An Application Placement Technique for Concurrent IoT Applications in Edge and Fog Computing Environments

Goudarzi, Mohammad; Wu, Huaming; Palaniswami, Marimuthu; Buyya, Rajkumar

doi:10.1109/tmc.2020.2967041

Cited by 259 publications

(135 citation statements)

References 24 publications

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“…• Baseline 2: We implement virtual machines dynamic computation offloading framework, which is adopted by these strategies [4]- [6]. The aim is to offload entire applications to heterogeneous servers when there are sufficient resources to fulfil the requirements.…”

Section: A Baseline Framework and Algorithmic Approachesmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Amir Masoud Rahmani . paradigm introduced for IoT applications with small end to end latency and placed at the edge of the radio access network [4]. Many studies [5]- [8] introduced computational offloading frameworks for resource constraint devices in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Microservice Based Computational Offloading Framework and Cost Efficient Task Scheduling Algorithm in Heterogeneous Fog Cloud Network

Zhao

Huang

2020

IEEE Access

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Nowadays, the usage of the fog cloud-based Internet of Things (IoT) applications among users has been growing progressively. These applications may be E-Transport, E-Healthcare, Augmented Reality, and 3D-Game. Generally, contemporary cloud frameworks offer services based on virtual machines. These frameworks incurred with following issues such as long boot-time, overhead and unnecessary cost to run the IoT applications. We propose a new Microservice container fog system (MSCFS) based framework to run the mobility and delay-sensitive applications with minimum cost. The study the cost-efficient task scheduling problem in the heterogeneous fog servers. Furthermore, the study introduces the Cost Aware Computational Offloading and task scheduling (CACOTS)framework, which solves the task scheduling into multiple steps. Such as task sequencing step, resource matching step and scheduling step. The experimental results prove that the proposed MSCFS and CACOTS schemes can enhance server utilization. As decrease the services latency and average services bootup time more effectively, and minimize costs. INDEX TERMS Task scheduling, fog servers, boot-up time, microservices, container.

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Section: A Baseline Framework and Algorithmic Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microservice Based Computational Offloading Framework and Cost Efficient Task Scheduling Algorithm in Heterogeneous Fog Cloud Network

Zhao

Huang

2020

IEEE Access

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“…Zhang et al [24] proposed an adaptive task offloading algorithm to optimize and balance the energy consumption of terminal devices and the overall task execution time. Goudarzi et al [25] proposed a parallel IoT batch application layout decision approach based on MEME algorithm to minimize the execution time and energy consumption of IoT applications in a computing environment with multiple IoT devices, multiple Fog/Edge servers and cloud servers. Cheng et al [26] studied the task allocation algorithm in the mobile edge computing system of data sharing, and then proposed three algorithms to reduce the delay and energy consumption needed to process global tasks and separable tasks respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Guo et al [29] studied the optimal allocation scheme of energy saving resources for multi-user mobile edge computing systems with inelastic computing tasks and non-negligible task execution time, and proposed a low complexity algorithm to solve the suboptimal solution by combining the optimization problem with the three-stage pipeline scheduling problem and using Johnson algorithm and convex optimization technology. The above studies [7,[19][20][21][22][23][24][25][26][27][28][29] all solve the corresponding joint optimization problem through the corresponding calculation offloading algorithm. However, their disadvantage lies in that the designed cost does not take into account the bandwidth resource consumption between tasks deployed on different edge servers and the energy consumption of edge clouds.…”

Section: Related Workmentioning

confidence: 99%

An Application Deployment Approach for City IoT Applications in Resource-constrained Edge Computing Environments

Liu¹,

Huang²

2020

Preprint

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With the development and utilization of more and more city Internet of Things (IoT) applications with high resource requirements, how to reduce the consumption of energy, processor resources and bandwidth resources in resource-constrained edge clouds while ensuring the execution delay of these applications is an urgent problem to be solved. Therefore, an optimal energy-bandwidth tradeoff deployment approach for city IoT application is proposed for resource-constrained edge clouds. In this approach, the city IoT applications are first divided into multiple collaborative tasks and offload to edge clouds. Secondly, a joint optimization model including energy consumption, resource wastage, resource load imbalance and bandwidth resource consumption is established for the task offloading scheme. Thirdly, the city IoT application deployment problem is optimized under the constraints of resource and execution delay. Finally, a comprehensive simulation test is conducted to analyze the deployment approaches from the aspects of performance and effectiveness. The experimental results show that our deployment approach is superior to other related approaches.

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“…However, due to the limited capacity (CPU, storage, etc.) of FENs, only a subset of service images can be placed on each FEN [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment

Choi

Ahn

2021

Sensors

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In recent years, we observed the proliferation of cloud data centers (CDCs) and the Internet of Things (IoT). Cloud computing based on CDCs has the drawback of unpredictable response times due to variant delays between service requestors (IoT devices and end devices) and CDCs. This deficiency of cloud computing is especially problematic in providing IoT services with strict timing requirements and as a result, gives birth to fog/edge computing (FEC) whose responsiveness is achieved by placing service images near service requestors. In FEC, the computing nodes located close to service requestors are called fog/edge nodes (FENs). In addition, for an FEN to execute a specific service, it has to be provisioned with the corresponding service image. Most of the previous work on the service provisioning in the FEC environment deals with determining an appropriate FEN satisfying the requirements like delay, CPU and storage from the perspective of one or more service requests. In this paper, we determined how to optimally place service images in consideration of the pre-obtained service demands which may be collected during the prior time interval. The proposed FEC environment is scalable in the sense that the resources of FENs are effectively utilized thanks to the optimal provisioning of services on FENs. We propose two approaches to provision service images on FENs. In order to validate the performance of the proposed mechanisms, intensive simulations were carried out for various service demand scenarios.

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An Application Placement Technique for Concurrent IoT Applications in Edge and Fog Computing Environments

Cited by 259 publications

References 24 publications

Microservice Based Computational Offloading Framework and Cost Efficient Task Scheduling Algorithm in Heterogeneous Fog Cloud Network

Microservice Based Computational Offloading Framework and Cost Efficient Task Scheduling Algorithm in Heterogeneous Fog Cloud Network

An Application Deployment Approach for City IoT Applications in Resource-constrained Edge Computing Environments

Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment

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An Application Placement Technique for Concurrent IoT Applications in Edge and Fog Computing Environments

Cited by 259 publications

References 24 publications

Microservice Based Computational Offloading Framework and Cost Efficient Task Scheduling Algorithm in Heterogeneous Fog Cloud Network

Microservice Based Computational Offloading Framework and Cost Efficient Task Scheduling Algorithm in Heterogeneous Fog Cloud Network

An Application Deployment Approach for City IoT&nbsp;Applications in Resource-constrained Edge Computing Environments

Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment

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Product

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An Application Deployment Approach for City IoT Applications in Resource-constrained Edge Computing Environments