Energy-aware Actor-based Distributed Programming

**Energy-aware actor-based distributed programming**:

- Réf **ABG-132510**
- Sujet de Thèse
- 13/06/2025
- Contrat doctoral
- IMT Atlantique (Nantes)
- Lieu de travail- Nantes - Pays de la Loire - France
- Intitulé du sujet- Energy-aware actor-based distributed programming
- Champs scientifiques- Informatique
- Sciences de l’ingénieur
- Mots clés- Distributed systems, frugal computing, energy quotas, actor programming

**Description du sujet**:
*Energy-aware actor-based distributed programming*

The SEED program (academic track)

1 Definition

1.1 Domain and scientific/technical context

notably in the domains of the Cloud-Edge-IoT (CEI) continuum and
AI-intensive software systems is a major challenge for today's
societies. Controlling the energy consumption is notably crucial in
two different contexts: (i) the management/optimization of the overall
energy consumption of large-scale software systems among with
AI-intensive software systems now occupy a place of choice and (ii)
the integration of massive numbers and large varieties of small,
battery powered IoT devices in large-scale distributed systems.

Currently, energy consumption is typically handled by runtime
monitoring of components of the software systems running on
distributed systems, e.g., data centers, grid architectures, sensor
networks and other distributed cyber-physical infrastructures. Based
on measured energy consumption and (configured) available energy
quotas, some scheduling component then dynamically enables
computations based on energy policies. [SDU+23, HCW+21, CKC+21]

1.2 Scientific/technical challenges

Current distributed software/hardware systems almost never provide
guarantees that future computations (that are typically encapsulated
in some software component) can be executed given the currently
available energy. Furthermore, energy-related information is
currently almost always expressed at the API and systems-level
[RWS+24]: energy-related properties therefore cannot be expressed at
the programming level. Such guarantees require a precise link between
energy-consuming computations, i.e., distributed programs, energy
sources (available electricity networks, batteries) and available
energy quotas determined based through dynamic monitoring.

Such a link has been proposed for non-distributed programs (through
program language extensions or transformations [CY17, ZLL15, YVS17] as
well as static analysis [MSP+21, KKK16]) but is lacking for
distributed systems. Currently, software systems for distributed
systems are typically structured in terms of separate programs that
are deployed on different machines, often using containers [SDU+23],
and cooperate at runtime. More precise energy policies and
energy-aware schedulers can thus be defined only in terms of
specifications that are difficult to link to the set of executing
programs.

1.3 Considered methods, targeted results and impacts

The main goal of this PhD is the *precise definition and efficient
enforcement of energy contracts over distributed programs*. We are
targeting a distributed programming language extension that allows
required and available energy quotas of programs to be partially
defined by the programmer and partially provided dynamically by the
distributed environment in which the program is executed. Required
energy quotas are then checked using static analysis or dynamic tests
against the energy available from the execution environment. Energy
quotas are managed and enforced through contexts that energy-aware
programs use to store energy-related environmental
information. Contexts are also harnessed in order to actively manage
energy-consuming computations using placement of computations,
optimization of communication, rate limiting, scaling etc.

Distributed computations are represented and energy contexts
maintained in the envisioned programming method by means of *software
actors* [MSD18, HSF16], components that encapsulate computations and
data that can be flexibly deployed and executed in distributed
systems.

1.4 Interdisciplinarity aspects

The topic straddles the domains of software engineering, energy
efficiency and frugal computing that is useful in numerous other
domains. It includes strong interdisplinary aspects, notably through
of data analyses) and the Industry of the Future (e.g., the
optimization of monolithic batch processes that are currently used
frequently). These interdisciplinary aspects will be explored, in
particular, together with the non academic partners of this topic.

1.5 References
- [CY17] Anthony Canino and Yu David Liu. 2017. Proactive and adaptive

energy-aware programming with mixed typechecking. In Proceedings of
the 38th ACM SIGPLAN Conference on Programming Language Design and
Implementation (PLDI 2017). Association for Computing Machinery, New
- [ZLL15] Haitao Steve Zhu, Chaoren Lin, and Yu David Liu. 2015. A

programming model for sustainable software. In Proceedings of the
37th International Conference on Software Engi