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StarShip: Mitigating I/O Bottlenecks in serverless computing for ScientificWorkflows

PROCEEDINGS OF THE ACM ON MEASUREMENT AND ANALYSIS OF computing SYSTEMS 2024年第1期8卷 1-29页

作者： Roy, Rohan Basu Tiwari, Devesh Northeastern Univ Boston MA 02115 USA

This work highlights the significance of I/O bottlenecks that data-intensive HPC workflows face in serverless environments - an issue that has been largely overlooked by prior works. To address this challenge, we propose a novel framework, StarShip, which effectively addresses I/O bottlenecks for HPC workflows executing in serverless environments by leveraging different storage options and multi-tier functions, co-optimizing for service time and service cost. StarShip exploits the Levenberg-Marquardt optimization method to find an effective solution in a large, complex search space. StarShip achieves significantly better performance and cost compared to competing techniques, improving service time by 45% and service cost by 37.6% on average over state-of-the-art solutions.

关键词： serverless computing HPC Workflows I/O Overhead

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An autoscalable approach to optimize energy consumption using smart meters data in serverless computing

SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION

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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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Will serverless computing Revolutionize NFV?

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PROCEEDINGS OF THE IEEE 2019年第4期107卷 667-678页

作者： Aditya, Paarijaat Akkus, Istemi Ekin Beck, Andre Chen, Ruichuan Hilt, Volker Rimac, Ivica Satzke, Klaus Stein, Manuel Nokia Bell Labs D-70435 Stuttgart Germany

Communication networks need to be both adaptive and scalable. The last few years have seen an explosive growth of software-defined networking (SDN) and network function virtualization (NFV) to address this need. Both technologies help enable networking software to be decoupled from the hardware so that software functionality is no longer constrained by the underlying hardware and can evolve independently. Both SDN and NFV aim to advance a software-based approach to networking, where networking functionality is implemented in software modules and executed on a suitable cloud computing platform. Achieving this goal requires the virtualization paradigm used in these services that play an important role in the transition to software-based networks. Consequently, the corresponding computing platforms accompanying the virtualization technologies need to provide the required agility, robustness, and scalability for the services executed. serverless computing has recently emerged as a new paradigm in virtualization and has already significantly changed the economics of offloading computations to the cloud. It is considered as a low-latency, resource-efficient, and rapidly deployable alternative to traditional virtualization approaches, such as those based on virtual machines and containers. serverless computing provides scalability and cost reduction, without requiring any additional configuration overhead on the part of the developer. In this paper, we explore and survey how serverless computing technology can help building adaptive and scalable networks and show the potential pitfalls of doing so.

关键词： Application virtualization cloud computing edge computing network function virtualization (NFV) serverless computing software-defined networking (SDN)

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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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Deep reinforcement learning for application scheduling in resource-constrained, multi-tenant serverless computing environments

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年第1期143卷 277-292页

作者： Mampage, Anupama Karunasekera, Shanika Buyya, Rajkumar Univ Melbourne Sch Comp & Informat Syst Cloud Comp & Distributed Syst CLOUDS Lab Melbourne Australia

serverless computing has sparked a massive interest in both the cloud service providers and their clientele in recent years. This model entails the shift of the entire matter of resource management of user applications to the service provider. In serverless systems, the provider is highly motivated to attain cost efficient usage of their infrastructure, given the granular billing modules involved. However, due to the dynamic and multi-tenant nature of the serverless workloads and systems, achieving efficient resource management while maintaining function performance is a challenging task. Rapid changes in demand levels for applications cause variations in actual resource usage patterns of function instances. This leads to performance variations in co-located functions which compete for similar resources, due to resource contentions. Most existing serverless scheduling works offer heuristic techniques for function scheduling, which are unable to capture the true dynamism in these systems caused by multi-tenancy and varying user request patterns. Further, they rarely consider the often contradicting dual objectives of achieving provider resource efficiency along with application performance. In this article, we propose a novel technique incorporating Deep Reinforcement Learning (DRL) to overcome the aforementioned challenges for function scheduling in a highly dynamic serverless computing environment with heterogeneous computing resources. We train and evaluate our model in a practical setting incorporating Kubeless, an open-source serverless framework, deployed on a 23-node Kubernetes cluster setup. Extensive experiments done on this testbed environment show promising results with improvements of up to 24% and 34% in terms of application response time and resource usage cost respectively, compared to baseline techniques. (c) 2023 Elsevier B.V. All rights reserved.

