Functionalities, Challenges and Enablers for a Generalized FaaS

based Architecture as the Realizer of Cloud/Edge

Continuum Interplay

George Kousiouris

and Dimosthenis Kyriazis

Department of Informatics & Telematics, Harokopio University of Athens, 9, Omirou Str. 177 78, Tavros, Greece

Department of Digital Systems, University of Piraeus, Karaoli & A. Dimitriou 80, 18534 Piraeus, Greece

Keywords: Function as a Service, Cloud Computing, Cloud Architecture, Service Platforms, Middleware.

Abstract: The availability of decentralized edge computing locations as well as their combination with more centralized

Cloud solutions enables the investigation of various trade-offs for application component placement in order

to optimize application behaviour and resource usage. In this paper, the goal is to investigate key

functionalities and operations needed by a middleware layer so that it can serve as a generalized architectural

and computing framework in the implementation of a Cloud/Edge computing continuum. As a primary

candidate, FaaS frameworks are taken under consideration, given their significant benefits such as flexibility

in execution, maturity of the underlying tools, event driven nature and enablement of incorporation of arbitrary

and legacy application components triggered by diverse actions and rules. Related work, gaps and enablers

for three different layers (application design and implementation, semantically enriched runtime

adaptation/configuration and deployment optimization) are highlighted. These aid in detecting necessary

building blocks of a proposed generalized architecture in order to enclose the needed functionalities, covering

aspects such as diverse service environments and links with the underlying platforms for orchestration,

dynamic configuration, deployment and operation.

1 INTRODUCTION

The current Cloud computing landscape is

characterized by an extreme diversity of offerings and

services, incorporating multiple solutions. These

include centralized Cloud providers (such as typical

VM offerings, dedicated nodes, hardware enhanced

resources such as GPUs and FPGAs etc), edge and

fog environments, HPC facilities, mobile computing

applications etc. implementing the Everything as a

Service approach. On the other hand, applications are

typically consisted of a multitude of components,

others in need of locality and others in need of

significant computational resources. These

applications portray varying abilities to exploit the

underlying services, varying requirements for

operation, control and technologies/programming

structures on which they rely (Ferrer et al, 2017).

For achieving a true smart cloud computing

continuum, i.e. a unified approach on available

resources, one should examine the domain from three

main perspectives:

- Perspective 1: Continuum in terms of application

design and definition, leading to a generalized

adaptive approach for building the software or

service stack. Adaptation to dynamic conditions

(unexpected loads and failures), to different

computing paradigms (microservices and

functions) and functionalities of the new

execution environment is paramount. Enabling

the combination of edge/cloud resources can

significantly enhance aspects such as latency

(Bittencourt et al, 2018).

- Perspective 2: Continuum in terms of

functionalities, deployment and management

approaches. The scale and differentiation in the

new cloud/edge interplay has become quite

complex, thus managing dependencies and

mechanisms across continuum resources has

become a daunting task (Lynn et al, 2020). Once an

application has been defined, can it be seamlessly

managed by an underlying framework and

distributed based on a given set of goals and

constraints? Can the application exploit specific

Kousiouris, G. and Kyriazis, D.

Functionalities, Challenges and Enablers for a Generalized FaaS based Architecture as the Realizer of Cloud/Edge Continuum Interplay.

DOI: 10.5220/0010412101990206

In Proceedings of the 11th International Conference on Cloud Computing and Services Science (CLOSER 2021), pages 199-206

ISBN: 978-989-758-510-4

199

features of the used service, such as GPU

processing, multiple cores in the node etc.? If yes,

which executable version of it and with which

parameter set should be used during deployment,

without user intervention? In order to achieve that,

a relevant set of semantic descriptions should exist.

- Perspective 3: Continuum in terms of the

relativity inserted between space (of deployment)

and time (of execution) (Foster, 2019). Utilizing a

centralized service far away from the observer

may introduce latency or other factors that render

this selection as more time consuming than a

localized deployment. The analysis of this trade-

off should result in a unified space-time

combinatorial approach for service selection

while considering space-time continuum

distorting factors like multitenancy and resulting

performance interference (Kousiouris et al, 2011).

Therefore, dynamic incorporation of this factor’s

weight on the final optimization should be

measured and taken under consideration.

