Nalini Venkatasubramanian

Mobile Cloud Computing: A Survey, State of Art and Future Directions

In the recent years, cloud computing frameworks such as Amazon Web Services, Google AppEngine and Windows Azure have become increasingly popular among IT organizations and developers. Simultaneously, we have seen a phenomenal increase in the usage and deployment of smartphone platforms and applications worldwide. This paper discusses the current state of the art in the merger of these two popular technologies, that we refer to as Mobile Cloud Computing (MCC). We illustrate the applicability of MCC in various domains including mobile learning, commerce, health/wellness and social medias. We further identify research gaps covering critical aspects of how MCC can be realized and effectively utilized at scale. These include improved resource allocation in the MCC environment through efficient task distribution and offloading, security and privacy.

Privacy protecting data collection in media spaces

Around the world as both crime and technology become more prevalent, officials find themselves relying more and more on video surveillance as a cure-all in the name of public safety. Used properly, video cameras help expose wrongdoing but typically come at the cost of privacy to those not involved in any maleficent activity. What if we could design intelligent systems that are more selective in what video they capture, and focus on anomalous events while protecting the privacy of authorized personnel? This paper proposes a novel way of combining sensor technology with traditional video surveillance in building a privacy protecting framework that exploits the strengths of these modalities and complements their individual limitations. Our fully functional system utilizes off the shelf sensor hardware (i.e. RFID, motion detection) for localization, and combines this with a XML-based policy framework for access control to determine violations within the space. This information is fused with video surveillance streams in order to make decisions about how to display the individuals being surveilled. To achieve this, we have implemented several video masking techniques that correspond to varying user privacy levels. These results were achievable in real-time at acceptable frame rates, while meeting our requirements for privacy preservation.

A Software Defined Networking architecture for the Internet-of-Things

The growing interest in the Internet of Things (IoT) has resulted in a number of wide-area deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multi-hop ad-hoc and MANET routing protocols, they all must be effectively integrated to create a seamless communication platform. Managing these open, geographically distributed, and heterogeneous networking infrastructures, especially in dynamic environments, is a key technical challenge. In order to take full advantage of the many opportunities they provide, techniques to concurrently provision the different classes of IoT traffic across a common set of sensors and networking resources must be designed. In this paper, we will design a software-defined approach for the IoT environment to dynamically achieve differentiated quality levels to different IoT tasks in very heterogeneous wireless networking scenarios. For this, we extend the Multinetwork INformation Architecture (MINA), a reflective (self-observing and adapting via an embodied Observe-Analyze-Adapt loop) middleware with a layered IoT SDN controller. The developed IoT SDN controller originally i) incorporates and supports commands to differentiate flow scheduling over task-level, multi-hop, and heterogeneous ad-hoc paths and ii) exploits Network Calculus and Genetic Algorithms to optimize the usage of currently available IoT network opportunities. We have applied the extended MINA SDN prototype in the challenging IoT scenario of wide-scale integration of electric vehicles, electric charging sites, smart grid infrastructures, and a wide set of pilot users, as targeted by the Artemis Internet of Energy and Arrowhead projects. Preliminary simulation performance results indicate that our approach and the extended MINA system can support efficient exploitation of the IoT multinetwork capabilities.

Information collection services for QoS-Aware mobile applications

Efficient resource provisioning that allows for cost-effective enforcement of application QoS relies on accurate system state information. However, maintaining accurate information about available system resources is complex and expensive, especially in mobile environments where system conditions are highly dynamic. Resource provisioning mechanisms for such dynamic environments must therefore be able to tolerate imprecision in system state while ensuring adequate QoS to the end-user. In this paper, we address the information collection problem for QoS-based services in mobile environments. Specifically, we propose a family of information collection policies that vary in the granularity at which system state information is represented and maintained. We empirically evaluate the impact of these policies on the performance of diverse resource provisioning strategies. We generally observe that resource provisioning benefits significantly from the customized information collection mechanisms that take advantage of user mobility information. Furthermore, our performance results indicate that effective utilization of coarse-grained user mobility information renders better system performance than using fine-grained user mobility information. Using results from our empirical studies, we derive a set of rules that supports seamless integration of information collection and resource provisioning mechanisms for mobile environments. These results have been incorporated into an integrated middleware framework AutoSeC (Automatic Service Composition) to provide support for dynamic service brokering that ensures effective utilization of system resources over wireless networks.

