Using real options to select stable Middleware-induced software architectures

The requirements that force decisions towards building distributed system architectures are usually of a non-functional nature. Scalability, openness, heterogeneity, and fault-tolerance are examples of such non-functional requirements. The current trend is to build distributed systems with middleware, which provide the application developer with primitives for managing the complexity of distribution, system resources, and for realising many of the non-functional requirements. As non-functional requirements evolve, the `coupling' between the middleware and architecture becomes the focal point for understanding the stability of the distributed software system architecture in the face of change. It is hypothesised that the choice of a stable distributed software architecture depends on the choice of the underlying middleware and its flexibility in responding to future changes in non-functional requirements. Drawing on a case study that adequately represents a medium-size component-based distributed architecture, it is reported how a likely future change in scalability could impact the architectural structure of two versions, each induced with a distinct middleware: one with CORBA and the other with J2EE. An option-based model is derived to value the flexibility of the induced-architectures and to guide the selection. The hypothesis is verified to be true for the given change. The paper concludes with some observations that could stimulate future research in the area of relating requirements to software architectures

Runtime UI Reloading for Java

Tarkvaraarendajad peavad tegema hulga rutiinseid töid, et testida kirjutatud koodi muudatuse tulemust. Olenevalt rakenduse keerukusest võib neil selleks minna paarist sekundist kuni paari minutini. Oma töö tulemust soovitakse katsetada tunni aja jooksul keskmiselt kolm kuni neli korda, mis tähendab märkimisväärset ajakulu teiste täitmist vajavate tööülesannete arvelt. Seda probleemi saab aga vältida, kui kasutada mõnda dünaamilisel viisil klasse taaslaadivat tarkvara, nagu seda on näiteks JRebel. Kahjuks tänasel päeval ei toeta mitte ükski olemasolev toode kasutajaliidese uuendamist töölaua programmide jaoks, nende töötamise ajal. Käesolevas lõputöös otsitakse lahendusi, et tuua käitusaegne kasutajaliidese uuendamine Swing teegiga kirjutatud programmidele. Selleks loodi pistikprogramm, mis täiendab JRebeli olemasolevat funktsionaalsust ning suudab tuvastada arendaja tehtud muudatused ja kanda need üle töötavasse rakendusse, ilma et seal juba olevad andmed kaduma läheksid. Soovitud tulemuse saavutamiseks oli vajalik lahendada järgmised probleemid: kuidas leida, mida arendaja täpselt muutis, kuidas uuendused saaksid rakenduse töötamise ajal jõustuda, kuidas luua uus isend ükskõik millisest kasutajaliidese komponendist ning seejärel kindlaks teha, kuidas vanalt isendilt uuele andmed üle kanda, kui pole teada, mis täpselt üleviimist vajab. Selle töö tulemusena saavad arendajad kasutada pistikprogrammi nimega UiReload, mis koostöös JRebeliga võimaldab neil näha enda töö tulemusi vaid millisekundite jooksul. Seesugune kiire tulemus oli saavutatav ainult lahendusega, mis välistaks tarkvara taaskäivitamise, õigesse kohta navigeerimise ja vormide täitmise vajaduse.In order to test if an edit works as expected, developers have to go through many routine tasks. Depending on the application given it may span from multiple seconds to minutes per change. Developer tests a change on average three to four times per hour - it is obvious that the process is excessively time-consuming. To minimize the amount of time, dynamic class updating software, like JRebel, can be used. Sadly, up to the present moment, none of the available products support reloading the UI of a running desktop application. For that reason, the aim of this thesis is to bring out the possible approaches that would enable runtime UI reloading for Swing programs. Accordingly a plugin UiReload for JRebel is implemented which can detect what was changed and therefore apply the modifications to running UI components whilst losing none of the existing state. In order to reach the expected result some major challenges had to be overcome. For a start, it was necessary to determine what was changed and which objects were affected by those editions. Secondly, to make sure how to propagate those changes to a running application. Thirdly, to ascertain how to create new instances of components and finally, how to replace an old instance of a component with new, in a way that the fresh version maintains the state of the previous one. For finding the state an automatic approach was devised. As a result, with UiReload, developers can test the changes in milliseconds. This implies to the fact that there is no more need to restart the application, navigate to the changed view nor fill in forms on the way. Overall, testing changes reaches the maximum speed and simplicity

Decentralized Coordination of Dynamic Software Updates in the Internet of Things

Large scale IoT service deployments run on a high number of distributed, interconnected computing nodes comprising sensors, actuators, gateways and cloud infrastructure. Since IoT is a fast growing, dynamic domain, the implementation of software components are subject to frequent changes addressing bug fixes, quality insurance or changed requirements. To ensure the continuous monitoring and control of processes, software updates have to be conducted while the nodes are operating without losing any sensed data or actuator instructions. Current IoT solutions usually support the centralized management and automated deployment of updates but are restricted to broadcasting the updates and local update processes at all nodes. In this paper we propose an update mechanism for IoT deployments that considers dependencies between services across multiple nodes involved in a common service and supports a coordinated update of component instances on distributed nodes. We rely on LyRT on all IoT nodes as the runtime supporting local disruption-minimal software updates. Our proposed middleware layer coordinates updates on a set of distributed nodes. We evaluated our approach using a demand response scenario from the smart grid domain

