Combining Heuristics to Optimize and Scale the Placement of IoT Applications in the Fog (original) (raw)
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Proceedings of the 33rd Annual ACM Symposium on Applied Computing - SAC '18, 2018
As fog computing brings compute and storage resources to the edge of the network, there is an increasing need for automated placement (i.e., selection of hosting devices) to deploy distributed applications. Such a placement must conform to applications' resource requirements in a heterogeneous fog infrastructure. The placement decision-making is further complicated by Internet of Things (IoT) applications that are tied to geographical locations of physical objects/things. This paper presents a model, an objective function, and a mechanism to address the problem of placing distributed IoT applications in the fog. Based on a backtrack search algorithm and accompanied heuristics, the proposed mechanism is able to deal with large scale problems, and to efficiently make placement decisions that fit the objective-to lower placed applications' response time. The proposed approach is validated through comparative simulations of different combinations of the algorithms and heuristics on varying sizes of infrastructures and applications. CCS CONCEPTS • Software and its engineering → Distributed systems organizing principles;
Placement of IoT Microservices in Fog Computing Systems: A Comparison of Heuristics
Algorithms
In the last few years, fog computing has been recognized as a promising approach to support modern IoT applications based on microservices. The main characteristic of this application involve the presence of geographically distributed sensors or mobile end users acting as sources of data. Relying on a cloud computing approach may not represent the most suitable solution in these scenario due to the non-negligible latency between data sources and distant cloud data centers, which may represent an issue in cases involving real-time and latency-sensitive IoT applications. Placing certain tasks, such as preprocessing or data aggregation, in a layer of fog nodes close to sensors or end users may help to decrease the response time of IoT applications as well as the traffic towards the cloud data centers. However, the fog scenario is characterized by a much more complex and heterogeneous infrastructure compared to a cloud data center, where the computing nodes and the inter-node connecting...
Mapo a Multi Objective Model for Iot Application Placement in a Fog Environment
2019
The emergence of the Fog computing paradigm that leverages in-network virtualized resources raises important challenges in terms of resource and IoT application management in a heterogeneous environment offering only limited computing resources. In this work, we propose a novel Pareto-based approach for application placement close to the data sources called Multiobjective IoT Application Placement in fOg (MAPO). MAPO models applications based on a finite state machine and uses three conflicting optimization objectives, namely completion time, energy consumption, and economic cost, considering both the computation and communication aspects. In contrast to existing solutions that optimize a single objective value, MAPO enables multi-objective energy and cost-aware application placement. To evaluate the quality of the MAPO placements, we created both simulated and real-world testbeds tailored for a set of medical IoT application case studies. Compared to the stateof-the-art approaches, MAPO reduces the economic cost by up to 27%, while decreasing the energy requirements by 23-68%, and optimizes the completion time by up to 7.3 times.
IEEE Transactions on Mobile Computing
Fog/Edge computing emerges as a novel computing paradigm that harnesses resources in the proximity of the Internet of Things (IoT) devices so that, alongside with the cloud servers, provide services in a timely manner. However, due to the everincreasing growth of IoT devices with resource-hungry applications, fog/edge servers with limited resources cannot efficiently satisfy the requirements of the IoT applications. Therefore, the application placement in the fog/edge computing environment, in which several distributed fog/edge servers and centralized cloud servers are available, is a challenging issue. In this article, we propose a weighted cost model 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. Besides, a new application placement technique based on the Memetic Algorithm is proposed to make batch application placement decision for concurrent IoT applications. Due to the heterogeneity of IoT applications, we also propose a lightweight pre-scheduling algorithm to maximize the number of parallel tasks for the concurrent execution. The performance results demonstrate that our technique significantly improves the weighted cost of IoT applications up to 65 percent in comparison to its counterparts.
IET Communications
Fog computing is a decentralised model which can help cloud computing for providing high quality-of-service (QoS) for the Internet of Things (IoT) application services. Service placement problem (SPP) is the mapping of services among fog and cloud resources. It plays a vital role in response time and energy consumption in fog-cloud environments. However, providing an efficient solution to this problem is a challenging task due to difficulties such as different requirements of services, limited computing resources, different delay, and power consumption profile of devices in fog domain. Motivated by this, in this study, we propose an efficient policy, called MinRE, for SPP in fog-cloud systems. To provide both QoS for IoT services and energy efficiency for fog service providers, we classify services into two categories: critical services and normal ones. For critical services, we propose MinRes, which aims to minimise response time, and for normal ones, we propose MinEng, whose goal is reducing the energy consumption of fog environment. Our extensive simulation experiments show that our policy improves the energy consumption up to 18%, the percentage of deadline satisfied services up to 14% and the average response time up to 10% in comparison with the second-best results.
