Task Classification Based Energy-Aware Consolidation in Clouds

Choi, Hyeon Seok; Lim, Joo Hyun; Yu, Heonchang; Lee, Eun-Young

doi:10.1155/2016/6208358

Cited by 16 publications

(4 citation statements)

References 26 publications

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“…Our current effort 'sees' the problem as a process that classifies a task into two classes, i.e., the local execution or the transfer to another node/Fog/Cloud. In the respective literature, one can find a number of research efforts that handle a resource allocation model as a classification problem [8], [15], [31], [45], [52]. The advantage is that classification models may incorporate the relationships between the adopted parameters and can be based on past experiences as represented by historical values.…”

Section: The Task Allocation Schemementioning

confidence: 99%

Multi-criteria optimal task allocation at the edge

Kolomvatsos

Anagnostopoulos

2019

Future Generation Computer Systems

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In Internet of Things (IoT), numerous nodes produce huge volumes of data that are subject of various processing tasks. Tasks execution on top of the collected data can be realized either at the edge of the network or at the Fog/Cloud. Their management at the network edge may limit the required time for concluding responses and return the final outcome/analytics to endusers or applications. IoT nodes, due to their limited computational and resource capabilities, can execute a limited number of tasks over the collected contextual data. A challenging decision is related to which tasks IoT nodes should execute locally. Each node should carefully select such tasks to maximize the performance based on the current contextual information, e.g., tasks' characteristics, nodes' load and energy capacity. In this paper, we propose an intelligent decision making scheme for selecting the tasks that will be locally executed. The remaining tasks will be transferred to peer nodes in the network or the Fog/Cloud. Our focus is to limit the time required for initiating the execution of each task by introducing a two-step decision process. The first step is to decide whether a task can be executed locally; if not, the second step involves the sophisticated selection of the most appropriate peer to allocate it. When, in the entire network, no node is capable of executing the task, it is, then, sent to the Fog/Cloud facing the maximum latency. We comprehensively evaluate the proposed scheme demonstrating its applicability and optimality at the network edge.

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Section: The Task Allocation Schemementioning

confidence: 99%

Multi-criteria optimal task allocation at the edge

Kolomvatsos

Anagnostopoulos

2019

Future Generation Computer Systems

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“…34 One observation for consolidating the cloud is that the relationship between the CPU utilization and energy consumption is not linear. 35 In fact, the energy consumption of CPU grows more than linearly as the CPU utilization increases. In particular, when the CPU utilization exceeds 90%, the energy consumption quickly increases.…”

Section: Cpu-aware Schedulingmentioning

confidence: 99%

An edge cloud–based body data sensing architecture for artificial intelligence computation

Kim

Lim

2019

International Journal of Distributed Sensor Networks

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As various applications and workloads move to the cloud computing system, traditional approaches of processing sensor data cannot be applied. Specifically, tenants may experience incompatibility and unpredictable performance variation due to inefficient implementations. In this article, we present an edge cloud-based body data sensing architecture for artificial intelligence computation. The main rationale for designing the edge cloud-based sensing architecture is as follows. By analyzing physical body data on the edge cloud computing system, we can identify the relationship between body activities and health conditions for persons. In addition, we can support real-time applications without catastrophic failures by our efficient and stable implementation of the sensing architecture. Our cloud storage architecture is designed to support both stateful and stateless applications, which are compatible with traditional infrastructures and provide server consolidation with a CPU-aware scheduling of virtual machines. Performance results show that our edge cloud-based architecture outperforms the previous architecture in terms of failures, processing time, and scalability.

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“…In 2016, HeeSeok Choi et al [24] proposed a task consolidation algorithm based on task classification and resource utilization for the cloud data center. Furthermore, they designed a VM consolidation algorithm to balance task execution time and energy consumption without violating a predefined service level agreement (SLA).…”

Section: Related Workmentioning

confidence: 99%

Resource Scheduling Based on Improved Spectral Clustering Algorithm in Edge Computing

et al. 2018

Scientific Programming

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With the development of Internet of Things (IoT), the massive data generated by it forms big data, and the complexity of dealing with big data brings challenges to resource scheduling in edge computing. In order to solve the problem of resource scheduling and improve the satisfaction of users in edge computing environment, we propose a user-oriented improved spectral clustering scheduling algorithm (ISCM) in this paper. Based on the improved k-means algorithm, the ISCM algorithm solves the problem that the clustering result is sensitive to the initial value and realizes the reclustering, which makes the obtained clustering results more stable. Finally, the edge computing resource scheduling scheme is obtained based on the clustering results. The experimental results show that the resource scheduling scheme based on improved spectral clustering algorithm is superior to traditional spectral clustering algorithm in edge computing environment.

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Task Classification Based Energy-Aware Consolidation in Clouds

Cited by 16 publications

References 26 publications

Multi-criteria optimal task allocation at the edge

Multi-criteria optimal task allocation at the edge

An edge cloud–based body data sensing architecture for artificial intelligence computation

Resource Scheduling Based on Improved Spectral Clustering Algorithm in Edge Computing

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