雾计算中的最后期限感知任务调度策略

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"本文主要探讨了在分层物联网（IoT）基础设施中，如何通过引入雾计算（Fog Computing）实现最后期限感知的任务调度策略，以优化服务延迟并支持需要实时响应的移动应用。" 物联网（IoT）的快速发展使得传统的“云中心”架构面临挑战，因为它无法有效地处理物联网的数据处理和通信需求，特别是在处理需要近乎实时响应的新兴移动应用时。为了解决这一问题，雾计算作为一种新的计算范式应运而生。雾计算将计算、通信、控制和存储功能分布到更接近终端用户的位置，即在“云到事物”的连续体中，从而减少了数据传输延迟，提高了服务质量。本文的核心是提出了一种基于最后期限感知的任务调度机制，它适用于分层IoT架构下的雾计算环境。在这种机制中，服务提供商利用自身雾节点与租用的云端资源之间的协作，以高效地执行用户卸载的任务。任务调度的目标是在满足任务最后期限的同时，优化资源利用率和系统效率。作者Jianhua Fan、Xianglin Wei、Tongxiang Wang（来自南京电信技术研究所）、Tian Lan和Suresh Subramaniam（来自乔治华盛顿大学）通过理论分析和模拟实验，研究了不同任务调度策略对服务延迟、任务完成率和资源利用率的影响。他们强调了在考虑任务优先级和资源约束的情况下，如何制定有效的调度决策，以确保任务能够在其预定的最后期限内完成。最后期限感知的任务调度策略通常涉及以下几个关键步骤： 1. **任务建模**：首先，需要对每个任务进行建模，包括任务的执行时间、依赖关系以及最后期限。 2. **资源分配**：根据任务需求和可用资源，确定最佳的雾节点或云资源分配。 3. **调度算法设计**：设计能够考虑最后期限的调度算法，可能包括优先级调度、贪心算法或混合方法。 4. **动态调整**：由于物联网环境的动态性，调度策略需要能够适应资源变化和新任务的出现，进行实时调整。 5. **性能评估**：通过模拟或实际部署，评估调度策略的性能，如平均服务延迟、任务完成率和资源利用率。这项研究对于理解如何在复杂的IoT环境中实现高效的任务调度具有重要意义，为未来物联网应用的设计和优化提供了理论依据。同时，它也为服务提供商和系统开发者提供了一种有效的方法，以应对不断增长的实时数据处理需求。

Deadline-Aware Task Scheduling in a Tiered IoT

Infrastructure

Jianhua Fan, Xianglin Wei, Tongxiang Wang

Nanjing Telecommunications Technology Institute

Nanjing, China

fjh7659@163.com, wei_xianglin@163.com

Tian Lan, Suresh Subramaniam

The George Washington University

Washington DC, US

tlan@email.gwu.edu, suresh@email.gwu.edu

Abstract—With the proliferation of the Internet of Things

(IoT), the current “cloud-only” architectures cannot efficiently

handle IoT’s data processing and communications needs, while

providing satisfactory service latency to support emerging

mobile applications on the horizon that require almost real-

time responses. fog computing is introduced as a new

computing paradigm that distributes computation,

communication, control, and storage closer to the end users

along the “cloud-to-things” continuum. In this paper, we

present a deadline-aware task scheduling mechanism for fog

computing in a tiered IoT infrastructure, where service

providers exploit the collaboration between their own fog

nodes and the rented cloud resources to efficiently execute

users’ offloaded tasks, at large geographical scale. We first

formulate the task-scheduling problem in such a cloud-fog

environment as a multi-dimensional 0-1 knapsack problem

that is NP-hard, and then propose an efficient algorithmic

solution based on ant colony optimization heuristic. The main

objective is to maximize the profits of fog service provider

while meeting the tasks’ deadline constraint. Extensive

experimental results show that our proposed optimization and

solution significantly improves the system performance

compared with existing heuristics.

I. INTRODUCTION

As smart cities, smart transportation, and smart homes

are transitioning toward the real world, cloud computing,

which employs computation resources located far away from

the end users and relies on robust network infrastructure,

falls short on providing low-latency guarantees to deadline-

sensitive applications that are very common in Internet of

Things (IoT) scenarios [1].

To address this issue, fog computing (also known as

Edge Computing) extends the cloud computing paradigm to

the edge of the network, in close proximity to end devices

that serve as both data generator and consumer. Many

characteristics of fog computing make it the appropriate

platform to support critical IoT services and applications. By

placing fog resources (i.e., local computing infrastructures)

within one-hop of the IoT devices, fog computing can bring

a number of key advantages including: a) Low service

latency and location awareness; b) Wide-spread geographical

distribution and coverage; c) Mobility; d) Scalability with

respect to system/network scale, e) Ubiquitous wireless

access, f) Strong support for real-time and ultra-low-latency

applications, g) Heterogeneity and diversity [2]. A typical

architecture of fog computing is shown in Fig. 1. The

cloudlet entities act as the local fog computing platforms,

connecting to the remote datacenters through the Internet.

IoT devices, residing on the network edge, connect to the

cloudlet through a wireless network (e.g., cellular or WiFi)

and can offload their computation tasks to the cloudlet or the

datacenters, if the desired application deadlines can be met.

Figure 1. Architecture of a tiered IoT system including cloudlets for edge

computing, datacenters for cloud computing, and IoT devices.

Harnessing both fog and cloud computing can enable

more agile and context-aware services since the cloudlet

entities and the datacenter usually have much more resources

than IoT devices and are able to more rapidly execute

computation-intensive tasks. Moreover, placing computing

resources at the edge of the network allows fog nodes to

efficiently process latency-sensitive tasks in a timely manner,

while large-scale and latency-tolerant tasks can still be

efficiently processed by the cloud that is equipped with more

computing power. Many IoT applications such as big data

analytics [3] require the interplay and cooperation between

the edge (fog) and the core (cloud), calling for a joint

optimization of both fog and cloud resources in the network.

In this paper, we present a deadline-constrained task

scheduling framework for IoT systems with a joint fog and

cloud computing architecture. Our goal is to maximize the

total net profit received by service providers through task

scheduling and placement, while meeting application

deadline requirements and satisfying resource capacity

constraints in both fog and cloud networks. A fog service

provider can exploit the collaboration between its own fog

nodes (including cloudlet and participating mobile user

devices) and the rented cloud resources for efficiently

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