In this tutorial, we will present serverless computing, survey existing serverless platforms from industry, academia, and open source projects, identify key characteristics and use cases, and describe technical challenges and open problems. Our tutorial will involve a hands-on experience of using the serverless technologies available from different cloud providers (e.g. IBM, Amazon, Google and Microsoft). We expect our users to have basic knowledge of programming and basic knowledge of cloud computing.

Location-based services are growing in popularity due to the ubiquity of smartphone users. The relevance of location-based query results is very important, especially for mobile phones with limited screen size. Location-based data frequently changes; this introduces challenges in indexing and ranking places. The growing popularity of mobile social networks, such as Twitter, FourSquare and Facebook Places, presents an opportunity to build better location-based services by leveraging user interactions on these networks. In this paper, we present SocialTelescope, a location-based service that automatically compiles, indexes and ranks locations, based on user interactions with locations in mobile social networks. We implemented our system as a location-based search engine that uses geo-tweets by Twitter users to learn about places. We evaluated the coverage and relevance of our system by comparing it against current state-of-the-art approaches including page-rank (Google Local Search), expert-based (Zagat) and user-review based (Yelp). Our results show that a crowd-sourced location-based service returns results that match those returned by current approaches in relevance, at a substantially lower cost.

This work aims at discovering community structure in rich media social networks through analysis of time-varying, multirelational data. Community structure represents the latent social context of user actions. It has important applications such as search and recommendation. The problem is particularly useful in the enterprise domain, where extracting emergent community structure on enterprise social media can help in forming new collaborative teams, in expertise discovery, and in the long term reorganization of enterprises based on collaboration patterns. There are several unique challenges: (a) In social media, the context of user actions is constantly changing and coevolving; hence the social context contains time-evolving multidimensional relations. (b) The social context is determined by the available system features and is unique in each social media platform; hence the analysis of such data needs to flexibly incorporate various system features. In this article we propose MetaFac (MetaGraph Factorization), a framework that extracts community structures from dynamic, multidimensional social contexts and interactions. Our work has three key contributions: (1) metagraph, a novel relational hypergraph representation for modeling multirelational and multidimensional social data; (2) an efficient multirelational factorization method for community extraction on a given metagraph; (3) an online method to handle time-varying relations through incremental metagraph factorization. Extensive experiments on real-world social data collected from an enterprise and the public Digg social media Web site suggest that our technique is scalable and is able to extract meaningful communities from social media contexts. We illustrate the usefulness of our framework through two prediction tasks: (1) in the enterprise dataset, the task is to predict users’ future interests on tag usage, and (2) in the Digg dataset, the task is to predict users’ future interests in voting and commenting on Digg stories. Our prediction significantly outperforms baseline methods (including aspect model and tensor analysis), indicating the promising direction of using metagraphs for handling time-varying social relational contexts.

This paper aims at discovering community structure in rich media social networks, through analysis of time-varying, multi-relational data. Community structure represents the latent social context of user actions. It has important applications in information tasks such as search and recommendation. Social media has several unique challenges. (a) In social media, the context of user actions is constantly changing and co-evolving; hence the social context contains time-evolving multi-dimensional relations. (b) The social context is determined by the available system features and is unique in each social media website. In this paper we propose MetaFac (MetaGraph Factorization), a framework that extracts community structures from various social contexts and interactions. Our work has three key contributions: (1) metagraph, a novel relational hypergraph representation for modeling multi-relational and multi-dimensional social data; (2) an efficient factorization method for community extraction on a given metagraph; (3) an on-line method to handle time-varying relations through incremental metagraph factorization. Extensive experiments on real-world social data collected from the Digg social media website suggest that our technique is scalable and is able to extract meaningful communities based on the social media contexts. We illustrate the usefulness of our framework through prediction tasks. We outperform baseline methods (including aspect model and tensor analysis) by an order of magnitude.

