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Towards a Security-Aware Benchmarking Framework for Function-As-A-Service
Towards a Security-Aware Benchmarking Framework for Function-as-a-Service Roland Pellegrini1, Igor Ivkic1 and Markus Tauber1 1University of Applied Sciences Burgenland, Eisenstadt, Austria {1610781022, igor.ivkic, markus.tauber}@fh-burgenland.at Keywords: Cloud Computing, Benchmarking, Cloud Security, Framework, Function-as-a-Service Abstract: In a world, where complexity increases on a daily basis the Function-as-a-Service (FaaS) cloud model seams to take countermeasures. In comparison to other cloud models, the fast evolving FaaS increasingly abstracts the underlying infrastructure and refocuses on the application logic. This trend brings huge benefits in application and performance but comes with difficulties for benchmarking cloud applications. In this position paper, we present an initial investigation of benchmarking FaaS in close to reality production systems. Furthermore, we outline the architectural design including the necessary benchmarking metrics. We also discuss the possibility of using the proposed framework for identifying security vulnerabilities. 1 INTRODUCTION illustrates the technical workflow between the components: Cloud computing, as defined by Mell and Grance (2011), is a model for enabling on-demand network access to a shared pool of configurable resources. Cloud vendors provide these resources in the service models Infrastructure as a Service (IaaS), Platform- as-a-Service (PaaS) and Software-as-a-Service (SaaS). Through virtualization, the IaaS service model provides computing resources (e.g.: servers, storage, network) to consumers so they can deploy Figure 1: FaaS architecture, based on Pientka (2017) and run software. In other words, the consumers do not control the infrastructure, but are able to manage However, Cloud Service Providers (CSP) often the running operating systems and applications. -
Nfaas: Named Function As a Service Michał Król Ioannis Psaras University College London University College London [email protected] [email protected]
NFaaS: Named Function as a Service Michał Król Ioannis Psaras University College London University College London [email protected] [email protected] ABSTRACT functionality to be incorporated. Powerful end-user devices and In the past, the Information-centric networking (ICN) community new applications (e.g., augmented reality [1]) demand minimum has focused on issues mainly pertaining to traditional content de- service delay, while the Internet of Things (IoT) [2] generates huge livery (e.g., routing and forwarding scalability, congestion control amounts of data that flow in the reverse direction from traditional and in-network caching). However, to keep up with future Internet flows (that is, from the edge towards the core for processing). As architectural trends the wider area of future Internet paradigms, a result, computation needs to be brought closer to the edge to there is a pressing need to support edge/fog computing environ- support minimum service latencies and to process huge volumes ments, where cloud functionality is available more proximate to of IoT data. where the data is generated and needs processing. In contrast to cloud computing, edge and fog computing promote With this goal in mind, we propose Named Function as a Service the usage of resources located closer to the network edge to be (NFaaS), a framework that extends the Named Data Networking used by multiple different applications, effectively reducing the architecture to support in-network function execution. In contrast transmission delay and the amount of traffic flowing towards the to existing works, NFaaSbuilds on very lightweight VMs and allows network core. -
2.5 AWS Lambda
Bachelorarbeit Jules Fehr Serverlose Architektur: Function as a Service mit AWS Lambda am Beispiel einer Web-Anwendung für Fotografen Fakultät Technik und Informatik Faculty of Engineering and Computer Science Department Informatik Department of Computer Science Jules Fehr Serverlose Architektur: Function as a Service mit AWS Lambda am Beispiel einer Web-Anwendung für Fotografen Abschlussarbeit eingereicht im Rahmen der Bachelorprüfung im Studiengang Wirtschaftsinformatik am Department Informatik der Fakultät Technik und Informatik der Hochschule für Angewandte Wissenschaften Hamburg Betreuender Prüfer: Prof. Dr. Ulrike Steffens Zweitgutachter: Prof. Dr. Stefan Sarstedt Abgegeben am 19.07.2018 Jules Fehr Thema der Arbeit Serverlose Architektur: Function as a Service mit AWS Lambda Beispiel einer Web-Anwendung für Fotografen Stichworte FaaS, Serverless, AWS Lambda, Web-Anwendung Kurzzusammenfassung Das Ziel dieser Bachelorarbeit ist die Konzeption und Entwicklung einer Function as a Service Web-Anwendung für Fotografen. In dieser Arbeit werden die Prinzipien von serverloser Architektur behandelt. Es wird eine Referenzarchitektur vorgestellt und es wird eine Anforderungsanalyse für die Anwendung erstellt. Basierend auf der Analyse und den Prinzipien der serverlosen Architektur wird die Umsetzung wichtiger Komponenten erläutert. Jules Fehr Title of the paper Serverless Architecture: Function as a Service with AWS Lambda on the example of a web application for photographers Keywords FaaS, Serverless, AWS Lambda, Web-application Abstract The goal of this bachelor thesis is the conception and development of a Function as a Service web-application for photographers. The principles of serverless architecture will be discussed. A reference architecture will be presented and a requirement analysis will be made. The implementation of important components will be made based on the analysis and the principles of serverless architecture. -
