Yun Ma

Data-Driven Composition for Service-Oriented Situational Web Applications

The convergence of Services Computing and Web 2.0 gains a large space of opportunities to compose “situational” web applications from web-delivered services. However, the large number of services and the complexity of composition constraints make manual composition difficult to application developers, who might be non-professional programmers or even end-users. This paper presents a systematic data-driven approach to assisting situational application development. We first propose a technique to extract useful information from multiple sources to abstract service capabilities with a set tags. This supports intuitive expression of users desired composition goals by simple queries, without having to know underlying technical details. A planning technique then exploits composition solutions which can constitute the desired goals, even with some potential new interesting composition opportunities. A browser-based tool facilitates visual and iterative refinement of composition solutions, to finally come up with the satisfying outputs. A series of experiments demonstrate the efficiency and effectiveness of our approach.

Demystifying the Imperfect Client-Side Cache Performance of Mobile Web Browsing

The web browser is one of the most significant applications on mobile devices such as smartphones. However, the user experience of mobile web browsing is undesirable because of the slow resource loading. To improve the performance of web resource loading, client-side cache has been adopted as a key mechanism. However, the existing passive measurement studies cannot comprehensively characterize the “client-side” cache performance of mobile web browsing. For example, most of these studies mainly focus on client-side implementations but not server-side configurations, suffer from biased user behaviors, and fail to study “miscached” resources. To address these issues, in this article, we present a proactive approach to making a comprehensive measurement study on client-side cache performance. The key idea of our approach is to proactively crawl resources from hundreds of websites periodically with a fine-grained time interval. Thus, we are able to uncover the resource update history and cache configurations at the server side, and analyze the cache performance in various time granularities. Based on our collected data, we build a new cache analysis model and study the upper bound of how high percentage of resources could potentially be cached and how effectively the caching works in practice. We report detailed analysis results of different websites and various types of web resources, and identify the problems caused by unsatisfactory cache performance. In particular, we identify two major problems - Redundant Transfer and Miscached Resource, which lead to unsatisfactory cache performance. We investigate three main root causes: Same Content, Heuristic Expiration, and Conservative Expiration Time, and discuss what mobile web developers can do to mitigate those problems.

Characterizing RESTful Web Services Usage on Smartphones: A Tale of Native Apps and Web Apps

The burst of Web-based Restful services brings us a number of facilities in our life and work. We are used to take smartphones to access these Web services, like location-based services, weather search, mapping, social networking, et al. On smartphones, we have two options of service consumers, a.k.a, Native apps and Web apps. Despite the platform-independence, Web apps are claimed to provide the same features and comparable user experiences with native apps. However, one fact is that more and more people prefer native apps rather than Web apps. In this paper, we make an empirical study on characterizing the performance disparity of native apps and Web apps. Given the same functionalities provided by the same service providers, we explore the Restful Web services that are used by native apps and Web apps. With HTTP-level trace analysis, we demystify the workflows on how native apps and Web apps use Web services and summarize different service usage patterns from architectural style perspective. Then we characterize the performance differences between native apps and Web apps on realizing Restful Web services including GET, DELETE, PUT & POST, in terms of number of network connections, response time, and data drain, given the same functional features. Our observations reveal that Web apps do not always perform worse than native apps using Restful Web services under the same context. We further propose some implications to improve both native apps and Web apps on smartphones.

Model-Based Automated Navigation and Composition of Complex Service Mashups

Service computing promotes a large number of web-delivered services, including web services, APIs and data feeds. Composing data, functionalities and even UI from these web-delivered services into a single web application, usually called service mashup, becomes a popular web development paradigm. The web-delivered services can be modeled as mashup components, while the development of mashup actually yields a set of inter-connected mashup components. The growing popularity of mashup components enriches functionality and user experiences, while the possible connections among components are complex and difficult to mashup developers, who might be non-professional programmers or even end-users, as actions over one component may have potential impacts on another. This paper proposes a novel approach for recommending developers in terms of navigation and completion of mashup components with a large-scale components repository. From data-driven perspective, we model the relationships between mashup components by a generic layered-graph model. Developers are allowed to select some initial components as starting point, while a graph-based algorithm recommends how to navigate to potentially relevant mashup components and complete the relevant mashup application. We experimentally demonstrate the efficiency and effectiveness of our approach for rapid mashup construction.

Data-driven synthesis of multiple recommendation patterns to create situational Web mashups

As a typical situational application, Web mashup reflects and accommodates some key features of Internetware paradigm. Mashup provides a development fashion that integrates data, computation and UI elements from multiple resources into a single Web application, and promises the quick rollout of creating potential new functionalities opportunistically. This paper focuses on the problem of recommending useful suggestions for developing data-driven mashups by synthesis of multiple patterns. We present a rapid and intuitive system called iMashupAdvisor, for aiding mashup development based on a novel automated suggestion mechanism. The key observation guiding the development of iMashupAdvisor is that mashups developed by different users might share some common patterns, for instance, selecting similar mashup components for similar goals, and gluing them in a similar manner. Such patterns could reside in multiple sources, e.g., the data dependency between mashup components, the interaction between users and mashup components, or the collective intelligence from existing applications created and maintained by programmers, etc. iMashupAdvisor leverages the synthesis of these patterns to recommend useful suggestions for a partial mashup, such as the missing components, connections between them, or potentially relevant options, to assist mashup completion. This paper presents the data model and ranking metrics of the synthesis process, and introduces efficient algorithms for the retrieval of recommendations. We also experimentally demonstrate the efficiency of our approach for benefiting the proposed rapid mashup development.

