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SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION 2024年第1期79卷

作者： Kaur, Jasmine Chana, Inderveer Bala, Anju Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala 147004 Punjab India

serverless computing has evolved as a prominent paradigm within cloud computing, providing on-demand resource provisioning and capabilities crucial to Science and Technology for Energy Transition (STET) applications. Despite the efficiency of the auto-scalable approaches in optimizing performance and cost in distributed systems, their potential remains underutilized in serverless computing due to the lack of comprehensive approaches. So an auto-scalable approach has been designed using Q-learning, which enables optimal resource scaling decisions. This approach proves useful for adjusting resources dynamically to maximize resource utilization by automatically scaling up or down resources as needed. Further, the proposed approach has been validated using AWS Lambda with key performance metrics such as probability of cold start, average response time, idle instance count, energy consumption, etc. The experimental results demonstrate that the proposed approach performs better than the existing approach by considering the above parameters. Finally, the proposed approach has also been validated to optimize the energy consumption of smart meter data.

关键词： serverless computing Autoscaling Q-learning Performance Energy consumption

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Mitigating Cold Start Problem in serverless computing: A Reinforcement Learning Approach

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IEEE INTERNET OF THINGS JOURNAL 2023年第5期10卷 3917-3927页

作者： Vahidinia, Parichehr Farahani, Bahar Aliee, Fereidoon Shams Shahid Beheshti Univ Fac Comp Sci & Engn Tehran 983969411 Iran Shahid Beheshti Univ Cyberspace Res Inst Tehran 983969411 Iran

serverless computing has revolutionized the world of cloud-based and event-driven applications with the introduction of Function as a Service (FaaS) as the latest cloud computing model. This computational model increases the level of abstraction from the infrastructure and breaks the program into small units called functions. Thus, it brings benefits, such as ease of development, saving resources, and reducing product launch time for enterprises and developers. Thanks to the scale-to-zero feature of this computational model, idle functions with no traffic will be depreciated from memory. However, this cost-saving approach adversely impacts delay leading to the cold start problem. Unfortunately, the existing solutions to alleviate the cold start delay are not resource efficient as they follow a fixed policy over time. Thereby, this article proposes a novel two-layer adaptive approach to tackle this issue. The first layer utilizes a holistic reinforcement learning algorithm to discover the function invocation patterns over time for determining the best time to keep the containers warm. The second layer is designed based on a long short-term memory (LSTM) to predict the function invocation times in the future to determine the required prewarmed containers. The experimental results on the Openwhisk platform show that the proposed approach reduces the memory consumption by 12.73% and improves the execution invocations on prewarmed containers by 22.65% compared to the Openwhisk platform.

关键词： Containers Delays Cloud computing serverless computing Computational modeling Internet of Things Costs Cold start delay Function as a Service (FaaS) memory consumption openwhisk reinforcement learning serverless computing serverless platforms

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Freyr⁺: Harvesting Idle Resources in serverless computing via Deep Reinforcement Learning

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第11期35卷 2254-2269页

作者： Yu, Hanfei Wang, Hao Li, Jian Yuan, Xu Park, Seung-Jong Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA SUNY Stony Brook Dept Appl Math Coll Engn & Appl Sci Stony Brook NY 11790 USA SUNY Stony Brook Coll Engn & Appl Sci Dept Stat & Comp Sci Stony Brook NY 11790 USA Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Missouri Univ Sci & Technol Coll Engn & Comp Comp Sci Dept Rolla MO 65409 USA

serverless computing has revolutionized online service development and deployment with ease-to-use operations, auto-scaling, fine-grained resource allocation, and pay-as-you-go pricing. However, a gap remains in configuring serverless functions-the actual resource consumption may vary due to function types, dependencies, and input data sizes, thus mismatching the static resource configuration by users. Dynamic resource consumption against static configuration may lead to either poor function execution performance or low utilization. This paper proposes Freyr(+), a novel resource manager (RM) that dynamically harvests idle resources from over-provisioned functions to accelerate under-provisioned functions for serverless platforms. Freyr+ monitors each function's resource utilization in real-time and detects the mismatches between user configuration and actual resource consumption. We design deep reinforcement learning (DRL) algorithms with attention-enhanced embedding, incremental learning, and safeguard mechanism for Freyr+ to harvest idle resources safely and accelerate functions efficiently. We have implemented and deployed a Freyr+ prototype in a 13-node Apache OpenWhisk cluster using AWS EC2. Freyr+ is evaluated on both large-scale simulation and real-world testbed. Experimental results show that Freyr+ harvests 38% of function invocations' idle resources and accelerates 39% of invocations using harvested resources. Freyr+ reduces the 99th-percentile function response latency by 26% compared to the baseline RMs.

