Hariharasudhan Viswanathan

Research challenges in computation, communication, and context awareness for ubiquitous healthcare

A new paradigm for ubiquitous healthcare characterized by pervasive continuous vital sign data collection, real-time processing of monitored data to derive meaningful physiological parameters, and context-aware data- and patient-centric decision making, is central to deliver personalized healthcare solutions to the elderly and the physically challenged. However, this new paradigm requires real-time processing of wirelessly collected vital signs using inherently complex physiological models and analysis of the processed information under context (e.g., location, ambient conditions, current physical activity) to extract knowledge about the health condition of patients. As the computational capabilities of biomedical sensor nodes are insufficient to run these models, this article presents an innovative resource provisioning framework that organizes and harnesses the computing capabilities of under-utilized electronic devices in the vicinity (e.g., laptops, tablets, PDAs, DVRs, medical terminals) in home and hospital settings. Novel wireless communication solutions for reliable vital sign transmission and algorithms for acquiring context awareness to support this framework are also discussed.

Energy-Aware Application-Centric VM Allocation for HPC Workloads

Virtualized data centers and clouds are being increasingly considered for traditional High-Performance Computing (HPC) workloads that have typically targeted Grids and conventional HPC platforms. However, maximizing energy efficiency, cost-effectiveness, and utilization of data center resources while ensuring performance and other Quality of Service (QoS) guarantees for HPC applications requires careful consideration of important and extremely challenging tradeoffs. An innovative application-centric energy-aware strategy for Virtual Machine (VM) allocation is presented. The proposed strategy ensures high resource utilization and energy efficiency through VM consolidation while satisfying application QoS. While existing VM allocation solutions are aimed at satisfying only the resource utilization requirements of applications along only one dimension (CPU utilization), the proposed approach is more generic as it employs knowledge obtained through application profiling along multiple dimensions. The results of our evaluation show that the proposed VM allocation strategy enables significant reduction either in energy consumption or in execution time, depending on the optimization goals.

Energy-Efficient Thermal-Aware Autonomic Management of Virtualized HPC Cloud Infrastructure

Virtualized datacenters and clouds are being increasingly considered for traditional High-Performance Computing (HPC) workloads that have typically targeted Grids and conventional HPC platforms. However, maximizing energy efficiency and utilization of datacenter resources, and minimizing undesired thermal behavior while ensuring application performance and other Quality of Service (QoS) guarantees for HPC applications requires careful consideration of important and extremely challenging tradeoffs. Virtual Machine (VM) migration is one of the most common techniques used to alleviate thermal anomalies (i.e., hotspots) in cloud datacenter servers as it reduces load and, hence, the server utilization. In this article, the benefits of using other techniques such as voltage scaling and pinning (traditionally used for reducing energy consumption) for thermal management over VM migrations are studied in detail. As no single technique is the most efficient to meet temperature/performance optimization goals in all situations, an autonomic approach that performs energy-efficient thermal management while ensuring the QoS delivered to the users is proposed. To address the problem of VM allocation that arises during VM migrations, an innovative application-centric energy-aware strategy for Virtual Machine (VM) allocation is proposed. The proposed strategy ensures high resource utilization and energy efficiency through VM consolidation while satisfying application QoS by exploiting knowledge obtained through application profiling along multiple dimensions (CPU, memory, and network bandwidth utilization). To support our arguments, we present the results obtained from an experimental evaluation on real hardware using HPC workloads under different scenarios.

VMAP: Proactive thermal-aware virtual machine allocation in HPC cloud datacenters

Clouds provide the abstraction of nearly-unlimited computing resources through the elastic use of federated resource pools (virtualized datacenters). They are being increasingly considered for HPC applications, which have traditionally targeted grids and supercomputing clusters. However, maximizing energy efficiency and utilization of cloud datacenter resources, avoiding undesired thermal hotspots (due to overheating of over-utilized computing equipment), and ensuring quality of service guaran-tees for HPC applications are all conflicting objectives, which require joint consideration of multiple pairwise tradeoffs. The novel concept of heat imbalance, which captures the unevenness in heat generation and extraction, at different regions inside a HPC cloud datacenter is introduced. This thermal awareness enables proactive datacenter management through prediction of future temperature trends as opposed to the state-of-the-art reactive management based on current temperature measurements. VMAP, an innovative proactive thermal-aware virtual machine consolidation technique is proposed to maximize computing resource utilization, to minimize datacenter energy consumption for computing, and to improve the efficiency of heat extraction. The effectiveness of the proposed technique is verified through experimental evaluations with HPC workload traces under single-as well as federated-datacenter scenarios (in the machine rooms at Rutgers University and University of Florida).

Uncertainty-Aware Autonomic Resource Provisioning for Mobile Cloud Computing

Mobile platforms are becoming the predominant medium of access to Internet services due to the tremendous increase in their computation and communication capabilities. However, enabling applications that require real-time in-the-field data collection and processing using mobile platforms is still challenging due to i) the insufficient computing capabilities and unavailability of complete data on individual mobile devices and ii) the prohibitive communication cost and response time involved in offloading data to remote computing resources such as cloud datacenters for centralized computation. A novel resource provisioning framework for organizing the heterogeneous sensing, computing, and communication capabilities of static and mobile devices in the vicinity in order to form an elastic resource pool-a hybrid static/mobile computing grid (also called a loosely-coupled mobile device cloud)-is presented. This local computing grid can be harnessed to enable innovative data-and compute-intensive mobile applications such as ubiquitous context-aware health and wellness monitoring of the elderly, distributed rainfall and flood-risk estimation, distributed object recognition and tracking, and content-based distributed multimedia search and sharing. In orderto address challenges such as the inherent uncertainty in the hybrid grid (in terms of network connectivity and device availability), the proposed role-based resource provisioning framework is imparted with autonomic capabilities, namely, self-organization, self-optimization, and self-healing. A thorough experimental analysis aimed at verifying and demonstrating the benefits brought by autonomic capabilities of the framework is also presented in detail.