Mash Droid: An Approach to Mobile-Oriented Dynamic Services Discovery and Composition by In-App Search

The popularity of smartphones and tablet computers in recent years makes mobile apps burst. Mobile apps have become the main consumers of the Internet-based services. Compared to traditional applications in the desktop computing era, mobile devices with their apps bring new opportunities and challenges to service computing community, in various aspects like service publication, discovery, interaction, composition, et al. In this paper, we propose a novel data-driven, content-based mobile apps composition approach, called MashDroid, by leveraging a novel In-App Search mechanism, i.e., Discovering relevant services for the data and content in apps. Rather than existing techniques that usually integrate fixed Web services, our approach relies on the dynamic service discovery and flexible data exchange between several apps. The unique feature of our approach is enabling the data communication channel between apps by the content index services provided by a leading Android appstore, Wandoujia, which now has over 1,000,000 apps and 200 million users. We employ the In-App Search mechanism to define a Restful-style app model and resource-oriented app description model. Based on the models, we design a framework for dynamically discovering relevant apps that could be composed with current apps contexts. We implement a prototype to demonstrate our approach.

Model-Based Management of Service Composition

Promoted by the Service Computing paradigm, service composition has played an important role in modern software development. Currently, available services have covered a wide spectrum of heterogeneity, including SOAP services, Restful services and other data services. The composite services should continuously serve for a large number of users. The heterogeneity and open dynamic network environment bring grand challenges to the management of service composition. Based upon our previous work on service composition middleware - Star link, and a runtime system management tool - SM@RT, this paper proposes a model-based approach to service composition management at run time. A runtime model enables casual connections between applications and supporting platforms, provides a global view of a running system, abstracts underlying technical details, and performs automated generation of management code. By constructing the runtime model of Star link and using the SM@RT tool to generate synchronization between model and running composition, our approach makes the following contributions to service composition management: (1) a more comprehensive view of service composition management, (2) an easy-of-use manner to perform management operations at model level without underlying tedious details, (3) an on-the-fly effect on running system by means of synchronization between the model and composite services. We demonstrate that our approach can tackle the challenge of service composition management by using a case study of a photo sharing composite service application.

ShuffleDog: Characterizing and Adapting User-Perceived Latency of Android Apps

Numerous complains have been made by Android users who severely suffer from the sluggish response when interacting with their devices. However, very few studies have been conducted to understand the user-perceived latency or mitigate the UI-lagging problem. In this paper, we conduct the first systematic measurement study to quantify the user-perceived latency using typical interaction-intensive Android apps in running with and without background workloads. We reveal the insufficiency of Android system in ensuring the performance of foreground apps and therefore design a new system to address the insufficiency accordingly. We develop a lightweight tracker to accurately identify all delay-critical threads that contribute to the slow response of user interactions. We then build a resource manager that can efficiently schedule various system resources including CPU, I/O, and GPU, for optimizing the performance of these threads. We implement the proposed system on commercial smartphones and conduct comprehensive experiments to evaluate our implementation. Evaluation results show that our system is able to significantly reduce the user-perceived latency of foreground apps in running with aggressive background workloads, up to 10x, while incurring negligible system overhead of less than 3.1 percent CPU and 7 MB memory.

Programming Situational Mobile Web Applications with Cloud-Mobile Convergence: An Internetware-Oriented Approach

Mobile Web applications (a.k.a., Web apps) stand for an important trend for next-generation Internet-based software. Currently popular mobile Web apps need to be adapted to various and ever-changing contexts and personalized user requirements. Based on our over-decade research experiences and practice on the Internetware paradigm, this position article describes an Internetware-oriented approach to designing, developing, and deploying situational mobile Web apps, by synthesizing the resources and services of mobile and cloud. Guided by a novel Service-Model-View-Controller (SMVC) software model, a mobile Web app is organized into a well-defined structure that facilitates adaptation including online/offline data access, computation offloading, user interface optimization, hybrid composition, etc. We provide efficient runtime support spanning mobile and cloud to make mobile Web apps more flexibly adaptive. The proof-of-concept evaluation demonstrates that our approach can benefit end-users with optimized user experience of mobile Web apps.

i - Jacob : An Internetware-Oriented Approach to Optimizing Computation-Intensive Mobile Web Browsing

Web browsing is always a key requirement of Internet users. Current mobile Web apps can contain computation-intensive JavaScript logics and thus affect browsing performance. Learning from our over-decade research and development experiences of the Internetware paradigm, we present the novel and generic i-Jacob approach to improving the performance of mobile Web browsing with effective JavaScript-code offloading. Our approach proposes a programming abstraction to make mobile Web situational and adaptive to contexts, by specifying the computation-intensive and “ offloadable ” code, and develops a platform-independent lightweight runtime spanning the mobile devices and the cloud. We demonstrate the efficiency of i-Jacob with some typical computation-intensive tasks over various combinations of hardware, operating systems, browsers, and network connections. The improvements can reach up to 49× speed-up in response time and 90% saving in energy.