Erasure Coding for Real-Time Streaming

We consider a real-time streaming system where messages are created sequentially at the source, and are encoded for transmission to the receiver over a packet erasure link. Each message must subsequently be decoded at the receiver within a given delay from its creation time. The goal is to construct an erasure correction code that achieves the maximum message size when all messages must be decoded by their respective deadlines under a specified set of erasure patterns (erasure model). We present an explicit intrasession code construction that is asymptotically optimal under erasure models containing a limited number of erasures per coding window, per sliding window, and containing erasure bursts of a limited length.Comment: Extended version of a conference paper in the IEEE International Symposium on Information Theory (ISIT), July 2012. 12 pages, 3 figure

Symmetric Allocations for Distributed Storage

We consider the problem of optimally allocating a given total storage budget in a distributed storage system. A source has a data object which it can code and store over a set of storage nodes; it is allowed to store any amount of coded data in each node, as long as the total amount of storage used does not exceed the given budget. A data collector subsequently attempts to recover the original data object by accessing each of the nodes independently with some constant probability. By using an appropriate code, successful recovery occurs when the total amount of data in the accessed nodes is at least the size of the original data object. The goal is to find an optimal storage allocation that maximizes the probability of successful recovery. This optimization problem is challenging because of its discrete nature and nonconvexity, despite its simple formulation. Symmetric allocations (in which all nonempty nodes store the same amount of data), though intuitive, may be suboptimal; the problem is nontrivial even if we optimize over only symmetric allocations. Our main result shows that the symmetric allocation that spreads the budget maximally over all nodes is asymptotically optimal in a regime of interest. Specifically, we derive an upper bound for the suboptimality of this allocation and show that the performance gap vanishes asymptotically in the specified regime. Further, we explicitly find the optimal symmetric allocation for a variety of cases. Our results can be applied to distributed storage systems and other problems dealing with reliability under uncertainty, including delay tolerant networks (DTNs) and content delivery networks (CDNs).Comment: 7 pages, 3 figures, extended version of an IEEE GLOBECOM 2010 pape

Privacy Management and Optimal Pricing in People-Centric Sensing

With the emerging sensing technologies such as mobile crowdsensing and Internet of Things (IoT), people-centric data can be efficiently collected and used for analytics and optimization purposes. This data is typically required to develop and render people-centric services. In this paper, we address the privacy implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared to the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.Comment: 16 page

Optimal content delivery with network coding

We present a unified linear program formulation for optimal content delivery in content delivery networks (CDNs), taking into account various costs and constraints associated with content dissemination from the origin server to storage nodes, data storage, and the eventual fetching of content from storage nodes by end users. Our formulation can be used to achieve a variety of performance goals and system behavior, including the bounding of fetch delay, load balancing, and robustness against node and arc failures. Simulation results suggest that our formulation performs significantly better than the traditional minimum k-median formulation for the delivery of multiple content, even under modest circumstances (small network, few objects, low storage budget, low dissemination costs)

Distributed Storage Allocation Problems

We investigate the problem of using several storage nodes to store a data object, subject to an aggregate storage budget or redundancy constraint. It is challenging to find the optimal allocation that maximizes the probability of successful recovery by the data collector because of the large space of possible symmetric and nonsymmetric allocations, and the nonconvexity of the problem. For the special case of probability-l recovery, we show that the optimal allocation that minimizes the required budget is symmetric. We further explore several storage allocation and access models, and determine the optimal symmetric allocation in the high-probability regime for a case of interest. Based on our experimental investigation, we make a general conjecture about a phase transition on the optimal allocation

Distributed Storage Allocations for Optimal Delay

We examine the problem of creating an encoded distributed storage representation of a data object for a network of mobile storage nodes so as to achieve the optimal recovery delay. A source node creates a single data object and disseminates an encoded representation of it to other nodes for storage, subject to a given total storage budget. A data collector node subsequently attempts to recover the original data object by contacting other nodes and accessing the data stored in them. By using an appropriate code, successful recovery is achieved when the total amount of data accessed is at least the size of the original data object. The goal is to find an allocation of the given budget over the nodes that optimizes the recovery delay incurred by the data collector; two objectives are considered: (i) maximization of the probability of successful recovery by a given deadline, and (ii) minimization of the expected recovery delay. We solve the problem completely for the second objective in the case of symmetric allocations (in which all nonempty nodes store the same amount of data), and show that the optimal symmetric allocation for the two objectives can be quite different. A simple data dissemination and storage protocol for a mobile delay-tolerant network is evaluated under various scenarios via simulations. Our results show that the choice of storage allocation can have a significant impact on the recovery delay performance, and that coding may or may not be beneficial depending on the circumstances.Comment: Extended version of an IEEE ISIT 2011 paper. 10 pages, 4 figure

VIP: A Framework for Joint Dynamic Forwarding and Caching in Named Data Networks

Emerging information-centric networking architectures seek to optimally utilize both bandwidth and storage for efficient content distribution. This highlights the need for joint design of traffic engineering and caching strategies, in order to optimize network performance in view of both current traffic loads and future traffic demands. We present a systematic framework for joint dynamic interest request forwarding and dynamic cache placement and eviction, within the context of the Named Data Networking (NDN) architecture. The framework employs a virtual control plane which operates on the user demand rate for data objects in the network, and an actual plane which handles Interest Packets and Data Packets. We develop distributed algorithms within the virtual plane to achieve network load balancing through dynamic forwarding and caching, thereby maximizing the user demand rate that the NDN network can satisfy. Numerical experiments within a number of network settings demonstrate the superior performance of the resulting algorithms for the actual plane in terms of low user delay and high rate of cache hits

Optometric Care of the Patient with Diabetes

The Canadian Association of Optometrists (CAO) is the national voice of optometry and is dedicated to collaboratively advancing the highest standard of primary eye care through the promotion of optimal vision and eye health, in partnership with all Canadians. Optometrists are the front line of eye health and vision care. They are experts in primary eye care and are well-positioned to help combat the vision related complications of diabetes. CAO assembled the Diabetes Guidelines Working Group to create national guidelines on the clinical management of diabetes mellitus in an effort to further educate Canadian optometrists and assist them in the management of this chronic disease. The Working Group consists of optometrists from private practice, research and academia, chosen on the basis of their expertise, experience and representation from across Canada