=Paper=
{{Paper
|id=Vol-2326/paper5
|storemode=property
|title=A Migration-based Approach to execute Long-Duration Multi-Cloud Serverless
Functions
|pdfUrl=https://ceur-ws.org/Vol-2326/paper5.pdf
|volume=Vol-2326
|authors=Boubakeur Soltani,Afifa Ghenai,Nadia Zeghib
|dblpUrl=https://dblp.org/rec/conf/icaase/SoltaniGZ18
}}
==A Migration-based Approach to execute Long-Duration Multi-Cloud Serverless
Functions==
https://ceur-ws.org/Vol-2326/paper5.pdf
A Migration-Based Approach to Execute Long-Duration Multi-
Cloud Serverless Functions
Boubaker Soltani Afifa Ghenai Nadia Zeghib
LIRE Laboratory
Constantine2 – Abdelhamid Mehri University, Constantine, Algeria
{boubaker.soltani, afifa.ghenai, nadia.zeghib} @univ-constantine2.dz
approach use is illustrated by a case study: a
generic machine learning application built
Abstract over the scientific platform ANTDROID.
Serverless Computing is emerging as an
undeniable paradigm for the deployment of
1. Introduction
(multi)cloud applications. It is mainly Cloud computing is now commonly used to describe
characterized by the use of stateless loosely- the delivery of software, infrastructure and storage
coupled functions that are composed together services over the internet. In this field, there are
to perform useful actions. This approach, generally two parts that may be made available under
contrarily to monolithic one, makes easier the the client control: the application code and the
maintenance and the evolution of the underlying infrastructure hosting that application. In
applications, since the functions can be this context, Serverless computing provides a great
independently revised and reprogrammed. opportunity for developers seeking relief from the
However, one principle in Serverless burden of infrastructure. This computing model
computing is that function execution should allows building and running applications and services
be within a short duration (five minutes max without having to manage infrastructure. In fact,
in most Cloud provider platforms), after Serverless Computing is an event-driven approach
which the function is abruptly terminated that abstracts the infrastructure management away
even if it has not completed its task. from the client. Aspects like scalability, provisioning
Moreover, the max duration cannot be and fault tolerance are automatically handled by a
extended without a negative effect on the Serverless platform, while the Cloud user focuses
platform performance. This leads to prevent only on his functional code [Bald17]. This code
functions requiring longer time from being comes in the form of a set of stateless functions that
adopted as Serverless functions. This paper are agnostic of where are they going to be executed
deals with this drawback. It proposes a [Bald17]. Serverless platforms adopt a pay-per-
distributed migration-based approach which execution billing strategy, i.e., contrarily to traditional
promotes the execution of long-duration Cloud platforms that host the client program in a
Serverless functions: each running function listening running server (hence, the client pays as
that reaches the maximum duration limit is long as the server is running even if there are no
repeatedly transferred to another cloud requests), a Serverless platform does not start a server
platform where it is carried on. In this aim, until a request is made. In this way, the client is billed
the migration-based system architecture, the for each invocation, meaning that no payment if no
migration technique and the migration requests [Kuh17]. The Serverless architecture is
algorithm are described. The proposed relatively a new paradigm and only few pioneers have
Copyright © by the paper’s authors. Copying permitted only for private
and academic purposes.
In: Proceedings of the 3rd Edition of the International Conference on
Advanced Aspects of Software Engineering (ICAASE18), Constantine,
Algeria, 1,2-December-2018, published at http://ceur-ws.org
Page 42
A Migration-Based Approach to Execute Long-Duration Multi- Cloud Serverless Functions ICAASE'2018
investigated this domain. Amazon AWS Lambda is a [Groz14]. This distribution and independence allows
compute service that supports many languages like us to apply our repeated migration-based approach to
node.js, C#, Python and Java on AWS infrastructure. cope with the aforementioned problem.
