WarMops: A Workload-Aware Resource Management Optimization Strategy for IaaS Private Clouds

Resource management is a significant task of scheduling and allocating resources to applications to meet the required Quality of Service (QoS) limitations by the minimization of overhead with an effective resource utilization. This paper presents a Fog-enabled Cloud computing resource management model for smart homes by the Improved Grey Wolf Optimization Strategy. Besides, Kernel Support Vector Machine (KSVM) model is applied for series forecasting of time and also of processing load of a distributed server and determine the proper resources which should be allocated for the optimization of the service response time. The presented IGWO-KSVM model has been simulated under several aspects and the outcome exhibited the outstanding performance of the presented model.

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Thinking About the Economic Management of Universities Based on Capital and Cost Management Optimization

Proceedings of Business and Economic Studies ◽

10.26689/pbes.v3i4.1420 ◽

2020 ◽

Vol 3 (4) ◽

Author(s):

Lin Liu

Keyword(s):

Cost Management ◽

Colleges And Universities ◽

Optimization Strategy ◽

Economic Management ◽

Current Implementation ◽

Full Discussion ◽

Educational Career ◽

Management Optimization

The economic management of colleges and universities has always been a topic of great concern to China's educational career, therefore, this paper will firstly make the necessary analysis of the current implementation of the economic management of colleges and universities in China, and then the reasons for the problems of economic management of colleges and universities in China is realized a detailed investigation, and finally, the economic management of colleges and universities based on capital and cost management optimization strategy is made a full discussion, looking forward to providing the necessary guidance for researchers in this field.

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Energy-Saving QoS Resource Management of Virtualized Networked Data Centers for Big Data Stream Computing

Big Data ◽

10.4018/978-1-4666-9840-6.ch040 ◽

2016 ◽

pp. 848-886

Author(s):

Nicola Cordeschi ◽

Mohammad Shojafar ◽

Danilo Amendola ◽

Enzo Baccarelli

Keyword(s):

Big Data ◽

Resource Management ◽

Real Time ◽

Data Stream ◽

Data Centers ◽

Software As A Service ◽

Stream Computing ◽

Management Optimization ◽

Hard Real Time ◽

Optimal Scheduler

In this chapter, the authors develop the scheduler which optimizes the energy-vs.-performance trade-off in Software-as-a-Service (SaaS) Virtualized Networked Data Centers (VNetDCs) that support real-time Big Data Stream Computing (BDSC) services. The objective is to minimize the communication-plus-computing energy which is wasted by processing streams of Big Data under hard real-time constrains on the per-job computing-plus-communication delays. In order to deal with the inherently nonconvex nature of the resulting resource management optimization problem, the authors develop a solving approach that leads to the lossless decomposition of the afforded problem into the cascade of two simpler sub-problems. The resulting optimal scheduler is amenable of scalable and distributed adaptive implementation. The performance of a Xen-based prototype of the scheduler is tested under several Big Data workload traces and compared with the corresponding ones of some state-of-the-art static and sequential schedulers.

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