High-level Distribution for the Rapid Production
of Robust Telecoms Software:
Comparing C++ and Erlang∗
J.H. Nystr¨om P.W. Trinder D.J. King
Currently most distributed telecoms software is engineered using low and
mid-level distributed technologies, but there is a drive to use high-level
distribution. This paper reports the ﬁrst systematic comparison of a
high-level distributed programming language in the context of substan-
tial commercial products. Our research strategy is to reengineer some
C++/CORBA telecoms applications in Erlang, a high-level distributed
language, and make comparative measurements.
Investigating the potential advantages of the high-level Erlang tech-
nology shows that two signiﬁcant beneﬁts are realised. Firstly, robust
conﬁgurable systems are easily developed using the high-level constructs
for fault tolerance, and distribution. The Erlang code exhibits resilience:
sustaining throughput at extreme loads and automatically recovering when
load drops; availability: remaining available despite repeated and multiple
failures; dynamic reconﬁgurability: with throughput scaling near-linearly
when resources are added or removed. Secondly, Erlang delivers signiﬁ-
cant productivity and maintainability beneﬁts: the Erlang components
are less than one third of the size of their C++ counterparts. The pro-
ductivity gains are attributed to speciﬁc language features, for example,
high-level communication saves 22%, and automatic memory management
saves 11%—compared with the C++ implementation.
Investigating the feasibility of the high-level Erlang technology demon-
strates that it fulﬁls several essential requirements. The requisite dis-
tributed functionality is readily speciﬁed, even although control of low-
level distributed coordination aspects is abrogated to the Erlang im-
plementation. At the expense of additional memory residency, excellent
time performance is achieved, e.g. three times faster than the C++ imple-
mentation, due to Erlang’s lightweight processes. Erlang interoperates
at low cost with conventional technologies, allowing incremental reengi-
neering of large distributed systems. The technology is available on the
required hardware/operating system platforms, and is well supported.
Keywords: Distributed Systems, Erlang, Telecom Software
∗The project is in part funded by the UK EPSRC Research Project, GR/R88137 and is
a joint venture between Motorola Software and Systems Engineering Research Group, Bas-
ingstoke and Heriot-Watt University, Edinburgh.
Telecommunication systems are amongst the most challenging to design and
realise since they integrate distributed resources under soft real-time constraints,
and must be highly available and reconﬁgurable. Since the telecommunications
sector is highly competitive with rapidly evolving technology, new products
require the least possible time to market, and need to be readily extended and
For the distributed software component that is the focus of this paper,
there are three levels of technologies: low-level distribution like sockets or
MPI, mid-level distribution like Java/RMI or CORBA, and high-level distri-
bution like UML 2.0 State Machines , SDL , or high-level distributed
languages [8, 1, 25]. Low-level distribution technologies still dominate current
telecommunications products, but mid-level technologies are gaining ground.
High-level technologies in the form of modelling languages such as SDL, have
long been used for design. With modelling language tools acquiring ever more
sophistication, however, there is a strong movement towards high-level tech-
niques, and especially model driven development.
This paper evaluates high-level distributed languages in comparison with
mid-level technologies like CORBA, in conjunction with C++. High-level lan-
guages like Erlang , or Glasgow distributed Haskell (GdH)  automati-
cally manage many distributed coordination aspects making the programs con-
cise, with the potential for rapid development and improved maintainability.
Moreover, the languages have high-level distributed coordination and sophis-
ticated fault tolerance, facilitating the construction of robust, reconﬁgurable
systems. Signiﬁcantly, many of the coordination aspects automatically and cor-
rectly managed by the language implementations are some of the most critical
and diﬃcult issues for distributed software, like storage management, data mar-
shalling communication and fault tolerance.
1.1 Research Goals
This paper investigates whether Erlang high-level distributed language tech-
nology can deliver the potential beneﬁts for realistic telecoms software develop-
ment, by considering the following research questions.
Q1 Can robust, conﬁgurable systems be more readily developed?
Does the fault tolerance provided by the language readily enable the de-
velopment of systems with resilience, high availability and reconﬁgurabil-
Q2 Can productivity and maintainability be improved? We argue
that productivity and maintainability are crucially related to code size:
shorter programs are faster to develop and easier to maintain. So are the
high-level programs more concise, and what is the potential for reuse?
We further investigate the feasibility of the Erlang high-level distributed lan-
guage technology for realistic telecoms software development by considering the
following research questions.
Q3 Can the required distributed functionality be speciﬁed? As con-
trol of low-level distributed coordination aspects has been abrogated to the
language implementation, can the programmer still specify the required
Q4 Can acceptable performance be achieved? Typically automatic co-
ordination management incurs space and time penalties. Despite these
costs, can a high-level language implementation meet the time and space
performance requirements of telecoms applications?
Q5 What are the costs of interoperating with conventional technol-
ogy? Many distributed systems are large, and monolithic reengineering
is prohibitively expensive. A new technology that can interoperate with
existing technologies allows incremental reengineering, providing that the
performance penalties of the interaction between components using diﬀer-
ent technologies are small.
Q6 Is the technology practical? Pragmatics are a key issue in software
technology selection. For example is the technology available on the prod-
uct hardware/operating system platform(s)? Are suitable libraries avail-
able? How well is it supported?
1.2 Research Strategy
Our research strategy is to reengineer some C++/CORBA telecoms applications
in Erlang, and make comparative measurements of both implementations for
Time and Space Performance (Section 5), Robustness (Section 6) and Software
Productivity (Section 7).
Realistic comparisons of software technologies are hard for a number of rea-
sons. It is well known that software development costs do not scale linearly
with size, and it is simply too expensive to duplicate large systems, e.g. those
with more than 100K lines of code. Worse still, comparing just one system is
not suﬃcient, as while that system may be a good match for a particular tech-
nology, many other systems in the same application area are not. Moreover, a
comparison must either be based on systems constructed by diﬀerent teams of
software engineers, or the same team will beneﬁt from building the ﬁrst system
when building the second.
