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Function-Aware Resource Management Framework for serverless edge computing

IEEE INTERNET OF THINGS JOURNAL 2023年第2期10卷 1310-1319页

作者： Ko, Haneul Pack, Sangheon Kyung Hee Univ Dept Elect Engn Yongin 17104 Gyeonggi South Korea Korea Univ Sch Elect Engn Seoul 02841 South Korea

serverless edge computing is an emerging concept where only required functions are defined and executed as container instances at the edge cloud. The edge cloud has finite resources;therefore, sophisticated resource management is indispensable to accommodate more requests. In this article, we propose a function-aware resource management (FARM) framework for serverless edge computing that defines per-function queues to maximally utilize edge cloud resources. The FARM framework optimally determines: 1) which container instances should be maintained as warm status and 2) the amount of computing resources assigned to them. The FARM framework specifically formulates a constrained Markov decision process problem to minimize the memory resource consumption for the warm status maintenance while guaranteeing on-time task completion and converts it to a linear programming model to derive the optimal solution. The evaluation results show that the FARM framework can reduce the memory resource consumption of the edge cloud while meeting the on-time task completion.

关键词： Function-aware resource management (FARM) joint optimization serverless edge computing warm start

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Dynamic Time-of-use Pricing for serverless edge computing with Generalized Hidden Parameter Markov Decision Processes 44

Dynamic Time-of-use Pricing for Serverless Edge Computing wi...

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44th IEEE International Conference on Distributed computing Systems (ICDCS)

作者： Tutuncuoglu, Feridun Ben-Ameur, Ayoub Dan, Gyorgy Araldo, Andrea Chahed, Tijani KTH Royal Inst Technol Div Network & Syst Engn Stockholm Sweden Inst Polytech Paris Telecom SudParis SAMOVAR Paris France

ISBN: (纸本)9798350386066;9798350386059

The commercial adoption of edge computing (EC) will require pricing schemes that cater to the financial interests of the operators and of the users. Pricing in EC is particularly challenging as it has to take into account the limited amount of edge resources as well as the stochasticity of user workloads due to location-specific workload characteristics and differences in user activity. We formulate the problem of maximizing the revenue of a serverless edge operator through dynamically pricing compute and memory resources under time varying workloads as a sequential decision making problem under uncertainty. We provide analytical results for the optimal pricing strategy in a Markovian setting in steady state. For the general case, we propose a novel Generalized Hidden Parameter Markov Decision Process (GHP-MDP) formulation of the revenue maximization problem, and we propose a dual Bayesian neural network approximator as a solution. The key novelty of the proposed solution is that it can be pre-trained on synthetic traces and adapts fast to previously unseen workload characteristics. We use simulations based on synthetic and real traffic traces to show that the proposed solution is sample-efficient thanks to effective transfer learning, and it outperforms state-of-the-art learning approaches in terms of revenue and learning rate by up to 50% on real traces.

关键词： serverless edge computing dynamic pricing resource management queuing theory transfer learning

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faas-sim: A trace-driven simulation framework for serverless edge computing platforms

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SOFTWARE-PRACTICE & EXPERIENCE 2023年第12期53卷 2327-2361页

作者： Raith, Philipp Rausch, Thomas Furutanpey, Alireza Dustdar, Schahram TU Wien Distributed Syst Grp Vienna Austria

This paper presents faas-sim, a simulation framework tailored to serverless edge computing platforms. In serverless computing, platform operators are tasked with efficiently managing distributed computing infrastructure completely abstracted from application developers. To that end, platform operators and researchers need tools to design, build, and evaluate resource management techniques that efficiently use of infrastructure while optimizing application performance. This challenge is exacerbated in edge computing scenarios, where, compared to cloud computing, there is a lack of reference architectures, design tools, or standardized benchmarks. faas-sim bridges this gap by providing (a) a generalized model of serverless systems that builds on the function-as-a-service abstraction, (b) a simulator that uses trace data from real-world edge computing testbeds and representative workloads, and (c) a network topology generator to model and simulate distributed and heterogeneous edge-cloud systems. We present the conceptual design, implementation, and a thorough evaluation of faas-sim. By running experiments on both real-world test beds and replicating them using faas-sim, we show that the simulator provides accurate results and reasonable simulation performance. We have profiled a wide range of edge computing infrastructure and workloads, focusing on typical edge computing scenarios such as edge AI inference or data processing. Moreover, we present several instances where we have successfully used faas-sim to either design, optimize, or evaluate serverless edge computing systems.