Coordinating Resource Use in Open Distributed Systems

In an open distributed system, computational resources are peer-owned, and distributed over time and space. The system is open to interactions with its environment, and the resources can dynamically join or leave the system, or can be discovered at runtime. This dynamicity leads to opportunities to carry out computations without statically owned resources, harnessing the collective compute power of the resources connected by the Internet. However, realizing this potential requires efficient and scalable resource discovery, coordination, and control, which present challenges in a dynamic, open environment. In this thesis, I present an approach to address these challenges by separating the functionality concerns of concurrent computations from those of coordinating their resource use, with the purpose of reducing programming complexity, and aiding development of correct, efficient, and resource-aware concurrent programs. As a first step towards effectively coordinating distributed resources, I developed DREAM, a Distributed Resource Estimation and Allocation Model, which enables computations to reason about future availability of resources. I then developed a fine-grained resource coordination scheme for distributed computations. The coordination scheme integrates DREAM-based resource reasoning into a distributed scheduler, for deciding and enforcing fine-grained resource-use schedules for distributed computations. To control the overhead caused by the coordination, a tuner is implemented which explicitly balances the overhead of the control mechanisms against the extent of control exercised. The effectiveness and performance of the resource coordination approach have been evaluated using a number of case studies. Experimental results show that the approach can effectively schedule computations for supporting various types of coordination objectives, such as ensuring Quality-of-Service, power-efficient execution, and dynamic load balancing. The overhead caused by the coordination mechanism is relatively modest, and adjustable through the tuner. In addition, the coordination mechanism does not add extra programming complexity to computations

Towards Implicit Parallel Programming for Systems

Multi-core processors require a program to be decomposable into independent parts that can execute in parallel in order to scale performance with the number of cores. But parallel programming is hard especially when the program requires state, which many system programs use for optimization, such as for example a cache to reduce disk I/O. Most prevalent parallel programming models do not support a notion of state and require the programmer to synchronize state access manually, i.e., outside the realms of an associated optimizing compiler. This prevents the compiler to introduce parallelism automatically and requires the programmer to optimize the program manually. In this dissertation, we propose a programming language/compiler co-design to provide a new programming model for implicit parallel programming with state and a compiler that can optimize the program for a parallel execution. We define the notion of a stateful function along with their composition and control structures. An example implementation of a highly scalable server shows that stateful functions smoothly integrate into existing programming language concepts, such as object-oriented programming and programming with structs. Our programming model is also highly practical and allows to gradually adapt existing code bases. As a case study, we implemented a new data processing core for the Hadoop Map/Reduce system to overcome existing performance bottlenecks. Our lambda-calculus-based compiler automatically extracts parallelism without changing the program's semantics. We added further domain-specific semantic-preserving transformations that reduce I/O calls for microservice programs. The runtime format of a program is a dataflow graph that can be executed in parallel, performs concurrent I/O and allows for non-blocking live updates

Towards Implicit Parallel Programming for Systems

Multi-core processors require a program to be decomposable into independent parts that can execute in parallel in order to scale performance with the number of cores. But parallel programming is hard especially when the program requires state, which many system programs use for optimization, such as for example a cache to reduce disk I/O. Most prevalent parallel programming models do not support a notion of state and require the programmer to synchronize state access manually, i.e., outside the realms of an associated optimizing compiler. This prevents the compiler to introduce parallelism automatically and requires the programmer to optimize the program manually. In this dissertation, we propose a programming language/compiler co-design to provide a new programming model for implicit parallel programming with state and a compiler that can optimize the program for a parallel execution. We define the notion of a stateful function along with their composition and control structures. An example implementation of a highly scalable server shows that stateful functions smoothly integrate into existing programming language concepts, such as object-oriented programming and programming with structs. Our programming model is also highly practical and allows to gradually adapt existing code bases. As a case study, we implemented a new data processing core for the Hadoop Map/Reduce system to overcome existing performance bottlenecks. Our lambda-calculus-based compiler automatically extracts parallelism without changing the program's semantics. We added further domain-specific semantic-preserving transformations that reduce I/O calls for microservice programs. The runtime format of a program is a dataflow graph that can be executed in parallel, performs concurrent I/O and allows for non-blocking live updates