The diversity issue is addressed a limited way by reengineering and measur-
ing two C++ telecoms applications, and by considering the results reported for
other components in related work (Section 8). We address the issue of scale by
reengineering a medium-scale (15K line) Dispatch Call Controller (DCC) 
(Section 4.2), and a smaller (3K line) Data Mobility (DM) component that
is closely integrated with ﬁve other components of a base radio network (Sec-
tion 4.1). Moreover, our results conﬁrm those of a more superﬁcial study of an
extremely large system: 1M lines of code (Section 8). To minimise the learn-
ing eﬀect both Erlang implementations are engineered by a programmer not
involved in the original Motorola C++/CORBA implementations. For com-
parability, we assume that the Erlang and C++/CORBA implementers are
similarly expert with the technologies used.
We believe the investigation reported here is the ﬁrst systematic comparative
evaluation of a high-level distributed programming language in the context of
substantial commercial products. In earlier work we have proposed the current
investigation , the results of which are reported here. An investigation into
the robustness of the DCC is reported in , eﬀectively a thorough exploration
of research question Q1. This paper reports a far broader range of research
questions Q1 -Q6, and in addition covers the robustness of both the DCC and
the DM. Other related work is covered in Section 8.
2 Challenges for Distributed Telecoms Software
The telecoms sector is rapidly growing, with new devices and technologies ap-
pearing almost daily. This adds to the complexity of telecoms systems, which
by their very nature have a distributed architecture, an array of diﬀerent hard-
ware, operating systems, networks, in addition to application software. On-time
delivery and quality are priorities to keep and win more customers against stiﬀ
competition. For example, failing to get the latest mobile phone into the shops
before Christmas, could lose market share. Poor quality can have a long-term
detrimental eﬀect on brand image, in addition to short-term ﬁnancial losses,
e.g. when a faulty device is recalled. Reliability and availability are key aspects
of software quality. Customers don’t want their phone to crash, and they want
the network infrastructure to be always available. For example, when there was
a surge in demand after the London bombings on 7 July, 2005 many mobile
networks couldn’t cope, and had to shut down or drop calls. This was at great
expense to network providers. Typically, a telecoms provider will aspire to the
5-nines of 99.999% availability, which equates to a downtime including main-
tenance of no more than 5 minutes and 15 seconds in one year. This is rarely
2.1 Technical Challenges
The intention of constructing high-availability software is being addressed by
pursuing the following speciﬁc technical challenges. High Level Programming:
using high level programming paradigms in application development releases
the programmer from dealing with awkward, low level, technical issues such
as memory management and communication details. Correctness: telecoms
systems are typically too large for the correctness to be shown using formal
proof. Hence, the importance of thorough testing that can take over 50% of
the software lifecycle. Additionally, abstraction can help with correctness, since
it is easier to demonstrate properties or model check, if the speciﬁcation or
implementation is given in a high-level formal notation. Fault tolerance: most
downtime is caused not by hardware faults, but by system and application
software failure. Recovering from a software crash, or processor failure, improves
availability. Maintainability: which includes both debugging existing systems,
and adding new features.
Application software for telecoms systems have particular requirements that
also pose challenges, including the following. Managing multiple interactions:
application software needs to be expressive enough to manage the interactions
between multiple geographically-distributed components, i.e. hardware, soft-
ware, networks, and operating systems. Soft real-time: systems need to
respond and react without delay to new requests, often a time-bound for a
computation is necessary. Scalability: systems need to adapt to cope with
increased demand, by the incremental addition of hardware. Resilience: the
system performance should downgrade gracefully when overloaded. Dynamic
reconﬁgurability: to ensure high availability the system has to be able to adapt
dynamically to both software and hardware upgrades.
2.2 Current Technologies
Currently many distributed telecoms systems are implemented in C with SDL on
real-time operating systems with trends towards using C++/CORBA, JAVA/RMI
and UML 2.0 State Machines. Higher level programming language technologies
like Erlang  are attractive because of the potential to reduce development
time, and improve reliability and maintainability. Clearly the language technol-
ogy used must also meet the other functional requirements of telecommunication
applications, e.g. real-time requirements.
3 Erlang and other Distributed Functional Lan-
3.1 Distributed Coordination Levels
A distributed system speciﬁcation must include both an algorithmic aspect, i.e.
a correct and eﬃcient algorithm describing what to compute, and a coordina-
tion aspect specifying how to organise the computations across the processors.
Distributed coordination typically includes partitioning the program into tasks,
placing the tasks on the processors, communicating between and synchronis-
ing tasks, and both detecting and recovering from failures. The computational
aspect of a distributed program may be speciﬁed at a range of levels of abstrac-
tion, e.g. relatively low level like assembler or C, or at a high-level like Prolog
Like the computation aspect, the coordination aspect of a distributed system
may be speciﬁed at a range of levels of abstraction. At the lowest level the
programmer manages all coordination explicitly, e.g. using send/receive and
fork/join. Example technologies providing low-level distributed coordination
include sockets and the PVM or MPI communication standards [7, 18]. Mid-
level coordination abstracts over several low-level coordination actions, e.g. a
remote procedure call (RPC) combines a send,fork,receive triple. Example
technologies providing mid-level distributed coordination include Java/RMI and
CORBA. High-level coordination aims to be yet more abstract, e.g. modelling
distributed processes as state machines. Example technologies providing high-
level distributed coordination include UML 2.0 State Machines , SDL ,
and high-level distributed languages like Facile  and Erlang.
High-level distributed languages like Erlang provide high-level communi-
cation, sophisticated fault tolerance mechanisms and automatic storage man-
agement. Hence, it is potentially easier to engineer robust software, programs
are shorter and hence faster to develop and easier to maintain. The high-level
language requires a sophisticated implementation to automatically control many
low-level coordination aspects, e.g. synchronisation and data marshalling. As
we shall see, relatively few of these sophisticated language implementations have
Distributed coordination may be integral to a distributed programming lan-
guage, e.g. Java/RMI, or provided by an external library, e.g. CORBA or MPI.
3.2 Distributed Functional Languages
In addition to Erlang, a number of distributed functional languages have been
constructed with a range of models of processes and communication e.g. Kali
Scheme , Facile , OZ , and Glasgow distributed Haskell (GdH) .
