Autonomous Workload Balancing in Cloud Federation Environments with Different Access Restrictions

Amjad, Anas; Sharma, Mak; Abozariba, Raouf; Asaduzzaman, Md; Benkhelifa, Elhadj; Patwary, Mohammad

doi:10.1109/mass.2017.68

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…Using deep learning, the learned model can prevent exhaustive computations to find the best solution. Anas et al [24] took computational utilization and access probability into consideration and developed a performance model based on queuing theory to address the workload balancing between service providers within a federated cloud environment.…”

Section: Related Workmentioning

confidence: 99%

Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

Arif

Ajesh

Shamsudheen

et al. 2022

Security and Communication Networks

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The use of application media, gamming, entertainment, and healthcare engineering has expanded as a result of the rapid growth of mobile technologies. This technology overcomes the traditional computing methods in terms of communication delay and energy consumption, thereby providing high reliability and bandwidth for devices. In today’s world, mobile edge computing is improving in various forms so as to provide better output and there is no room for simple computing architecture for MEC. So, this paper proposed a secure and energy-efficient computational offloading scheme using LSTM. The prediction of the computational tasks is done using the LSTM algorithm, the strategy for computation offloading of mobile devices is based on the prediction of tasks, and the migration of tasks for the scheme of edge cloud scheduling helps to optimize the edge computing offloading model. Experiments show that our proposed architecture, which consists of an LSTM-based offloading technique and routing (LSTMOTR) algorithm, can efficiently decrease total task delay with growing data and subtasks, reduce energy consumption, and bring much security to the devices due to the firewall nature of LSTM.

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Section: Related Workmentioning

confidence: 99%

Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

Arif

Ajesh

Shamsudheen

et al. 2022

Security and Communication Networks

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“…It schedules the execution location of tasks based on computational, storage or network resources. Anas et al [20] take computational utilization and access probability into consideration, and develop a performance model based on queuing theory to address the workload balancing between service providers within a federated cloud environment. Ma et al [21] consider cooperation among edge nodes and investigate the workload scheduling with the objective of minimizing the service response time as well as the outsourcing traffic in mobile edge computing.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-Based Workload Scheduling for Edge Computing

Zheng¹,

Wan

Zhang

et al. 2021

Preprint

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Edge computing is a new paradigm for providing cloud computing capacities at the edge of network near mobile users. It offers an effective solution to help mobile devices with computation-intensive and delay-sensitive tasks. However, the edge of network presents a dynamic environment with large number of devices, high mobility of the end user, heterogeneous applications and intermittent traffific. In such environment, edge computing always encounters workload scheduling problem of how to effificiently schedule incoming tasks from mobile devices to edge servers or cloud servers, which is a hard and online problem. In this work, we focus on the workload scheduling problem with the goal of balancing the workload, reducing the service time and minimizing the failed task rate. We proposed a reinforcement learning-based approach, which can learn from the previous actions and achieve best scheduling in the absence of a mathematical model of the environment. Simulation results show that our proposed approach achieves the best performance in aspects of service time, virtual machine utilization, and failed tasks rate compared with other approaches. Our reinforcement learning-based approach can provide an effificient solution to the workload scheduling problem in edge computing.

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“…(3) Load balancing: it is usual to have more than one provider to process the user request in the federated cloud environment. In such situations, the strategy needed to allocate the user request equally between CSPs using load balancing methods becomes complicated for sharing the workload transparently [97].…”

Section: Challenges In Cloud Federationmentioning

confidence: 99%

A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

Molo

Badejo

Adetiba

et al. 2021

Applied Computational Intelligence and Soft Computing

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Cloud computing is a technology that allows dynamic and flexible computing capability and storage through on-demand delivery and pay-as-you-go services over the Internet. This technology has brought significant advances in the Information Technology (IT) domain. In the last few years, the evolution of cloud computing has led to the development of new technologies such as cloud federation, edge computing, and fog computing. However, with the development of Internet of Things (IoT), several challenges have emerged with these new technologies. Therefore, this paper discusses each of the emerging cloud-based technologies, as well as their architectures, opportunities, and challenges. We present how cloud computing evolved from one paradigm to another through the interplay of benefits such as improvement in computational resources through the combination of the strengths of various Cloud Service Providers (CSPs), decrease in latency, improvement in bandwidth, and so on. Furthermore, the paper highlights the application of different cloud paradigms in the healthcare ecosystem.

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Autonomous Workload Balancing in Cloud Federation Environments with Different Access Restrictions

Cited by 5 publications

References 19 publications

Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

Reinforcement Learning-Based Workload Scheduling for Edge Computing

A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

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