Current and Future Challenges of Software Engineering for Services and Applications

Casale, Giuliano; Chesta, Cristina; Deussen, Peter; Nitto, Elisabetta Di; Gouvas, Panagiotis; Koussouris, Sotirios; Stankovski, Vlado; Symeonidis, Andreas L.; Vlassiou, Vlassis; Zafeiropoulos, Anastasios; Zhao, Zhiming

doi:10.1016/j.procs.2016.08.278

Cited by 48 publications

(33 citation statements)

References 4 publications

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“…For example, it is important to extend existing software development and delivery methodologies with reusable abstractions for designing, orchestrating and managing IoT, Fog/Edge computing, Big Data, and serverless computing technologies and platforms. Early efforts in these directions are already underway [38].…”

Section: Application Development and Deliverymentioning

confidence: 99%

A Manifesto for Future Generation Cloud Computing

et al. 2018

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The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to * Corresponding

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Section: Application Development and Deliverymentioning

confidence: 99%

A Manifesto for Future Generation Cloud Computing

et al. 2018

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“…However, the definition and calculation of software productivity have changed a lot in the DevOps environment. Cois [3] proposed the influence of software communication and data driving on software productivity in DevOps environment in 2014, and Casale [10] proposed the challenges and existing methods of software productivity in the new environment. These new definitions of software productivity combine the impact of new software development models in the DevOps environment on traditional software productivity.…”

Section: Related Workmentioning

confidence: 99%

Software Productivity in DevOps

Liu¹,

Qin²,

Zhu³

et al. 2019

JSW

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The investigation of multi-source, heterogeneous, multi-cycle data in DevOps has been attracting lots of attention in recent years. Although productivity is crucial for assuring instant release of DevOps, it has not been well studied based on merging effort features and unobserved cost features for open source software. An innovative software productivity estimation model in DevOps is proposed in this paper by recasting the definition of effort and cost. The proposed productivity model takes account of committed "outcomes" as cost instead of traditional man-month, and extended effort to consist of various commits (code, issue, scripts). Four open source projects are studied, with 95481 commits and 95828 issues in total. The experiment results illustrate the productivity changes with life cycle. The non-traditional code work ratio in productivity can represent iteration frequency of a software production and increases drastically before important releases. Thus we can monitor the life cycle and predicting large change of a production with productivity.

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“…Workflow scheduling problems are increasingly common in many scientific communities [1] [2]. To workflow applications, a popular representation is the Directed Acyclic Graph (DAG), where each vertex represents a task and edges encode precedence constraints.…”

Section: Introductionmentioning

confidence: 99%

Learning Workflow Scheduling on Multi-Resource Clusters

Laat

Zhao

2019

2019 IEEE International Conference on Networking, Architecture and Storage (NAS)

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Workflow scheduling is one of the key issues in the management of workflow execution. Typically, a workflow application can be modeled as a Directed-Acyclic Graph (DAG). In this paper, we present GoDAG, an approach that can learn to well schedule workflows on multi-resource clusters. GoDAG directly learns the scheduling policy from experience through deep reinforcement learning. In order to adapt deep reinforcement learning methods, we propose a novel state representation, a practical action space and a corresponding reward definition for workflow scheduling problem. We implement a GoDAG prototype and a simulator to simulate task running on multiresource clusters. In the evaluation, we compare the GoDAG with three state-of-the-art heuristics. The results show that GoDAG outperforms the baseline heuristics, leading to less average makespan to different workflow structures.

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Current and Future Challenges of Software Engineering for Services and Applications

Cited by 48 publications

References 4 publications

A Manifesto for Future Generation Cloud Computing

A Manifesto for Future Generation Cloud Computing

Software Productivity in DevOps

Learning Workflow Scheduling on Multi-Resource Clusters

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