Almost all the languages are research languages used to investigate high-level
distributed coordination integrated with the language. Consequently, these lan-
guages are available on few hardware/operating system platforms and have
limited support in the form of libraries and tools. In contrast Erlang is a
production language designed to aid the rapid production of robust distributed
systems. It is available on a range of hardware/operating system platforms and
supported by the OTP tools and libraries.
Erlang is a distributed functional language originally developed in Ericsson
for constructing highly reliable telecommunications systems . The language
has integrated distribution, including ﬁrst-class processes, sophisticated fault
tolerance mechanisms, automatic storage management i.e. garbage collection,
soft real-time support, and sophisticated availability support e.g. hot-loading
hardware and software upgrades into a running system.
Erlang has been used by a number of companies to construct a wide range
of applications, primarily in the telecoms sector, but increasingly in other sec-
tors, e.g. banking. Examples include the ﬁrst implementation of GPRS for
standard packet data in GSM systems , and the Intelligent Network Service
Creation Environment . The largest application to date is the AXD 301
scalable and robust backbone ATM switch , currently utilising up to 288
Processing Elements (PEs). The code comprises over 1.7 Million lines of new
Erlang code [13, p.6], 300K lines of mostly-reused C and 8K lines of Java,
developed by a team peaking at 50 software engineers .
Erlang has several general features that facilitate the construction of large
distributed real-time systems. The module system allows the structuring of
very large programs into conceptually manageable units. Erlang supports
single-assignment variables, and has an explicit notion of time, enabling it to
support soft real-time applications, i.e. where response times are in the order of
milliseconds. The following subsections outline the speciﬁc features of Erlang
that impact the comparisons in Sections 6 and 7.
3.3.1 Fault Tolerance
The Erlang reliability philosophy is to separate the functionality and error-
handling concerns. That is, the programmer writes simple code for the successful
case that may fail, raising an exception. The key to this “let it fail” ethos is
that the language incorporates ﬁrst class processes that can fail without dam-
aging other processes. True processes, while common in operating systems, are
extremely unusual in production programming languages. A common reason for
a failure is a timeout and the exception raised may be handled within a process
by an exception handler, or by a monitoring process.
Figure 1: Erlang/C DM Supervision Tree
The monitoring of one process by another is suﬃciently common that it
is encapsulated by the supervisor behaviour . An Erlang behaviour is a
high-level distributed coordination abstraction. In the supervisor behaviour the
supervising, or parent, process spawns child processes and declares a number
of coordination aspects. An important aspect is the action to perform in the
event of a failure, e.g. restart the child process, kill the child process, kill all the
child processes. A second important aspect is the frequency of failures to be
tolerated, e.g. one per hour. As the supervised processes may supervise other
processes, a supervision tree can be constructed.
Figure 1 shows the supervision tree for the Erlang/C DM component de-
scribed in Section 4.1. In this tree, Supervisor 2 will restart either of the Erlang
receiver or transmitter processes at most once an hour. Supervisor 1 will fail
gracefully if supervisor 2 fails or either of the C drivers fail, reﬂecting the fact
that it has no way of restarting the C drivers. This supervision tree provides
much of the DM robustness, and is less than 10 source lines of code.
Figure 2: Erlang DM Communication
3.3.2 High-Level Communication
Communication between Erlang processes is high-level asynchronous message
passing. The communication mechanisms provide automatic data marshalling,
error detection, communication and synchronisation. Figure 2 and Figure 3 give
a dramatic comparison of the same communication in Erlang and in C++.
An Erlang cast is a point-to-point send primitive. The C++ version contains
considerable amounts of data marshalling and defensive code, e.g. lines 30-35
detect and report an error. The Erlang version crucially relies on automatic
error detection, and that the failure will be handled elsewhere, most probably
by a supervising process.
3.3.3 Automatic Memory Management
It is easy to make errors when explicitly managing memory, moreover space leaks
are hard to detect and correct, and cannot be allowed in long running telecoms
applications. Like many modern programming languages, Erlang provides
automatic memory management, supported by garbage collection. This both
relieves the programmer from specifying a signiﬁcant and awkward aspect of
the program, and improves reliability be guaranteeing safe storage manage-
ment. However, even with garbage collection, some space leaks may need to be
explicitly detected and eliminated.
Erlang programs typically contain no explicit storage management, and
hence there is none in Figure 2. In contrast, lines 9 and 13 of the C++ code in
Figure 3 calculate a size and allocate an object of that size.
1 void DataMobilityRxProcessor::processUnsupVer(void)
3 MSG_PTR msg_buf_ptr;
4 MM_DEVICE_INFO_MSG *msg_ptr;
5 RETURN_STATUS ret_status;
6 UINT16 msg_size;
8 // Determine size of ici message
9 msg_size = sizeof( MM_DEVICE_INFO_MSG);
11 // Create ICI message object to send to DMTX so it sends a Device Info
12 // message to Q1 and Q2 clients
13 IciMsg ici_msg_object( MM_DEVICE_INFO_OPC, ICI_DMTX_TASK_ID, msg_size);
15 // Retrieve ICI message buffer pointer
16 msg_buf_ptr = ici_msg_object.getIciMsgBufPtr();
18 // Typecast pointer from (void *) to (MM_DEVICE_INFO_MSG *)
19 msg_ptr = (MM_DEVICE_INFO_MSG *)msg_buf_ptr;
21 // Populate message buffer
22 SET_MM_DEVICE_INFO_DEVICE_TYPE( msg_ptr, SERVER);
23 SET_MM_DEVICE_INFO_NUM_VER_SUPPORTED( msg_ptr, NUM_VER_SUPPORTED);
24 SET_MM_DEVICE_INFO_FIRST_SUP_PROTO_VERS( msg_ptr, PROTO_VERSION_ONE);
26 // Send message to the DMTX task
27 ret_status = m_ici_io_ptr->send(&ici_msg_object);
29 // Check that message was sent successfully
30 if (ret_status != SUCCESS)
32 // Report problem when sending ICI message
33 sz_err_msg( MAJOR, SZ_ERR_MSG_ERR_OPCODE, __FILE__, __LINE__,
34 "DataMobilityRxProcessor processUnsupVer: failure sending "
35 " device info message to DMTX");
Figure 3: C++ DM Communication
3.3.4 Tools and Support
To help reduce time to market Erlang is supplied with the Open Telecom
Platform (OTP) libraries . The OTP includes, inter alia, libraries, design
principles, and productivity, proﬁling and debugging tools. Example libraries
include support for HTTP, FTP, SSL, SSH and TCP/IP protocols, for SNMP
agents, CORBA and the H248 protocol stack. There is an Abstract Syntax
Notation One (ASN.1) compiler . Database support includes an ODBC im-
plementation and Mnesia, a bespoke in-memory distributed database.
