OSCAR: A Collaborative Bandwidth Aggregation System

The exponential increase in mobile data demand, coupled with growing user expectation to be connected in all places at all times, have introduced novel challenges for researchers to address. Fortunately, the wide spread deployment of various network technologies and the increased adoption of multi-interface enabled devices have enabled researchers to develop solutions for those challenges. Such solutions aim to exploit available interfaces on such devices in both solitary and collaborative forms. These solutions, however, have faced a steep deployment barrier. In this paper, we present OSCAR, a multi-objective, incentive-based, collaborative, and deployable bandwidth aggregation system. We present the OSCAR architecture that does not introduce any intermediate hardware nor require changes to current applications or legacy servers. The OSCAR architecture is designed to automatically estimate the system's context, dynamically schedule various connections and/or packets to different interfaces, be backwards compatible with the current Internet architecture, and provide the user with incentives for collaboration. We also formulate the OSCAR scheduler as a multi-objective, multi-modal scheduler that maximizes system throughput while minimizing energy consumption or financial cost. We evaluate OSCAR via implementation on Linux, as well as via simulation, and compare our results to the current optimal achievable throughput, cost, and energy consumption. Our evaluation shows that, in the throughput maximization mode, we provide up to 150% enhancement in throughput compared to current operating systems, without any changes to legacy servers. Moreover, this performance gain further increases with the availability of connection resume-supporting, or OSCAR-enabled servers, reaching the maximum achievable upper-bound throughput

POEM: Pricing Longer for Edge Computing in the Device Cloud

Multiple access mobile edge computing has been proposed as a promising technology to bring computation services close to end users, by making good use of edge cloud servers. In mobile device clouds (MDC), idle end devices may act as edge servers to offer computation services for busy end devices. Most existing auction based incentive mechanisms in MDC focus on only one round auction without considering the time correlation. Moreover, although existing single round auctions can also be used for multiple times, users should trade with higher bids to get more resources in the cascading rounds of auctions, then their budgets will run out too early to participate in the next auction, leading to auction failures and the whole benefit may suffer. In this paper, we formulate the computation offloading problem as a social welfare optimization problem with given budgets of mobile devices, and consider pricing longer of mobile devices. This problem is a multiple-choice multi-dimensional 0-1 knapsack problem, which is a NP-hard problem. We propose an auction framework named MAFL for long-term benefits that runs a single round resource auction in each round. Extensive simulation results show that the proposed auction mechanism outperforms the single round by about 55.6% on the revenue on average and MAFL outperforms existing double auction by about 68.6% in terms of the revenue.Comment: 8 pages, 1 figure, Accepted by the 18th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP

Mobile device clusters as edge compute resources: Design, deployment, and role in the computing ecosystem

Edge computing offers an alternative to centralized, in-the-cloud compute services. Among the potential advantages of edge-computing are lower latency that improves responsiveness, reduced wide-area network congestion, and possibly greater privacy by keeping data more local. However, widely deploying the needed edge-compute resources requires (1) provisioning the load introduced at various locations, (2) huge initial deployment cost and management expenses, and (3) continuous upgrades to keep up with the increase in demand. The availability of under-utilized mobile and personal computing devices at the edge provides a potential solution to these deployment challenges. In this thesis, we propose taking advantage of clusters of co-located mobile devices to offer an edge computing platform. Scenarios with co-located devices include, but are not limited to, passengers with mobile devices using public transit services, students in classrooms and groups of people sitting in a coffee shop. We propose, design, implement and evaluate the Femtocloud system which provides a dynamic, self-configuring and multi-device mobile cloud out of a cluster of mobile devices. Within the Femtocloud system, we develop a variety of adaptive mechanisms and algorithms to manage the workload on the edge-resources and effectively mask their churn. These mechanisms enable building a reliable and efficient edge computing service on top of unreliable, voluntary resources. Our work also includes building a system that enable mobile devices to accurately and efficiently acquire knowledge of the existing compute service providers, their compute capacities, and the network parameters while communicating with each of these providers. Such data is acquired through measurements that involve a set of voluntary mobile devices and is be used to allow allow mobile devices to select the compute service provider that matches their demand and meets their target level of quality of experience. The data acquired by our system can also be used by compute service providers to identify potential locations for service deployment and discover any shortcomings in their existing deployments.Ph.D

Facade: High-Throughput, Deniable Censorship Circumvention Using Web Search

Abstract Censorship circumvention systems that use HTTP as cover traffic make tradeoffs between deniability and performance by offering either deniability at the expense of performance (e.g., Infranet) or performance at the expense of deniability (e.g., StegoTorus). These systems do so because HTTP is typically very asymmetric, with very little capacity to carry covert data in each HTTP GET request; higher throughput channels achieve performance by generating sequences of HTTP GET requests that do not mimic normal user traffic patterns. Fortunately, the emergence of new web services makes it increasingly common for any individual HTTP GET requests to contain more entropy. For example, site-specific search services create GET requests that contain sequences of search terms that can encode more bits than a single deniable HTTP request otherwise would. In this paper, we design a new encoding technique that uses web search terms to encode hidden messages in an upstream channel for censorship circumvention; implement the encoding technique in a system that resists fingerprinting attacks; and compare the security and performance of Facade to existing censorship circumvention systems that use HTTP as cover traffic