关键词： Nearest neighbor methods serverless computing Memory management Dynamic scheduling Training Heuristic algorithms Deep reinforcement learning Attention incremental learning reinforcement learning resource harvesting serverless computing

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An Improved Task Allocation Scheme in serverless computing Using Gray Wolf Optimization (GWO) Based Reinforcement Learning (RIL) Approach

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WIRELESS PERSONAL COMMUNICATIONS 2021年第3期117卷 2403-2421页

作者： Yuvaraj, N. Karthikeyan, T. Praghash, K. ICT Acad Res & Dev Chennai Tamil Nadu India Annamacharya Inst Technol & Sci Dept Elect & Commun Engn Rajampet Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Vaddeswaram Andhra Pradesh India

serverless computing offers a wide variety of event-driven integrations and cloud services, easy development and implementation frameworks, and complex balancing and control of costs. With these benefits into consideration, the growing implementation of serverless systems means that the performance of the serverless system is measured and new techniques created to maximize the potential of the software. The serverless or system runtime features have shown major performance and cost advantages for event-driven cloud applications. While serverless runtimes are limited to applications requiring lightweight data and storage, such as the prediction and inference of machine learning, these applications have been improved beyond other cloud runtimes. In this paper, we propose a machine learning model to parallelize the jobs allocated to the event queue and the dispatcher of the serverless framework. We hence use Gray Wolf Optimization (GWO) model to improve the process of task allocation. Further, to optimize GWO, we use the Reinforcement Learning (RIL) approach that simultaneously optimizes the parameters of GWO and improves the task allocation. The simulation studies show that the proposed GWO-RIL offers reduced runtimes and it adapts with varying load conditions.

关键词： serverless computing Reinforcement Learning (RL) Gray Wolf Optimization (GWO) Resource allocation Run time

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Towards providing a priority-based vital sign offloading in healthcare with serverless computing and a fog-cloud architecture

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 157卷 51-66页

作者： Cassel, Gustavo Andre Setti Righi, Rodrigo da Rosa da Costa, Cristiano Andre Bez, Marta Rosecler Pasin, Marcelo Univ Vale Rio Dos Sinos Software Innovat Lab SOFTWARELAB Appl Comp Grad Program Sao Leopoldo RS Brazil Creat Ind Feevale Novo Hamburgo RS Brazil Univ Neuchatel Inst Informat Neuchatel Switzerland

Smart cities and healthcare services have been gaining much attention in recent years, as the benefits offered by this field of research are significant and improve quality of life. Systems can proactively detect health problems by monitoring a person's vital signs and make automated decisions to prevent these problems from worsening. With this in mind, we highlight two essential requirements that smart city architectures should consider to achieve elevated quality of experience in the healthcare domain. The first is to process high-priority vital signs with short response times, so people with comorbidities can have health problems identified as soon as possible. The second is to employ scalability techniques to deal with high usage peaks resulting from people concentrating in specific city neighborhoods. This paper contributes to this field of research by proposing SmartVSO - a computational model of a hierarchic, scalable, tree-based, fog-cloud architecture that executes healthcare services with reduced response time for urgent vital signs. Fog computing is employed to reduce response times, and cloud computing is used to provide virtually infinite computing resources. serverless computing is the main technology we consider for deploying and running healthcare services because this allows authorized companies to implement their own services in a distributed and pluggable approach, without recompiling the proposed modules and reducing scalability concerns for engineers. An experiment with 80,000 vital signs indicates that our solution processes 60% of the very critical ones in no more than 5.3 s, while an architecture without fog computing and without prioritization takes up to 231 min (around 3 h and 51 min) to process 60% of very critical vital signs.

关键词： Healthcare Internet of Things Priority Fog Cloud serverless computing

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Performance Optimization of serverless computing for Latency-Guaranteed and Energy-Efficient Task Offloading in Energy-Harvesting Industrial IoT

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IEEE INTERNET OF THINGS JOURNAL 2023年第3期10卷 1897-1907页

作者： Ko, Haneul Pack, Sangheon Leung, Victor C. M. Korea Univ Dept Comp & Informat Sci Seoul 136713 South Korea Korea Univ Sch Elect Engn Seoul 136713 South Korea Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

serverless architecture enables various intelligent applications to be run without managing infrastructure. In this architecture, the computing cost is generally proportional to the number of requested stateless functions and this number can affect the task completion time and, thus, it is prominent to decide an appropriate number of requested stateless functions. In this article, we propose a latency-guaranteed and energy-efficient task offloading (LETO) system where an Internet of Things (IoT) device decides the number of stateless functions requested to the cloud by considering the deadline on the task completion time and its energy level. To minimize the computing cost while guaranteeing sufficiently short task completion time and low energy outage probability, we formulate a constrained Markov decision process (CMDP) problem and convert the CMDP problem into an equivalent linear programming (LP) model. By solving the LP model, the optimal policy on the number of requested stateless functions can be achieved. Evaluation results illustrate that LETO can cut down the operating expenditure (OPEX) by up to 59% compared to a latency-guaranteed offloading scheme while keeping the task completion time and the energy outage probability below desirable levels.