A compiler  and a bytecode interpreter are both available open source for
Erlang. The language is also supported by commercial training courses, con-
sultancy, and other technical services. There are annual international research
and user conferences, books  and online reference material.
Figure 4: Abstract DM Architecture
4 Basis of Comparison
This section describes the telecom software components that were reengineered
as the basis of our comparison between C++ and Erlang.
Communication over XXX
C, C++ test rig
Figure 5: Erlang DM Architecture
4.1 Data Mobility Server (DM) and Platform
The following description of the Data Mobility server (DM) avoids using the
proper product names, and some details are made abstract to preserve commer-
cial conﬁdentiality. The DM is a small component of a radio communications
subsystem (RCS) which is responsible for communication between the RCS and
data mobility devices. Both the RCS and DM were developed by Motorola as
part of an existing product that follows an international standard.
4.1.1 DM Design
The abstract DM architecture is shown in Figure 4, where PZ is a participating
zone manager, RM is a resource manager, CM is a conﬁguration manager, and
IHLR is an individual home location register. Key aspects of the architecture
are as follows. The DM has two main components a receiver (DM Rx) and a
transmitter (DM Tx). The DM communicates with data mobility devices using
UDP, and with ﬁve other components of the RCS.
4.1.2 DM Measurement platform
The RCS hardware/operating system platform is SUN/Solaris, and the C/C++
and Erlang DM measurements reported in the following sections have been
performed on a 167MHz Sun Ultra 1, with 128Mbytes of RAM, running SunOS
5.8. The C++ compiler is gcc 3.3.3, and the Erlang bytecode compiler is
BEAM R10B-6. The BEAM compiler is the most common Erlang platform,
although the HiPE compiler that combines native and bytecode code gives better
performance for some applications .
The architecture of the Erlang/C DM is shown in Figure 5, where DME Rx
and DME Tx are Erlang receiver/transmitter processes and DME Rx-drv and
DME Tx-drv are C receiver/transmitter drivers. Key aspects of the architecture
are that it combines Unix processes, C threads, and Erlang processes; and that
it interoperates with the same C RCS test harness as the C++ DM.
4.2 Dispatch Call Controller (DCC) and Platform
The second component reengineered is a prototype dispatch call system devel-
oped at Motorola Labs in Illinois . Dispatch call processing is a prevalent
feature of many wireless communication systems. Managing the call processing
with a distributed paradigm enables the processing to be scaled as system usage
grows, with work dynamically distributed to the resources available. The essence
of the application is a group call manager that generates instances of a group
call factory dynamically on the resources available. Each factory generates call
handlers to manage individual calls sent to it by the manager.
The DCC requires the following functionality. It must provide dynamic
scalability, i.e. the ability to adapt to use additional resources while the system
is running. It must reclaim resources to enable continuous execution, i.e. ensure
that once a service instance has terminated, all of its resources are reclaimed.
It must be fault tolerant, and in particular provide continued service despite
failures. It must meet soft real time performance criteria, i.e. call management
mustn’t interrupt the call.
4.2.1 DCC Design
The requirements for the DCC model are derived from the technical report by
Lillie  and from a set of functional requirements . For the purpose of
comparison we use a model of the DCC service that only deals with regular voice
point-to-point calls and hand-oﬀs between the base radio stations. Calls received
when the workers are already executing the maximum number of instances are
rejected. The system may have between 1 and 4 worker nodes running on
separate processing elements and 13 processing elements dedicated to the traﬃc
generator and system interface [19, 20].
The model of the DCC service comprises two distinct parts, the subscribers
of the system generating traﬃc and the ﬁxed-end terminating the service.
Subscribers are simulated by two processes. The ﬁrst process issues hand-oﬀ
messages to the ﬁxed-end notifying it that the subscriber is now associated with
a new base radio station, thereby simulating the roaming of the subscriber. The
second process sets up the call and generates simulated voice traﬃc. The call is
initiated by a request from the caller process which waits for the ﬁxed-end to set
up the call and route the traﬃc. A timer is used to model the user terminating
the call if it is not commenced within a short period.
The rates of hand-oﬀs and call requests are higher than one would normally
expect from a real system, in order to generate large enough volumes of traf-
ﬁc without having an unwieldy number of subscribers in the generator. The
requests and duration of calls are evenly distributed over the expected rates,
namely one hand-over every two minutes, one call request every ten minutes,
and each call lasts half a minute. During a call the initiating party, who is
also the sending party as this is simplex traﬃc, will issue voice data each 90
The ﬁxed-end is simulated in the framework by a service dealing with both
hand-oﬀs and call requests. The service uses a database table to associate
subscribers with base radio stations. In consequence a hand-oﬀ only changes
the record of the subscriber who has moved. For calls a service instance is
maintained as a handler for the duration of the call. The handler is maintained
for a certain time after the call is completed, and is reused should a new call
be setup for the same subscriber within a short period. The call handler in the
ﬁxed-end also deals with hand-oﬀs during the call.
4.2.2 DCC Measurement platform
The hardware platform is a 32-node Beowulf cluster, where each node consists of
a Pentium-III 530 MHz processor with 256 Mbytes of RAM, interconnected by
a switched 100Mb/s Ethernet. The C++ compiler is gcc 3.3.2, and the Erlang
bytecode compiler is BEAM R10B-6.
The software test platform is made up of a number of subsystems described
below and the overall architecture is shown in Figure 6.
Test Management Responsible for starting and controlling the System Man-
agement and Traﬃc Generator subsystems during the test. The Test Man-
Test Management System Management
Figure 6: Erlang DCC Architecture
ager will also inject the faults into the non-testing subsystems.
