Aquarium

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\begin{document}

\conferenceinfo{ScalaDays '12}{London, UK.}

\copyrightyear{2012}

\copyrightdata{1-59593-056-6/05/0006}

\titlebanner{DRAFT---Do not distribute}

\title{Aquarium: Billing for the Cloud in the Cloud}

\authorinfo{Georgios Gousios \and Christos KK Loverdos \and Nectarios Koziris}

{GRNet SA}

{\{gousiosg,loverdos,nkoziris\}@grnet.gr}

\maketitle

\begin{abstract}

    This paper describes the architecture for the Aquarium cloud infrastructure

    software. Aquarium is a new software we have built whose main function is to associate cloud resources usage with charging policies.

\end{abstract}

\category{D.3.3}{Programming Languages}{Language Constructs and Features}[Control structures]

\terms

    Object-Oriented Programming, Philosophy

\keywords

    OOP, Ontology, Programming Philosophy

\section{Introduction}

\section{Requirements}

Aquarium was designed on a clean sheet to serve a particular purpose,

namely the provision of billing services to an IaaS infrastructure,

and also be extensible to new services. In the following sections,

we briefly present the requirements that shaped Aquarium's design.

\subsection{Application Environment}

Aquarium developed as part of the Okeanos project at GRNet. The

Okeanos project is building a full stack public IaaS system for Greek

universities, and several services on top of it. Several components comprise

the Okeanos infrastructure:

\begin{description}

    \item[Synnefo] is an IaaS management console. Users can create and start

        VMs, monitor their usage, create private internal networks among VMs

        and connect to them over the web. The service backend is based on

        Google's Ganneti for VM host management and hundrends of physical

        VM container nodes.

    \item[Archipelago] is a storage service, based on the Rados

        distributed object store. It is currently under development, and the

        plan is to act as the single point of storage for VM images, shared

        volumes and user files, providing clonable snapshots and distributed

        fault tolerance.

    \item[Pithos] is a user oriented file storage service. Currently it its

        second incarnation, it supports content deduplication, sharing of files

        and folders and a multitude of clients.

    \item[Astakos] is an identity consolidation system that also acts as the

        entry point to the entire infrastructure. Users can login using 

        identities from multiple systems, such as the Shibboleth (SAML) 

        federation enabled across all Greek universities or their Twitter 

        accounts.

\end{description}

While all the above systems (and several prospective ones) have different 

user interfaces and provide distinct functionality in the context of

the GRnet IaaS, they all share a common notion of \emph{resources}, access

and manipulation options to which they offer to users. 

\subsection{Sharing}

The Okeanos IaaS will support the Greek higher education, an estimated

population of 100.000 students and researchers. Each member will be granted

access to a collection of resources using her institutional account. To enforce

a limit to the resources that can be acquired from the platform, each user will

have a limited amount of credits, renewable each month, which will be allowed

to spend on any resource available through the infrastructure. Resources can

also be shared; for example files on the Pithos file service or virtual machine

images on the Archipelago storage are potentially subject to concurrent usage from 

multiple users. This means that charges for the use of a single resource

may need to be distributed among several users. Also this may mean that in order

for sharing to work correctly, users may need to transfer credits among them.

\subsection{Configuration}

Billing systems are by nature open ended. As new services are deployed, new

resources appear, while others might be phased out.  Moreover, changes to

company policies may trigger changes to price lists for those resources, while

ad-hoc requests for large scale computational resources may require special

pricing policies. In order for a billing system to be able to successfully

adapt to changing requirements, it must be able to accommodate such changes

without requiring changes to the application itself. This means that all

information required for Aquarium in order to perform a billing operation,

must be provided to it externally. Moreover, to ensure high-availability,

billing configuration should be updatable while Aquarium is running, or at

least with minimal downtime, without affecting the operation of external

systems.