Integrated power management for video streaming to mobile handheld devices

Optimizing user experience for streaming video applications on handheld devices is a significant research challenge. In this paper, we propose an integrated power management approach that unifies low level architectural optimizations (CPU, memory, register), OS power-saving mechanisms (Dynamic Voltage Scaling) and adaptive middleware techniques (admission control, optimal transcoding, network traffic regulation). Specifically, we identify interaction parameters between the different levels and optimize them to significantly reduce power consumption. With knowledge of device configurations, dynamic device parameters and changing system conditions, the middleware layer selects an appropriate video quality and fine tunes the architecture for optimized delivery of video. Our performance results indicate that architectural optimizations that are cognizant of user level parameters(e.g. transcoded video quality) can provide energy gains as high as 57.5% for the CPU and memory. Middleware adaptations to changing network noise levels can save as much as 70% of energy consumed by the wireless network interface. Furthermore, we demonstrate how such an integrated framework, that supports tight coupling of inter-level parameters can enhance user experience on a handheld substantially.

MAPCloud: Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture

The rise in popularity of mobile applications creates a growing demand to deliver richer functionality to users executing on mobile devices with limited resources. The availability of cloud computing platforms has made available unlimited and scalable resource pools of computation and storage that can be used to enhance service quality for mobile applications. This paper exploits the observation that using local resources in close proximity to the user, i.e. local clouds, can increase the quality and performance of mobile applications. In contrast, public cloud offerings (e.g. Amazon Web Services) offer scalability at the cost of higher delays, higher power consumption and higher price on the mobile device. In this paper we introduce MAP Cloud, a hybrid, tiered cloud architecture consisting of local and public clouds and show how it can be leveraged to increase both performance and scalability of mobile applications. We model the mobile application as a workflow of tasks and aim to optimally decompose the set of tasks to execute on the mobile client and 2-tier cloud architecture considering multiple QoS factors such as power, price, and delay. Such an optimization is shown to be NP-Hard, we propose an efficient simulated annealing based heuristic, called CRAM that is able to achieve about84% of optimal solutions when the number of users is high. We evaluate CRAM and the 2-tier approach via implementation(on Android G2 devices and Amazon EC2, S3 and Cloud Front)and extensive simulation using two rich mobile applications(Video-Content Augmented Reality and Image processing). Our results indicate that MAP Cloud provides improved scalability as compared to local clouds, improved efficiency (power/delay)(about 32% lower delays and power) and about 40% decrease in price in comparison to only using public cloud.

MuSIC: Mobility-Aware Optimal Service Allocation in Mobile Cloud Computing

This paper exploits the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. User Mobility introduces new complexities in enabling an optimal decomposition of tasks that can execute cooperatively on mobile clients and the tiered cloud architecture while considering multiple QoS goals such application delay, device power consumption and user cost/price. In this paper, we propose a novel framework to model mobile applications as a location-time workflows (LTW) of tasks, here user mobility patterns are translated to a mobile service usage patterns. We show that an optimal mapping of LTWs to tiered mobile cloud resources is an NP-hard problem. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (78% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing and video streaming applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint, Manhattan models and is resilient to errors/uncertainty in prediction of mobile user location-time workflows.

Load management in distributed video servers

We define and formulate various policies for load management in distributed video servers. We propose a predictive placement policy that determines the degree of replication necessary for popular videos using a cost based optimization procedure based on a priori predictions of expected subscriber requests. For scheduling requests, we propose an adaptive scheduling policy that compares the relative utilization of resources in a video server to determine an assignment of requests to replicas. To optimize storage utilization, we also devise methods for dereplication of videos based on changes in their popularities and in server usage patterns. Performance evaluations indicate that a load management procedure which uses a judicious combination of the different policies performs best for most server configurations. Advances in storage technologies are making high performance video servers a reality. These video servers are being deployed over emerging broadband networks to deliver a variety of interactive, digital video services to thousands of residential subscribers. To meet the scalability requirements in such large deployments, distributed video server architectures are being considered (M. Buddhikot and G. Parulkar, 1995). We propose various methods for load management that are targeted at improving the cost effectiveness of distributed video servers.

Energy efficient data collection in distributed sensor environments

Sensors are typically deployed to gather data about the physical world and its artifacts for a variety of purposes that range from environment monitoring, control, to data analysis. Since sensors are resource constrained, often sensor data is collected into a sensor database that resides at (more powerful) servers. A natural tradeoff exists between the sensor resources (bandwidth, energy) consumed and the quality of data collected at the server. Blindly transmitting sensor updates at a fixed periodicity to the server results in a suboptimal solution due to the differences in stability of sensor values and due to the varying application needs that impose different quality requirements across sensors. We propose adaptive data collection mechanisms for sensor environments that adjusts to these variations while at the same time optimizing the energy consumption of sensors. Our experimental results show significant energy savings compared to the naive approach to data collection.

Dynamic backlight adaptation for low-power handheld devices

Backlight power minimization can effectively extend battery life for mobile handheld devices. This article proposes an adaptive middleware-based approach to optimize backlight power consumption when playing streaming video. The technique simultaneously minimizes the negative impact on perceived video quality.