Nanolambda: Implementing Functions As a Service at All Resource Scales for the Internet of Things
NanoLambda: Implementing Functions as a Service at All Resource Scales for the Internet of Things. Gareth George, Fatih Bakir, Rich Wolski, and Chandra Krintz Computer Science Department Univ. of California, Santa Barbara Abstract—Internet of Things (IoT) devices are becoming in- decompose their applications into multiple functions, each re- creasingly prevalent in our environment, yet the process of sponsible for some short-lived piece of processing. A function programming these devices and processing the data they produce is typically a small, single-entry code package (50 megabytes remains difficult. Typically, data is processed on device, involving arduous work in low level languages, or data is moved to the or less) written in a high level language (e.g. Python, node.js, cloud, where abundant resources are available for Functions or Java). Thanks to their small size and self-contained design, as a Service (FaaS) or other handlers. FaaS is an emerging these functions are easily invoked and scaled automatically category of flexible computing services, where developers deploy by the FaaS platform in response to incoming events (data self-contained functions to be run in portable and secure con- arrival, messages, web requests, storage updates, etc.). Many tainerized environments; however, at the moment, these functions are limited to running in the cloud or in some cases at the “edge” functions, potentially belonging to different tenants, execute on of the network using resource rich, Linux-based systems. a single server, using Linux containers to provide secure iso- In this paper, we present NanoLambda, a portable platform lation and a consistent execution environment. -
Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider
Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider Mohammad Shahrad, Rodrigo Fonseca, Íñigo Goiri, Gohar Chaudhry, Paul Batum, Jason Cooke, Eduardo Laureano, Colby Tresness, Mark Russinovich, and Ricardo Bianchini July 15, 2020 What is Serverless? •Very attractive abstraction: • Pay for Use • Infinite elasticity from 0 (and back) • No worry about servers • Provisioning, Reserving, Configuring, patching, managing •Most popular offering: Function-as-a-Service (FaaS) • Bounded-time functions with no persistent state among invocations • Upload code, get an endpoint, and go For the rest of this talk, Serverless = Serverless FaaS What is Serverless? Bare Metal VMs (IaaS) Containers Functions (FaaS) Unit of Scale Server VM Application/Pod Function Provisioning Ops DevOps DevOps Cloud Provider Init Time Days ~1 min Few seconds Few seconds Scaling Buy new hardware Allocate new VMs 1 to many, auto 0 to many, auto Typical Lifetime Years Hours Minutes O(100ms) Payment Per allocation Per allocation Per allocation Per use State Anywhere Anywhere Anywhere Elsewhere Serverless “…more than 20 percent of global enterprises will have deployed serverless computing technologies by 2020.” Gartner, Dec 2018 Serverless Source: CNCF Cloud Native Interactive Landscape https://landscape.cncf.io/format=serverless Serverless December 2019 “… we predict that (…) serverless computing will grow to dominate the future of cloud computing.” So what are people doing with FaaS? • Interesting Explorations • MapReduce (pywren) -
A Brief History of Cloud Application Architectures
applied sciences Review A Brief History of Cloud Application Architectures Nane Kratzke ID Lübeck University of Applied Sciences, Department of Electrical Engineering and Computer Science, 23562 Lübeck, Germany; [email protected] Received: 14 July 2018; Accepted: 27 July 2018; Published: 14 August 2018 Featured Application: This paper features system and software engineering use cases for large-scale (business) Cloud-native applications (e.g., Netflix, Twitter, Uber, Google Search). Such Cloud-native applications (CNA) provide web-scalability and independent deployability of their components and enable exponential user growth. Furthermore, migration and architecture transformation use cases of existing tiered and on-premise (business) applications are additionally of interest. Thus, questions of how existing and not cloud-ready applications are migratable into cloud environments are covered as well. Abstract: This paper presents a review of cloud application architectures and its evolution. It reports observations being made during a research project that tackled the problem to transfer cloud applications between different cloud infrastructures. As a side effect, we learned a lot about commonalities and differences from plenty of different cloud applications which might be of value for cloud software engineers and architects. Throughout the research project, we analyzed industrial cloud standards, performed systematic mapping studies of cloud-native application-related research papers, did action research activities in cloud engineering projects, modeled a cloud application reference model, and performed software and domain-specific language engineering activities. Two primary (and sometimes overlooked) trends can be identified. First, cloud computing and its related application architecture evolution can be seen as a steady process to optimize resource utilization in cloud computing. -
Architectural Implications of Function-As-A-Service Computing