Source code is provided as ZIP file and deployed in a The remainder of this paper is organized as follows:
container that is allocated the necessary hardware Section 2 discuses some works based on monolithic
resources. The combination of code, configuration application decomposition that are relevant to our
and dependencies is what is called a Lambda function work. Section 3 is dedicated to the presentation of our
[Sbar17]. The author admits that a Lambda function migration-based system architecture. Section 4
can only run for a maximum of five minutes. Azure illustrates our proposal via a case study. Finally,
Functions [Kum17] is Microsoft’s version of a Section 5 concludes the paper with ongoing work.
Serverless platform. They provide a technique named
the consumption plan to enhance function scalability 2. Related Work
during execution [Azr18]. A function in such plan is
limited (by default) to five minutes, but this value can The investigated literature is classified switch to two
be increased up to ten minutes [Azr18]. Google Cloud categories: works that focused on well-designing
Functions [Stig18] is another promising Serverless functions at design time, i.e., from the beginning, be
platform which limits the function to nine minutes careful to program functions that do not surpass the
max [Goo18]. Moreover, according to [Fox17], maximum limit; and works that focused on
Serverless platforms are still hosting short- running decomposing pre-existing monolithic applications to
functions due to the faced difficulty of scheduling smaller independent functions.
long-running tasks and their SLA (service level
agreements) management, but the authors advocate 2.1. Design-Time Function Creation
that it is possible in the future that Serverless In [Ader17], set of requirements assisting the
platforms will host such tasks. Current procedure to development of microservices benchmark application
execute long-duration workloads is not to provision for repeated empirical research in software
them as Serverless functions (e.g. as a listening server architectures was provided [Ader17]. The e-
instead) [Fox17] [Sbar17]. commerce European software Otto is planned to be
Consequently, in current Serverless platforms, the rebuilt from scratch switch to a microservice concept
function execution should be within a short duration of Verticals (isolated parts from each other, no shared
(five minutes max in most Cloud provider platforms), states or infrastructures) [Hass17]. Sascha et al.
after which the function is abruptly terminated even if [Alpr15] propose a microservice-based architecture
it has not completed its task. This leads to prevent for business process modeling. An application in their
functions requiring longer time from being adopted as model is decomposed to resources and entities, where
Serverless functions. a service is dedicated for each resource. Authors of
In the aim to overcome this drawback, we take [Tarm17] are tackling the limit of the traditional
advantage from Multi-cloud paradigm features and method for analyzing and designing microservice
propose a distributed migration-based approach architectures by proposing a new one based on
which promotes the execution of long-duration holistic analysis and design (from identifying the
Serverless functions. organizational identity and objectives to modeling the
The Multi-Cloud paradigm allows considering the microservice dependency graph) [Tarm17].
distribution and provider-independence aspects. In Containerized microservice [Venu17] is a typical
fact, Multi Cloud [Groz14] is the usage of multiple, combination nowadays, due to the promising
independent clouds by a client or a service. This container technology solutions like Docker.
paradigm is dedicated to accomplish the task of Nevertheless, authors notice lack of performance
scheduling workloads to resources deployed across consideration in microservice building and identify
multiple clouds. A Multi-cloud is, however, not the open issues and research guidelines. The Table 1
same as Cloud federation which refers to a set of summaries the main advantages and limits of the
cloud providers that intentionally interconnect their design–time function creation approaches.
infrastructures to make a unified resource pool
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Table 1: Design-Time Function Creation Approaches Summary
Papers Application domain Advantages Limits
[Ader17] Software empirical research Evaluative Requirements Function execution time is
[Hass17] E-commerce apps Scalability and agility once fixed in design-time. So,
(in/de)creasing the max limit
[Alpr15] Business process modeling Flexibility and agility
requires redesigning the
[Tarm17] Holistic analysis/design More details are dragged function to meet the new time
[Venu17] Containerized microservices Lightweight virtualization limit.
