Mobile and IoT devices are becoming increasingly capable computing platforms that are often underutilized. In this paper, we propose RAMOS, a system that leverages the idle compute cycles in a group of heterogeneous mobile and IoT devices that can be clustered to form an edge FemtoCloud. At the heart of this system, we formulate a multi-objective, resource-aware task assignment and scheduling problem. The scheduler runs in two main modes; latency-minimization and energy-efficiency. Under the latency-minimization mode, it strives to maximize the computational throughput of the constructed FemtoCloud while maintaining the energy consumption below an operator specified threshold. Under the energy-efficient mode, it minimizes the total energy consumed in the FemtoCloud while meeting defined tasks deadlines. Due to the NP-Completeness of this scheduling problem, we design a set of heuristics to solve it. We implement a prototype of our system and use it to evaluate its performance and efficiency. Our results demonstrate the system's ability to meet different scheduling objectives while adhering to pre-specified time and energy constraints. Compared to other schedulers, RAMOS achieves 10 to 40 percent completion time improvement under latency minimization mode and up to 30 percent more energy-efficiency under the energy-efficient mode.

Mobile devices are becoming increasingly capable computing platforms with significant processor power and memory. However, mobile compute capabilities are often underutilized. In this paper we consider how a collection of co-located devices can be orchestrated to provide a cloud service at the edge. 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. To this end, we propose the femtocloud system which provides a dynamic, self-configuring and multi-device mobile cloud out of a cluster of mobile devices. We present the femtocloud system architecture designed to enable multiple mobile devices to be configured into a coordinated cloud computing service despite churn in mobile device participation. We develop a prototype of our femtocloud system and use it in addition to simulations to evaluate the performance of the system showing its efficiency and ability to leverage the available devices' compute capacity. We contribute to a line of research on small, local and possibly private clouds.

With the advent of wearable computing and the resulting growth in mobile application market, we investigate mobile opportunistic cloud computing where mobile devices leverage nearby computational resources in order to save execution time and consumed energy. Our goal is to enable generic computation offloading to heterogeneous devices that include Cloud, mobile devices, and cloudlets. We propose a generic and flexible architecture that maximizes the computation gain with respect to various objective functions such as, minimizing the response time, reducing the overall energy consumption, and increasing the network lifetime. This novel architecture is designed to automate computation offloading to numerous compute resources over disrupted network connections.

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 allow researchers to develop solutions for those challenges. Such solutions exploit available interfaces on these devices in both local and collaborative forms. These solutions, however, have faced a formidable deployment barrier. Therefore, in this paper, we present OSCAR, a multi-objective, incentive-based, collaborative, and deployable bandwidth aggregation system, designed to exploit multiple network interfaces on modern mobile devices. Oscar's architecture does not introduce any intermediate hardware nor require changes to current applications or legacy servers. This architecture estimates the interfaces characteristics and application requirements, schedules various connections and/or packets to different interfaces, and provides users with incentives for collaboration and bandwidth sharing. We formulate the OSCAR scheduler as a multi-objective scheduler that maximizes system throughput while achieving user-defined efficiency goals for both cost and energy consumption. We implement a small scale prototype of our OSCAR system, which we use to evaluate its performance. Our evaluation shows that we provide up to 150% enhancement in the throughput compared to current operating systems with only minor updates to the client devices.

There is great potential for boosting the performance of mobile devices by offloading computation-intensive parts of mobile applications to the cloud. The full realization of this potential is hindered by a mismatch between how individual mobile devices demand computing resources and how cloud providers offer them: offloading requests from a mobile device usually require quick response, may be infrequent, and are subject to variable network connectivity, whereas cloud resources incur relatively long setup times, are leased for long time quanta, and are indifferent to network connectivity. In this paper, we present the design and implementation of the COSMOS system, which bridges this gap by providing computation offloading as a service to mobile devices. COSMOS efficiently manages cloud resources for offloading requests to both improve offloading performance seen by mobile devices and reduce the monetary cost per request to the provider. COSMOS also effectively allocates and schedules offloading requests to resolve the contention for cloud resources. Moreover, COSMOS makes offloading decisions in a risk-controlled manner to overcome the uncertainties caused by variable network connectivity and program execution. We have implemented COSMOS for Android and explored its design space through computation offloading experiments to Amazon EC2 across different applications and in various settings. We find that COSMOS, configured with the right design choices, has significant potential in reducing the cost of providing cloud resources to mobile devices while at the same time enabling mobile computation speedup.