Architectural Implications of Function-as-a-Service Computing Mohammad Shahrad Jonathan Balkind David Wentzlaff Princeton University Princeton University Princeton University Princeton, USA Princeton, USA Princeton, USA [email protected] [email protected] [email protected] ABSTRACT Network Serverless computing is a rapidly growing cloud application model, popularized by Amazon’s Lambda platform. Serverless cloud ser- Scheduling vices provide fine-grained provisioning of resources, which scale Platform (priorwork) automatically with user demand. Function-as-a-Service (FaaS) appli- Queueing Management cations follow this serverless model, with the developer providing 35% decrease in IPC Interference their application as a set of functions which are executed in response due to interference 6x variation due to to a user- or system-generated event. Functions are designed to Memory BW invocation pattern 20x MPKI for be short-lived and execute inside containers or virtual machines, Branch MPKI >10x exec time short functions introducing a range of system-level overheads. This paper studies for short functions Cold Start Server the architectural implications of this emerging paradigm. Using (500ms cold start) Up to 20x (thispaper) Container the commercial-grade Apache OpenWhisk FaaS platform on real slowdown servers, this work investigates and identifies the architectural im- Native plications of FaaS serverless computing. The workloads, along with Execution Figure 1: We characterize the server-level overheads of the way that FaaS inherently interleaves short functions from many Function-as-a-Service applications, compared to native exe- tenants frustrates many of the locality-preserving architectural cution. This contrasts with prior work [2–5] which focused structures common in modern processors. -
Persistent Helper Functions in a Serverless Offering
Journal of Software Engineering and Applications, 2020, 13, 278-287 https://www.scirp.org/journal/jsea ISSN Online: 1945-3124 ISSN Print: 1945-3116 Result-as-a-Service (RaaS): Persistent Helper Functions in a Serverless Offering Arshdeep Bahga, Vijay K. Madisetti, Joel R. Corporan Georgia Institute of Technology, Atlanta, USA How to cite this paper: Bahga, A., Madi- Abstract setti, V.K. and Corporan, J.R. (2020) Re- sult-as-a-Service (RaaS): Persistent Helper Serverless Computing or Functions-as-a-Service (FaaS) is an execution model Functions in a Serverless Offering. Journal for cloud computing environments where the cloud provider executes a piece of Software Engineering and Applications, of code (a function) by dynamically allocating resources. When a function 13, 278-287. https://doi.org/10.4236/jsea.2020.1310018 has not been executed for a long time or is being executed for the first time, a new container has to be created, and the execution environment has to be in- Received: September 21, 2020 itialized resulting in a cold start. Cold start can result in a higher latency. We Accepted: October 24, 2020 propose a new computing and execution model for cloud environments Published: October 27, 2020 called Result-as-a-Service (RaaS), which aims to reduce the computational Copyright © 2020 by author(s) and cost and overhead while achieving high availability. In between successive Scientific Research Publishing Inc. calls to a function, a persistent function can help in successive calls by pre- This work is licensed under the Creative computing the functions for different possible arguments and then distribut- Commons Attribution International License (CC BY 4.0). -
How Entle Core™ Reduces Risk with Proven Technologies
How Entle Core™ reduces risk with proven technologies What is Entle Core™? cloud computing had become well-established, and serverless computing took that a step further to make On the basis of over a decade in the web elastic infrastructure even more accessible. development industry, Entle has developed a mature, In contrast to a constantly-running server or elastic risk-reducing, and highly performant website server group that responds to visitor requests (either architecture called Entle Core™. Websites built with using bare-metal physical machines, virtual machines, Entle Core™ are heavily optimised for performance, or containers), serverless functions use cloud native SEO, and ultimately, conversion. systems’ inherent elasticity to consume resources on To achieve this, Entle Core™ combines a serverless demand and in a fully abstracted manner. Server hosting architecture with the JAMstack (JavaScript, management is abstracted to the cloud provider’s APIs, and markup) pattern. expert team, in the same way that they are responsible for all their other cloud infrastructure (e.g. in AWS’ The key elements of Entle Core™ are: case, the underlying infrastructure for EC2, EKS, Aurora • Serverless hosting using AWS S3 storage and AWS or Redshift). This significantly reduces the DevOps Cloudfront CDN, and AWS Lamda to provide workload on the website developer and thereby dynamic functionality; reduces the risk associated with designing, testing, and maintaining one’s own server setup. Serverless is the next step in the evolution of A static site -
A Systematic Mapping Study on Engineering Function-As-A-Service Platforms and Tools
