A defining, unique aspect of distributed systems lies in interprocess communication (IPC) through which distributed components interact and collaborate toward the holistic system behaviors. This highly decoupled construction intuitively contributes to the scalability, performance, and resiliency advantages of distributed software, but also adds largely to their greater complexity, compared to centralized software. Yet despite the importance of IPC in distributed systems, little is known about how to quantify IPC-induced behaviors in these systems through IPC measurement and how such behaviors may be related to the quality of distributed software . To answer these questions, in this article, we present DistMeasure , a framework for measuring distributed software systems via the lens of IPC hence enabling the study of its correlation with distributed system quality. Underlying DistMeasure is a novel set of IPC metrics that focus on gauging the coupling and cohesion of distributed processes. Through these metrics, DistMeasure quantifies relevant runtime characteristics of distributed systems and their quality relevance, covering a range of quality aspects each via respective direct quality metrics. Further, DistMeasure enables predictive assessment of distributed system quality in those aspects via learning-based anomaly detection with respect to the corresponding quality metrics based on their significant correlations with related IPC metrics. Using DistMeasure , we demonstrated the practicality and usefulness of IPC measurement against 11 real-world distributed systems and their diverse execution scenarios. Among other findings, our results revealed that IPC has a strong correlation with distributed system complexity, performance efficiency, and security. Higher IPC coupling between distributed processes tended to be negatively indicative of distributed software quality, while more cohesive processes have positive quality implications. Yet overall IPC-induced behaviors are largely independent of the system scale, and higher (lower) process coupling does not necessarily come with lower (higher) process cohesion. We also show promising merits (with 98% precision/recall/F1) of IPC measurement (e.g., class-level coupling and process-level cohesion) for predictive anomaly assessment of various aspects (e.g., attack surface and performance efficiency) of distributed system quality.

Selection of good locations in a map is an indispensable function in many applications. In order to select specific locations, we have to specify detailed selection criteria. However, it is not easy especially for users of mobile devices. Therefore, we used an idea of skyline queries, which are known to be easy and effective to retrieve interesting data from a database. In our previous work, we have proposed area skyline query that selects good locations in a map. However, the query is not fast enough for handling “big data”. We simplify and revise the algorithm of the query in this paper by using MapReduce framework so that we can use it for big data. Experiments' results demonstrate that the performance and scalability are superior to previous area skyline algorithm and are able to handle big data.