Traﬃc Generator The Traﬃc Generator sends a sequence of calls to the
Service Port and acts as a sink for all messages from service instances to
System Management Responsible for starting, stopping and management of
the Service Port and the worker nodes. The System Management subsys-
tem is also responsible for restarting the Service Port for any worker that
Service Port The Port is responsible for starting and maintaining all the in-
terfaces used by the services to communicate with the Workers and relays
calls from the subscribers of the services to the Worker responsible for
Service Admission acting as gatekeeper.
Worker There are one or more Worker subsystems in the system and they are
responsible for executing of the dispatch call handlers. One of the workers
is the designated leader of the workers and is responsible for admission
control and distribution of calls between the available Workers. The leader
is also responsible for redistributing the call handled by a worker should
it fail and for restarting the System Management subsystem should it fail.
Should there only be the leader it will also act as a normal worker.
C++ DM Erlang/C DM Erlang DM
480 230 940
Table 1: Maximum DM Throughput at 100% QoS
The leader of the workers is selected using a distributed leader election
protocol based on functionality in a library enabling distributed name
registration. Should the leader fail a new leader is elected using the same
This section compares the time and space performance of three DM imple-
mentations: a C++ implementation, a pure Erlang implementation and an
Erlang/C implementation that reuses some C DM libraries. The latter im-
plementation allows the measurement of the costs of interfacing Erlang with
foreign code. Some performance measurements for the Erlang DCC are re-
ported in Section 6, but an installation of the C++/CORBA DCC was not
available to make systematic performance comparisons.
5.1 Data Mobility
The nominal throughput for the data mobility server is less than 1 query/s.
Table 1 shows the maximum throughput on the 167MHz Sun Ultra platform
speciﬁed in Section 4.1.2, for a 100% quality of service(QoS), i.e. handling 100%
of all queries. The maximum Erlang DM throughput is approximately double
the C++ DM throughput, which is in turn approximately double that of the
Erlang/C DM. Given the nominal throughput, we conclude that both the
pure Erlang and interoperating Erlang/C DMs are fast enough for
the Data Mobility application.
5.1.2 Round Trip Times
The round trip times for both types of query, and for a failed query are shown
in Figure 7. The Erlang implementations handle failed queries slightly faster
than the C++ implementation. The pure Erlang implementation is ap-
proximately three times faster than the C++ implementation. How-
ever, primarily due to the extra layer of communication to the linked in driver,
the Erlang/C implementation is 26% slower for successful type 1 queries, and
50% slower for successful type 2 queries.
Figure 7: DM Round Trip Times
The maximum residency of the C++ and Erlang DMs depicted in Figure 8
shows that the Erlang implementations use signiﬁcantly more memory. The
Erlang DM uses 150% more memory and the Erlang/C DM uses 170%
more. The dominating space cost (5.2Mb) is the ﬁxed-size Erlang runtime
system (ERTS) which would be a smaller percentage of the total memory cost
for applications larger than the 3000-line DM. To put it another way, only the
top sections of the Erlang and Erlang/C bars, are expected to increase with
application size. In the following section we will see the robustness beneﬁts
provided by the sophisticated ERTS.
Figure 8: Memory Residency
5.1.4 Performance Discussion
It may initially seem surprising that a bytecode interpreted language like Er-
lang outperforms compiled C++. It is straightforward to deduce that for the
DM, as with most distributed applications, computation speed does not limit
performance, but rather performance is dominated by communication and pro-
cess management. Erlang implementations support lightweight processes and
are designed to provide fast process management and interprocess communica-
tion. In contrast, a sequential language like C or C++ must rely on operating
system process management and interprocess communication. This entails mak-
ing expensive system calls that switch from user to superuser space, and relying
on relatively slow and heavyweight operating system threads. Indeed inter-
process communication remains expensive even using shared-memory as in the
This section investigates the robustness of the DM and DCC applications, cov-
ering resilience, availability, and dynamic reconﬁgurability. Additional measure-
ments of the DCC availability can be found in .
Overloading the system should result in a graceful degradation of performance,
and the system should recover without human intervention once the load is
reduced. Resilience in response to extreme overload is critical in the telecoms
and other service-oriented sectors where extremely high service demands occur
regularly. For example network traﬃc peaks immediately after dramatic events
like a bombing.
Figure 9: Erlang/C DM Resilience
6.1.1 DM Resilience
Like most telecoms systems the Data Mobility server controls load by declining
new requests. Figure 9 depicts the throughput as the C++, Erlang/C, and
Erlang DMs are overloaded beyond the nominal load of 1 query/s. The key
features are that although the performance of the Erlang DM degrades,
it never completely fails, e.g. both Erlang DMs handle approximately 50q/s
at a load of 25000q/s. Moreover, without human intervention the Erlang DM
recovers after load drops, whereas the C++ DM has crashed by 920q/s and
would require a manual restart.
Of course the C++ DM could be reengineered to decline excess requests,
and also to recover. However this would make the program even larger and
more complex, as compared with the Erlang program (see Section 7.1). In
short, fault tolerance comes more or less for free in Erlang, while it requires
considerable extra eﬀort to include and validate it in most other languages
including C++. Consequently, applications in these languages are typically far
less resilient in practise.
Call / s
Number of workers
Figure 10: Erlang DCC Resilience
6.1.2 DCC Resilience
For the DCC, an overload is more simultaneous service calls than the system
can handle. Like the DM, the DCC controls load by declining requests. DCC
resilience is investigated by subjecting conﬁgurations of the system to 100%,
200% and 1000% load with a varying number of worker nodes.
The results are reported in Figure 10, and show several interesting features.
Throughput at 1000% is always less than throughput at 200%. Surprisingly, up
to 3 nodes there is a small increase in both request and message throughput for
both 200% and 1000% load. Thereafter, both 200% and 100% result in lower
throughput. The reason that an overload at a small number of nodes gives a
small increase in throughput is that at 100% load there are times when one or
more of the worker nodes execute less than the maximum of service instances.
We have also exposed a single worker DCC to 50000% load and the through-
put was still 110%. Limitations of the load generation technology prevent mea-
surements of larger systems at such exceptionally high loads.