关键词： co-simulation edge-cloud continuum serverless edge computing simulation

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Distributed Task Scheduling in serverless edge computing Networks for the Internet of Things: A Learning Approach

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IEEE INTERNET OF THINGS JOURNAL 2022年第20期9卷 19634-19648页

作者： Tang, Qinqin Xie, Renchao Yu, Fei Richard Chen, Tianjiao Zhang, Ran Huang, Tao Liu, Yunjie Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Purple Mt Labs Dept Future Networks Nanjing 211111 Peoples R China Carleton Univ Sch Informat Technol Ottawa ON K1S 5B6 Canada

By delegating the infrastructure management, such as provisioning or scaling to third-party providers, serverless edge computing has recently been widely adopted in several applications, especially Internet of Things (IoT) applications. Task scheduling is a critical issue in serverless edge computing as it significantly impacts the quality of user experience. In contrast to the centralized scheduling in the cloud center, serverless edge task scheduling is more challenging due to the heterogeneous and resource-constrained nature of edge resources. This article aims to study the distributed task scheduling for the IoT in serverless edge computing networks, in which heterogeneous serverless edge computing nodes are rational individuals with interests to optimize their own scheduling utility while the nodes only have access to local observations. The task scheduling competition process is formulated as a partially observable stochastic game (POSG) to enable serverless edge computing nodes to noncooperatively schedule tasks and allocate computing resources depending on their locally observed system state, which takes into account the associated task generation state, data queue state, communication channel state, and previous computing resource allocation state. To solve the proposed POSG and deal with the partial observability, a multiagent task scheduling algorithm based on the dueling double deep recurrent Q-network (D3RQN) method is developed to approximate the optimal task scheduling and resource allocation solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme.

关键词： Deep reinforcement learning (DRL) distributed task scheduling Internet of Things (IoT) serverless edge computing stochastic game

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Workflow Scheduling Using Hybrid PSO-GA Algorithm in serverless edge computing for the Internet of Things 95

Workflow Scheduling Using Hybrid PSO-GA Algorithm in Serverl...

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IEEE 95th Vehicular Technology Conference: (VTC-Spring)

作者： Xie, Renchao Gu, Dier Tang, Qinqin Huang, Tao Yu, F. Richard Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada

ISBN: (数字)9781665482431

ISBN: (纸本)9781665482431

In this paper, we design a task scheduling scheme for Internet of Things (IoT) workflow applications in serverless edge computing. Notice the fact that complex applications in traditional serverless computing are decomposed into several stateless, dependent functions, whose execution environments are pre-deployed at the resource-finite edge domain, we model the workflow application as Directed Acyclic Graph (DAG) by considering the distribution of edge resources and the deployment of serverless functions. We further formulate the scheduling problem as a multi-objective optimization problem to reduce the time consumption, energy consumption, and cost simultaneously. Then, considering the diversity of solution space and the fast convergence to optimal solutions, an improved hybrid algorithm that combines Particle Swarm Optimization and Genetic Algorithm (PSO-GA) is introduced and utilized to make the scheduling decision. Finally, extensive simulation experiments are conducted to validate the superiority of the proposed scheme.

关键词： serverless edge computing workflow scheduling multi-objective optimization PSO-GA

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SFSM: A serverless Function Scheduling Method for FaaS Applications over edge computing

SFSM: A Serverless Function Scheduling Method for FaaS Appli...

