Joint Container Placement and Task Provisioning in Dynamic Fog Computing

Mseddi, Amina; Jaafar, Wael; Elbiaze, Halima; Ajib, Wessam

doi:10.1109/jiot.2019.2935056

Cited by 60 publications

(26 citation statements)

References 35 publications

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“…(4) Using real-life data (daily call arrivals in 6 months of 2020) and synthetic data (capabilities of devices), we experimentally verify the effectiveness and efficiency of our proposed method. Compared with five stateof-the-art methods for task scheduling, i.e., linear programming (LP [21]), integer programming (IP [22]), MIP [16]), improved particle swarm optimization (IPSO [23]), and multiobjective evolutionary algorithm-based decomposition (MOEAD [24]), we find that PACAM is at least two orders of magnitude faster than the above methods.…”

Section: Motivation Example (Internet Of Vehicle)mentioning

confidence: 95%

PACAM: A Pairwise-Allocated Strategy and Capability Average Matrix-Based Task Scheduling Approach for Edge Computing

Hong

Zhang

Cao

et al. 2022

Security and Communication Networks

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With the development of the smart Internet of Things (IoT), an increasing number of tasks are deployed on the edge of the network. Considering the substantially limited processing capability of IoT devices, task scheduling as an effective solution offers low latency and flexible computation to improve the system performance and increase the quality of services. However, limited computing resources make it challenging to assign the right tasks to the right devices at the edge of the network. To this end, we propose a polynomial-time solution, which consists of three steps, i.e., identifying available devices, estimating device quantity, and searching for feasible schedules. In order to shrink the number of potential schedules, we present a pairwise-allocated strategy (PA). Based on these, a capability average matrix (CAM)-based index is designed to further boost efficiency. In addition, we evaluate the schedules by the technique for order preference by similarity to an ideal solution (TOPSIS). Extensive experimental evaluation using both real and synthetic datasets demonstrates the efficiency and effectiveness of our proposed approach.

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Section: Motivation Example (Internet Of Vehicle)mentioning

confidence: 95%

PACAM: A Pairwise-Allocated Strategy and Capability Average Matrix-Based Task Scheduling Approach for Edge Computing

Hong

Zhang

Cao

et al. 2022

Security and Communication Networks

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“…Unlike traditional 5G telco data centers, these are characterized by sporadic resources availability, mobility, and increased flexibility. Proposals made on distributed redundant placement frameworks, resource management algorithms and server selection algorithm [21] and [22] are primarily done to bring microservice applications on cloud closer to the devices i.e. network edge.…”

Section: Related Workmentioning

confidence: 99%

“…The aforementioned algorithms and design are most suitable with fog/edge computing especially when combined with Internet of Things type of data. These proposals [20], [21] and [22] focus on enabling ability to have stateless services deployed on the fly but do not share light on how heavy stateful services can be managed on a containerization and micro-service 5G telco cloud environment.…”

Section: Related Workmentioning

confidence: 99%

CASE: A Context-Aware Storage Placement and Retrieval Ecosystem

et al. 2022

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Emerging cloud-native technologies, such as container-runtime and container-orchestrator offer unprecedented agility in developing and running applications, especially when combined with microservice-style architecture. Several existing 5G-Telecom network products such as element Management System (e-MS), 5G-Core and 5G-Access are being redesigned to fit the microservice paradigm. Cloud environment allows enterprises to scale their application on-demand with minimum cost; however, it is often difficult to use containers without sacrificing many benefits that cloud-native technology offers. The e-MS is characterized to orchestrate 5G network elements (5GNEs) deployed nationwide, and systematically store terabytes of stateful data per second. Containers are characterized to have an ephemeral state, hence 'statefulness' aspect of e-MS makes orchestration complex. In this paper, different challenges around stateful storage selection, content placement and content retrieval operations within e-MS microservices are described. To overcome these challenges, to this end, we propose CASE -A Context-Aware Storage placement and retrieval Ecosystem -which enables context-based operations to be intrinsically supported by the underlying e-MS application. Our approach has been designed to maintain the location-independent philosophy of cloud-native by associating context information directly to 5GNE rather than fixed storage entities, thereby ensuring scalability. Through simulation with real data-set obtained from one of the world's largest terrestrial telecom operator, we show that based on such location-independent context information, CASE with e-MS can facilitate high performance despite dynamic 5GNE count agility, stateless e-MS replication and stateful storage scaling, while not posing a significant signaling burden on the cloud environment.

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“…We also introduce different placement mechanisms to account for device discovery, failures and overloads. Mseddi et al (2019) introduce service placement implemented by the means of particleswarm optimization, a greedy algorithm, and an exact optimization. The goal of their placement is to maximize the number of executed applications adhering to their time constraints.…”

Section: Service Placementmentioning

confidence: 99%

FogFrame: a framework for IoT application execution in the fog

Skarlat¹,

Schulte²

2021

PeerJ Computer Science

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Recently, a multitude of conceptual architectures and theoretical foundations for fog computing have been proposed. Despite this, there is still a lack of concrete frameworks to setup real-world fog landscapes. In this work, we design and implement the fog computing framework FogFrame—a system able to manage and monitor edge and cloud resources in fog landscapes and to execute Internet of Things (IoT) applications. FogFrame provides communication and interaction as well as application management within a fog landscape, namely, decentralized service placement, deployment and execution. For service placement, we formalize a system model, define an objective function and constraints, and solve the problem implementing a greedy algorithm and a genetic algorithm. The framework is evaluated with regard to Quality of Service parameters of IoT applications and the utilization of fog resources using a real-world operational testbed. The evaluation shows that the service placement is adapted according to the demand and the available resources in the fog landscape. The greedy placement leads to the maximum utilization of edge devices keeping at the edge as many services as possible, while the placement based on the genetic algorithm keeps devices from overloads by balancing between the cloud and edge. When comparing edge and cloud deployment, the service deployment time at the edge takes 14% of the deployment time in the cloud. If fog resources are utilized at maximum capacity, and a new application request arrives with the need of certain sensor equipment, service deployment becomes impossible, and the application needs to be delegated to other fog resources. The genetic algorithm allows to better accommodate new applications and keep the utilization of edge devices at about 50% CPU. During the experiments, the framework successfully reacts to runtime events: (i) services are recovered when devices disappear from the fog landscape; (ii) cloud resources and highly utilized devices are released by migrating services to new devices; (iii) and in case of overloads, services are migrated in order to release resources.

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Joint Container Placement and Task Provisioning in Dynamic Fog Computing

Cited by 60 publications

References 35 publications

PACAM: A Pairwise-Allocated Strategy and Capability Average Matrix-Based Task Scheduling Approach for Edge Computing

PACAM: A Pairwise-Allocated Strategy and Capability Average Matrix-Based Task Scheduling Approach for Edge Computing

CASE: A Context-Aware Storage Placement and Retrieval Ecosystem

FogFrame: a framework for IoT application execution in the fog

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