A Scalable and Flexible Platform for Service Placement inMulti-Fog and Multi-Cloud Environments
Research Square (Research Square), 2023
Provisioning services for Internet of Things (IoT) devices leads to several challenges: heterogeneity of IoT devices, varying Quality of Services (QoS) requirements, and increasing availability of both Cloud and Fog resources. The last of these is most significant to cope with the limitations of Cloud infrastructure providers (CIPs) for latency-sensitive services. Many Fog infrastructure providers (FIPs) have recently emerged and their number is increasing continually. FLEX is proposed in this work as a platform for selecting a location for service placement in a multi-Fog and multi-Cloud environment. For each service, FLEX broadcasts service requirements to the resource managers (RMs) of the available Fog and Cloud service providers and then selects the most suitable provider for that service. FLEX is scalable and flexible as it leaves it up to the RMs to have their own policy for the placement of submitted services. Service placement and resource selection has been formulated as an optimization problem and an efficient heuristic algorithm is proposed to solve it. Results show that the proposed algorithm can be used across both Cloud and Fog-based providers.
SCATTER: Service Placement in Real-Time Fog-Assisted IoT Networks
Journal of Sensor and Actuator Networks, 2021
Internet of Things (IoT) networks dependent on cloud services usually fail in supporting real-time applications as there is no response time guarantees. The fog computing paradigm has been used to alleviate this problem by executing tasks at the edge of the network, where it is possible to provide time bounds. One of the challenging topics in a fog-assisted architecture is to task placement on edge devices in order to obtain a good performance. The process of task mapping into computational devices is known as Service Placement Problem (SPP). In this paper, we present a heuristic algorithm to solve SPP, dubbed as clustering of fog devices and requirement-sensitive service first (SCATTER). We provide simulations using iFogSim toolkit and experimental evaluations using real hardware to verify the feasibility of the SCATTER algorithm by considering a smart home application. We compared the SCATTER with two existing works: edge-ward and cloud-only approaches, in terms of Quality of Serv...
BADEP: Bandwidth and delay efficient application placement in fog‐based IoT systems
Transactions on Emerging Telecommunications Technologies, 2020
Nowadays, fog computing is considered as a new computing method that provides the resources required to provide various services and applications to Internet of Things (IoT) devices near them. Nonetheless, in recent years, the increasing growth of IoT devices with heavily dependent on resources has made it impossible for fog computing to provide these resources efficiently in many cases, which will lead to delay in the delivery of various services. Thus, the placement of application/service in fog computing‐based environments will turn into a challenging issue. This was the motivation for this paper to present a method for placement of various IoT applications in fog computing‐based environments to reduce the delay in these systems. In the proposed method, it is tried to reduce the resulting delay by reducing the network usage in the fog computing layer. In doing so, the proposed method tried to place the applications so that the interdependent modules in these applications, with a ...
FLEX: A Platform for Scalable Service Placement in Multi-Fog and Multi-Cloud Environments
Australasian Computer Science Week 2022, 2022
With the recent development in the Internet of Things (IoT), big data, and machine learning, the number of services has dramatically increased. These services are heterogeneous in terms of the amount of resources and quality of service (QoS) requirements. To cope with the limitations of Cloud infrastructure providers (CIPs) for latency-sensitive services, many Fog infrastructure providers (FIPs) have recently emerged and their numbers are increasing continually. Due to difficulties such as the different requirements of services, location of end-users, and profile cost of IPs, distributing services across multiple FIPs and CIPs has become a fundamental challenge. Motivated by this, a flexible and scalable platform, FLEX, is proposed in this work for the service placement problem (SPP) in multi-Fog and multi-Cloud computing. For each service, FLEX broadcasts the service's requirements to the resource managers (RMs) of all providers and then based on the RMs' responses, it selects the most suitable provider for that service. The proposed platform is flexible and scalable as it leaves it up to the RMs to have their own policy for service placement. The problem is formulated as an optimization problem and an efficient heuristic algorithm is proposed to solve it. Our simulation results show that the proposed algorithm can meet the requirements of services.
An Efficient Application Deployment in Fog
International Journal of Information and Communication Technology Research, 2023
Fog computing emerged to meet to the needs of modern IoT applications, such as low latency, high security, etc. To this end, it brings the network resources closer to the end user. The properties of fog computing, such as heterogeneity, distribution, and resource limitations, have challenged application deployment in this environment. Smart service placement means deploying services of the IoT applications on fog nodes in a way that their service quality requirements are met and fog resources are used effectively. This paper proposes an efficient application deployment method in fog computing using communities. In contrast to previous research, the proposed method uses more factors than topological features to distribute network capacity more evenly between communities. This results in efficient use of network resources and better fulfillment of application requirements. In addition, according to our argument, using multiple criteria to prioritize applications will lead to better deployment and more effective use of resources. For this purpose, we use the number of application requests besides the deadline factor for application prioritization. Extensive simulation results showed that the proposed method significantly outperforms the state-of-the-art methods in terms of meeting deadlines, decreasing delays, increasing resource utilization, and availability by about 17, 33, 7, and 11 percent, respectively.