The term model-driven engineering (MDE) is typically used to describe software development approaches in which abstract models of software systems are created and systematically transformed to concrete implementations. In this paper we give an overview of current research in MDE and discuss some of the major challenges that must be tackled in order to realize the MDE vision of software development. We argue that full realizations of the MDE vision may not be possible in the near to medium-term primarily because of the wicked problems involved. On the other hand, attempting to realize the vision will provide insights that can be used to significantly reduce the gap between evolving software complexity and the technologies used to manage complexity.

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Model-driven Development of Complex Software: A Research Roadmap

[서평]「Component Software」 - Beyond Object-Oriented Programming -

Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities

A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

Designing an Super-Peer Network

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Virtual testbeds model them by seamlessly integrating physical, simulated, and emulated sensor nodes and radios in real time.

Flexible experimentation in wireless sensor networks

The cloud is scalable and cost-efficient, but it isn't ideal for hosting all applications. Fog computing proposes an alternative of offloading some computation to the edge. Which applications to offload, where to send them, and when this should occur isn't entirely clear yet due to a lack of understanding of potential edge infrastructures. Through a number of experiments, the authors showcase the feasibility and readiness of micro-clouds formed by collections of Raspberry Pis to host a range of fog applications, particularly for network-constrained environments.

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On Using Micro-Clouds to Deliver the Fog

In order to provide an increasing number of functionalities and benefit from sophisticated and application-tailored services from the network, distributed applications are led to integrate an ever-widening range of networking technologies. As these applications become more complex, this requirement for 'network heterogeneity' is becoming a crucial issue in their development. Although progress has been made in the networking community in addressing such needs through the development of network overlays, we claim in this paper that the middleware community has been slow to integrate these advances into middleware architectures, and, hence, to provide the foundational bedrock for heterogeneous distributed applications. In response, we propose our 'open overlays' framework. This framework, which is part of a wider middleware architecture, accommodates 'overlay plug-ins', allows physical nodes to support multiple overlays, supports the stacking of overlays to create composite protocols, and adopts a declarative approach to configurable deployment and dynamic reconfigurability. The framework has been in development for a number of years and supports an extensive range of overlay plug-ins including popular protocols such as Chord and Pastry. We report on our experiences with the open overlays framework, evaluate it in detail, and illustrate its application in a detailed case study of network heterogeneity.

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Experiences with open overlays: a middleware approach to network heterogeneity

Data analytic applications built upon big data processing frameworks such as Apache Spark are an important class of applications. Many of these applications are not latency-sensitive and thus can run as batch jobs in data centers. By running multiple applications on a computing host, task co-location can significantly improve the server utilization and system throughput. However, effective task co-location is a non-trivial task, as it requires an understanding of the computing resource requirement of the co-running applications, in order to determine what tasks, and how many of them, can be co-located. State-of-the-art co-location schemes either require the user to supply the resource demands which are often far beyond what is needed; or use a one-size-fits-all function to estimate the requirement, which, unfortunately, is unlikely to capture the diverse behaviors of applications. In this paper, we present a mixture-of-experts approach to model the memory behavior of Spark applications. We achieve this by learning, off-line, a range of specialized memory models on a range of typical applications; we then determine at runtime which of the memory models, or experts, best describes the memory behavior of the target application. We show that by accurately estimating the resource level that is needed, a co-location scheme can effectively determine how many applications can be co-located on the same host to improve the system throughput, by taking into consideration the memory and CPU requirements of co-running application tasks. Our technique is applied to a set of representative data analytic applications built upon the Apache Spark framework. We evaluated our approach for system throughput and average normalized turnaround time on a multi-core cluster. Our approach achieves over 83.9% of the performance delivered using an ideal memory predictor. We obtain, on average, 8.69x improvement on system throughput and a 49% reduction on turnaround time over executing application tasks in isolation, which translates to a 1.28x and 1.68x improvement over a state-of-the-art co-location scheme for system throughput and turnaround time respectively.

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Improving spark application throughput via memory aware task co-location: a mixture of experts approach

Middleware solutions for sensor networks have so far mainly focused on communication abstractions, ad-hoc message routing protocols, and power conservation techniques. We argue that customisation and dynamic reconfiguration of sensor network middleware are additional important dimensions to consider. This paper describes a sensor middleware that can be customised to suit different sensor application types, and provides a reflective approach for co-ordinated network-wide dynamic reconfiguration of sensor behaviour. To evaluate our approach we illustrate customisation and dynamic reconfiguration of the Gridkit sensor middleware in a flood-monitoring scenario.

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Barry Porter

Papers

Flexible experimentation in wireless sensor networks

On Using Micro-Clouds to Deliver the Fog

Experiences with open overlays: a middleware approach to network heterogeneity

Improving spark application throughput via memory aware task co-location: a mixture of experts approach

Dynamic reconfiguration in sensor middleware