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IEEE International Conference on Web Services (IEEE ICWS)

作者： Tian, Hao Chen, Cheng Dai, Fei Dou, Wanchun Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Southwest Forestry Univ Coll Big Data & Intelligent Engn Kunming Yunnan Peoples R China

ISBN: (纸本)9798350368567;9798350368550

serverless edge computing is emerging as an enabler to provision scalable and flexible Function-as-a-Service (FaaS) applications with lightweight function instances at network edge. In serverless edge computing, the function instances with inter-dependencies are scheduled to proximate edge nodes in a distributed manner. However, the heterogeneity and unpredictability of edge networks bring significant challenge in realizing optimal scheduling decision to guarantee execution performance of applications without any prior. In view of this challenge, a serverless Function Scheduling Method, named SFSM, is proposed in this paper for FaaS applications over edge computing. First, a long-term optimization problem is formulated to reduce completion time and decoupled into time-slot sub-problems via Lyapunov optimization. To avoid the cross-edge redundant data transmission overhead of inter-functions, a two-level graph optimization is designed with vertical and horizontal data merging. Then, SFSM incorporates an online multi-armed bandit-based scheduling algorithm that only requires the context of requests without complete information of edge networks. Finally, extensive experimental results based on real-world datasets demonstrate the effectiveness and superiority of SFSM.

关键词： serverless edge computing serverless function scheduling graph optimization online learning multi-armed bandit

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The Potential of serverless edge-powered Islands for Web Development

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JOURNAL OF WEB ENGINEERING 2025年第1期24卷 1-38页

作者： Vepsalainen, Juho Vuorimaa, Petri Hellas, Arto Aalto Univ Dept Comp Sci Espoo Finland

Web developers face two significant challenges when developing their applications and websites: latency and payload size. Given that web services rely on servers, the related communication incurs a cost in terms of latency. In contrast, the payload passed to the client incurs a communication cost, not to mention the computational cost to the client. The concept of serverless edge computing, built on top of content delivery networks (CDNs), is an approach that has begun to gain the attention of web developers for its promise of lower latencies due to its efficiencies in communication thanks to globally distributed networks and replication. Islands architecture is a technical approach that addresses payload size by giving developers easy ways to defer and potentially even avoid the cost of loading content. Combined, these two approaches form edge-powered islands and, in this article, we examine how the combination can help to address these two notable costs web developers have to consider in their daily work. Our findings indicate that edge-powered islands can provide a way to introduce interactivity to otherwise static websites while wrapping dynamic portions of a page within islands to gain the benefits of static approaches in more dynamic contexts, such as storefronts. In addition, islands can provide loading benefits even for more application-like websites, such as social networks, and give web developers an additional control layer in their development work.

关键词： formance serverless edge computing web applications web development

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SimuScale: Optimizing Parameters for Autoscaling of serverless edge Functions through Co-Simulation 17

SimuScale: Optimizing Parameters for Autoscaling of Serverle...

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IEEE 17th International Conference on Cloud computing (IEEE CLOUD)

作者： Raith, Philipp Nastic, Stefan Dustdar, Schahram TU Wien Distributed Syst Grp Vienna Austria

ISBN: (纸本)9798350368543;9798350368536

serverless edge computing is growing in popularity, and while commercial providers are starting to offer edgeoriented products, much research is still being done on orchestrating functions (e.g., autoscaling). These approaches range from threshold- to AI-based strategies and support various Service Level Objectives (SLOs), such as Round-Trip-Time (RTT) and resource usage. However, the Quality of Service (QoS) continuously deteriorates due to the dynamic edge-cloud continuum and static parameterization of orchestration strategy parameters. Platforms must adapt the orchestration parameters during runtime to counteract this drift that causes SLO violations. To this end, we introduce the Orchestration Parameter Optimization Problem (OPOP), which aims to find parameters for orchestration strategies to minimize SLO violations. We propose a novel selfadaptive Simulation-based Scaling (SimuScale) approach that uses co-simulation to solve OPOP for autoscalers during runtime. SimuScale uses live monitoring data to feed the simulation and perform parameter optimization. Our Proof of Concept is integrated with Kubernetes and evaluated on a real-world edge-cloud testbed. While this work focuses on a threshold-based autoscaler, it can be extended to optimize other orchestration components (e.g., schedulers). Our experimental results show that SimuScale finds parameters that decrease RTT SLO violations between 15% and 40%. SimuScale also can reduce resource usage by 29.87% while maintaining the target 95th RTT percentile. Moreover, it can reduce variance caused by different request patterns, making orchestration strategies more resilient in realistic scenarios.