\subsection{Scaling}

In the context of the Okeanos system, Aquarium provides billing services on a

per user basis for all resources exposed by other systems. As such, it is in

the critical path of user requests that modify resource state; all supported

applications must query Aquarium in order to ensure that the user has enough

credits to create a new resource. This means that for a large number of users

(given previous GRNet systems usage by the Greek research community, we

estimate a concurrency level of 30.000 users), Aquarium must update and

maintain in a queryable form their credit status, 

with soft realtime guarantees. 

Being on the critical path also means that Aquarium must be highly resilient,

too. If Aquarium fails, all supported systems will also fail. Even if Aquarium

fails for a short period of time, it must not loose any billing events, as this

will allow users to use resources without paying for them. Moreover, in case of

failure, Aquarium must not corrupt any billing data under any circumstances,

while it should reach an operating state very fast after a service restart.

\section{Domain Modeling}

\subsection{Basic terminology}

We have already mentioned several entities in our description so far. Let us be a bit more specific on several key terms.

\begin{description}

\item[Credits]

The analog of money. Credits are the `universal money` within Aquarium.

\item[Resource]

A billable/chargeable entity. We generally need credits to use a resource. When a resource is used,  then consume credits. Examples of resources are the `download bandwidth` and, respectively,  the 'upload bandwidth', the `disk space` and the `VM time` to name a few. Generally speaking, the ``resource'' term specifies the type. A resource may have several properties attached, i.e. it name, unit of measure, a description of how its consumption translates to credit and whether it can have more than one instances.

\item[Resource instance]

A user may have several instances of a resource type. For example, regarding a ``Virtual Machine'' resource, a user may have more then one of them. They are distinguished by their unique resource instance identifier. We call resources that can have more than one instance ``complex'' resources.

\item[Resource event]

An event that is generated by a system, which is responsible for the resource.

The resource event describes a state change for the resource. In particular, a resource event records the time when that state change occurred (`occurredMillis` attribute) and the changed value (`value` attribute).

\item[Cost policy]

A cost policy refers to a resource and it is the policy used in order to charge credits for resource usage. Cost policies come in three flavors, namely \textsf{continuous}, \textsf{discrete} and \textsf{onoff}.

\item[Pricelists] assign a price tag to each resource, within a timeframe.

\item[Charging algorithms] specify the way a resource event can generate consumed credits. 

A charging algorithm can be as simple as a direct multiplication of the 

        chargeable resource quantity with the applicable price. We offer the option, though, to define more complex charging  scenarios, the Aquarium DSL supports a simple imperative language with

        a number of implicit variables (e.g. \texttt{price, volume, date}) 

        that enable administrators to specify, e.g. billing algorithms that

        scale with billable volume. Similarily to price lists, charging algorithms

        have an applicability timeframe attached to them.

\item[Credit plans] define a number of credits to give to users and a repetition

        period.

\item[Agreements] assign a name to a charging algorithm, pricelist and creditplan triplets,

        which is then assigned to each user.

\item[Resource instance state]

A value which is associated with a resource instance. Usually a floating point number, as in the $10.5$ MB designation regarding a possible ``total current downloading bandwidth''.

\item[User]

An owner of resources and credits. Users are defined externally of Aquarium.

\item[Resource event store]

A datatabase, in the general sense, where resource events are stored.

\item[User bill]

A, usually, periodic statement of how many credits the user has consumed. It may contain detailed analysis that relates consumed credits to resources.

\item[Billing period]

A time period at the end of which we issue a user bill.

A billing period is made of a starting date and a duration that is a multiple of a week.

A usual billing period starts on a particular month date (eg. 3rd) and lasts for a month.

Each resource type designates what happens to its accumulated value (if any) at the beginning of the billing period. Usually, at the beginning of the billing period, the accumulating amounts of resources are set accumulating amount. For example, for a monthly billing period, the total uploading bandwidth is reset to zero every month.

\item[User state]

The user state is made of the following distinct parts, of which the first two can be integrated to a unifying ``resource'' concept.

\begin{enumerate}

\item User credit state, that is the total credit amount for the user.

