Energy-Aware Task Allocation for Multi-Cloud Networks

Mishra, Sambit Kumar; Mishra, Sonali; Alsayat, Ahmed; Jhanjhi, N. Z.; Humayun, Mamoona; Sahoo, Kshira Sagar; Luhach, Ashish Kr.

doi:10.1109/access.2020.3026875

Cited by 64 publications

(18 citation statements)

References 31 publications

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“…There exist several work that addressed different constraints in task allocation such as resource constraints [32], [33], time and spatial constraints [34]- [36], and envy minimization [37]. Previous attempts at addressing communication constraints in multiagent algorithms can be classified into two categories.…”

Section: Related Workmentioning

confidence: 99%

Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments

2022

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This paper presents a deep reinforcement learning framework in which agents learn how to schedule and censor themselves amongst the other agents competing for access to a limited communication medium. In particular, the process learns to schedule the communication between agents to improve the performance of task allocation in environments with constrained communication in terms of limited bandwidth and message collision. The proposed approach, called Communication-Aware Consensus-Based Bundle Algorithm (CA-CBBA), extends the previous CBBA [1] in that the learned communication policy enables efficient utilization of the shared medium by prioritizing agents with messages that are important for the mission. Furthermore, agents in denser parts of the network are censored appropriately to alleviate the message collision and hidden node problems. We evaluate our approach in various task assignment scenarios, and the results show that CA-CBBA outperforms CBBA in terms of convergence time, rate of conflict resolution, and task allocation reward. Moreover, we show that CA-CBBA yields a policy that generalizes beyond the training set to handle larger team sizes. Finally, the results on time-critical problems, such as a search-and-rescue mission, show that CA-CBBA also outperforms the baselines considered (e.g., CBBA, MCDGA [2], and ACBBA [3]) in terms of number of unassigned and conflicted tasks in most of the scenarios.

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Section: Related Workmentioning

confidence: 99%

Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments

2022

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“…CC is a concept that allows for the instantaneous leasing of computer resources with little or no communication with the provider. Cloud simplifies operations in this sense since it eliminates the need for rigorous resource dimensioning and planning, allowing for flexible usage without the user's previous commitment [47,48]. Cloud users benefit from almost all the resources they need, and they may either utilize or supply everything as a service.…”

Section: B Cloud Computing and Ir 50mentioning

confidence: 99%

Industrial Revolution 5.0 and the Role of Cutting Edge Technologies

Humayun¹

2021

IJACSA

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IR 4.0 emphasizes the interconnection of machines and systems to achieve optimal performance and productivity gains. IR 5.0 is said to take it a step further by fine-tuning the human-machine connection. IR 5.0 is more collaboration between the two: automated technology's ultra-fast accuracy combines with a human's intelligence and creativity. The driving force behind IR 5.0 is customer demand for customization and personalization, necessitating a greater human involvement in the production process. As IR 5.0 evolves, we may expect to see a slew of breakthroughs across various industries. However, just automating jobs or digitizing processes will not be enough; the finest and most successful businesses will be those that can combine the dual powers of technology and human ingenuity. IR 5.0 focuses on the use of modern cutting-edge technologies, namely, AI, IoT, big data, cloud computing, Blockchain, Digital twins, edge computing, collaborative robots, and 6G along with leveraging human creativity and intelligence. Wherever possible, IR 5.0 will change industrial processes worldwide by removing mundane, filthy, and repetitive activities from human workers. Intelligent robotics and systems will have unparalleled access to industrial supply networks and production floors. However, to understand and leverage the benefits of IR 5.0 better, there is a need to understand the role of modern CET in industrial revolution 5.0. To fill this gap, this article will examine IR 5.0 prospective, uses, supporting technologies, opportunities, and issues involved that need to be understood for leveraging the potentials of IR 5.0.

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“…Therefore, many energy-saving areas are being explored. Following the development of edge computing, cloud computing, and 5G networks, the most recent focus includes migration methods and scheduling certain tasks from local devices to remote cloud processing [38,39], energy management systems focused on security [40], task allocation algorithms in multi-cloud networks [41], and energy-aware video streaming for mobile devices [42,43]. Increasingly more works are also related to energy saving in the context of Internet of Things networks, e.g., [44][45][46].…”

Section: Related Workmentioning

confidence: 99%

Fluid-Flow Approximation in the Analysis of Vast Energy-Aware Networks

Nycz

Czachórski

2021

Mathematics

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The paper addresses two issues: (i) modeling dynamic flows transmitted in vast TCP/IP networks and (ii) modeling the impact of energy-saving algorithms. The approach is based on the fluid-flow approximation, which applies first-order differential equations to analyze the evolution of queues and flows. We demonstrate that the effective implementation of this method overcomes the constraints of storing large data in numerical solutions of transient problems in vast network topologies. The model is implemented and executed directly in a database system. It can analyze transient states in topologies of more than 100,000 nodes, i.e., the size which was not considered until now. We use it to investigate the impact of an energy-saving algorithm on the performance of a vast network. We find that it reduces network congestion and save energy costs but significantly lower network throughput.

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Energy-Aware Task Allocation for Multi-Cloud Networks

Cited by 64 publications

References 31 publications

Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments

Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments

Industrial Revolution 5.0 and the Role of Cutting Edge Technologies

Fluid-Flow Approximation in the Analysis of Vast Energy-Aware Networks

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