the proposed strategy has better performance than the benchmark strategy.Sensors 2020, 20, 122 2 of 20 aggregation site, etc. [5]. On the other side, the fog computing nodes are not necessarily deployed around these access points. To distinguish these two concepts, in this paper, nodes with sufficient communication and computing resources scattered among IoT devices are referred to as fog nodes. The nano-server clusters deployed at the access points are referred to as MEC nodes, which usually have more resources than fog nodes. The collaborative computing of the IoT-end nodes, the fog nodes, the MEC nodes and the data center are referred to as IoT-Fog-Edge computing in this context.The studies on collaboration of Fog, MEC, and Cloud computing usually focus on finding an optimal solution to allocate the IoT tasks to appropriate virtual machines which are hosted on fog, MEC or cloud nodes. In [10], the authors propose to allocate the workload among local MEC servers, neighborhood MEC servers or cloud servers to minimize the energy consumption of MEC nodes subject to delay constraints. Lyapunov drift-plus-penalty-based dynamic queue evaluation is used for the online allocation algorithm. In [11], an optimal algorithm is put forward to determine that the tasks should be allocated to clouds near the end devices or to the one far from the end devices for the energy efficient big data processing. The delay constraints to tasks in near clouds and far cloud are taken into account. In [12], an optimal algorithm for joint task allocation among mobile devices, the computing access point and the remote cloud is proposed, where computing access point can be treated as an MEC node. The studies introduced above are not correlated with the continuous data-fl...

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“…For the implementation of FESDA scheme, [26] Tahir et al [27] PPFA [9] Figure 2: Aggregation cost comparison we have used the data-set of Irish smart grid [32], while, parameters used in our implementation are shown in Table III. Although, there are some cloud-fog simulators i.e., iFogSim [33] and YAFS [34] which have been used in various studies to calculate the latency, network congestion, energy consumption, and cost, etc., for cloud-fog scenarios. However, these simulators do not have support to implement the cryptographic schemes.…”

Section: Performance Evaluationmentioning

confidence: 99%

FESDA: Fog-Enabled Secure Data Aggregation in Smart Grid IoT Network

Saleem

Khan

Malik

et al. 2020

IEEE Internet Things J.

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With advances in Fog and edge computing, various problems such as data processing for large Internet of things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for smart grids incur high computation and communication costs, and in recent years there have been efforts to leverage fog computing with smart grids to overcome these limitations. In this paper, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of Paillier crypto-system is used to encrypt consumption data of the smart meter users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the smart meters malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the PPFA aggregation scheme.

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YAFS: A Simulator for IoT Scenarios in Fog Computing

Cited by 172 publications

References 32 publications

Resilient Service Chains through Smart Replication

Resilient Service Chains through Smart Replication

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

FESDA: Fog-Enabled Secure Data Aggregation in Smart Grid IoT Network

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