The C++/CORBA DCC does not contain resilience mechanisms, e.g. to de-
cline requests. In consequence we predict that the C++/CORBA DCC would,
like the C++ DM, fail catastrophically when signiﬁcantly overloaded. However
without a usable C++/CORBA DCC we are unable to substantiate this pre-
diction. Like the DM, the C++/CORBA DCC could be be reengineered with
resilience mechanisms at the cost of program size and complexity.
kill kill kill kill kill kill kill kill kill
Issued per 15s
One worker killed every 5 minutes
Figure 11: DCC Repeated failures
Hardware and software redundancy enables distributed systems to be highly
available, that is to continue to function despite hardware or software failures.
Ideally the system should continue to function not only in the event of a single
component failing, but also when components repeatedly fail and when several
components fail simultaneously. One reason that it is important that the system
can deal with multiple components failing is that it is not uncommon that the
behaviour of a faulty component causes other components to fail.
We investigate availability using the DCC primarily as it has multiple pro-
cesses on multiple nodes. In contrast the DM only comprises four processes in a
single node. Even so we have measured the DM with transient errors induced by
injecting errors that trigger randomly, and ﬁnd that the eﬀect on the through-
put was too small distinguish from normal ﬂuctuations induced by the varying
latency of the network.
To test the DCC for single failures, repeated failures and multiple failures we
have subjected a system with 5 worker nodes to the series of tests outlined below.
Additional measurements of the DCC availability can be found in , including
the failure of diﬀerent node types, including the failure of the leader, and of
the leader-elect. In the results of the tests reported in Figures 11, 12, and 14,
Acknowledged Calls are the key throughput measure, recording the total number
of calls handled by the system. The diﬀerence between the Acknowledged Calls
and (total) Calls is a quality of service measure, and the initial throughput in
the ﬁgures represents 100% throughput at the nominal quality of service.
kill 1 node kill 2 nodes kill 3 nodes kill 4 nodes kill 5 nodes
Issued per 15s
One worker killed every 5 minutes
Figure 12: DCC Multiple failures
The availability measurements undertaken are as follows.
- Repeated software and hardware failures. Figure 11 shows that the Er-
lang DCC remains available despite repeated software failures simulated
by destroying the Erlang node on a processor. Figure 14 shows that the
Erlang DCC remains available despite repeated hardware failures simu-
lated by removing processors. After removing a processor, the throughput
is reduced proportional to the number of surviving processors. In sum-
mary, the Erlang DCC remains available despite repeated hard-
ware and software failures; and that performance doesn’t degrade
with repeated failures as the throughput after the repeated failures in
Figure 11 is the same as at the start.
- Groups of nodes of increasing size are crashed. Figure 12 shows that when
more components fail, throughput drops lower and recovery takes longer.
However the system recovers to its original throughput within a reasonable
time even if all but one of the nodes has failed. In summary, the Erlang
DCC resists the simultaneous failure of multiple components.
- Repeated and multiple failures of the service instance processes. Figure 13
shows that the Erlang DCC throughput is not signiﬁcantly reduced
when a small percentage of messages crash the service instance.
10m 20m 30m 40m 50m
Issued per 15s
0.01% of messages crash the handler
Calls without fails
Rejected Calls without fails
Calls with fails
Rejected Calls with fails
Figure 13: DCC Service Instance Failures
As the C++/CORBA DCC does not contain fault tolerance mechanisms, e.g.
to detect and recover from process failures, we predict that the C++/CORBA
DCC would fail catastrophically in the event of a hardware or software failure.
However without a usable C++/CORBA DCC we are unable to substantiate
this prediction. The C++/CORBA DCC could be be reengineered with fault
tolerance mechanisms, or to use a fault-tolerant CORBA, at the cost of program
size and complexity.
remove remove remove remove add add add add
Issued per 15s
Calls Acknowledged Calls Rejected Calls
Figure 14: DCC Dynamic Reconﬁgurability
6.3 Dynamic Reconﬁgurability
To provide high availability, telecoms systems should adapt quickly to changing
demands, such as the maximum throughput required. It is not only important
that computational and other resources can be added quickly, and without major
performance costs during the reconﬁguration, but also that the same resources
can be readily removed when they are no longer needed.
The Erlang DCC behaviour as computational resources are added and
removed is evaluated as follows. The system is started with 5 worker nodes
and nodes are removed until only the leader node remains and then the worker
nodes are added back until the original conﬁguration is obtained. The system
has been exposed to a 100% load for 5 worker nodes, using the same notion of
load as in the previous section. Figure 14 shows the result of the experiment
and illustrates the following properties.
- The second half of the graph shows near-linear throughput scaling as
resources are added. For example the approximate number of acknowl-
edged calls per 15s rises from 500 on 1 node to 950 on 2 nodes and 1800
on 4 nodes.
Appl. DM DCC
Lang. C/C++ Erlang Total C++ IDL Erlang Total
C++ 3101 3101 14774 83 14857
Erlang/C 247 616 863
Erlang 398 398 4882 4882
Table 2: DM and DCC Code Sizes (SLOC)
- The ﬁrst half of the graph shows near-linear decrease in throughput
as resources are removed. For example, the approximate number of
acknowledged calls per 15s falls from 1800 on 4 nodes to 950 on 2 nodes
and 500 on 1 node.
-The cost of adding or removing a processor is small: the fall in
throughput as Erlang nodes are added and deleted is small.
The C++ DCC provides dynamic reconﬁgurability using CORBA, although
systematic measurements are not reported .
This section compares software productivity measures of the C++ and Erlang
DM and DCC implementations. The signiﬁcance of software size is well estab-
lished: shorter programs are faster to produce [15, 26], and hence programmers
working in higher level languages are more productive. The reduced develop-
ment time crucially reduces time to market for the product. More signiﬁcantly,
given that more than half of programming eﬀort is expended on maintenance,
shorter programs are easier to maintain .
The metric we use for software size is logical source lines of code (SLOC).
There are numerous software complexity metrics, e.g. McCabe’s cyclomatic com-
plexity , and a good survey is available in . Indeed McCabe’s cyclomatic
complexity is a Motorola corporate standard but is unavailable for either the
DCC or the DM in isolation. SLOC has the advantages of simplicity, relatively
wide use, and cross-paradigm applicability. That is SLOC can be used on both
the object-oriented C++ and the functional Erlang.