In order to achieve these objectives, suitable

middleware frameworks need to be designed,

deployed and operated across the continuum. The aim

of this paper is to investigate what is the status in

some of the key areas mentioned above, in order to

highlight existing capabilities as well as according

gaps and enablers. Section 2 includes the suitability

of the FaaS model for the specific purpose as well as

investigation of the current status in application

design and adaptation (Perspective 1), Section 3

investigates the usage of middleware and semantic

technologies (Perspective 2), while Section 4 studies

the space-time continuum capabilities (Perspective 3)

Finally the paper proposes a high level architectural

approach that would enhance the ability of FaaS

frameworks to meet this diverse role in Section 5 and

Section 6 concludes the paper.

2 FaaS CHARACTERISTICS AND

APPLICATION DESIGN

2.1 Why FaaS? Key FaaS

Characteristics of Interest

One of the main benefits of the FaaS model is the fact

that it is built around the most sophisticated variation

of the pay-as-you-go concept, the pay-as-you-

execute model, thus only charging when the

application code is actually executed (including

billing factors such as function runtime and memory).

What is more it alleviates from server environment

maintenance. Break down into functions enables

easier scalability and elasticity of the applications,

thus better ability to exploit elastic resources and

services, as well as software modularity and

maintenance. Therefore it strengthens the benefits of

a migration towards a cloud/edge service

environment. This function-based break-down

enables the easier distribution of tasks between

centralized and edge resources available.

This is further strengthened by the fact that

application structure is fed in the underlying FaaS

platform (such as Openwhisk) which handles many of

the deployment, configuration and orchestration

needs. FaaS has been among the highest growth

public cloud service types while the need to optimize

cost savings from cloud services is the top priority for

cloud users in 2020 (Flexera, 2020).

One of the main abilities of FaaS platforms is to

include diverse components and behaviours and adapt

them to event driven sequences and workflows, a

feature known as polyglot ability. Its operators include

notions such as Actions (application code that may

include pure function code enforced on input message

data, legacy non-FaaS components, arbitrary and

diverse executables of any programming language in

docker containers) and Rules (used to associate one

event trigger with one or multiple actions, therefore

enabling the definition of complex workflows). One

key aspect is that the event sources can be anything i.e

messages arriving on Message Queues, changes in

databases, web interactions, service APIs etc. This

enables bridging the FaaS model with other popular

existing approaches such as microservices, REST

services, legacy web applications or any arbitrary

legacy component.

Functions can typically range from small and

lightweight to larger and more computationally

demanding, therefore very suitable for the cloud/edge

interplay scope and offloading trade-offs investigation,

depending on available hardware on the edge, latency

considerations etc. However careful consideration

should be given to aspects such as the distribution of

the load, auto-scaling mechanisms, operational tasks

and function limitations (Kuhlenkamp et al, 2020). A

thorough analysis of numerous open and closed source

FaaS frameworks (Van Eyk et al, 2019) indicates the

fact that in many cases there are misconceptions

around characteristics, while costs may also differ from

the initial considerations.

Other benefits of a FaaS platform middleware

include the abstraction of the underlying resources and

infrastructure services used. Each platform may span

across different providers, resource types etc in a

manner that is agnostic to the end user. Locally in each

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service resource used, the FaaS platform (through its

distributed managers and agents) handles aspects such

as function deployment, enabling seamless federation

and distribution of the components through underlying

container orchestrators.

The need to offload computation exploiting

various trade-offs and capabilities as well as

transform monolithic applications in order to be

decomposed in smaller components, executed

separately and on the most suitable resources, is

proposed by the FuncX framework (Chard et al,

2020). Furthermore, approaches at an architectural

level have started to emerge in order to scale and

distribute FaaS platforms across different providers in

multi-cloud or hybrid cloud scenarios (Vieira et al,

2020) as close as possible to the client.

Other approaches such as A3-E (Baresi et al,

2019) enable applications to execute parts of their

logic on different infrastructures, with the goal of

minimizing latency and battery consumption and

maximizing availability. Cross-site orchestration has

been investigated in the context of the AWS Lambda

service in GlobalFlow (Zheng et al, 2019). Other

frameworks based on e.g. WebAssembly (Hall et al,

2019) have emerged, in an effort to reduce container

performance overheads in environments with need

for low-latency response or hardware platforms with

limited resources, however their tool support is not

near the maturity of platforms such as Openwhisk.

