22 research outputs found
Energy-aware Graph Job Allocation in Software Defined Air-Ground Integrated Vehicular Networks
The software defined air-ground integrated vehicular (SD-AGV) networks have
emerged as a promising paradigm, which realize the flexible on-ground resource
sharing to support innovative applications for UAVs with heavy computational
overhead. In this paper, we investigate a vehicular cloud-assisted graph job
allocation problem in SD-AGV networks, where the computation-intensive jobs
carried by UAVs, and the vehicular cloud are modeled as graphs. To map each
component of the graph jobs to a feasible vehicle, while achieving the
trade-off among minimizing UAVs' job completion time, energy consumption, and
the data exchange cost among vehicles, we formulate the problem as a
mixed-integer non-linear programming problem, which is Np-hard. Moreover, the
constraint associated with preserving job structures poses addressing the
subgraph isomorphism problem, that further complicates the algorithm design.
Motivated by which, we propose an efficient decoupled approach by separating
the template (feasible mappings between components and vehicles) searching from
the transmission power allocation. For the former, we present an efficient
algorithm of searching for all the subgraph isomorphisms with low computation
complexity. For the latter, we introduce a power allocation algorithm by
applying convex optimization techniques. Extensive simulations demonstrate that
the proposed approach outperforms the benchmark methods considering various
problem sizes.Comment: 14 pages, 7 figure
Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets
We investigate a data quality-aware dynamic client selection problem for
multiple federated learning (FL) services in a wireless network, where each
client offers dynamic datasets for the simultaneous training of multiple FL
services, and each FL service demander has to pay for the clients under
constrained monetary budgets. The problem is formalized as a non-cooperative
Markov game over the training rounds. A multi-agent hybrid deep reinforcement
learning-based algorithm is proposed to optimize the joint client selection and
payment actions, while avoiding action conflicts. Simulation results indicate
that our proposed algorithm can significantly improve training performance.Comment: 6 pages,8 figure
Seamless Service Provisioning for Mobile Crowdsensing: Towards Integrating Forward and Spot Trading Markets
The challenge of exchanging and processing of big data over Mobile
Crowdsensing (MCS) networks calls for the new design of responsive and seamless
service provisioning as well as proper incentive mechanisms. Although
conventional onsite spot trading of resources based on real-time network
conditions and decisions can facilitate the data sharing over MCS networks, it
often suffers from prohibitively long service provisioning delays and
unavoidable trading failures due to its reliance on timely analysis of complex
and dynamic MCS environments. These limitations motivate us to investigate an
integrated forward and spot trading mechanism (iFAST), which entails a new
hybrid service trading protocol over the MCS network architecture. In iFAST,
the sellers (i.e., mobile users with sensing resources) can provide long-term
or temporary sensing services to the buyers (i.e., sensing task owners). iFast
enables signing long-term contracts in advance of future transactions through a
forward trading mode, via analyzing historical statistics of the market, for
which the notion of overbooking is introduced and promoted. iFAST further
enables the buyers with unsatisfying service quality to recruit temporary
sellers through a spot trading mode, upon considering the current
market/network conditions. We analyze the fundamental blocks of iFAST, and
provide a case study to demonstrate its superior performance as compared to
existing methods. Finally, future research directions on reliable service
provisioning for next-generation MCS networks are summarized
Matching-based Hybrid Service Trading for Task Assignment over Dynamic Mobile Crowdsensing Networks
By opportunistically engaging mobile users (workers), mobile crowdsensing
(MCS) networks have emerged as important approach to facilitate sharing of
sensed/gathered data of heterogeneous mobile devices. To assign tasks among
workers and ensure low overheads, a series of stable matching mechanisms is
introduced in this paper, which are integrated into a novel hybrid service
trading paradigm consisting of futures trading mode and spot trading mode to
ensure seamless MCS service provisioning. In the futures trading mode, we
determine a set of long-term workers for each task through an
overbooking-enabled in-advance many-to-many matching (OIA3M) mechanism, while
characterizing the associated risks under statistical analysis. In the spot
trading mode, we investigate the impact of fluctuations in long-term workers'
resources on the violation of service quality requirements of tasks, and
formalize a spot trading mode for tasks with violated service quality
requirements under practical budget constraints, where the task-worker mapping
is carried out via onsite many-to-many matching (O3M) and onsite many-to-one
matching (OMOM). We theoretically show that our proposed matching mechanisms
satisfy stability, individual rationality, fairness and computational
efficiency. Comprehensive evaluations also verify the satisfaction of these
properties under practical network settings, while revealing commendable
performance on running time, participators' interactions, and service quality
DISCO: Achieving Low Latency and High Reliability in Scheduling of Graph-Structured Tasks over Mobile Vehicular Cloud
To effectively process data across a fleet of dynamic and distributed
vehicles, it is crucial to implement resource provisioning techniques that
provide reliable, cost-effective, and real-time computing services. This
article explores resource provisioning for computation-intensive tasks over
mobile vehicular clouds (MVCs). We use undirected weighted graphs (UWGs) to
model both the execution of tasks and communication patterns among vehicles in
a MVC. We then study low-latency and reliable scheduling of UWG asks through a
novel methodology named double-plan-promoted isomorphic subgraph search and
optimization (DISCO). In DISCO, two complementary plans are envisioned to
ensure effective task completion: Plan A and Plan B.Plan A analyzes the past
data to create an optimal mapping () between tasks and the MVC in
advance to the practical task scheduling. Plan B serves as a dependable backup,
designed to find a feasible mapping () in case fails during
task scheduling due to unpredictable nature of the network.We delve into into
DISCO's procedure and key factors that contribute to its success. Additionally,
we provide a case study that includes comprehensive comparisons to demonstrate
DISCO's exceptional performance in regards to time efficiency and overhead. We
further discuss a series of open directions for future research