Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.

https://www.cs.wm.edu/~liqun/paper/mobidata15-fog.pdf

A Survey of Fog Computing: Concepts, Applications and Issues

A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well-organized green computing paradigm to support the next generation of IoT applications.

/pdf/fog-computing-an-overview-of-big-iot-data-analytics-3qglv4a8yn.pdf

Fog Computing: An Overview of Big IoT Data Analytics

Fog data analytics: A taxonomy and process model

The Web browser is a killer app 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, caching has been adopted as a key mechanism. However, the existing passive measurement studies cannot comprehensively characterize the performance of mobile Web caching. 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 paper, we present a proactive approach for a comprehensive measurement study on mobile Web 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 effective 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.

/pdf/measurement-and-analysis-of-mobile-web-cache-performance-58c5yidh38.pdf

Measurement and Analysis of Mobile Web Cache Performance

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.

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

Efficient web caching in mobile apps eliminates unnecessary network traffic, reduces web accessing latency, and improves smartphone battery life. However, recent research has indicated that current mobile apps suffer from poor implementations of web caching. In this work, we first conducted a comprehensive survey of over 1000 Android apps to identify how different types of mobile apps perform in web caching. Based on our analysis, we designed CacheKeeper, an OS web caching service transparent to mobile apps for smartphones. CacheKeeper can not only effectively reduce overhead caused by poor web caching of mobile apps, but also utilizes cross-app caching opportunities in smartphones. Furthermore, CacheKeeper is backward compatible, meaning that existing apps can take advantage of CacheKeeper without any modifications. We have implemented a prototype of CacheKeeper in Linux kernel. Evaluation on 10 top ranked Android apps shows that our CacheKeeper prototype can save 42% networks traffic with real user browsing behaviors and increase web accessing speed by 2x under real 3G settings. Experiments also show that our prototype incurs negligible overhead in most aspects on cache misses.

/pdf/cachekeeper-a-system-wide-web-caching-service-for-1bvo8wrmv8.pdf

Li Qun

Papers

CacheKeeper: a system-wide web caching service for smartphones