A lightweight decentralized service placement policy for performance optimization in fog computing

Guerrero, Carlos; Lera, Isaac; Juiz, Carlos

doi:10.1007/s12652-018-0914-0

Cited by 126 publications

(85 citation statements)

References 48 publications

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“…The OMNeT++ simulator was used to show how MigCEP improves live migration against a static and a greedy strategy over a dynamic large‐scale infrastructure (ie, 1000 emulated connected cars). No codebase was released for either the works of Guerrero et al or Ottenwälder et al…”

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

“…There were only four papers that included more than one of those application constraints, more concretely, the workload and dependency constraints. Guerrero et al considered both the request rate of the applications to prioritize the placement of some application and the interrelation between the services. They considered that the interrelated services of an application should be allocated in devices within the network shortest path between the user and the cloud provider, and the placement order was determined by the topological order of the services, placing the initial services closer to the users.…”

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

“…In some other cases, the network latency is not measured directly, and indicators such as the hop count are considered . Guerrero et al also minimized the hop count between the users and the placement of the services with the objective to reduce the application latency and the network usage. The number of migrations was also included in the metrics evaluated in the experimental phase.…”

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

“…The OMNeT++ 64 simulator was used to show how MigCEP improves live migration against a static and a greedy strategy over a dynamic large-scale infrastructure (ie, 1000 emulated connected cars). No codebase was released for either the works of Guerrero et al 27 or Ottenwälder et al 28 In this context, Brogi and Forti 29 proposed both an exhaustive and a greedy backtracking algorithm to solve FAPP based on the (hardware, software, IoT, and QoS) requirements of multicomponent applications. The greedy heuristic attempts the placement of components sorted in ascending order on the number compatible nodes (ie, fail-first), considering candidate nodes one by one sorted in decreasing order on the available resources (ie, fail-last).…”

Section: Search-based Algorithmsmentioning

confidence: 99%

“…26,29,61 Mahmud et al 44 also considered a general resource value, but they additionally defined the service demand and device capacity in terms of the expected and offered processing times. On the contrary, this model was sometimes simplified to a scalar value that represents a general capacity unit 27,35,[51][52][53] or with general resources slots. 42,59 Finally, some other papers defined the hardware resources of the fog computing nodes, but they did not include this constraint in the optimization process to simplify it.…”

Section: Node Constraintsmentioning

confidence: 99%

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How to place your apps in the fog: State of the art and open challenges

et al. 2019

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Summary Fog computing aims at extending the cloud towards the Internet of things so to achieve improved quality of service and to empower latency‐sensitive and bandwidth‐hungry applications. The fog calls for novel models and algorithms to distribute multiservice applications in such a way that data processing occurs wherever it is best placed, based on both functional and nonfunctional requirements. This survey reviews the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open‐source prototypes and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog.

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Section: Analysis Of the State Of The Artmentioning

confidence: 99%

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

Section: Search-based Algorithmsmentioning

confidence: 99%

Section: Node Constraintsmentioning

confidence: 99%

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How to place your apps in the fog: State of the art and open challenges

et al. 2019

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Optimization policy for file replica placement in fog domains

Guerrero

Lera

Juiz

2019

Concurrency and Computation

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Fog computing architectures distribute computational and storage resources along the continuum from the cloud to things. Therefore, the execution of services or the storage of files can be closer to the users. The main objectives of fog computing domains are to reduce the user latency and the network usage. Availability is also an issue in fog architectures because the topology of the network does not guarantee redundant links between devices. Consequently, the definition of placement polices is a key challenge. We propose a placement policy for data replication to increase data availability that contrasts with other storage policies that only consider a single replica of the files. The system is modeled with complex weighted networks and topological features, such as centrality indices. Graph partition algorithms are evaluated to select the fog devices that store data replicas. Our approach is compared with two other placement policies: one that stores only one replica and FogStore, which also stores file replicas but uses a greedy approach (the shortest path). We analyze 22 experiments with simulations. The results show that our approach obtains the shortest latency times, mainly for writing operations, a smaller network usage increase, and a similar file availability to FogStore.

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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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A lightweight decentralized service placement policy for performance optimization in fog computing

Cited by 126 publications

References 48 publications

How to place your apps in the fog: State of the art and open challenges

How to place your apps in the fog: State of the art and open challenges

Optimization policy for file replica placement in fog domains

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

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