\item User resource state, which refers to the state of each resource instance that the user owns.

\item Processing state \TODO

\end{enumerate}

\item[Resource event processing]

The set of algorithmic steps by which a resource event leads to state changes of the user state.

\end{description}

\subsection{The configuration DSL}

\label{sec:dsl}

The configuration requirements of Aquarium  were addressed by creating a new

domain specific language ({\sc dsl}), based on the YAML format.  The DSL

enables administrators to specify chargeable resources, charging policies and

price lists and combine them arbitrarily into agreements applicable to specific

users, user groups or the whole system. 

The DSL supports inheritance for policies, price lists and agreements and composition in the case of agreements.

It also facilitates the

definition of generic, repeatable debiting rules, which are then used by the

system to refill the user's account with credits on a periodic base.

From the previously specified term, the following five are used in the DSL:

\begin{enumerate}

\item Resources

\item Charging algorithms

\item Pricelists

\item Credit plans

\item Agreements

\end{enumerate}

\begin{figure}

\lstset{language=c, basicstyle=\footnotesize,

stringstyle=\ttfamily, 

flexiblecolumns=true, aboveskip=-0.9em, belowskip=0em, lineskip=0em}

\begin{lstlisting}

resources:

  - resource:

    name: bandwidthup

    unit: MB/hr

    complex: false

    costpolicy: continuous

pricelists:

  - pricelist: 

    name: default

    bandwidthup: 0.01

    effective:

      from: 0

  - pricelist: 

    name: everyTue2

    overrides: default

    bandwidthup: 0.1

    effective:

      repeat:

      - start: "00 02 * * Tue"

        end:   "00 02 * * Wed"

      from: 1326041177        //Sun, 8 Jan 2012 18:46:27 EET

algorithms:

  - algorithm:

    name: default

    bandwidthup: $price times $volume

    effective:

      from: 0

agreements:

  - agreement:

    name: scaledbandwidth

    pricelist: everyTue2

    algorithm:

      bandwidthup: |

        if $volume gt 15 then

          $volume times $price

        elsif $volume gt 15 and volume lt 30 then

          $volume times $price times 1.2

        else

          $volume times price times 1.4

        end

\end{lstlisting}

\caption{A simple billing policy definition.} 

\label{fig:dsl}

\end{figure}

In Figure~\ref{fig:dsl}, we present the definition of a simple (albeit valid) 

policy. The policy parsing is done top down, so the order of definition 

is important. The definition starts with a resource, whose name is then

re-used in order to attach a pricelist and a price calculation algorith to it.

In the case of pricelists, we present an example of \emph{temporal overloading};

the \texttt{everyTue2} pricelist overrides the default one, but only for 

all repeating time frames between every Tuesday at 02:00 and Wednesday at

02:00, starting from the timestamp indicated at the \texttt{from} field. Another

example of overloading is presented at the definition of the agreement, which

overloads the default algorithm definition using the imperative part of the

Aquarium {\sc dsl} to provide a scaling charge algorithm.

\subsection{Billing}

Commonly to most similar systems, billing in Aquarium is the application of the

provisions of a user's contract to an incoming billing event in order to

produce an entry for the user's wallet. However, in stark contrast to most

other systems, which rely on database transactions in order to securely modify

the user's balance, Aquarium performs account updates asynchronously and

concurrently for all users.

Per resource, the charging operation is affected by the cost policy and complexity

parameters. Specifically, the 3 available cost policies affect the calculation 

of the amount of resource usage to be charged as follows:

\begin{itemize}

    \item resources employing the \textsf{continuous} cost policy are charged for

        the actual resource usage through time. When a resource event arrives,

        the previous resource state between the previous charge operation and the

        current event event timestamp is charged and the resource state is then

        updated. More formally, for continuous resources, if $f(t)$ represents

        the function of resource usage through time and $p(t)$ is the function

        representing the pricelist at time $t$, 

        then the total cost up to a 

        $c(t) = \sum_{i=0}^{t} {p(t) \times \int_0^{t}{f(t)dt}}$. Most resources

        in Aquarium are continuous, for example bandwidth and disk space.

