15,788 research outputs found
Multi-Task Offloading via Graph Neural Networks in Heterogeneous Multi-access Edge Computing
In the rapidly evolving field of Heterogeneous Multi-access Edge Computing
(HMEC), efficient task offloading plays a pivotal role in optimizing system
throughput and resource utilization. However, existing task offloading methods
often fall short of adequately modeling the dependency topology relationships
between offloaded tasks, which limits their effectiveness in capturing the
complex interdependencies of task features. To address this limitation, we
propose a task offloading mechanism based on Graph Neural Networks (GNN). Our
modeling approach takes into account factors such as task characteristics,
network conditions, and available resources at the edge, and embeds these
captured features into the graph structure. By utilizing GNNs, our mechanism
can capture and analyze the intricate relationships between task features,
enabling a more comprehensive understanding of the underlying dependency
topology. Through extensive evaluations in heterogeneous networks, our proposed
algorithm improves 18.6\%-53.8\% over greedy and approximate algorithms in
optimizing system throughput and resource utilization. Our experiments showcase
the advantage of considering the intricate interplay of task features using
GNN-based modeling
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics
Autonomous robotic systems, like autonomous vehicles and robotic search and
rescue, require efficient on-device training for continuous adaptation of Deep
Reinforcement Learning (DRL) models in dynamic environments. This research is
fundamentally motivated by the need to understand and address the challenges of
on-device real-time DRL, which involves balancing timing and algorithm
performance under memory constraints, as exposed through our extensive
empirical studies. This intricate balance requires co-optimizing two pivotal
parameters of DRL training -- batch size and replay buffer size. Configuring
these parameters significantly affects timing and algorithm performance, while
both (unfortunately) require substantial memory allocation to achieve
near-optimal performance.
This paper presents R^3, a holistic solution for managing timing, memory, and
algorithm performance in on-device real-time DRL training. R^3 employs (i) a
deadline-driven feedback loop with dynamic batch sizing for optimizing timing,
(ii) efficient memory management to reduce memory footprint and allow larger
replay buffer sizes, and (iii) a runtime coordinator guided by heuristic
analysis and a runtime profiler for dynamically adjusting memory resource
reservations. These components collaboratively tackle the trade-offs in
on-device DRL training, improving timing and algorithm performance while
minimizing the risk of out-of-memory (OOM) errors.
We implemented and evaluated R^3 extensively across various DRL frameworks
and benchmarks on three hardware platforms commonly adopted by autonomous
robotic systems. Additionally, we integrate R^3 with a popular realistic
autonomous car simulator to demonstrate its real-world applicability.
Evaluation results show that R^3 achieves efficacy across diverse platforms,
ensuring consistent latency performance and timing predictability with minimal
overhead.Comment: Accepted by RTSS 202
Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud
With the advent of cloud computing, organizations are nowadays able to react
rapidly to changing demands for computational resources. Not only individual
applications can be hosted on virtual cloud infrastructures, but also complete
business processes. This allows the realization of so-called elastic processes,
i.e., processes which are carried out using elastic cloud resources. Despite
the manifold benefits of elastic processes, there is still a lack of solutions
supporting them.
In this paper, we identify the state of the art of elastic Business Process
Management with a focus on infrastructural challenges. We conceptualize an
architecture for an elastic Business Process Management System and discuss
existing work on scheduling, resource allocation, monitoring, decentralized
coordination, and state management for elastic processes. Furthermore, we
present two representative elastic Business Process Management Systems which
are intended to counter these challenges. Based on our findings, we identify
open issues and outline possible research directions for the realization of
elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and
P. Hoenisch (2015). Elastic Business Process Management: State of the Art and
Open Challenges for BPM in the Cloud. Future Generation Computer Systems,
Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00
Congestion control in multi-serviced heterogeneous wireless networks using dynamic pricing
Includes bibliographical references.Service providers, (or operators) employ pricing schemes to help provide desired QoS to subscribers and to maintain profitability among competitors. An economically efficient pricing scheme, which will seamlessly integrate usersâ preferences as well as service providersâ preferences, is therefore needed. Else, pricing schemes can be viewed as promoting social unfairness in the dynamically priced network. However, earlier investigations have shown that the existing dynamic pricing schemes do not consider the usersâ willingness to pay (WTP) before the price of services is determined. WTP is the amount a user is willing to pay based on the worth attached to the service requested. There are different WTP levels for different subscribers due to the differences in the value attached to the services requested and demographics. This research has addressed congestion control in the heterogeneous wireless network (HWN) by developing a dynamic pricing scheme that efficiently incentivises users to utilize radio resources. The proposed Collaborative Dynamic Pricing Scheme (CDPS), which identifies the users and operatorsâ preference in determining the price of services, uses an intelligent approach for controlling congestion and enhancing both the usersâ and operatorsâ utility. Thus, the CDPS addresses the congestion problem by firstly obtaining the users WTP from usersâ historical response to price changes and incorporating the WTP factor to evaluate the service price. Secondly, it uses a reinforcement learning technique to illustrate how a price policy can be obtained for the enhancement of both users and operatorsâ utility, as total utility reward obtained increases towards a defined âgoal stateâ
Resource Allocation in Networking and Computing Systems: A Security and Dependability Perspective
In recent years, there has been a trend to integrate networking and computing systems, whose management is getting increasingly complex. Resource allocation is one of the crucial aspects of managing such systems and is affected by this increased complexity. Resource allocation strategies aim to effectively maximize performance, system utilization, and profit by considering virtualization technologies, heterogeneous resources, context awareness, and other features. In such complex scenario, security and dependability are vital concerns that need to be considered in future computing and networking systems in order to provide the future advanced services, such as mission-critical applications. This paper provides a comprehensive survey of existing literature that considers security and dependability for resource allocation in computing and networking systems. The current research works are categorized by considering the allocated type of resources for different technologies, scenarios, issues, attributes, and solutions. The paper presents the research works on resource allocation that includes security and dependability, both singularly and jointly. The future research directions on resource allocation are also discussed. The paper shows how there are only a few works that, even singularly, consider security and dependability in resource allocation in the future computing and networking systems and highlights the importance of jointly considering security and dependability and the need for intelligent, adaptive and robust solutions. This paper aims to help the researchers effectively consider security and dependability in future networking and computing systems.publishedVersio
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Electricity Network Investment and Regulation for a Low Carbon Future
The requirement for significantly higher electricity network investment in the UK seems certain as the capacity of distributed generation and large scale renewables increases on the system. In this paper, which forms a chapter in the forthcoming Book âDelivering a Low Carbon Electricity System: Technologies, Economics and Policyâ, the authors make a number of significant suggestions for improvement to the current system of network regulation. First, they suggest that the RPI-X system needs to be overhauled in favour of a simpler yardstick based system and which allows for more merchant transmission investments. Second, future regulation should involve more negotiated regulation involving agreements between network owners and purchasers of network services. This would be particularly advantageous for decisions on new network investments. Third, more extensive use needs to be made of locational pricing within the transmission and distribution system in order to facilitate the least cost expansion of low carbon generation, including micropower. Fourth, consideration needs to be given to ownership unbundling of distribution networks from retail supply. This would better facilitate the entry of distributed generation and the development of appropriate competition between grid and off-grid generation supply and demand side management. Finally, there needs to be a significant increase in R&D expenditure in electricity networks supported by customer levies
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