Social media websites promote diverse user interaction on media objects as well as user actions with respect to other users. The goal of this work is to discover community structure in rich media social networks, and observe how it evolves over time, through analysis of multi-relational data. The problem is important in the enterprise domain where extracting emergent community structure on enterprise social media, can help in forming new collaborative teams, aid in expertise discovery, and guide long term enterprise reorganization. Our approach consists of three main parts: (1) a relational hypergraph model for modeling various social context and interactions; (2) a novel hypergraph factorization method for community extraction on multi-relational social data; (3) an on-line method to handle temporal evolution through incremental hypergraph factorization. Extensive experiments on real-world enterprise data suggest that our technique is scalable and can extract meaningful communities. To evaluate the quality of our mining results, we use our method to predict users' future interests. Our prediction outperforms baseline methods (frequency counts, pLSA) by 36-250% on the average, indicating the utility of leveraging multi-relational social context by using our method.

In the absence of generic programming abstractions for dynamic data in most enterprise programming environments, individual applications treat data streams as a special case requiring custom programming. With the growing number of live data sources such as RSS feeds, messaging and presence servers, multimedia streams, and sensor data. a general-purpose client-server programming model is needed to easily incorporate live data into applications. In this paper, we present Live Data Views, a programming abstraction that represents live data as a time-windowed view over a set of data streams. Live Data Views allow applications to create and retrieve stateful abstractions of dynamic data sources in a uniform manner, via the application of intra- and inter- stream operators. We provide details of our model and evaluate a proof-of-concept Live Data Views implementation to monitor traffic conditions on a highway. We also provide the preliminary design of a J2EE-based implementation, and outline some of the research challenges raised by this abstraction in a distributed computing environment.

Distributed hash table (DHT) overlay networks offer an efficient and robust technique for wire-area data storage and queries. Workload from real applications that use DHT networks will likely exhibit significant skews that can result in bottlenecks and failures that limit the overall scalability of the DHT approach. We present the content and load-aware scalable hashing (CLASH) protocol that can enhance the load distribution behavior of a DHT. CLASH relies on a variable-length identifier key scheme, where the length of any individual key is a function of load. CLASH uses variable-length keys to cluster content-related objects on single nodes to achieve processing efficiencies, and minimally disperse objects across multiple servers when hotspots occur. We demonstrate the performance benefits of CLASH through analysis and simulation.

Wireless local-area networks are becoming increasingly popular. They are commonplace on university campuses and inside corporations, and they have started to appear in public areas [17]. It is thus becoming increasingly important to understand user mobility patterns and network usage characteristics on wireless networks. Such an understanding would guide the design of applications geared toward mobile environments (e.g., pervasive computing applications), would help improve simulation tools by providing a more representative workload and better user mobility models, and could result in a more effective deployment of wireless network components.Several studies have recently been performed on wire-less university campus networks and public networks. In this paper, we complement previous research by presenting results from a four week trace collected in a large corporate environment. We study user mobility patterns and introduce new metrics to model user mobility. We also analyze user and load distribution across access points. We compare our results with those from previous studies to extract and explain several network usage and mobility characteristics.We find that average user transfer-rates follow a power law. Load is unevenly distributed across access points and is influenced more by which users are present than by the number of users. We model user mobility with persistence and prevalence. Persistence reflects session durations whereas prevalence reflects the frequency with which users visit various locations. We find that the probability distributions of both measures follow power laws.

The bulk of proposed pervasive computing devices such as PDAs and cellular telephones operate as thin clients within a larger infrastructure. To access services within their local environment, these devices participate in a service discovery protocol which involves a master directory that registers all services available in the local environment. These directories typically are isolated from each other. Devices that move across service discovery domains have no access to information outside their current local domain. In this paper we propose an application-level protocol called VIA that enables data sharing among discovery domains. Each directory maintains a table of active links to other directories that share related information. A set of linked directories forms a data cluster that can be queried by devices for information. The data cluster is distributed, self-organizing, responsive to data mobility, and robust to failures. Using application-defined data schemas, clusters organize themselves into a hierarchy for efficient querying and network resource usage. Through analysis and simulation we describe the behavior of VIA under different workloads and show that the protocol overhead for both maintaining a cluster and handling failures grows slowly with the number of gateways.