Functional programming languages in computing clouds: practical and theoretical explorations

Cloud platforms must integrate three pillars: messaging, coordination of workers and data. This research investigates whether functional programming languages have any special merit when it comes to the implementation of cloud computing platforms. This thesis presents the lightweight message queue CMQ and the DSL CWMWL for the coordination of workers that we use as artefact to proof or disproof the special merit of functional programming languages in computing clouds. We have detailed the design and implementation with the broad aim to match the notions and the requirements of computing clouds. Our approach to evaluate these aims is based on evaluation criteria that are based on a series of comprehensive rationales and specifics that allow the FPL Haskell to be thoroughly analysed. We find that Haskell is excellent for use cases that do not require the distribution of the application across the boundaries of (physical or virtual) systems, but not appropriate as a whole for the development of distributed cloud based workloads that require communication with the far side and coordination of decoupled workloads. However, Haskell may be able to qualify as a suitable vehicle in the future with future developments of formal mechanisms that embrace non-determinism in the underlying distributed environments leading to applications that are anti-fragile rather than applications that insist on strict determinism that can only be guaranteed on the local system or via slow blocking communication mechanisms

Functional programming languages in computing clouds: practical and theoretical explorations

Cloud platforms must integrate three pillars: messaging, coordination of workers and data. This research investigates whether functional programming languages have any special merit when it comes to the implementation of cloud computing platforms. This thesis presents the lightweight message queue CMQ and the DSL CWMWL for the coordination of workers that we use as artefact to proof or disproof the special merit of functional programming languages in computing clouds. We have detailed the design and implementation with the broad aim to match the notions and the requirements of computing clouds. Our approach to evaluate these aims is based on evaluation criteria that are based on a series of comprehensive rationales and specifics that allow the FPL Haskell to be thoroughly analysed. We find that Haskell is excellent for use cases that do not require the distribution of the application across the boundaries of (physical or virtual) systems, but not appropriate as a whole for the development of distributed cloud based workloads that require communication with the far side and coordination of decoupled workloads. However, Haskell may be able to qualify as a suitable vehicle in the future with future developments of formal mechanisms that embrace non-determinism in the underlying distributed environments leading to applications that are anti-fragile rather than applications that insist on strict determinism that can only be guaranteed on the local system or via slow blocking communication mechanisms

Cwmwl, a LINDA-based PaaS fabric for the cloud

In this paper we introduce a new Platform-as-a-Service cloud environment that combines the LINDA coordination language, an in-memory key-value store, with functional programming to facilitate efficient execution of tenant plugins and applications. In the implementation a tuple space plays a central role in introducing deterministic services for basic parallel programming, including message passing, persistent infinite message pools and transactions. Redis, a key-value store, serves as the in-memory tuple space that glues together parallel constructs (i.e. skeletons) of formerly monolithic business applications to form an elastic distributed application. Although functional programming languages have adopted new runtime technology to achieve parallel execu- tion, which is mostly focused on threads, it rarely offers an obvious way to match functions to threads. We find that the LINDA tuple space and its coordination model offers a general purpose paradigm to tackle synchronisation issues that ties into both domains of computing clouds: computation through supporting common skeletons and big data (analytics) through serving as an in-memory data grid