Identified Gap. While FaaS frameworks portray a

number of promising characteristics in terms of

execution on demand, improved cost, ease of

placement and inherent/direct parallelization

achieved, along with a long list of open source tooling

and approaches maturing, they come also with a

number of shortcomings that should be addressed.

Tooling availability related to deployment and

function reuse, remains a major difficulty, in current

FaaS systems (Leitner et al, 2019). Furthermore,

abstractions and programming models for building

non-trivial FaaS applications are limited. Currently

frameworks imply the need for full porting of the

application to the FaaS model, thus the redesign of

their execution model around short-lived functions,

leading to potential need for extensive application

rebuilding. Another challenge is the handling of state

in the FaaS model. The latter primarily targets at

stateless functions that do minimal I/O and

communication. Frameworks such as CloudBurst

(Shreekanti et al, 2020) have tried to extend the scope

to a broader range of applications and algorithms,

while incorporating key-value stores for state sharing

between functions. However new challenges emerge

when functions operate at a distributed and

federated cloud-edge environment, including data

consistency, locality and performance.

2.2 Application Design and Adaptation

to the FaaS Model

Visual environments have emerged in recent years as a

user friendly and abstract mean of development that

can speed up application development. Typically these

environments are based on flow programming and

offer palettes of readymade nodes or operators that

incorporate the major functionalities needed. Function

code is applied on the input message, transforming its

contents based on the function logic and passing it to

the next node in line. Furthermore, they encompass

means of extension for these nodes as well as external

repositories. Environments such as open-source Node-

RED for IoT event driven applications and KNIME

(mixture of open and proprietary models) for data

science flows have emerged, indicating that the need

for easier development is very relevant. Therefore they

can be extended and adapted to eventually deploy the

developed flows in a FaaS environment.

In terms of major open source FaaS platforms,

these typically do not come with a UI for workflow

definition (Van Eyk et al, 2019), with the exception

of Apache Airflow that also includes the

incorporation of operators to include typical cloud

services or processes. One drawback of Airflow is

that these operators are typically provider specific and

thus cannot be reused, while amplifying the vendor

lock-in. Also, they do not include advanced and

abstracted cloud design patterns. Fission workflows

are mainly programmatically defined. Proprietary

solutions also exist with an extensive list of

accompanying services such as the IBM Cloud

(formerly Bluemix) environment (and Blueworks) as

well as Google Composer.

In the cloud design patterns domain, Big Vendors

have promoted Pattern-Based development through

new programming and deployment paradigms in

order to build value added services. This development

methodology has the goal of providing complex

services and resources by interaction of simpler ones

and can be used to define proper orchestration actions

(Amato et al, 2017). Typical cloud design patterns

may include template structures and workflows such

as AI training and optimization (Giampa et al, 2020),

map/reduce types of structures, MPI based, data

ingestion, preprocessing, encryption, privacy and

transformation flows, load balancer structures,

preconfigured messaging structures (e.g.

publish/subscribe), data caching mechanisms, auto-

scaling and throttling functionalities, continuous

Functionalities, Challenges and Enablers for a Generalized FaaS based Architecture as the Realizer of Cloud/Edge Continuum Interplay

201

deployment patterns etc. Some of these patterns may

exist in implementation, even directly in the FaaS

model or enabling transformations to this through

suitable converter frameworks (Carvalho et al, 2019),

however they are in need of parameterization and/or

wrapping around the core design framework for

achieving maximum abstraction.

Identified Gap. While helpful in the sense of each

domain’s usability, current design environments lack

the ability to aid an application in exploiting cloud

benefits through ready-made supporting structures

that enhance functional and non-functional aspects.

Furthermore, they lack a unified and vertical

approach to enable application definition,

enhancement with features and creation of cloud

deployment specification in an integrated manner.

Either deployment specification or workflow

specification are supported but not combined and do

not include design patterns and functionality

automatically incorporated and configured in the

application graph. Many of the proposed

operators/environments are provider/platform

specific and increase vendor lock-in.