7.1 DM and DCC Code Sizes
The size of the C++ and Erlang DM and DCC are reported in table 2, and
the sizes of the DMs are depicted in Figure 15. The pure Erlang DM is
1/7th of the size of the C++ DM, and the Erlang/C DM is 1/3rd of the size
of the C++ DM. Both the pure Erlang DM, and the Erlang/C DCC
are less than 1/3rd of the size of the C++ implementations. These
results are consistent with other measurements , and with developer folklore
in companies like Ericsson, T-Mobile and Nortel.
Part Lines of Code Percentage Modules
Reusable Platform 2994 61% 26
Speciﬁc Service 147 3% 1
Testing/Statistics 1741 36% 11
Total 4882 100% 38
Table 3: Erlang DCC Reusablity
A key productivity issue is reuse, covering both generic services and libraries.
The Erlang/C DM is dramatically smaller than the C++ DM together with
the reused RCS libraries: 1/18th of the size (18000 vs 870 SLOC). The library
functions are not required by the Erlang DM. The DCC is an instance of a
generic distributed server and Table 3 shows that the majority of the code (61%)
is a reusable platform. The table analyses the DCC code sizes into Platform:
the reusable generic parts; Testing/Statistics; and Service: the parts speciﬁc to
Figure 15: Source Code Sizes
7.2 Reasons for Size Diﬀerence
The reasons why Erlang programs are shorter become apparent from the anal-
ysis of the C++ and Erlang DM code presented in Figure 16 and in Tables 4
and 5. For simplicity we compare the C++ DM only with the pure Erlang
DM, and when interpreting the percentages, the reader should recall that the
Erlang DM is 1/7th of the size of the C++ DM.
-Erlang’s sophisticated fault tolerance mechanisms mean that the pro-
grammer can code for the successful case. Hence, there is far less de-
Code Type Erlang Erlang/C Erlang/C Erlang/C
Total Total Erlang C Part
Application 62.2% 61.8% 69.0% 43.7%
Defensive 0.5% 1.7% 0.0% 6.1%
Communication 15.1% 10.2% 10.9% 8.5%
Memory management 0.0% 3.2% 0.0% 11.3%
Type declarations 4.9% 6.1% 5.2% 8.5%
Deﬁnes 5.4% 5.7% 7.0% 2.4%
Includes 2.4% 5.7% 2.4% 13.8%
Process management 9.5% 5.6% 5.5% 5.7%
Table 4: Erlang DM Code Proportions
Code Type C++ Code RCS C libraries
Application 19.2% 12.1%
Defensive 25.3% 24.2%
Communication 22.1% 5.6%
Memory management 11.3% 7.1%
Type declarations 11.2% 11.6%
Deﬁnes 1.1% 23.6%
Includes 8.1% 8.6%
Process management 1.9% 7.1%
Table 5: C++ DM Code Proportions
fensive code in the Erlang implementations: 0.5% as opposed to
-Erlang’s high-level communication greatly reduces communica-
tion coding eﬀort of buﬀering, marshalling and error-checking: 15.1%
as opposed to 22.1%. Figures 2 and 3 give a dramatic comparison of the
same communication in Erlang and in C++.
-Erlang’s automatic garbage collection greatly reduces memory
management coding eﬀort: 11.3% as opposed to 0%.
Signiﬁcantly, much of the code that is omitted from the Erlang imple-
mentation is technically challenging, e.g. memory management and defensive
code are notoriously hard to get correct and to test. Moreover, Motorola engi-
neers observed that the DM has relatively little defensive code, and that many
components contain more than 50% defensive code.
8 Related Work
The impact of the programming language used on the quality and timeliness
of software engineering projects has been long established, e.g. . There are
numerous comparisons of programming languages, some general e.g. , some
comparing within a paradigm e.g. object-oriented languages  and others
comparing paradigms, e.g. scripting versus general purpose languages . Pro-
ponents of Erlang, along with proponents of other functional languages like
Clean and Haskell, have participated in these studies. Some of the compar-
isons incorporate hundreds of languages, but the great majority of benchmark
programs are sequential and are necessarily small kernels of larger applications.
Rather than small sequential programs, this paper compares two substantial
distributed product components, where robustness and conﬁgurability are key
Figure 16: Source Code Breakdown
In contrast to Erlang, other distributed functional languages like Kali
Scheme , Facile , OZ , and Glasgow distributed Haskell (GdH) 
are all research languages, and very few comparisons with other programming
languages have been reported. The comparisons that do exist, e.g. , are far
smaller scale than those reported here: considering smaller components, and
fewer aspects of distributed software engineering.
Comparisons between Erlang and other language technologies have been
reported. Ericsson have undertaken some unpublished comparative studies, al-
though a published study is based on the development of the AXD301 ATM
switch which is in excess of 1M SLOC. The study reports that the Erlang
systems have between 4 and 10 times less code than C/C++, Java or PLEX.
Moreover, the systems developed in Erlang and in conventional languages
exhibit similar error rates per unit SLOC, and similar SLOC per unit time pro-
grammer productivity. Hence, the Erlang system shows at least a fourfold
gain in productivity and reduction in errors . Our productivity results in
Section 7.1 are in close agreement with Wiger’s. In contrast to Wiger’s general
comparison of Erlang with other technologies, we report a systematic inves-
tigation of the six research questions identiﬁed in the introduction, and make
direct comparisons between corresponding C++ and Erlang implementations
of two components.
In another study Jantsch et al compare six languages including Erlang, for
system level description . They report that while Erlang is suitable for
specifying control software, mixed hardware and software systems and simple
hardware, it is less suitable for specifying purely-functional software and pure
hardware. The study reported here compares Erlang and C++ for distributed
software engineering, rather than for system level description.
We have investigated high-level distributed language technology for telecoms
software by reengineering two telecoms components in Erlang. Telecoms ap-
plications are challenging: requiring the integration of distributed resources
under soft real-time constraints and with high availability and conﬁgurability
requirements. To address the software comparison issues outlined in Section 1.2,
two components, one small, and one medium-scale have been engineered. The
components have been measured and compared with the existing C++ compo-
nents. Let us return to the research questions from the introduction, and ﬁrst
consider the potential beneﬁts of a high-level distributed language technology.