2.2. Monolithic Application Decomposition determining the different contexts that the monolith
encompasses, moving the source code selectively to
In [Kecs16], authors offer an approach based on each context package, then choosing one of them to
ENTICE project’s Image synthesis technique. The begin the separation process on it (priority criteria
principle is to generate dynamic VM images and their like: pace of change, security). In [Maz17], authors
required software dependencies (without irrelevant present a formal microservice extraction model to
parts) [Kecs16]. Service Cutter [Gys16] uses 16 allow algorithmic recommendation of microservice
software’s coupling criteria extracted from academia candidates in a refactoring scenario. This model is
and industry practices. It uses domain models and similar to [Gys16] approach. It has two
other artifacts as coupling information sources; transformations: construction, which converts the
represents them as a graph; before estimating the monolith to graph and clustering, which extracts
parts that may be split to independent loosely-coupled microservices from graph. The major advantages and
services. Authors in [New15] give the outlines of limits of these approaches is summarized in Table 2.
decomposing a monolithic application by
Table 2: Monolithic Application Decomposition Approaches Summary
Paper Decomposition based on Advantages Limits
[Kecs16] VM images Facilitated by Cloud platforms Requires manual
intervention.
[Gys16] Design artifacts Closer to user goals isolation
Decomposition may be
[New15] Context packages Explicit domain isolation impracticable.
Application altering may
[Maz17] Source code Directly executable result
be undesirable.
2.3. Discussion
which does not imply any structure reforming (no
According to the above works summarized in Table redesign consideration concerns). It is mainly based
1 and Table 2, the decomposition may be on the migration principle in Multi-cloud computing.
impracticable or leads to fixed-duration functions (do The key idea is that each running serverless function
not fit a frequently-changing limit environment). that reaches the maximum duration limit is repeatedly
Moreover, it may require manual intervention, or may transferred to another cloud platform where it is
have a specific application domain. Hence, it seems carried on.
that the decomposition has significant limits and it is
not suitable to deal with long-duration Serveless
functions. In the following, we propose an approach
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3. The Proposed Migration Approach
In this section, we propose a distributed migration
approach based on Multi-Cloud Serverless
architecture. First, we describe briefly this
architecture that uses Docker containers at runtime
for executing user functions. This platform is the
distributed link that makes the different clouds find
each other. Second, we describe the used migration-
based technique which is accomplished in two
phases: monitoring and migration.
3.1. Distributed Serverless Architecture Figure1: Distributed Serverless Architecture[Solt18]
Docker [Vohr16] is a container technique that 3.2. Migration-Based Thechnique
bundles the dependencies of a specific application in
a single deployable unit called Docker image that can First, we define a variable in our system: MaxLimit.
be executed at any node running Docker engine. It represents the maximum allowed time (in seconds)
Docker Containers can be connected to combine their for a function to be executed before it is scheduled for
resources for the software running within. Dockerfile migration (abruptly terminated in traditional
is a collection that embraces all commands of Serverless) and it is configurable by system
configuration and downloads required API for the administrators switch to their needs (five minutes by
application to be ready for execution. Then it builds default).
the container either from scratch or upon other base The novel contribution is adding new specific
image (usually, a base container consists of an images and functions to the Container Image
operating system and some very basic tools) Repository (CIR) and the Function Registry (FM)
[Vohr16]. Each function in the system needs a finite respectively. The new added elements details are as
set of container images in order to be able to properly follows:
work (its software dependencies).
3.2.1. Function Monitor Image (FMI): It is the
We adopt our distributed Serverless architecture runtime image for the monitoring task. We are
shown in Figure 1 [Solt18]. This architecture is a Peer choosing Prometheus as implementation for it.
to Peer (P2P) system that spans several Cloud Prometheus [Prm1t18] is an open-source system
provider platforms (a Middleware for Multi Cloud). monitoring and alerting toolkit and it can be
In this paper, we show the role of two needed configured with Docker [Prm2t18]. This image
components.
has the interfacing API for monitor (see next
(a) Container Image Registry (CIR): a registry of
point 3.2.2) to use Prometheus. The Monitor API
container images for different programming language
Docker image is represented in Figure 2.
runtimes. This registry is extensible, so runtimes can
be added/ removed from it and each node in the Multi
Cloud system has its own CIR (for its functions) 3.2.2. Monitor: It is a specific function that has as
[Solt18]. role to count the consumed time during
(b) The Function Manager (FM): among its roles, it execution, for each function in the local node
ensures the communication between different nodes using FMI API and all functions that have ran
in the Multi Cloud Serverless system. In this paper, more than or equals MaxLimit are passed to FUS
we use it to migrate functions from node to another for migration (see next point). The monitor is
along with its all dependencies, so, no worry about periodically invoked to ensure continuous
their existence in destination [Solt18]. function refresh (thirty second by default, but it is
configurable).