Proposed Enablers for FaaS Application Design

Linked to FaaS Frameworks. Cloud design and

programming patterns offered as FaaS reusable

components may significantly aid application

adaptation or extension in order to embed this

functionality alongside its current implementation.

Furthermore, visual environments for workflow

creation can significantly aid developers in their

transition and application adaptation. Thus

incorporation of such patterns in arbitrary flows and

instantiation of them with the specific software

artefacts/functions needed may be performed.

Furthermore, a vertical approach in these tools is

needed in the sense of the ability to produce directly

the application deployment specification to a FaaS

framework from the application design.

3 RESOURCE SEMANTIC

ENRICHMENT AND USAGE IN

RUNTIME DEPLOYMENT

Currently, a significantly high number of available

cloud services exist in the market. This makes the task

of matchmaking between user demands and service

capabilities difficult. Approaches have been

developed in order to cater for differences in

semantics for the service composition process (Di

Martino et al, 2017). However what is needed is a

tighter link between semantics and platform self-

configuration processes in order to fully exploit

semantic descriptions usage during runtime. In some

specialized cases, such transformations are performed

for exploiting special purpose cloud services e.g.

cloud-based FPGA’s (Chen et al, 2019). In terms of

fully integrated runtime usage and management

through semantics, the AffectUs framework

(Kousiouris et al, 2019) has used a combination of

ontologies, semantic inference, REST services and

flow based programming adapters in order to

integrate the use of ontologies in a functional manner

in the life-cycle of a supply chain management

application.

Extensive cloud service description frameworks

have been proposed in recent years (Ghazouani et al,

2020), covering a wide variety of common cloud

service characteristics (type, deployment model,

evaluation, functionality and operations, accessibility

and authorization features, QoS capabilities, legal

issues, pricing, resource control).

Identified Gap. Very interesting works exist in the

field of cloud service descriptions, thus exploitable in

the context of the Linked Data paradigm. What is yet

to be accomplished is a fully integrated use of

ontologies and semantics, not only for a preselection

or interface composition process but also to enable

functionally more automation of configuration.

Proposed Enablers for Usage of Semantics in

Deployment Configuration and Automation. First of

all, relevant ontologies need to be enriched or

integrated in order to capture specific aspects of an

application configuration, linked to the way this

application is configured, deployed and managed.

Afterwards, instantiated tuples (examples in Table 1)

would enable inference on the way the application

should be configured or whether it can exploit

specific resource characteristics. This can also be

extended at the function or cloud template pattern

level and can be easily supported during runtime (e.g.

multiple image versions with different characteristics

for a given software artefact).

However, this needs to be coupled by a

middleware layer that will bridge service/resource

descriptions with software artefact descriptions,

inferring whether capabilities of the former can be

exploited through the needs/characteristics of the

latter. Purposes for such inference can include usage

during deployment and/or application adaptation, in

order to enhance both functionally and non

functionally the automated and seamless application

adaptation to diverse environments. Moreover, a

semantically enriched controlling logic can improve

its agility. One could retrieve the specific resource

type in which currently the application component is

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running and infer whether changing a specific

parameter would be expected to make a difference.

Other aspects such as locality constraints, e.g.

imposed by legal (Barnitzke et al, 2011), ethical or

other requirements, can be expressed. Finally,

through modelled information like endpoints and

their relation to QoS features (monitoring or

controlling parameter endpoints), automated

incorporation of controller logic can be inserted in the

designed workflows and parameterized on the fly.

Table 1: Example Ontology Relations Usable for

Deployment Optimization.

Subject

(example

instance)

Predicate

Object Class

(example instance)

cliOption (-

threads)

setsParameter

Parameter

(numberOfThreads)

Resource

(aws.medium)

isA ResourceType (VM)

ResourceType

(VM)

hasParameter

Parameter

(numberOfCores)

Parameter

(numberOfCores)

isUsedBy

Parameter

(numberOfThreads)

Endpoint

(/setThreads)

managesPara

mete

Parameter (e.g.

numberOfThreads)

Endpoint

(/setThreads)

isA HTTPEndpoint

Parameter

(numberOfThread

Affects

QoSMetric (e.g.