Run-time Variability with First-class Contexts

Software must be regularly updated to keep up with changing requirements. Unfortunately, to install an update, the system must usually be restarted, which is inconvenient and costly. In this dissertation, we aim at overcoming the need for restart by enabling run-time changes at the programming language level. We argue that the best way to achieve this goal is to improve the support for encapsulation, information hiding and late binding by contextualizing behavior. In our approach, behavioral variations are encapsulated into context objects that alter the behavior of other objects locally. We present three contextual language features that demonstrate our approach. First, we present a feature to evolve software by scoping variations to threads. This way, arbitrary objects can be substituted over time without compromising safety. Second, we present a variant of dynamic proxies that operate by delegation instead of forwarding. The proxies can be used as building blocks to implement contextualization mechanisms from within the language. Third, we contextualize the behavior of objects to intercept exchanges of references between objects. This approach scales information hiding from objects to aggregates. The three language features are supported by formalizations and case studies, showing their soundness and practicality. With these three complementary language features, developers can easily design applications that can accommodate run-time changes

Towards Implicit Parallel Programming for Systems

Multi-core processors require a program to be decomposable into independent parts that can execute in parallel in order to scale performance with the number of cores. But parallel programming is hard especially when the program requires state, which many system programs use for optimization, such as for example a cache to reduce disk I/O. Most prevalent parallel programming models do not support a notion of state and require the programmer to synchronize state access manually, i.e., outside the realms of an associated optimizing compiler. This prevents the compiler to introduce parallelism automatically and requires the programmer to optimize the program manually. In this dissertation, we propose a programming language/compiler co-design to provide a new programming model for implicit parallel programming with state and a compiler that can optimize the program for a parallel execution. We define the notion of a stateful function along with their composition and control structures. An example implementation of a highly scalable server shows that stateful functions smoothly integrate into existing programming language concepts, such as object-oriented programming and programming with structs. Our programming model is also highly practical and allows to gradually adapt existing code bases. As a case study, we implemented a new data processing core for the Hadoop Map/Reduce system to overcome existing performance bottlenecks. Our lambda-calculus-based compiler automatically extracts parallelism without changing the program's semantics. We added further domain-specific semantic-preserving transformations that reduce I/O calls for microservice programs. The runtime format of a program is a dataflow graph that can be executed in parallel, performs concurrent I/O and allows for non-blocking live updates

Towards Implicit Parallel Programming for Systems

Multi-core processors require a program to be decomposable into independent parts that can execute in parallel in order to scale performance with the number of cores. But parallel programming is hard especially when the program requires state, which many system programs use for optimization, such as for example a cache to reduce disk I/O. Most prevalent parallel programming models do not support a notion of state and require the programmer to synchronize state access manually, i.e., outside the realms of an associated optimizing compiler. This prevents the compiler to introduce parallelism automatically and requires the programmer to optimize the program manually. In this dissertation, we propose a programming language/compiler co-design to provide a new programming model for implicit parallel programming with state and a compiler that can optimize the program for a parallel execution. We define the notion of a stateful function along with their composition and control structures. An example implementation of a highly scalable server shows that stateful functions smoothly integrate into existing programming language concepts, such as object-oriented programming and programming with structs. Our programming model is also highly practical and allows to gradually adapt existing code bases. As a case study, we implemented a new data processing core for the Hadoop Map/Reduce system to overcome existing performance bottlenecks. Our lambda-calculus-based compiler automatically extracts parallelism without changing the program's semantics. We added further domain-specific semantic-preserving transformations that reduce I/O calls for microservice programs. The runtime format of a program is a dataflow graph that can be executed in parallel, performs concurrent I/O and allows for non-blocking live updates

Towards Implicit Parallel Programming for Systems

Multi-core processors require a program to be decomposable into independent parts that can execute in parallel in order to scale performance with the number of cores. But parallel programming is hard especially when the program requires state, which many system programs use for optimization, such as for example a cache to reduce disk I/O. Most prevalent parallel programming models do not support a notion of state and require the programmer to synchronize state access manually, i.e., outside the realms of an associated optimizing compiler. This prevents the compiler to introduce parallelism automatically and requires the programmer to optimize the program manually. In this dissertation, we propose a programming language/compiler co-design to provide a new programming model for implicit parallel programming with state and a compiler that can optimize the program for a parallel execution. We define the notion of a stateful function along with their composition and control structures. An example implementation of a highly scalable server shows that stateful functions smoothly integrate into existing programming language concepts, such as object-oriented programming and programming with structs. Our programming model is also highly practical and allows to gradually adapt existing code bases. As a case study, we implemented a new data processing core for the Hadoop Map/Reduce system to overcome existing performance bottlenecks. Our lambda-calculus-based compiler automatically extracts parallelism without changing the program's semantics. We added further domain-specific semantic-preserving transformations that reduce I/O calls for microservice programs. The runtime format of a program is a dataflow graph that can be executed in parallel, performs concurrent I/O and allows for non-blocking live updates