关键词： Task analysis Costs Heuristic algorithms Computer architecture serverless computing Optimization Energy harvesting Constrained Markov decision process (CMDP) energy harvesting serverless computing task offloading

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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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Automated Fine-Grained CPU Cap Control in serverless computing Platform

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2020年第10期31卷 2289-2301页

作者： Kim, Young Ki HoseinyFarahabady, M. Reza Lee, Young Choon Zomaya, Albert Y. Univ Sydney Camperdown NSW 2006 Australia Macquarie Univ Macquarie Pk NSW 2109 Australia

serverless computing has emerged as a new cloud computing execution model that liberates users and application developers from explicitly managing 'physical' resources, leaving such a resource management burden to service providers. In this article, we study the problem of resource allocation for multi-tenant serverless computing platforms explicitly taking into account workload fluctuations including sudden surges. In particular, we investigate different root causes of performance degradation in these platforms where tenants (their applications) have different workload characteristics. To this end, we develop a fine-grained CPU cap control solution as a resource manager that dynamically adjusts CPU usage limit (or CPU cap) concerning applications with same/similar performance requirements, i.e., application groups. The adjustment of CPU caps applies primarily to co-located worker processes of serverless computing platforms to minimize resource contention, which is the major source of performance degradation. The actual adjustment decisions are made based on performance metrics (e.g., throttled time and queue length) using a group-aware scheduling algorithm. The extensive experimental results performed in our local cluster confirm that the proposed resource manager can effectively eliminate the burden of explicit reservation of computing capacity, even when fluctuations and sudden surges in the incoming workload exist. We measure the robustness of the proposed resource manager by comparing it with several heuristics which extensively used in practice, including the enhanced version of round robin and the least length queue scheduling policies, under various workload intensities driven by real-world scenarios. Notably, our resource manager outperforms other heuristics by decreasing skewness and average response time up to 44 and 94 percent, respectively, while it does not over-use the CPU resources.

关键词： serverless computing virtualized cloud platforms operating system process management dynamic CPU scheduling performance modeling

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ATSSC:An Attack Tolerant System in serverless computing

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China Communications 2024年第6期21卷 192-205页

作者： Zhang Shuai Guo Yunfei Hu Hongchao Liu Wenyan Wang Yawen Information Engineering University Zhengzhou 450002China

serverless computing is a promising paradigm in cloud computing that greatly simplifies cloud *** serverless computing,developers only provide function code to serverless platform,and these functions are invoked by its driven ***,security threats in serverless computing such as vulnerability-based security threats have become the pain point hindering its wide *** ideas in proactive defense such as redundancy,diversity and dynamic provide promising approaches to protect against ***,these security technologies are mostly applied to serverless platform based on“stacked”mode,as they are designed independent with serverless *** lack of security consideration in the initial design makes it especially challenging to achieve the all life cycle protection for serverless application with limited *** this paper,we present ATSSC,a proactive defense enabled attack tolerant serverless *** integrates the characteristic of redundancy,diversity and dynamic into serverless seamless to achieve high-level security and ***,ATSSC constructs multiple diverse function replicas to process the driven events and performs cross-validation to verify the *** order to create diverse function replicas,both software diversity and environment diversity are ***,a dynamic function refresh strategy is proposed to keep the clean state of serverless *** implement ATSSC based on Kubernetes and *** and experimental results demonstrate that ATSSC can effectively protect serverless computing against cyberattacks with acceptable costs.

关键词： active defense attack tolerant cloud computing security serverless computing

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SMSS: Stateful Model Serving in Metaverse With serverless computing and GPU Sharing

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第3期42卷 799-811页

作者： Cai, Zinuo Chen, Zebin Ma, Ruhui Guan, Haibing Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Dept Comp Sci & Engn Shanghai 200240 Peoples R China

With the rapid development of information technology, the concept of the Metaverse has swept the world and set off a new wave of the industrial revolution. The construction of living and manufacturing scenes based on the Metaverse requires the joint participation of scientists and engineers from various fields where "human" is at the core. In the Metaverse, predicting human behavior and response based on the deep learning model is meaningful because the prediction results can provide more satisfactory services for participants. Therefore, how to deploy a multi-stage machine learning reasoning model has become the bottleneck to improving the development level of Metaverse. Thanks to its scalability and pay-as-you-go billing model, the emerging serverless computing can effectively cope with the workload of machine learning inference. However, the statelessness of serverless computing and the lack of good GPU resource-sharing support make it difficult to deploy the machine learning model directly on the serverless computing platform to play its advantages. Therefore, we propose SMSS, a stateful model inference service, which is deployed on a serverless computing platform that supports GPU sharing. Since the serverless computing platform does not support stateful workflow execution, SMSS adopts log-based workflow runtime support. We also design a mechanism of two-layer GPU sharing to fully explore the potential of inter-model and intra-model GPU sharing. We evaluate the effectiveness of SMSS with real workloads. Our experimental results show that log-based stateful workflow operation support can ensure the stateful execution of tasks with low overhead but facilitate error location and recovery. Two-layer GPU Sharing can reduce the cold start time of inference tasks to two orders of magnitude at most.

关键词： Metaverse serverless computing model serving stateful workflow GPU sharing

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