The recent proliferation of API hosting frameworks has dramatically eased the development of interesting web mashups and provided monetization opportunities for enterprises offering high value APIs. Most of these mashups are based on request/response REST model that is widely used in the web world. However, REST is not the primary vehicle for communication oriented services such as Internet telephony, chat, presence or live video communications. Session Initiation Protocol (SIP) is commonly used for communication services and has well defined Java APIs for use by applications and application developers. In this paper, we present "Talking Cloud" - a API hosting platform on the cloud, for enabling the composition of asynchronous, communication-oriented services to create high value mashups such as object detection in real-time video. Our platform is based on a unique combination of HTTP and SIP, and provides a number of useful features such as service customization, media signaling and routing, social network interactions, elasticity and scaling, besides standard API management functionalities such as usage metering. Our platform provides a flexible framework that allows applications to place hooks within the service invocation workflow, and invoke application logic that is executed remotely, enabling flexible and context sensitive handling of calls/media. We describe our platform architecture and show how communication mashups can interact with it using a combination of REST and SIP interfaces that are exposed by the platform. We describe a prototype implementation and present two usecases - nearest available helpdesk agent and real time object detection within live video streams.

Large-scale, predictive social analytics have proven effective. Over the last decade, research and industrial efforts have understood the potential value of inferences based on online behavior analysis, sentiment mining, influence analysis, epidemic spread, etc. The majority of these efforts, however, are not yet designed with realtime responsiveness as a first-order requirement. Typical systems perform a post-mortem analysis on volumes of historical data and validate their “predictions” against already-occurred events.We observe that in many applications, real-time predictions are critical and delays of hours (and even minutes) can reduce their utility. As examples: political campaigns could react very quickly to a scandal spreading on Facebook; content distribution networks (CDNs) could prefetch videos that are predicted to soon go viral; online advertisement campaigns can be corrected to enhance consumer reception. This paper proposes CrowdCast, a cloud-based framework to enable real-time analysis and prediction from streaming social data. As an instantiation of this framework, we tune CrowdCast to observe Twitter tweets, and predict which YouTube videos are most likely to “go viral” in the near future. To this end, CrowdCast first applies online machine learning to map natural language tweets to a specific YouTube video. Then, tweets that indeed refer to videos are weighted by the perceived “influence” of the sender. Finally, the video’s spread is predicted through a sociological model, derived from the emerging structure of the graph over which the video-related tweets are (still) spreading. Combining metrics of influence and live structure, CrowdCast outputs sets of candidate videos, identified as likely to become viral in the next few hours. We monitor Twitter for more than 30 days, and find that CrowdCast’s real-time predictions demonstrate encouraging correlation with actual YouTube viewership in the near future.

We design a framework for solution validation and couple it with the ability for VM and elasticity management. We implement our design in the form of a "dashboard which captures all elements of a comprehensive cloud solution management framework. We illustrate the applicability of the dashboard with a cloud solution we have developed.

In recent years, adoption of social networks such as Facebook, Twitter etc have witnessed a meteoric rise amongst users across the world. Much of this is due to such platforms' capability to create a community of users who are able to communicate updates in real-time and keep in constant touch with each other. Social networks are also enabling the delivery of a host of services irrespective of the user devices and the services delivered. Hence, social networks have drawn the attention of businesses and enterprises for various purposes such as new products advertising, trend mining, connecting to customers. This paper demonstrates how social networks can be used to create business entities like contact centers. In contrast to current day contact centers, where a user must visit a company's web-site to engage in live-chat or call a 1-800 number, social networks offer a real-time channel to enable companies to reach individual users through a medium of their choice and at a time of their choosing. Rather than requiring customers to visit a company's website, companies are now able to interact with the customers where customers are already spending time, and can tailor the customer experience as a 1-on-1 channel for personalized, real-time interaction. A presence based open contact center system (POCC) is presented in this paper. In the proposed system, presence of a customer i.e. the availability of a customer on social media is detected and then based on the capabilities of the social network, a real-time communication channel such as livechat is established between an agent and the customer. The core components of the POCC are implemented using industry standard SIP protocol. The architecture of the POCC is flexible such that adding a new social network service is only a matter of writing a service specific adapter that can be easily plugged into the POCC.

Crowdsourced video often provides engaging and diverse perspectives not captured by professional videographers. Broad appeal of user-uploaded video has been widely confirmed: freely distributed on YouTube, by subscription on Vimeo, and to peers on Facebook/Google+. Unfortunately, user-generated multimedia can be difficult to organize; these services depend on manual "tagging" or machine-mineable viewer comments. While manual indexing can be effective for popular, well-established videos, newer content may be poorly searchable; live video need not apply. We envisage video-sharing services for live user video streams, indexed automatically and in realtime, especially by shared content. We propose FOCUS , for Hadoop-on-cloud video-analytics. FOCUS uniquely leverages visual, 3D model reconstruction and multimodal sensing to decipher and continuously track a video's line-of-sight. Through spatial reasoning on the relative geometry of multiple video streams, FOCUS recognizes shared content even when viewed from diverse angles and distances. In a 70-volunteer user study, FOCUS' clustering correctness is roughly comparable to humans .

One of the major security threats that public cloud computing platforms face today is that the active cloud virtual machine instances are visible and accessible via the public internet, which allows hackers to carry out several types of attacks such as Denial of Service (DoS) and intrusion over along durations which increases the probabilities of successful penetration. Security logs of the failed attempts attest to the real threat and the intensity and duration of these. Most systems running on public cloud instances today are not security hardened to withstand such persistent and long attacks. It is not only dangerous but also disastrous for the enterprise that uses such instances to deliver cloud services, for the users that use such services, and for the cloud provider that provides the cloud infrastructure. Therefore, what is required is a network level access control solution that facilitates delivery of cloud services while protecting the network perimeter of the solution in a useable and dynamically customisable manner. In this paper, we have described such a network-based access control solution for public cloud services that we have designed and developed and is applicable to any of the various cloud platforms available today. We have deployed our solution as part of the "Security-as-a-Service" model on IBM Smart Cloud Enterprise (SCE), and has been used for commercial delivery of cloud services. These applications have led to not only high level of security with no security attacks via network exposure on the services, but also significant savings on the cost of maintaining the security of such instances and services. We have also studied the challenges that network address translators (NATs) pose for network-based access control on public cloud, and have developed solutions for such challenges.

The authors present a large-scale presence virtualization and federation platform that federates across heterogeneous presence domains, letting applications exploit cross-domain contextual data. The platform also provides a programmable interface for customized context queries.