12,280 research outputs found
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
Budget-constrained Edge Service Provisioning with Demand Estimation via Bandit Learning
Shared edge computing platforms, which enable Application Service Providers
(ASPs) to deploy applications in close proximity to mobile users are providing
ultra-low latency and location-awareness to a rich portfolio of services.
Though ubiquitous edge service provisioning, i.e., deploying the application at
all possible edge sites, is always preferable, it is impractical due to often
limited operational budget of ASPs. In this case, an ASP has to cautiously
decide where to deploy the edge service and how much budget it is willing to
use. A central issue here is that the service demand received by each edge
site, which is the key factor of deploying benefit, is unknown to ASPs a
priori. What's more complicated is that this demand pattern varies temporally
and spatially across geographically distributed edge sites. In this paper, we
investigate an edge resource rental problem where the ASP learns service demand
patterns for individual edge sites while renting computation resource at these
sites to host its applications for edge service provisioning. An online
algorithm, called Context-aware Online Edge Resource Rental (COERR), is
proposed based on the framework of Contextual Combinatorial Multi-armed Bandit
(CC-MAB). COERR observes side-information (context) to learn the demand
patterns of edge sites and decides rental decisions (including where to rent
the computation resource and how much to rent) to maximize ASP's utility given
a limited budget. COERR provides a provable performance achieving sublinear
regret compared to an Oracle algorithm that knows exactly the expected service
demand of edge sites. Experiments are carried out on a real-world dataset and
the results show that COERR significantly outperforms other benchmarks
Profitable Task Allocation in Mobile Cloud Computing
We propose a game theoretic framework for task allocation in mobile cloud
computing that corresponds to offloading of compute tasks to a group of nearby
mobile devices. Specifically, in our framework, a distributor node holds a
multidimensional auction for allocating the tasks of a job among nearby mobile
nodes based on their computational capabilities and also the cost of
computation at these nodes, with the goal of reducing the overall job
completion time. Our proposed auction also has the desired incentive
compatibility property that ensures that mobile devices truthfully reveal their
capabilities and costs and that those devices benefit from the task allocation.
To deal with node mobility, we perform multiple auctions over adaptive time
intervals. We develop a heuristic approach to dynamically find the best time
intervals between auctions to minimize unnecessary auctions and the
accompanying overheads. We evaluate our framework and methods using both real
world and synthetic mobility traces. Our evaluation results show that our game
theoretic framework improves the job completion time by a factor of 2-5 in
comparison to the time taken for executing the job locally, while minimizing
the number of auctions and the accompanying overheads. Our approach is also
profitable for the nearby nodes that execute the distributor's tasks with these
nodes receiving a compensation higher than their actual costs
- …