ResponseTime)

applicationCompo

nent

hasSpecificLo

calityConstrai

nts

ProviderDC (if

specific location and

provider is needed)

applicationVersio

n (myImage)

isA ContainerImage

ContainerImage(

myImage)

isOptimizedF

ResourceType

(GPUEnabledVM)

CloudPattern

(MessageMQTT)

isOptimizedF

ResourceType

(EdgeResource)

ResourceType

(EdgeResourceA)

hasMeasured

Performance

QoSMetric (Bench

results)

One aspect that needs to be stressed is that this

mechanism needs to be linked with respective

annotation capabilities of typical orchestrator systems

(e.g. Docker Compose, Kubernetes). Annotating the

abilities of nodes (feasible in current orchestrators

through e.g. node naming) will bridge the gap

between selection and enforcement of a given

deployment scheme.

4 SPACE-TIME CONTINUUM

EVALUATION/ OPTIMIZATION

Even though the FaaS model promises reductions of

cost compared to IaaS and PaaS offerings, its billing

mechanisms typically include function invocation

numbers as well as execution time. However, in this

context costs are less predictable, especially because

they are tied to function performance (as well as the

provider’s environment). Reports (Bortolini et al,

2019) have observed significant differences (up to

8.5× in performance and 67 × in cost between

providers, 16.8× in performance and 67.2× in cost

between programming languages). The problem of

the assessment of black box Cloud services

performance is especially intense in FaaS

environments (Pellegrini et al, 2019). Cloud Service

Providers usually restrict the maximum size of code,

memory and runtime of Cloud Functions. The

aforementioned work introduces a baseline FaaS

benchmarking tool, which allows users to evaluate

the performance of Cloud Functions. Challenges as

well as requirements for a future FaaS measurement

framework include taking into account notions of

cost, realistic workloads, more (open-source)

platforms, and cloud integration.

Baseline approaches such as FunctionBench (Kim

et al, 2019), including workloads such as ML,

network and micro-level benchmarks are a step

forward. Comparisons have been performed to better

understand the cost vs performance trade‐off between

FaaS and IaaS such that cloud users can decide which

approach is suitable for them. Reports (Malla et al,

2020) on comparisons between Google cloud's FaaS

(Cloud Functions) and its IaaS (Compute Engine) in

terms of cost and performance have indicated that

FaaS can be 14% to 40% less expensive than IaaS for

the same level of performance. However,

performance of FaaS exhibits higher variation.

In terms of deployment optimization and

placement, numerous approaches are available (Cao

et al, 2019), even from the initial Cloud era, focusing

on multi-cloud service placement (Ferrer et al, 2012)

or more recently for cloud-to-edge specific issues

(Meixner et al, 2019). In many cases the size of the

problem due to its NP hard nature is impossible to

calculate optimally without breaking another

constraint (e.g. time to reach the deployment

decision), given the range of possibilities to take

under consideration.

Identified Gap. There needs to be a closer link and

tight collaboration between multiple functionalities

and primarily resource capacity

measurement/evaluation (through e.g. benchmarks

that take under consideration other parameters such

as function based execution, time to completion,

memory used etc.) and deployment optimization.

Proposed Enablers for Enhanced Deployment

Optimization Across the Continuum. Through the

Functionalities, Challenges and Enablers for a Generalized FaaS based Architecture as the Realizer of Cloud/Edge Continuum Interplay

203

inclusion of periodic benchmarking/ evaluation

details for each resource type (integrated also with the

semantic descriptions of Section III), as well as

relevant factors included in the optimization model,

the space-time continuum approach (Foster, 2019)

may be implemented. Furthermore, the integration of

the semantic querying as an initial filter of resources

(and based on the discussion in Section 3), can

significantly reduce the search space of an

optimization algorithm and therefore potentially

enable the application of globally optimal algorithms

such as branch-and-bound. The overall process of the

previous sections can be summarized in the sequence

presented in Figure 1.

5 GENERALIZED

ARCHITECTURAL BLOCKS

The functionalities presented in the previous sections

are included in the form of generalized architectural

building blocks in Figure 2, responsible for

implementing the sequence of Figure 1. This

architecture does not assume that there is any special

connection or federation between the entities (e.g.