Institute of Architecture of Application Systems A Systematic Mapping Study on Engineering Function-as-a-Service Platforms and Tools Vladimir Yussupov, Uwe Breitenbücher, Frank Leymann, Michael Wurster Institute of Architecture of Application Systems, University of Stuttgart, Germany, {yussupov, breitenbuecher, leymann, wurster}@iaas.uni-stuttgart.de : @inproceedings{Yussupov2019_SystematicMappingStudyFaaS, author = {Vladimir Yussupov and Uwe Breitenb{\"u}cher and Frank Leymann and Michael Wurster}, title = {{A Systematic Mapping Study on Engineering Function-as-a-Service Platforms and Tools}}, booktitle = {Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2019)}, publisher = {ACM}, year = 2019, month = dec, pages = {229--240}, doi = {10.1145/3344341.3368803} } © Yussupov et al. 2019. This is an Open Access publication licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. A Systematic Mapping Study on Engineering Function-as-a-Service Platforms and Tools Vladimir Yussupov Uwe Breitenbücher Institute of Architecture of Application Systems Institute of Architecture of Application Systems University of Stuttgart, Germany University of Stuttgart, Germany [email protected] [email protected] Frank Leymann Michael Wurster Institute of Architecture of Application Systems Institute of Architecture of Application Systems University of Stuttgart, Germany University of Stuttgart, Germany [email protected] [email protected] ABSTRACT which creates a wrong impression that servers are absent. A recent Function-as-a-Service (FaaS) is a novel cloud service model allowing newcomer in the line of as-a-service offerings called Function-as- to develop fine-grained, provider-managed cloud applications. -
Rethinking Scalable Service Architectures for the Internet of Things
Devices-as-Services: Rethinking Scalable Service Architectures for the Internet of Things Fatih Bakir, Rich Wolski, Chandra Krintz Gowri Sankar Ramachandran Univ. of California, Santa Barbara Univ. of Southern California Abstract vices at the edge that augment device capabilities and enable We investigate a new distributed services model and architec- scale. In this paper, we outline this approach to implementing ture for Internet of Things (IoT) applications. In particular, Devices-as-Services and describe some of the capabilities of we observe that devices at the edge of the network, although an early prototype. resource constrained, are increasingly capable – performing Our work is motivated by the following observations. • IoT applications can and will likely be structured as collec- actions (e.g. data analytics, decision support, actuation, con- tions of services that require functionality from a device trol, etc.) in addition to event telemetry. Thus, such devices tier, an edge tier, and a cloud tier are better modeled as servers, which applications in the cloud • in-network data processing can significantly reduce re- compose for their functionality. We investigate the implica- sponse time and energy consumption [31], tions of this “flipped” IoT client-server model, for server dis- • edge isolation precludes the need for dedicated commu- covery, authentication, and resource use. We find that by com- nication channels between application and devices, and bining capability-based security with an edge-aware registry, facilitates privacy protection for both data and devices, this model can achieve fast response and energy efficiency. • actuation in device tier will require some form of request- 1 Introduction response protocol where the device fields the request, • the heterogeneity of devices militates for a single program- As the Internet of Things (IoT) grows in size and ubiquity, it ming paradigm and distributed interaction model, and is becoming critical that we perform data-driven operations • multi-function devices can and will be able to perform their (i.e. -
Serverless Computing: an Investigation of Factors Influencing Microservice Performance
Serverless Computing: An Investigation of Factors Influencing Microservice Performance Wes Lloyd1, Shruti Ramesh4, Swetha Chinthalapati2, Lan Ly3, Shrideep Pallickara5 5 Institute of Technology 4 Department of Computer Science University of Washington Microsoft Colorado State University Tacoma, Washington USA Redmond, Washington USA Fort Collins, Colorado USA 1wlloyd, 2swethach, [email protected] [email protected] [email protected] Abstract— Serverless computing platforms provide driving a paradigm shift rivaling a scale not seen since the function(s)-as-a-Service (FaaS) to end users while promising advent of cloud computing itself! reduced hosting costs, high availability, fault tolerance, and dynamic elasticity for hosting individual functions known as Fundamentally different than application hosting with IaaS microservices. Serverless Computing environments, unlike or Platform-as-a-Service (PaaS) clouds, with serverless Infrastructure-as-a-Service (IaaS) cloud platforms, abstract computing, applications are decomposed into many infrastructure management including creation of virtual machines microservices, which are essentially disparate functions. (VMs), operating system containers, and request load balancing Serverless environments leverage operating system containers from users. To conserve cloud server capacity and energy, cloud such as Docker to deploy and scale microservices [6]. Granular providers allow hosting infrastructure to go COLD, code deployment harnessing containers enables incremental, deprovisioning containers when service demand is low freeing rapid scaling of server infrastructure surpassing the elasticity infrastructure to be harnessed by others. In this paper, we present afforded by dynamically scaling virtual machines (VMs). Cloud results from our comprehensive investigation into the factors providers can load balance many small container placements which influence microservice performance afforded by serverless across servers helping to minimize idle server capacity better computing.