关键词： serverless computing Resource cost efficiency Function scheduling Resource contention Reinforcement learning Practical experiments

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Privacy-Preserving serverless computing Using Federated Learning for Smart Grids

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第11期18卷 7843-7852页

作者： Singh, Parminder Masud, Mehedi Hossain, M. Shamim Kaur, Avinash Muhammad, Ghulam Ghoneim, Ahmed Lovely Profess Univ Sch Comp Sci & Engn Phagwara 144411 India Taif Univ Coll Comp & Informat Technol Dept Comp Sci Taif 26571 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh 11543 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Comp Engn Riyadh 11543 Saudi Arabia Menoufia Univ Fac Sci Math & Comp Sci Dept Shibin Al Kawm 32511 Egypt

The smart power grid is a critical energy infrastructure where real-time electricity usage data is collected to predict future energy requirements. The existing prediction models focus on the centralized frameworks, where the collected data from various home area networks (HANs) are forwarded to a central server. This process leads to cybersecurity threats. This article proposes a federated learning based model with privacy preservation of smart grids data using serverless cloud computing. The model considers the blockchain-enabled dew servers in each HAN for local data storage and local model training. Advanced perturbation and normalization techniques are used to reduce the inverse impact of irregular workload on the training results. The experiment conducted on benchmarks datasets demonstrates that the proposed model minimizes the computation and communication costs, attacking probability, and improves the test accuracy. Overall, the proposed model enables smart grids with robust privacy preservation and high accuracy.

关键词： Computational modeling Smart grids Data models Data privacy Servers Training Collaborative work Blockchain dew computing federated learning privacy-preserving serverless computing smart grid

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Memory orchestration mechanisms in serverless computing: a taxonomy, review and future directions

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第5期27卷 5489-5515页

作者： Rad, Zahra Shojaee Ghobaei-Arani, Mostafa Ahsan, Reza Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran

serverless computing has become very popular in recent years due to its flexibility and cost efficiency. serverless computing is a cloud computing model that allows developers to write and deploy code without managing the underlying infrastructure. serverless computing has been welcomed due to its scalability, affordability, and ease of use. However, memory orchestration in serverless computing can be challenging due to the ephemeral nature of serverless functions, which are short-lived and stateless. This article has reviewed memory orchestration mechanisms and then presented a classification of memory orchestration mechanisms in serverless computing, which are classified into three main methods: Machine learning-based approach, Heuristic-based approach, and framework-based approach. The advantages and disadvantages of each mechanism as well as the challenges and performance metrics affecting their effectiveness have been investigated. Each memory orchestration approach, whether heuristic-based, framework-based, or machine learning-based, has its own advantages and disadvantages. Different mechanisms are suitable for different use cases. Therefore, it is important to carefully evaluate the trade-offs between performance, cost, and complexity when choosing a memory orchestration mechanism. Finally, the paper identifies several future research directions for memory orchestration in serverless computing, which include developing memory orchestration mechanisms and dynamic memory management, integrating memory orchestration mechanisms with other resource management mechanisms, security, memory sharing, compression, pricing, and investigating the relationships between memory orchestration with cost and performance in serverless computing.

关键词： serverless computing Memory configuration Memory orchestration Function as a service Performance evaluation Cloud computing Memory optimization Review

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Hardware-hardened Sandbox Enclaves for Trusted serverless computing

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ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION 2024年第1期21卷 1-25页

作者： Park, Joongun Kang, Seunghyo Lee, Sanghyeon Kim, Taehoon Park, Jongse Kwon, Youngjin Huh, Jaehyuk Korea Adv Inst Sci & Technol Daejeon South Korea ETRI Daejeon South Korea Korea Adv Inst Sci & Technol Daejeon 34141 South Korea ETRI Daejeon 34141 South Korea

In cloud-based serverless computing, an application consists of multiple functions provided by mutually distrusting parties. For secure serverless computing, the hardware-based trusted execution environment (TEE) can provide strong isolation among functions. However, not only protecting each function from the host OS and other functions, but also protecting the host system from the functions, is critical for the security of the cloud servers. Such an emerging trusted serverless computing poses new challenges: Each TEE must be isolated from the host system bi-directionally, and the system calls from it must be validated. In addition, the resource utilization of each TEE must be accountable in a mutually trusted way. However, the current TEE model cannot efficiently represent such trusted serverless applications. To overcome the lack of such hardware support, this article proposes an extended TEE model called Cloister, designed for trusted serverless computing. Cloister proposes four new key techniques. First, it extends the hardware-based memory isolation in SGX to confine a deployed function only within its TEE (enclave). Second, it proposes a trusted monitor enclave that filters and validates system calls from enclaves. Third, it provides a trusted resource accounting mechanism for enclaves that is agreeable to both service developers and cloud providers. Finally, Cloister accelerates enclave loading by redesigning its memory verification for fast function deployment. Using an emulated Intel SGX platform with the proposed extensions, this article shows that trusted serverless applications can be effectively supported with small changes in the SGX hardware.

