Revenue-optimal task scheduling and resource management for IoT batch jobs in mobile edge computing

Huang, Jiwei; Li, Songyuan; Chen, Ying

doi:10.1007/s12083-020-00880-y

Cited by 62 publications

(32 citation statements)

References 35 publications

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“…In [35], based on the queueing model, we formulated a stochastic optimization problem of energy efficient task scheduling for sensor hubs in IoT, and designed an efficient scheme to solve the problem. In [36], we further studied the task scheduling and resource management problem in mobile edge computing by designing a more efficient optimization algorithm. In [37], we applied the generalized SPN model and made a preliminary attempt to evaluate the performance and reliability simultaneously of IoT services.…”

Section: Discussionmentioning

confidence: 99%

A Simulation-Based Optimization Approach for Reliability-Aware Service Composition in Edge Computing

2020

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With the prevalence of Internet of Things (IoT), edge computing has emerged as a novel computing model for optimizing traditional cloud computing systems by moving part of the computational tasks to the edge of the network for better performance and security. With the technique of services computing, edge computing systems can accommodate the application requirements with more agility and flexibility. In large-scale edge computing systems, service composition as one of the most important problems in services computing suffers from several new challenges, i.e., complex layered architecture, failures and recoveries always in the lifecycle, and search space explosion. In this paper, we make an attempt at addressing these challenges by designing a simulation-based optimization approach for reliability-aware service composition. Composite stochastic Petri net models are proposed for formulating the dynamics of multi-layered edge computing systems, and their corresponding quantitative analysis is conducted. To solve the state explosion problem in large-scale systems or complex service processes, time scale decomposition technique is applied to improving the efficiency of model solving. Additionally, simulation schemes are designed for performance evaluation and optimization, and ordinal optimization technique is introduced to significantly reduce the size of the search space. Finally, we conduct experiments based on real-life data, and the empirical results validate the efficacy of the approach.

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Section: Discussionmentioning

confidence: 99%

A Simulation-Based Optimization Approach for Reliability-Aware Service Composition in Edge Computing

2020

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“…With queueing theory, we also proposed a multiqueue approach of energyefficient task scheduling for sensor hubs in IoT using Lyapunov optimization technique [33]. In [34], we investigated the task scheduling and resource management problem and designed an equivalent linear programming problem which could be efficiently and elegantly solved at polynomial computational complexity. In addition, we have explored generalized stochastic Petri net models for model-based performance evaluation and search-based optimization for both performance and reliability metrics [35].…”

Section: Discussionmentioning

confidence: 99%

Deploying GIS Services into the Edge: A Study from Performance Evaluation and Optimization Viewpoint

Zhang

Huang

2020

Security and Communication Networks

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Geographic information system (GIS) is an integrated collection of computer software and data used to view and manage information about geographic places, analyze spatial relationships, and model spatial processes. With the growing popularity and wide application of GIS in reality, performance has become a critical requirement, especially for mobile GIS services. To attack this challenge, this paper tries to optimize the performance of GIS services by deploying them into edge computing architecture which is an emerging computational model that enables efficient offloading of service requests to edge servers for reducing the communication latency between end-users and GIS servers deployed in the cloud. Stochastic models for describing the dynamics of GIS services with edge computing architecture are presented, and their corresponding quantitative analyses of performance attributes are provided. Furthermore, an optimization problem is formulated for service deployment in such architecture, and a heuristic approach to obtain the near-optimal performance is designed. Simulation experiments based on real-life GIS performance data are conducted to validate the effectiveness of the approach presented in this paper.

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“…where the second inequality follows from (14); and the third inequality follows from (13). PTAS-ALLOC searches all subsets Q when |Q| = q, so it certainly searches the set Q * and the set S * .…”

Section: B Propertiesmentioning

confidence: 99%

Energy-Efficient Task Allocation of Heterogeneous Resources in Mobile Edge Computing

Liu

2021

IEEE Access

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The edge cloud provides heterogeneous resources, such as cores, memory, and storage which are then allocated to mobile applications in mobile edge computing, which require multiple types of resources to execute. While current work on mobile edge computing focuses on tasks needing a single resource to run, we investigate the task allocation problem aiming to minimize total energy consumption while considering heterogeneous resource settings. Specifically, we consider the binary computation offloading mode, in which a task executes successfully as a whole. We formulate this as an integer programming problem, and transform that to the two subproblems of the resource allocation and the offloading decision. We propose a heuristic approach to solve the resource allocation subproblem and an approximation algorithm for the offloading decision subproblem. We show that our proposed approximation algorithm is a polynomial time approximation scheme, and hence it achieves a tradeoff between optimality loss and time complexity. To the best of our knowledge, this is the first study of task allocation considering both multiple types of resources and binary computation offloading mode. Experimental results demonstrate that the performance of our proposed algorithm scales very well for multi-resource allocation in different conditions, and it achieves a good balance of performance and speed.INDEX TERMS Mobile edge computing, computation offloading, resource allocation, energy efficient, polynomial time approximation scheme.

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Revenue-optimal task scheduling and resource management for IoT batch jobs in mobile edge computing

Cited by 62 publications

References 35 publications

A Simulation-Based Optimization Approach for Reliability-Aware Service Composition in Edge Computing

A Simulation-Based Optimization Approach for Reliability-Aware Service Composition in Edge Computing

Deploying GIS Services into the Edge: A Study from Performance Evaluation and Optimization Viewpoint

Energy-Efficient Task Allocation of Heterogeneous Resources in Mobile Edge Computing

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