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application with at least one long-running function.
Our illustrating case study is a generic machine
learning application of three Java-based Serverless
functions built over the scientific platform called
ANTDROID [Hadr12].
4.1. System Description
ANTDROID [Hadr12] is a federated SaaS available
to scientists for sensing the activities of mobile users
for their own experiments. The scientist component of
the platform provides an interface allowing them to
Figure 2: Monitor API Docker Image connect external services to the platform to extract
and reuse dataset collected from their experiments’
3.2.3. Function Urgent Scheduler participants (e.g., visualization, analysis) as shown in
(FUS): this function is the scheduler that receives Figure 4.
the list of functions that have reached MaxLimit
(from monitor). It selects, for each one, a random
node and informs the selected node for the
function’s dependencies (names only). The node
responds back by asking for its missing images;
the set passed by FUS to its local FM. Finally,
the local FM migrates the function towards the
remote FM all along with its missing images
(delta images) and its current memory state.
Algorithm 1 describes how FUS works.
Algorithm 1
Input: List long
Output: List migr Figure 4: AntDroid Architecture [Hadr12]
For each f in long: This interface will be exploited by a special docker
(1) fmid = Selects random node image of our Serverless system (stored
(2) sends(fname, dependenciesNames) to fmid in CIR) to build the AntDroid Connect Service or
(3) receives (deltaImages) from fmid ACS as shown in Figure 5.
(4) adds (f, fmid, deltaImages) to migr
End for;
(5) returns migr to local FM
(6) FM adds the complete deltaImages to migr
(7) FM sends each f to its target
Figure 3: Urgent Scheduler Algorithm
4. Case Study Figure 5: ACS Docker Image
In the aim of showing the actual contribution of our ACS image is now the dependency runtime for all
approach and its usefulness, we consider an the application’s three functions (as they do not need
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A Migration-Based Approach to Execute Long-Duration Multi- Cloud Serverless Functions ICAASE'2018
more than usual Java runtime environment given in Table3. The three functions are executed
functionality). The details of the functions as well as sequentially one after another switch to their order in
their minimum estimated time before termination are Table 3.
Table 3: Example of Functions Set
Function name Role Duration
Filter Accesses the scientist collected dataset to extract the subset of valuable data. 20 min
Learner Iterates the filtered data to acquire new knowledge (statistical indicators). 8 min
Viewer Shows the calculated results in graphical plots (like Pie plots). few seconds
4.2. Application of The Proposed Approach to
AntDroid
Since the dataset collection may be very large and
continuously fed by interactions (tens of gigabytes),
the two functions filter and learner are estimated to
be long, and hence, subjects for this paper procedure
to be applied upon. The filter function, hence, will be
migrated 3 times in the respective moments 5min,
10min and 15min after it starts. We show in the
following figures how does that scenario happen
(only once for filter for the sake of illustration). Table
4 discusses the three figures steps and meanings.
Figure 7: Migration Scenario (b)
Figure 6: Migration Scenario (a) Figure 8: Migration Scenario (c)
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A Migration-Based Approach to Execute Long-Duration Multi- Cloud Serverless Functions ICAASE'2018
Table 4: Migration Scenario Details
Figure part Details
(a) Application starting point. (1) The client calls the http link: fmid-url/filter.
(2) The FM selects the filter function and its dependency ACS image.
As shown in Figure 6.
(3) The ACS is started and filter function now executes.
(1) The local timer notifies the FM (each 30 sec).
(b) After 5 min. (2) The FM prepares to start the new monitoring cycle.
As shown in Figure 7. (3) The FMI image and monitor function are loaded to memory.
(4) monitor detects that filter reached its max limit. It notifies FM.
(1) FM prepares the FUS scheduler function.
(c) Migration phase. (2) FUS is started within its container.
As shown in Figure 8. (3) FUS executes its algorithm, determines the migration destination and informs FM.