Cloud or Edge providers), only a middle managing

entity like a broker/platform manager that creates and

operates resources on the cloud/edge.

Figure 1: From application design to deployable FaaS.

The entry point is the Application Design Layer,

that includes aspects such as management of the

application code basis, visual flow programming

environments enriched with ready-made software

pattern flows, FaaS operators, followed by suitable

parameterization and code insertion where needed. A

necessary step in this case is also the semantic

description of the application parts, whether these are

existing legacy components and executables or newly

created functions. This step aids in the creation of

semantic triples in an according knowledge base,

following the respective Ontological definitions. The

same step of semantic description population needs to

be undertaken also at the resource side, so that the

inference engine can exploit and combine the two

sources of semantics.

Next, the main management layer (Continuum

Deployment Layer) is responsible for receiving the

application graphs extracted from the design

environments and convert them to the action

sequence specification needed by the main FaaS

platform (e.g. Openwhisk). The optimization process

is only performed after a relevant query towards the

Inference Engine, that can apply functional or non

functional constraints and return the subset of

resources that address the requirements. These should

then be mapped on the application graph and

evaluated in terms of performance, cost or other goal

for which information is available (e.g. energy

efficiency). Benchmarking or historical monitoring

information for resources can be acquired from the

Performance Measurement block, that aims to capture

this either through monitoring of executions or from

periodic performance evaluation tests.

From this optimization process (applied in the

Global Continuum Placement Optimizer), the final

resulting deployment graph may be acquired and

forwarded for deployment to the mainstream FaaS

platform, properly annotated via the mechanisms

described in Section 3 in order to dictate the

deployment scheme. However this mainstream FaaS

platform needs to be managed by the Global FaaS

layer, in the sense that the latter will have the

responsibility to create according resource instances

(e.g. Openwhisk nodes) in each of the Cloud/Edge

federation resources. It is necessary to stress that this

resource creation does not assume any special

agreement or link between the different entities.

The Continuum Deployment layer can act like any

typical customer of the latter. A final step is the

incorporation of supporting structures to functionally

link these distributed resources (virtual networking

layers and/or in-memory data services for data

locality and state preservation for functions).

At the provider-local level, once the application is

deployed, the elasticity controllers (embedded in the

application graph) can check the detailed function

execution logs or application endpoints (obtained

from the semantic descriptions) for performance

information and upon detection of an under or over

performing application part they can toggle local

resources given by the local CSP (e.g. through API

calls). If despite these efforts this application part

does not suitably adapt to the desired QoS levels,

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204

Figure 2: Generalized Architectural Building Blocks.

then the problem can be redirected to the global

optimization plane for a new deployment

optimization.

6 CONCLUSIONS

As a conclusion, FaaS presents a set of characteristics

(e.g. granular execution, flexible deployment

structures, supporting platform tools) that enable its

usage across diverse devices as well as distributed

environments. However in order for this upcoming

computing model to reach its full potential in the

Cloud/Edge interplay scenario, a number of additions

need to be performed to enable easier application

creation, adaptation, and seamless management

across diverse locations.

The work in this paper proposed additions from

application design to deployment and operation.

Initially the application design and implementation

process, supported by implementation templates that

cover typical cloud oriented functionalities directly in

the FaaS model, as well as visual flow programming

environments, will aid in abstracting FaaS migration

processes or application functionality extensions. To

this end, the polyglot ability of FaaS frameworks is a

major strength.

Following, extensions in terms of semantic

descriptions and their incorporation during the

runtime selection and configuration is another key

enabler that aids in automatically adapting to the

diverse environments and optimizing application

setup. Optimization of deployment selection can

significantly benefit from applying such semantic

descriptions as well as runtime evaluation of

Cloud/Edge resources. The overall analysis has led to

a generalized architectural approach that can aid in

addressing functional and non-functional

requirements of complex applications deployed over

dynamic and volatile environments in an abstract

manner. The proposed architecture does not imply

any specific relation or exposure between providers

and is regulated by a brokering entity that has the goal

of managing the middleware layer.

ACKNOWLEDGEMENTS

The research leading to the results presented in this

paper has received funding from the European

Union's Project H2020 PHYSICS (GA 101017047).

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