Self-organizing sensing infrastructure for autonomic management of green datacenters

The scale and complexity of modern datacenters are growing at an alarming rate due to the rising popularity of the cloud computing paradigm as an effective means to cater to the ever increasing demand for computing and storage. The management of modern datacenters is rapidly exceeding human ability, making autonomic approaches essential. In this article, methods for acquiring thermal awareness using real-time measurements and heat and air circulation models as well as solutions for proactive autonomic datacenter management that exploit this awareness are discussed. Novel communication and coordination schemes that enable self-organization of a network of external heterogeneous sensors (e.g., thermal cameras, scalar temperature and humidity sensors, airflow meters) into a multitier sensing infrastructure capable of real-time datacenter monitoring are also presented.

An autonomic resource provisioning framework for mobile computing grids

Enabling data- and compute-intensive applications that require real-time in-the-field data collection and processing using mobile platforms is still a significant challenge due to i) the insufficient computing capabilities and unavailability of complete data on individual mobile devices and ii) the prohibitive communication cost and response time involved in offloading data to remote computing resources such as clouds for centralized computation. A novel resource provisioning framework is proposed for organizing the heterogeneous sensing, computing, and communication capabilities of static and mobile devices in the vicinity in order to form an elastic resource pool (a heterogeneous mobile computing grid) that can be harnessed to collectively process massive amounts of locally generated data in parallel. The proposed framework is imparted with autonomic capabilities, namely, self-optimization and self-organization, in order to be energy and uncertainty aware, respectively, in the dynamic mobile environment.

Cocktail Party in the Cloud: Blind Source Separation for Co-Operative Cellular Communication in Cloud RAN

Due to the rapid growing popularity of mobile Internet, broadband cellular wireless systems are expected to offer higher and higher data rates even in high-mobility environments. Cloud Radio Access Network (C-RAN) is a new centralized paradigm for broadband wireless access that addresses efficiently the fluctuation in capacity demand through real-time inter-Base Station (BS) cooperation. An innovative Blind Source Separation (BSS)-based cellular communication solution for CRANs, Cloud-BSS, which leverages the inter-BS cooperation, is proposed. Cloud-BSS groups contiguous cells into clusters - sets of neighboring cells inside which mobile stations do not need to perform handovers - and allows them to use all of the frequency channels. The proposed solution is studied under different network topologies, and a novel strategy, called Channel-Select, to improve the Signal-to-Noise Ratio (SNR) is introduced. Cloud-BSS enhances the cluster spectral efficiency, decreases handovers, eliminates the need for bandwidth-consuming channel estimation techniques, and mitigates interference. Simulation results, which are discussed along with concepts, confirm these expectations.

Proactive Thermal-Aware Resource Management in Virtualized HPC Cloud Datacenters

Clouds provide the abstraction of nearly-unlimited computing resources through the elastic use of federated resource pools (virtualized datacenters). They are being increasingly considered for HPC applications, which have traditionally targeted grids and supercomputing clusters. However, maximizing energy efficiency and utilization of cloud datacenter resources, avoiding undesired thermal hotspots (due to overheating of over-utilized computing equipment), and ensuring quality of service guarantees for HPC applications are all conflicting objectives, which require joint consideration of multiple pairwise tradeoffs. An innovative proactive thermal-aware virtual machine consolidation (involving allocations as well as migrations) technique is proposed to maximize computing resource utilization, to minimize datacenter energy consumption for computing, and to improve the efficiency of heat extraction. The capability to migrate virtual machines away from lightly-loaded servers in a thermal-aware manner opens up opportunity to improve resource consolidation over time and, hence, achieve the aforementioned goals. The effectiveness of the proposed technique is verified through experimental evaluations with HPC workload traces under single- as well as federated-datacenter scenarios.

SILENCE: distributed adaptive sampling for sensor-based autonomic systems

Adaptive sampling and sleep scheduling can help realize the much needed resource efficiency in densely deployed autonomic sensor-based systems that monitor and reconstruct physical or environmental phenomena. This paper presents a data-centric approach to distributed adaptive sampling aimed at minimizing the communication and processing overhead in autonomic networked sensor-based systems. The proposed solution exploits the spatio-temporal correlation in sensed data and eliminates redundancy in transmitted data through selective representation without compromising on accuracy of reconstruction of the monitored phenomenon at a remote monitor node. In addition, the solution also exploits the same correlations for adaptive sleep scheduling aimed at saving energy in Wireless Sensor Networks (WSNs) while also providing a mechanism for ensuring connectivity to the monitor node. The data-centric joint adaptive-sampling and sleep-scheduling solution, SILENCE, has been evaluated through real experiments on a testbed monitoring temperature and humidity distribution in a rack of servers as well as through extensive simulations on TOSSIM, the TinyOS simulator.