33,928 research outputs found
Multiple Linear Regression-Based Energy-Aware Resource Allocation in the Fog Computing Environment
Fog computing is a promising computing paradigm for time-sensitive Internet
of Things (IoT) applications. It helps to process data close to the users, in
order to deliver faster processing outcomes than the Cloud; it also helps to
reduce network traffic. The computation environment in the Fog computing is
highly dynamic and most of the Fog devices are battery powered hence the
chances of application failure is high which leads to delaying the application
outcome. On the other hand, if we rerun the application in other devices after
the failure it will not comply with time-sensitiveness. To solve this problem,
we need to run applications in an energy-efficient manner which is a
challenging task due to the dynamic nature of Fog computing environment. It is
required to schedule application in such a way that the application should not
fail due to the unavailability of energy. In this paper, we propose a multiple
linear, regression-based resource allocation mechanism to run applications in
an energy-aware manner in the Fog computing environment to minimise failures
due to energy constraint. Prior works lack of energy-aware application
execution considering dynamism of Fog environment. Hence, we propose A multiple
linear regression-based approach which can achieve such objectives. We present
a sustainable energy-aware framework and algorithm which execute applications
in Fog environment in an energy-aware manner. The trade-off between
energy-efficient allocation and application execution time has been
investigated and shown to have a minimum negative impact on the system for
energy-aware allocation. We compared our proposed method with existing
approaches. Our proposed approach minimises the delay and processing by 20%,
and 17% compared with the existing one. Furthermore, SLA violation decrease by
57% for the proposed energy-aware allocation.Comment: 8 Pages, 9 Figure
Adaptive Dispatching of Tasks in the Cloud
The increasingly wide application of Cloud Computing enables the
consolidation of tens of thousands of applications in shared infrastructures.
Thus, meeting the quality of service requirements of so many diverse
applications in such shared resource environments has become a real challenge,
especially since the characteristics and workload of applications differ widely
and may change over time. This paper presents an experimental system that can
exploit a variety of online quality of service aware adaptive task allocation
schemes, and three such schemes are designed and compared. These are a
measurement driven algorithm that uses reinforcement learning, secondly a
"sensible" allocation algorithm that assigns jobs to sub-systems that are
observed to provide a lower response time, and then an algorithm that splits
the job arrival stream into sub-streams at rates computed from the hosts'
processing capabilities. All of these schemes are compared via measurements
among themselves and with a simple round-robin scheduler, on two experimental
test-beds with homogeneous and heterogeneous hosts having different processing
capacities.Comment: 10 pages, 9 figure
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
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