Q1 Can robust, conﬁgurable systems be more readily developed?
Yes, as detailed below.
Resilience The Erlang DM and DCC both sustain throughput at ex-
treme loads and automatically recover to pre-overload throughput
when load drops (Figures 9 and 10). In contrast, as the C++ DM
and DCC both lack resilience mechanisms, the C++ DM fails catas-
trophically when substantially overloaded, and we predict that the
DCC would fail similarly.
Availability The Erlang DCC remains available despite repeated hard-
ware and software failures, and performance doesn’t degrade with
repeated failures (Figures 11 and 14). The Erlang DCC resists
the simultaneous failure of multiple components (Figure 12). The
throughput of the Erlang DCC is not signiﬁcantly reduced when a
small percentage of messages crash the service instance (Figure 13).
In contrast, as the C++/CORBA DCC lacks fault tolerance mecha-
nisms, we predict that it would fail catastrophically in the event of a
hardware or software failure.
Dynamic Reconﬁgurability The Erlang DCC shows near-linear through-
put scaling as resources are added and a near-linear decrease in
throughput as resources are removed. Moreover, the cost of adding or
removing a processor is small (Figure 14). The C++ DCC provides
similar dynamic reconﬁgurability using CORBA.
Q2 Can productivity and maintainability be improved?
Yes, using source lines of code (SLOC) as a metric, both the Erlang
DM and DCC are less than a third of the size of the C++ counterpart
(Table 2). Moreover, much of the Erlang DCC is a reusable generic
server (Table 3). The reasons for the reduced programming eﬀort are that
coding for the successful case saves 27%, high-level communications save
22%, and automatic memory management saves a further 11% (Tables 4
and 5). These productivity results are consistent with other measure-
ments , and with developer folklore.
Secondly, we consider the feasibility of the Erlang high-level distributed lan-
guage technology for realistic telecoms software development.
Q3 Can the required distributed functionality be speciﬁed?
Yes, even although low-level distributed coordination aspects are abro-
gated to the Erlang implementation, the requisite DCC and DM func-
tionality is readily speciﬁed.
Q4 Can acceptable performance be achieved?
Substantially Yes, Figure 7 shows that the Erlang DMs have acceptable
time performance, exceeding the throughput requirements. Indeed the
round trip times for the pure Erlang DM are a third of the C++ DM
times, and even the Erlang/C round trip times are no more than 50%
greater. It may seem surprising that a bytecode interpreted language like
Erlang outperforms compiled C++. However in this distributed context
where the C++ DM must rely on relatively slow and heavyweight operat-
ing system processes and inter-process communication, the Erlang DM
uses fast lightweight processes and inter-process communication integral
to the language.
In contrast to the excellent time performance, Figure 8 shows that for the
DM, as for other small applications, the Erlang memory residency is up
to 170% greater due to the large (5Mb) runtime system.
Q5 What are the costs of interoperating with conventional technology? Com-
bining the Erlang DMs with the C RCS test harness, and incorporating
the C drivers in the Erlang/C DM shows that Erlang components can
interoperate with legacy code. As Erlang components are readily made
robust, the robustness of a large distributed system can be incrementally
improved by (re)engineering critical components in Erlang. Figure 8
shows that the additional space cost of the interoperating Erlang/C
DM is small: 15%. However the time penalty for the additional commu-
nication with the C driver is high: and the Erlang/C DM round trip
times are 4 times slower (Figure 7) and maximum throughput is a quarter
of the pure Erlang DM (Figure 9).
Q6 Is the technology practical?
As far as required by the DCC and DM, Erlang has proved to be a us-
able technology. We have shown that Erlang is available on appropriate
hardware/operating system platforms for two typical telecoms products.
That is, it is available on the Sun/Solaris RCS product platform for the
DM (Section 4.1.2), and on a scalable platform, namely a Beowulf clus-
ter, for the DCC (Section 4.2.2). The technology is well supported with
training and consultancy, and many useful components are available in
the OTP libraries (Section 3.3.4).
We conclude that high-level distributed languages like Erlang can deliver the
required telecoms functionality and performance. Moreover, such languages
oﬀer improved robustness and productivity for distributed telecoms software.
The RCS product group have responded very favourably to the robustness and
productivity beneﬁts of the Erlang DMs. An Erlang DM has been installed
alongside the original C++ DM, and the product group are investigating reengi-
neering other parts of the RCS in Erlang.
Given that Erlang has signiﬁcant beneﬁts for the rapid production of ro-
bust distributed systems, and was developed at approximately the same time
as Java, one might ask why it hasn’t been more widely adopted. Although the
adoption of a programming language is strongly inﬂuenced by social and organi-
sational issues largely beyond the scope of this paper, there are some interesting
dissemination issues. One issue is that Erlang has only become open source
recently, unlike Java. Moreover, project managers require strong evidence of
potential beneﬁts before adopting a technology, and we hope that systematic
studies such as the work presented here will help provide this evidence.
There are also a number of technical issues inﬂuencing language choice.
Rather than the dominant object-oriented paradigm, Erlang supports an im-
pure functional programming paradigm. Consequently software engineers re-
quire rather specialist training, and do not have access to ubiquitous object-
oriented software engineering tools, although specialised tools are available in
the OTP. Erlang was developed in, and even now is primarily employed in
a single specialist sector, namely telecoms. Furthermore there is the percep-
tion that the performance of a high-level bytecode-interpreted language like
Erlang will compare unfavourably with compiled mid-level languages like C.
This perception is, however, mistaken for many distributed contexts where rel-
atively slow sequential execution is more than compensated for by Erlang’s
fast process management and interprocess communication, as demonstrated in
Section 5. Despite these issues the use of Erlang, as measured by implemen-
tation downloads, is growing exponentially and it is being applied in more and
The current work could be extended to explore Erlang’s potential for in-
service software upgrades, so-called hot-code loading . In ongoing work we
are investigating the impact of a range of language constructs on the engineering
of distributed telecoms software, again using the DCC and DM as a basis for
comparison. We consider Erlang, C++ and Glasgow distributed Haskell, and
the constructs of interest include the type system, process and communication
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