(4) FM migrates filter from Node1 to Node2.
From this presented illustration of our technique the adoption of virtualization techniques to simulate a
where the filter function was (same for learner) big number of physical machines, while in reality
migrated as much as needed (3 times here) until its there are only a few of them.
termination, the developer concerns himself less
about time constraints and any factors affecting them 5. Conclusion
during design-time (like unpredictable overloading of
Serverless infrastructures, data volumes), focusing In this paper, we have tackled the limit of the
more on functional requirements of his application. Serverless principle: all functions should terminate
The above case study shows clearly that our their execution in a short period (typically five
proposed approach allows the execution of long minutes). We have proposed a distributed migration
duration Serverless functions. We intend to consider approach based on Multi-Cloud Serverless
some key aspects to improve its efficiency. First, architecture. This migration-based technique deals
during the function migration, the destination cloud with Serverless functions that run for a long duration.
may not provide the same level of QoS (Quality of Our technique consists of transferring a target
Service) desired by the migrated function (although function that reaches its limit in term of execution
all the dependencies are ensured) for many reasons, time, to another Cloud platform to be resumed there.
e.g. the new host may be in a period of high charge By repeatedly applying this step, a function – no
workloads. Another issue is the time necessary to matter how long – finishes its task normally. This
execute the algorithm of this paper added to the algorithm is designed to be fast and simple in term of
required time to transfer the function state and its scheduling decision, because its main goal is not
dependencies. This time can differ from Cloud-pair optimizing resource utilization, but to save long
network links affected by parameters like geo- functions from being cut off. We have illustrated the
locations, links’ bandwidth and size of transferred use of the proposed approach through the case study
functions and libraries. The random aspect of the of a generic machine learning application built over
Algorithm1 will be studied as a more fair function the scientific platform ANTDROID. In future, we
distribution between nodes. An evaluation of the plan to investigate in the optimization of the
incurred latency of Algorithm1 will be conducted by scheduler algorithm. Another interesting work may
measuring the diverse parameters mentioned in this concern considering more fair function distribution
paragraph, which gives more realistic dimension to between nodes instead of mere randomness-based
this architecture. The main tool for this evaluation selection, all without losing the urgent aspect of the
will be Semi-physical simulation which is essentially algorithm.
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6. References [Groz14] N. Grozev and R. Buyya, “Inter-Cloud
architectures and application brokering:
[Bald17] Ioana Baldini, Paul Castro, Kerry Chang, taxonomy and survey,” in Journal of
Perry Cheng, Stephen Fink, Vatche Ishakian, Software: Practice and Experience, vol. 44,
Nick Mitchell,Vinod Muthusamy, Rodric no. 3, pp. 369–390, Mars. 2014.
Rabbah, Aleksander Slominski, Philippe
Suter. Serverless Computing: Current [Ader17] Aderaldo C., Mendonça N., Pahl C.,
Trends and Open Problems. Springer, Jamshidi P., Benchmark requirements for
December,2017. microservices architecture research. In
https://arxiv.org/pdf/1706.03178.pdf Proceedings of the 1st International
Workshop on Establishing the Community-
[Kuh17] Jorn Kuhlenkamp and Markus Klems. Wide Infrastructure for Architecture-Based
Costradamus: A Cost-Tracing System for Software Engineering, ECASE, May 2017,
Cloud-based Software Services. Service- Buenos Aires, Argentina, pp. 8-13.
Oriented Computing: 15th International
Conference, ICSOC 2017, Malaga, Spain, [Hass17] Wilhelm Hasselbring and Guido Steinacker.
November 13–16, 2017, pp.657-672. Microservice Architectures for Scalability,
Agility and Reliability in E-Commerce. 2017
[Sbar17] Peter Sbarski with Forewords by Patrick
IEEE International Conference on Software
Debois, Donald F. Ferguson. Serverless
Architecture Workshops (ICSAW), 5-7 April
Architectures on AWS. Manning Shelter
2017, Gothenburg, Sweden.
Island; 2017.
[Alpr15] Sascha Alpers and Christoph Becker.
