scholarly journals Using Ideal Time Horizon for Energy Cost Determination

2021 ◽  
pp. 9-13
Author(s):  
Raed Abdulkareem Hasan ◽  
Hadeel W. Abdulwahid ◽  
Arwa Sahib Abdalzahra
Keyword(s):  
Energy Cost ◽  
Time Horizon ◽  
Complete System ◽  
Proper Time ◽  
Active State ◽  
Sleep Mode ◽  
Vm Placement ◽  
Vm Migration ◽  
Cost Determination ◽  
High Cluster

In most optimal VM placement algorithms, the first step to determine the proper time horizon, T for the prediction of the expected maximum future load, L. However, T is dependent on the proper knowledge of the required time for servers to switch from their initial SLEEP/ACTIVE state to the next desired state. The activities implemented by this policy are (a) to relocate the VM from an encumbered server, a server that operates in an undesirably high regime with applications forecasted to rise their burdens to compute in the subsequent reallocation cycles; (b) to conduct VM migration from servers that operate within the undesirable regime to shift the server to a SLEEP mode; (c) putting an idle server to SLEEP mode and rebooting the servers from the SLEEP mode at high cluster loads. A novel mechanism for forwarding arriving client desires to the utmost suitable server is implemented; thus, in the complete system, balancing the requested load is possible.

KidVO: a kinodynamically consistent algorithm for online motion planning in dynamic environments

2016 ◽  
Vol 43 (1) ◽  
pp. 33-47 ◽  
Author(s):  
Mostafa Mahmoodi ◽  
Khalil Alipour ◽  
Hadi Beik Mohammadi
Keyword(s):  
Motion Planning ◽  
Time Horizon ◽  
Proper Time ◽  
Selection Procedure ◽  
Dynamic Environments ◽  
Content Type ◽  
Dynamic Constraints ◽  
Optimal Behavior ◽  
Hamilton Jacobi Bellman ◽  
Velocity Obstacle

Purpose – The purpose of this paper is to propose an efficient method, called kinodynamic velocity obstacle (KidVO), for motion planning of omnimobile robots considering kinematic and dynamic constraints (KDCs). Design/methodology/approach – The suggested method improves generalized velocity obstacle (GVO) approach by a systematic selection of proper time horizon. Selection procedure of the time horizon is based on kinematical and dynamical restrictions of the robot. Toward this aim, an omnimobile robot with a general geometry is taken into account, and the admissible velocity and acceleration cones reflecting KDCs are derived, respectively. To prove the advantages of the suggested planning method, its performance is compared with GVOs, the so-called Hamilton-Jacobi-Bellman equation and the rapidly exploring random tree. Findings – The obtained results of the presented scenarios which contain both computer and real-world experiments for complicated crowded environments indicate the merits of the suggested methodology in terms of its near-optimal behavior, successful obstacle avoidance both in static and dynamic environments and reaching to the goal pose. Originality/value – This paper proposes a novel method for online motion planning of omnimobile robots in dynamic environments while considering the real capabilities of the robot.

Optimal Dynamic Placement of Virtual Machines in Geographically Distributed Cloud Data Centers

2017 ◽  
Vol 26 (03) ◽  
pp. 1750001 ◽  
Author(s):  
Hana Teyeb ◽  
Nejib Ben Hadj-Alouane ◽  
Samir Tata ◽  
Ali Balma
Keyword(s):  
Virtual Machines ◽  
Time Frame ◽  
Cloud Infrastructure ◽  
Vm Placement ◽  
Vm Migration ◽  
Cloud Data ◽  
Geographically Distributed ◽  
Optimal Dynamic ◽  
And Migration ◽  
Distributed Cloud

In geo-distributed cloud systems, a key challenge faced by cloud providers is to optimally tune and configure the underlying cloud infrastructure. An important problem in this context, deals with finding an optimal virtual machine (VM) placement, minimizing costs, while at the same time, ensuring good system performance. Moreover, due to the fluctuations of demand and traffic patterns, it is crucial to dynamically adjust the VM placement scheme over time. It should be noted that most of the existing studies, however, dealt with this problem either by ignoring its dynamic aspect or by proposing solutions that are not suitable for a geographically distributed cloud infrastructure. In this paper, exact as well as heuristic solutions based on Integer Linear programming (ILP) formulations are proposed. Our work focuses also on the problem of scheduling the VM migration by finding the best migration sequence of intercommunicating VMs that minimizes the resulting traffic on the backbone network. The proposed algorithms execute within a reasonable time frame to readjust VM placement scheme according to the perceived demand. Our aim is to use VM migration as a tool for dynamically adjusting the VM placement scheme while minimizing the network traffic generated by VM communication and migration. Finally, we demonstrate the effectiveness of our proposed algorithms by performing extensive experiments and simulation.

scholarly journals A survey on application of machine learning to manage the virtual machines in cloud computing

2020 ◽  
Vol 11 (3) ◽  
pp. 197-208
Author(s):  
Varun Barthwal ◽  
M.M.S. Rauthan ◽  
Rohan Varma
Keyword(s):  
Machine Learning ◽  
Service Quality ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Performance Parameters ◽  
Vm Placement ◽  
Vm Migration ◽  
Cloud Datacenter ◽  
Management Policies ◽  
Selection Of