关键词： serverless edge computing Co-Simulation Self-Adaptive System Autoscaler edge-Cloud Continuum

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A survey on the scheduling mechanisms in serverless computing: a taxonomy, challenges, and trends

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第5期27卷 5981-5993页

作者： Ghorbian, Mohsen Ghobaei-Arani, Mostafa Esmaeili, Leila Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran

In recent years, serverless computing has received significant attention due to its innovative approach to cloud computing. In this novel approach, a new payment model is presented, and a microservice architecture is implemented to convert applications into functions. These characteristics make it an appropriate choice for topics related to the Internet of Things (IoT) devices at the network's edge because they constantly suffer from a lack of resources, and the topic of optimal use of resources is significant for them. Scheduling algorithms are used in serverless computing to allocate resources, which is a mechanism for optimizing resource utilization. This process can be challenging due to a number of factors, including dynamic behavior, heterogeneous resources, workloads that vary in volume, and variations in number of requests. Therefore, these factors have caused the presentation of algorithms with different scheduling approaches in the literature. Despite many related serverless computing studies in the literature, to the best of the author's knowledge, no systematic, comprehensive, and detailed survey has been published that focuses on scheduling algorithms in serverless computing. In this paper, we propose a survey on scheduling approaches in serverless computing across different computing environments, including cloud computing, edge computing, and fog computing, that are presented in a classical taxonomy. The proposed taxonomy is classified into six main approaches: Energy-aware, Data-aware, Deadline-aware, Package-aware, Resource-aware, and Hybrid. After that, open issues and inadequately investigated or new research challenges are discussed, and the survey is concluded.

关键词： serverless computing Function as a service Scheduling algorithms Energy-aware scheduling Data-aware scheduling Deadline-aware scheduling Package-aware scheduling Resource-aware scheduling Makespan serverless edge computing Resource management Performance evaluation

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QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 157卷 250-263页

作者： Hudson, Nathaniel Khamfroush, Hana Baughman, Matt Lucani, Daniel E. Chard, Kyle Foster, Ian Univ Chicago Dept Comp Sci Chicago IL 60637 USA Argonne Natl Lab Data Sci & Learning Div Lemont IL USA Univ Kentucky Dept Comp Sci Lexington KY USA Aarhus Univ Dept Elect & Comp Engn Aarhus Denmark

Resource constraints on the computing continuum require that we make smart decisions for serving AIbased services at the network edge. AI -based services typically have multiple implementations (e.g., image classification implementations include SqueezeNet, DenseNet, and others) with varying trade-offs (e.g., latency and accuracy). The question then is how should AI -based services be placed across Function -as -a -Service (FaaS) based edge computing systems in order to maximize total Quality -of -Service (QoS). To address this question, we propose a problem that jointly aims to solve (i) edge AI service placement and (ii) request scheduling. These are done across two time -scales (one for placement and one for scheduling). We first cast the problem as an integer linear program. We then decompose the problem into separate placement and scheduling subproblems and prove that both are NP - hard . We then propose a novel placement algorithm that places services while considering device -to -device communication across edge clouds to offload requests to one another. Our results show that the proposed placement algorithm is able to outperform a state-of-the-art placement algorithm for AI -based services, and other baseline heuristics, with regard to maximizing total QoS. Additionally, we present a federated learning -based framework, FLIES, to predict the future incoming service requests and their QoS requirements. Our results also show that our FLIES algorithm is able to outperform a standard decentralized learning baseline for predicting incoming requests and show comparable predictive performance when compared to centralized training.

关键词： Service placement Federated learning serverless edge computing edge intelligence Quality-of-service

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