A Performance Survey of Lightweight Virtualization Techniques

Plauth, Max; Feinbube, Lena; Polze, Andreas

doi:10.1007/978-3-319-67262-5_3

Cited by 33 publications

(9 citation statements)

References 16 publications

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“…While the survey has shown that many practitioners actually build serverless user-facing applications, many of our interviewees were not convinced about this particular use case. An often-voiced concern was that the response time and latency of functions (with a tail latency in the range of multiple seconds [20], when a new container instance is started by the provider) does not lend itself to usage directly in an end user facing request cycle. These interviewees have argued that the primary use case for FaaS should be in backend applications, such as for transforming images, processing logs or telemetry data, scheduling and executing backups, sending out notification emails, and similar tasks.…”

Section: Types Of Serverless Applicationsmentioning

confidence: 99%

A mixed-method empirical study of Function-as-a-Service software development in industrial practice

Leitner

Wittern²,

Spillner

et al. 2019

Journal of Systems and Software

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Function-as-a-Service (FaaS) describes cloud computing services that make infrastructure components transparent to application developers, thus falling in the larger group of "serverless" computing models. When using FaaS offerings, such as AWS Lambda, developers provide atomic and short-running code for their functions, and FaaS providers execute and horizontally scale them on-demand. Currently, there is no systematic research on how developers use serverless, what types of applications lend themselves to this model, or what architectural styles and practices FaaS-based applications are based on. We present results from a mixed-method study, combining interviews with practitioners who develop applications and systems that use FaaS, a systematic analysis of grey literature, and a Web-based survey. We find that successfully adopting FaaS requires a different mental model, where systems are primarily constructed by composing pre-existing services, with FaaS often acting as the "glue" that brings these services together. Tooling availability and maturity, especially related to testing and deployment, remains a major difficulty. Further, we find that current FaaS systems lack systematic support for function reuse, and abstractions and programming models for building non-trivial FaaS applications are limited. We conclude with a discussion of implications for FaaS providers, software developers, and researchers.

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Section: Types Of Serverless Applicationsmentioning

confidence: 99%

A mixed-method empirical study of Function-as-a-Service software development in industrial practice

Leitner

Wittern²,

Spillner

et al. 2019

Journal of Systems and Software

View full text Add to dashboard Cite

show abstract

“…Various Middleware Frameworks of Containers [21] Programming Sensors and IoT Devices (Small VMs Supported by Python Runtime Environments such as Spring and NodeJS) [22][23][24][25] Application Efficiency (Resource Virtualization Technology for Hardware Flexibility) [26] Lightweight Virtualization Solution [27] Virtualization Application to Lower Overhead [28] Definition of Container [29] Strengths of Containers, Lightening Up [30] The Connection between Lightweight Virtualization and Microservice [31] Strategies to Strengthen The Fuzzy Distribution of Microphone Services [32] Cluster Intelligence-Based Strategies [33] Reinforcement…”

Section: Cloud Computingmentioning

confidence: 99%

Improved Q Network Auto-Scaling in Microservice Architecture

et al. 2022

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Microservice architecture has emerged as a powerful paradigm for cloud computing due to its high efficiency in infrastructure management as well as its capability of largescale user service. A cloud provider requires flexible resource management to meet the continually changing demands, such as auto-scaling and provisioning. A common approach used in both commercial and open-source computing platforms is workload-based automatic scaling, which expands instances by increasing the number of incoming requests. Concurrency is a request-based policy that has recently been proposed in the evolving microservice framework; in this policy, the algorithm can expand its resources to the maximum number of configured requests to be processed in parallel per instance. However, it has proven difficult to identify the concurrency configuration that provides the best possible service quality, as various factors can affect the throughput and latency based on the workloads and complexity of the infrastructure characteristics. Therefore, this study aimed to investigate the applicability of an artificial intelligence approach to request-based auto-scaling in the microservice framework. Our results showed that the proposed model could learn an effective expansion policy within a limited number of pods, thereby showing an improved performance over the underlying auto expansion configuration.

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“…Unikernel is a specialized ultra-lightweight virtualization technique that allows the creation of a single address space machine image using library OS. It allows the creation of OS with the minimal set of OS constructs or libraries, as required by the applications, thus having a very small footprint [29]. Therefore, unikernel is ultra-lightweight and provides higher security and deployment efficiency as compared to VMs and containers.…”

Section: ) Lightweight Virtualizationmentioning

confidence: 99%

FENCE: Fast, ExteNsible, and ConsolidatEd Framework for Intelligent Big Data Processing

et al. 2020

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The proliferation of smart devices and the advancement of data-intensive services has led to explosion of data, which uncovers massive opportunities as well as challenges related to real-time analysis of big data streams. The edge computing frameworks implemented over manycore systems can be considered as a promising solution to address these challenges. However, in spite of the availability of modern computing systems with a large number of processing cores and high memory capacity, the performance and scalability of manycore systems can be limited by the software and operating system (OS) level bottlenecks. In this work, we focus on these challenges, and discuss how accelerated communication, efficient caching, and high performance computation can be provisioned over manycore systems. The proposed Fast, ExteNsible, and ConsolidatEd (FENCE) framework leverages the availability of a large number of computing cores and overcomes the OS level bottlenecks to provide high performance and scalability for intelligent big data processing. We implemented a prototype of FENCE and the experiment results demonstrate that FENCE provides improved data reception throughput, read/write throughput, and application processing performance as compared to the baseline Linux system.

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A Performance Survey of Lightweight Virtualization Techniques

Cited by 33 publications

References 16 publications

A mixed-method empirical study of Function-as-a-Service software development in industrial practice

A mixed-method empirical study of Function-as-a-Service software development in industrial practice

Improved Q Network Auto-Scaling in Microservice Architecture

FENCE: Fast, ExteNsible, and ConsolidatEd Framework for Intelligent Big Data Processing

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