    \item resources employing the \textsf{onoff} cost policy can be in two states:

        either switched on and actively used or switched off. Therefore, the unit

        of resource usage is time and not the actual resource usage, while the

        period of charging is calculated only when the resource is switched on.

        Virtual machines are examples of resources with the \textsf{onoff} cost

        policy.

    \item resources using the \textsf{distinct} cost policy are charged

        upon usage, without time playing a role in the charge. Such resources

        are useful for one off charges, such as the allocation of

        virtual machine or the migration of a virtual machine to a less busy

        host.

\end{itemize}

Billing events are obtained through a connection to a message queue. Upon

arrival, a billing event is stored in an immutable log, and then forwarded to

the user actor's mailbox; the calculation of the actual billing entries to be

stored in the user's wallet is done within the context of the user actor,

serially for each incoming events. This permits the actor to have mutable state

internally (as described in Section~\ref{sec:ustate}), without risking the

calculation correctness. The calculation process involves steps such as

validating the resource event, resolving the current state of resource affected

by the incoming resource event, deciding the value applicable pricelist and

algorithm, generating entries for the user's wallet and updating the current

resource state for the user. A significant source of complexity in the process

is the support for temporal overriding for pricelists and algorithms: within

the timeframe between resource updates, several policies or algorithms may be

active. The billing algorithm must therefore split the billing period to pieces

according the applicability of each policy/algorithm and make sure that at 

least a baseline policy is in effect in order to perform the calculation.

Consequently, a resource event might lead to several entries to the user's wallet.

The actual format of the event is presented in Figure~\ref{fig:resevt}.

\begin{figure}

\lstset{language=C, basicstyle=\footnotesize,

stringstyle=\ttfamily, 

flexiblecolumns=true, aboveskip=-0.9em, belowskip=0em, lineskip=0em}

\begin{lstlisting}

{

  "id":"4b3288b57e5c1b08a67147c495e54a68655fdab8",

  "occured":1314829876295,

  "userId":31,

  "cliendId":3,

  "resource":"vmtime",

  "eventVersion":1,

  "value": 1,

  "details":{

    "vmid":"3300",

    "action": "on"

  }

}

\end{lstlisting}

\caption{A billing event example} 

\label{fig:resevt}

\end{figure}

\subsection{User State}

\label{sec:ustate}

\section{Architecture}

\input{arch}

\section{Performance}

To evaluate the performance and scalability of Aquarium, we performed two

experiments: The first one is a micro-benchmark that measures the time required

for the basic processing operation performed by Aquarium, which is billing for

increasing number of messages. The second one demonstrates Aquarium's

scalability on a single node with respect to the number of users.  In both

cases, Aquarium was run on a MacBookPro featuring a quad core 2.33{\sc g}hz

Intel i7 processor and 8{\sc gb} of {\sc ram}. We selected Rabbit{\sc mq} and

Mongo{\sc db} as the queue and database servers, both of which were run on a

virtualised 4 core with 4{\sc gb} {\sc ram} Debian Linux server. Both systems

were run using current versions at the time of benchmarking (2.7.1 for

Rabbit{\sc mq} and 2.6 for Mongo{\sc db}).  The two systems were connected with

a full duplex 100Mbps connection.  No particular optimization was performed on

either back-end system, nor to the {\sc jvm} that run Aquarium. 

To simulate a realistic deployment, Aquarium was configured, using the policy

{\sc dsl} to handle billing events for 4 types of resources, using 3 overloaded

pricelists, 2 overloaded algorithms, all of which were combined to 10 different

agreements, which were randomly (uniformly) assigned to users. To drive the

benchmarks, we used a synthetic load generator that worked in two stages: it

first created a configurable number of users and then produced billing events

that 

All measurements were done using the first working version of the

Aquarium deployment, so no real optimisation effort did take place. 