The exponential increase in mobile data demand coupled with the rapid deployment of various wireless access technologies have led to the proliferation of multi-interface enabled devices. As a result, researchers focused on exploiting the available interfaces on such devices in both solitary and coll

Current routing protocols for cognitive radio networks are severely affected by the frequent activity of primary users. Nodes that are in the interference range of an appearing primary user are not allowed to transmit, and therefore existing routes which utilize such nodes are obliged to undergo a route maintenance phase. This naturally provides other routes to the destination that may incur extra delay or increase packet queuing overhead. In this work, a novel route maintenance protocol is proposed that allows existing routes to endure the event of primary user presence by forming cooperative links between neighboring nodes and nulling out transmission at the primary receiver using cooperative beamforming. Our proposed protocol can be used in conjunction with any of the existing routing protocols, thus achieving modularity. Extensive simulations are done which prove that our proposed protocol outperforms existing route maintenance techniques in terms of end-to-end delay and loss ratio, with minimal incurred overhead.

The widespread deployment of varying networking technologies, coupled with the exponential increase in end-user data demand, have led to the proliferation of multi-homed or multi-interface enabled devices. To date, these interfaces are mainly utilized one at a time based on network availability, cost, and user-choice. While researchers have focused on simultaneously leveraging these interfaces by aggregating their bandwidths, these solutions however, have faced a steep deployment barrier. In this demo, we demonstrate the OPERETTA optimal bandwidth aggregation system. OPERETTA aims to utilize all the available network interfaces on a mobile device by distributing the users traffic on them. It is implemented as a Layered Service Provider (LSP) in the Windows OS to intercept the network connections from the applications and schedule these connections on the most appropriate interfaces. The network interfaces are chosen with the goal to optimize the overall system power consumption while meeting a certain throughput constraint calculated based on a user defined utility. The demo shows the effect of applying different scheduling algorithms and user utility functions on the system throughput and energy consumption.

The widespread deployment of varying networking technologies, coupled with the exponential increase in end-user data demand, have led to the proliferation of multi-homed or multi-interface enabled devices. To date, these interfaces are mainly utilized one at a time based on network availability, cost, and user-choice. While researchers have focused on simultaneously leveraging these interfaces by aggregating their bandwidths, these solutions however, have faced a steep deployment barrier. In this paper, we propose a novel optimal, energy-efficient, and deployable bandwidth aggregation system (OPERETTA) for multiple interface enabled devices. OPERETTA satisfies three goals: achieving a user defined throughput level with optimal energy consumption over multiple interfaces, deployability without changes to current legacy servers, and leveraging incremental deployment to achieve increased performance gains. We present the OPERETTA architecture and formulate the optimal scheduling problem as a mixed integer programming problem yielding an efficient solution. We evaluate OPERETTA via implementation on the the Windows OS, and further verify our results with simulations on NS2. Our evaluation shows the tradeoffs between the energy and throughput goals. Furthermore, with no modifications to current legacy servers, OPERETTA achieves throughput gains up to 150% compared to current operating systems with the same energy consumption. In addition, with as few as 25% of the servers becoming OPERETTA enabled, OPERETTA performance reaches the throughput upper bound, highlighting its incremental deployment and performance gains.

The widespread deployment of varying networking technologies, coupled with the exponential increase in end-user data demand, have all led to the proliferation of multi-homed or multi-interface enabled devices. To date, these interfaces are mainly utilized one at a time based on network availability, cost, and user-choice. Researchers have recently focused on leveraging these interfaces simultaneously by proposing solutions to aggregate their bandwidths in order to ultimately increase throughput and satisfy the end-user's growing demand on data. These solutions, however, have faced a steep deployment barrier due to various system design choices and heavy demand on energy. In this paper, we propose a novel Green and Deployable Bandwidth Aggregation System (G-DBAS) for multiple interface enabled devices. G-DBAS addresses a set of challenges including automatically estimating the characteristics of applications and scheduling various connections to different interfaces along with meeting different energy consumption goals set by users. We fully implement G-DBAS on the Windows OS and evaluate various scheduling strategies that we propose. Our implementation and simulation results show that G-DBAS can achieve the user energy-throughput goals while operating as an out-of-the-box standard Windows executable, highlighting its deployability and ease of use.