关键词： Security Hardware serverless computing Trusted Execution Environment

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Improved QoS at the Edge Using serverless computing to Deploy Virtual Network Functions

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IEEE INTERNET OF THINGS JOURNAL 2020年第10期7卷 10673-10683页

作者： Chaudhry, Saqib Rasool Palade, Andrei Kazmi, Aqeel Clarke, Siobhan Trinity Coll Dublin Sch Comp Sci & Stat Dublin D02 PN40 Ireland

Multiaccess edge computing (MEC) will strengthen forthcoming 5G networks by improving the Quality of Service (QoS), in particular, reducing latency, increasing data processing rates, and providing real-time information to develop high-value Internet-of-Things (IoT) services. To enable data-intensive network services and support advanced analytics, many network operators have proposed to integrate MEC systems with network function virtualization (NFV) consolidating virtual network functions (VNFs) and edge capabilities on a shared infrastructure. As of yet, this integration is not fully established, with various architectural issues currently open, even at standardization level. For instance, any update to VNFs deployed in a MEC system requires a time-consuming manual effort, which affects the overall infrastructure operations. To address these pitfalls, VNFs can be decomposed into microservices, which maintain their own states and exhibit different resource consumption requirements. This article presents an approach to integration that leverages serverless computing to merge MEC and NFV at the system level and to deploy VNFs on demand, by combining MEC functional blocks with an NFV orchestrator using a Kubernetes cluster. We further investigate whether the resource utilization of a MEC system can be improved by leveraging networked FPGA-enabled MEC servers, through an extension of the edge layer that takes advantage of available programmable hardware. We quantitatively evaluate and demonstrate the improvement of 75% end-to-end latency, 99.96% VNF execution time, 26.9% resource utilization, and 15.8% energy consumption in comparison with traditional baselines of cloud, edge, and serverless-edge test cases for a high-definition real-time video streaming application.

关键词： Hardware Internet of Things Field programmable gate arrays Streaming media Servers Task analysis Quality of service Edge computing networked FPGA serverless computing virtual network functions (VNFs) and data security

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iFaaSBus: A Security- and Privacy-Based Lightweight Framework for serverless computing Using IoT and Machine Learning

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第5期18卷 3522-3529页

作者： Golec, Muhammed Ozturac, Ridvan Pooranian, Zahra Gill, Sukhpal Singh Buyya, Rajkumar Bursa Uludag Univ Elect & Elect Engn Dept TR-16370 Bursa Turkey Sisecam Co TR-34947 Istanbul Turkey Hepsiburada TR-34387 Istanbul Turkey Univ Surrey 5G & 6GIC Guildford GU2 7XH Surrey England Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Univ Melbourne Cloud Comp & Distributed Syst CLOUDS Lab Sch Comp & Informat Syst Melbourne Vic 3010 Australia

As data of COVID-19 patients is increasing, the new framework is required to secure the data collected from various Internet of Things (IoT) devices and predict the trend of disease to reduce its spreading. This article proposes security- and privacy-based lightweight framework called iFaaSBus, which uses the concept of IoT, machine learning (ML), and function as a service (FaaS) or serverless computing to diagnose the COVID-19 disease and manages resources automatically to enable dynamic scalability. iFaaSBus offers OAuth-2.0 Authorization protocol-based privacy and JSON Web Token & Transport Layer Socket protocol-based security to secure the patient's health data. iFaaSBus outperforms response time compared to nonserverless computing while responding to up to 1100 concurrent requests. Further, the performance of various ML models is evaluated based on accuracy, precision, recall, F-score, and area under the curve (AUC) values, and the K-nearest neighbor model gives the highest accuracy rate of 97.51%.

关键词： COVID-19 Servers Security Diseases Privacy Internet of Things Authorization Artificial intelligence (AI) function as a service (FaaS) Internet of Things (IoT) machine learning (ML) security and privacy serverless computing

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