A "No Data Center" Solution to Cloud Computing

Mengistu, Tessema M.; Alahmadi, Abdulrahman; Albuali, Abdullah; Alsenani, Yousef; Che, Dunren

doi:10.1109/cloud.2017.99

Cited by 35 publications

(13 citation statements)

References 5 publications

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“…However, the computing capability of a single MEC server has not been able to meet the challenge of the growing number of connected devices. Thus, computing offloading and resource allocation have become hot issues in edge computing [14]- [16]. Traditional resource allocation and calculation offloading are solved by clustering or convex optimization methods [17]- [21].…”

Section: Related Workmentioning

confidence: 99%

Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning

Wang

et al. 2020

IEEE Access

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As a mode of processing task request, edge computing paradigm can reduce task delay and effectively alleviate network congestion caused by the proliferation of Internet of things(IoT) devices compared with cloud computing. However, in the actual construction of the network, there are various edge autonomous subnets in the adjacent areas, which leads to the possibility of unbalance of server load among autonomous subnets during the peak period of task request. In this paper, a deep reinforcement learning algorithm is proposed to solve the complex computation offloading problem for the heterogeneous Edge Computing Server(ECS) collaborative computing. The problem is solved based on the real-time state of the network and the attributes of the task, which adopts Actor Critic and Policy Gradient's Deep Deterministic Policy Gradient(DDPG) to make optimized decisions of computation offloading. Considering multi-task, the heterogeneity of edge subnet and mobility of edge devices, the proposed algorithm can learn the network environment and generate the computation offloading decision to minimize the task delay.The simulation results show that the proposed DDPG-based algorithm is competitive compared with the Deep Q Network(DQN) algorithm and Asynchronous Advantage Actor-Critic(A3C) algorithm. Moreover, the optimal solutions are leveraged to analyze the influence of edge network parameters on task delay. INDEX TERMS Edge computing, computation offload, collaborative computing, reinforcement learning, DDPG.

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Section: Related Workmentioning

confidence: 99%

Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning

Wang

et al. 2020

IEEE Access

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“…This has not been done in the context of VANET. So, there is a need to implement in vehicular networks ''VC as a service'' (VCaaS), as proposed by Mengistu et al [6]. Similarly, the cuCloud [7], [8] is a system that can be called a genuine volunteer cloud computing system, which manifests the concept of VCaaS that finds significance in edge computing and related applications.…”

Section: Introductionmentioning

confidence: 99%

VANET-Based Volunteer Computing (VBVC): A Computational Paradigm for Future Autonomous Vehicles

2020

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Despite the surge in a vehicular ad-hoc network (VANET) and volunteer computing research, future high-end vehicles are expected to under-utilize the onboard computation, storage and communication resources. Therefore, this research envisions the next paradigm shift by merging VANET and volunteer computing, which we call VANET based volunteer computing (or VBVC). To date, the potential design system for VBVC has not been characterized. To fill up this gap, we first set forth the scientific classification of VBVC, which uses the automobiles alongside roadside units (RSU) to give computational administrations to different vehicles on the road. We propose a potential framework for different VBVC scenarios. Moreover, we provide an experimental evaluation of VBVC by comparing it with the traditional model in terms of job completion, latency, and throughput. The proposed VBVC performs better when compared with traditional approaches.

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“…Most of these PCs are underutilized, which has given rise to the idea of volunteer cloud computing using underutilized PCs (i.e., volunteer hosts). A volunteer cloud computing approach that makes use of spare resources has the potential to complement the traditional cloud computing model [6].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, the peerto-peer (P2P) paradigm relies on direct interaction and a measure of witnesses' observations (reputation) of a node to determine trust [7]. Volunteer cloud computing needs robust and adaptive mechanisms to prevent data loss and reduce task failures on untrustworthy volunteer hosts [6]. The nature of IoT applications that operate within a volunteer cloud environment is notoriously diverse and may include batch-type applications (e.g., a shopping application) with low priority and more performance-sensitive tasks/data with high priority (e.g., a health application) [8].…”

Section: Introductionmentioning

confidence: 99%

ProTrust: A Probabilistic Trust Framework for Volunteer Cloud Computing

et al. 2020

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With the exponential growth of large data produced by IoT applications and the need for lowcost computational resources, new paradigms such as volunteer cloud computing (VCC) have recently been introduced. In VCC, volunteers do not disclose resource information before joining the system. This leads to uncertainties about the level of trust in the system. The majority of available trust models are suitable for peer-to-peer (P2P) systems, which rely on direct and indirect interaction, and might cause memory consumption overhead concerns in large systems. To address this problem, this paper introduces ProTrust, a probabilistic framework that defines the trust of a host in VCC. We expand the concept of trust in VCC and develop two new metrics: (1) trustworthiness based on the priority of a task, named loyalty, and (2) trustworthiness affected by behavioral change. We first utilized a modified Beta distribution function, and the behavior of resources are classified into different loyalty levels. Then, we present a behavior detection method to reflect recent changes in behavior. We evaluated ProTrust experimentally with a real workload trace and observed that the framework's estimation of the trust score improved by approximately 15% and its memory consumption decreased by more than 65% compared to existing methods.

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A "No Data Center" Solution to Cloud Computing

Cited by 35 publications

References 5 publications

Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning

Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning

VANET-Based Volunteer Computing (VBVC): A Computational Paradigm for Future Autonomous Vehicles

ProTrust: A Probabilistic Trust Framework for Volunteer Cloud Computing

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