[Kum17] Praveen Kumar Sreeram. Azure Serverless Microservice Based Tool Support for Business
Computing Cookbook. Packt Publishing Process Modelling. Enterprise Distributed Object
Ltd;August,2017. Computing Workshop (EDOCW), IEEE 19th
https://azure.microsoft.com/mediahandler/files/res International, Sept. 2015.
ourcefiles/ff388333-6cd0-4b47-
a33e3c3296a5141b/Azure_Serverless_Comp [Tarm17]Ahmad Tarmizi Abdul Ghani, Mohd.
uting_Cookbook. pdf Shanudin Zakaria. A Method for Analyzing
and Designing Microservice Holistically.
[Azr18]https://docs.microsoft.com/en-us/azure/azure- (IJACSA) International Journal of Advanced
functions/functions-scale, accessed in Computer Science and Applications, Vol. 8,
5/23/2018. No. 12, 2017.
[Stig18] Maddie Stigler. Beginning Serverless [Venu17]M.V.L.N.Venugopal. Containerized
Computing, Developing with Amazon Web Microservices architecture. International
Services, Microsoft Azure and Google Journal Of Engineering And Computer
Cloud, Apress; 2018. Science ISSN:2319-7242 Vol.6(11), pp.
https://www.apress.com/us/book/9781484230831
23199-23208, November 2017.
[Goo18] Google Cloud Functions documentation:
[Kecs16] Kecskemeti G., Marosi A., Kertesz A., The
https://cloud.google.com/functions/quotas,
ENTICE approach to decompose monolithic
accessed in 5/23/2018.
services into microservices. In International
[Fox17] Geoffrey C. Fox, Vatche Ishakian, Vinod Conference on High Performance
Muthusamy and Aleksander Slominski Computing & Simulation (HPCS 2016), July
(IBM). Report from workshop and panel on 18-22, 2016, Innsbruck, Austria, pp. 591-
the Status of Serverless Computing and 596.
Function-as-a-Service (FaaS) in Industry and
[Gys16]Michael Gysel,Lukas Kölbener,
Research. First International Workshop on
Wolfgang Giersche and Olaf Zimmermann.
Serverless Computing (WoSC), June 5-8,
Service Cutter: A Systematic Approach to
2017, Atlanta,GA,USA.
Service Decomposition. 5th European
Conference on Service-Oriented and Cloud
International Conference on Advanced Aspects of Software Engineering Page 49
ICAASE, December, 01-02, 2018
A Migration-Based Approach to Execute Long-Duration Multi- Cloud Serverless Functions
Computing (ESOCC), Sep 2016, Vienna,
Austria. Springer International Publishing,
LNCS-9846, pp.185-200, 2016.
[New15] Sam Newman. Building Microservices:
Designing Fine-Grained Systems. O'Reilly
Media, Feb. 2015 (chapter 5).
[Maz17] Genc Mazlami, Jurgen Cito, Philipp Leitner.
Extraction of Microservices from Monolithic
Software Architectures. The 24th IEEE
International Conference on Web Services
(ICWS 2017) June 25-30, 2017, Honolulu,
HI, USA
[Vohr16] Deepak Vohra. Pro Docker. Apress, ISBN-
13 (pbk): 978-1-4842-1829-7, ISBN-13
(electronic): 978-1-4842-1830-3; 2016.
[Solt18] Boubaker Soltani, Afifa Ghenaï and Nadia
Zeghib. Towards Distributed Containerized
Serverless Architecture in Multi Cloud
Environments. The 15th International
Conference on Mobile Systems and
Pervasive Computing (MobiSPC) Gran
Canaria, Spain, August 13-15, 2018.
[Prm1t18] Prometheus - Monitoring system & time
series database. https://prometheus.io/,
accessed 27/05/2018.
[Prm2t18]https://docs.docker.com/config/thirdparty/prome
theus/, accessed 27/05/2018.
[Hadr12] N. Haderer, R. Rouvoy and L. Seinturier,
“AntDroid: A Distributed Platform for Mobile
Sensing,” Research Report RR-7885, Inria, Feb.
2012.
International Conference on Advanced Aspects of Software Engineering Page 50
ICAASE, December, 01-02, 2018