\section{Lessons Learned}

One of the topics of debate while designing Aquarium was the choice of

programming platform to use. With all user facing systems in the Okeanos cloud

being developed in Python and the initial Aquarium designers being beginner

Scala users (but experts in Java), the choice certainly involved risk that

management was initially reluctant to take. However, by breaking down the

requirements and considering the various safeguards that the software would

need to employ in order to satisfy them, it became clear that a

typesafe language was a hard requirement. Of the platforms examined, the {\sc

jvm} had the richest collection of ready made components; the Akka library was

particularly enticing for the scalability and distribution possibilities it

offered.

The choice of Scala at the moment it had been made was a high risk/high gain

bet for GRNet. However, the development team's experience has been generally

positive. Scala as a language was an enabling factor; case classes permitted

the expression of data models, including the configuration {\sc dsl}, that

could be easily be serialized or read back from wire formats while also

promoting immutability through the use of the \texttt{copy()} constructor. The

pervasive use of immutability allowed us to write strict, yet simple and

concise unit tests, as the number of cases to be examined was generally low.

The \textsf{Maybe}\footnote{\textsf{Maybe} works like \textsf{Option}, but it

has an extra possible state (\textsf{Failed}), which allows exceptions to be

encapsulated in the return type of a function, and then retrieved and accounted

for in a pattern matching operation with no side effects. More at

\url{https://github.com/loverdos/Maybe}} monad, enabled side-effect free

development of data processing functions, even in cases where exceptions were

the only way to go. Java interoperability was excellent, while thin Scala

wrappers around existing Java libraries enabled higher productivity and use of

Scala idioms in conjunction with Java code.

The Akka library, which is the backbone of our system, is a prime example of 

the simplicity that can be achieved by using carefully designed high-level

components. Akka's custom supervision hierarchies allowed us to partition the

system in self-healing sub-components, each of which can fail independently

of the other. For example, if the queue reader component fails due to a queue

failure, Aquarium will still be accessible and responsive for the {\sc rest}

interface. Also, Akka allowed us to easily saturate the processing components

of any system we tested Aquarium on, simply by tuning the number of threads (in

{\sc i/o} bound parts) and actors (in {\sc cpu} bound parts) per dispatcher. 

Despite the above, the experience was not as smooth as initially expected. The

most prominent problem we encountered was that of lacking documentation. The

Akka library documentation, extensive as is, only scratches the surface.

Several other libraries we use, for example Spray for {\sc rest} handling, have

non-existent documentation. The Java platform, and .Net that followed, has

shown that thorough and precise documentation are key to adoption, and we

expected a similar quality level. Related is the problem of shared community

wisdom; as most developers know, a search for any programming problem will

reveal several straightforward Java or scripting language sources. The

situation with Scala is usually the opposite; the expressive power of the

language makes it the current language of choice for treating esoteric

functional programming concepts, while simple topics are often neglected. Scala

has several libraries of algebraic datatypes but no {\sc yaml} parser. As Scala

gains mainstream adoption, we hope that such problems will fade.

From a software engineering point of view, the current state of the project was

reached using about 6 person months of effort, 2 of which were devoted to

requirements elicitation, prototype building and familiarizing with the

language. The source code currently consists of 5.000 lines of executable

statements (including about 1.000 lines of tests), divided in about 10

packages. The system is built using both {\sc sbt} and Maven. 

\section{Related Work}

\section{Conclusions and Future Work}

In this paper, we presented Aquarium, a high-performance, generic billing 

system, currently tuned for cloud applications. We presented the requirements

that underpinned its design, outlined the architectural decisions made

and analysed its implementation and performance.

Scala has been an enabling factor for the implementation of Aquarium, both

at the system prototyping phase and during actual development. 

Aquarium is still under development, with a first stable version 

being planned for early 2012. 

Aquarium is available under an open source license at 

\url{https://code.grnet.gr/projects/aquarium}.

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