AbstractVirtual machine (VM) management is a fundamental challenge in the cloud datacenter, as it requires not only scheduling and placement, but also optimization of the method to maintain the energy cost and service quality. This paper reviews the different areas of literature that deal with the resource utilization prediction, VM migration, VM placement and the selection of physical machines (PMs) for hosting the VMs. The main features of VM management policies were also examined using a comparative analysis of the current policies. Many research works include Machine Learning (ML) for detecting the PM overloading, the selection of VMs from over-utilized PM and VM placement as the main activities. This article aims to identify and classify research done in the area of scheduling and placement of VMs using the ML with resource utilization history. Energy efficiency, VM migration counts and Service quality were the key performance parameters that were used to assess the performance of the cloud datacenter.

Energy Cost Determination Using a Portable Accelerometer

1986 ◽  
Vol 66 (7) ◽  
pp. 1102-1107 ◽  
Author(s):  
Joseph A. Balogun ◽  
Nancy T. Farina ◽  
Erin Fay ◽  
Karen Rossmann ◽  
Lorene Pozyc
Keyword(s):  
Energy Cost ◽  
Cost Determination

scholarly journals Scheduling of Diesel Generators Operation with Restricted PCC in Microgrid

2021 ◽  
Vol 17 (2) ◽  
pp. 108-119
Author(s):  
Nabil Aklo ◽  
Mofeed Rashid
Keyword(s):  
Power Systems ◽  
Energy Cost ◽  
Time Horizon ◽  
Electrical Power ◽  
Cost Effective ◽  
Energy System ◽  
Optimization Method ◽  
Renewable Energy Resources ◽  
Electrical Power Systems ◽  
Diesel Generators

Smart Microgrid (MG) effectively contributes to supporting the electrical power systems as a whole and reducing the burden on the utility grid by the use of unconventional energy generation resources, in addition to backup Diesel Generators (DGs) for reliability increasing. In this paper, potential had been done on day-ahead scheduling of diesel generators and reducing the energy cost reached to the consumers side to side with renewable energy resources, where economical energy and cost-effective MG has been used based on optimization agent called Energy Management System (EMS). Improved Particle Swarm Optimization (IPSO) technique has been used as an optimization method to reduce fuel consumption and obtain the lowest energy cost as well as achieving the best performance to the energy system. Three scenarios are adopted to prove the efficiency of the proposed method. The first scenario uses a 24 hour time horizon to investigate the performance of the model, the second scenario uses two DGs and the third scenario depends on a 48-hour time horizon to validating the performance. The superiority of the proposed method is illustrated by comparing it with PSO and simulation results show using the proposed method can reducing the fuel demand and the energy cost by satisfying the user’s preference.

Clinical Update: Assessing Maximum Medical Improvement in Carpal Tunnel Syndrome

2003 ◽  
Vol 8 (4) ◽  
pp. 4-5
Author(s):  
Christopher R. Brigham ◽  
James B. Talmage
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Carpal Tunnel ◽  
Proper Time ◽  
Carpal Tunnel Release ◽  
Hand Function ◽  
Factors Affecting ◽  
Early Results ◽  
Medical Evaluation ◽  
Nerve Recovery ◽  
Tunnel Syndrome

Abstract Permanent impairment cannot be assessed until the patient is at maximum medical improvement (MMI), but the proper time to test following carpal tunnel release often is not clear. The AMA Guides to the Evaluation of Permanent Impairment (AMA Guides) states: “Factors affecting nerve recovery in compression lesions include nerve fiber pathology, level of injury, duration of injury, and status of end organs,” but age is not prognostic. The AMA Guides clarifies: “High axonotmesis lesions may take 1 to 2 years for maximum recovery, whereas even lesions at the wrist may take 6 to 9 months for maximal recovery of nerve function.” The authors review 3 studies that followed patients’ long-term recovery of hand function after open carpal tunnel release surgery and found that estimates of MMI ranged from 25 weeks to 24 months (for “significant improvement”) to 18 to 24 months. The authors suggest that if the early results of surgery suggest a patient's improvement in the activities of daily living (ADL) and an examination shows few or no symptoms, the result can be assessed early. If major symptoms and ADL problems persist, the examiner should wait at least 6 to 12 months, until symptoms appear to stop improving. A patient with carpal tunnel syndrome who declines a release can be rated for impairment, and, as appropriate, the physician may wish to make a written note of this in the medical evaluation report.

Energy cost of sexual activity

1970 ◽  
Vol 126 (3) ◽  
pp. 526a-526 ◽  
Author(s):  
A. N. Goldbarg
Keyword(s):  
Sexual Activity ◽  
Energy Cost

Before I Die

GeroPsych ◽  
2018 ◽  
Vol 31 (3) ◽  
pp. 151-162 ◽  
Author(s):  
Qiao Chu ◽  
Daniel Grühn ◽  
Ashley M. Holland
Keyword(s):  
End Of Life ◽  
Psychosocial Development ◽  
Time Horizon ◽  
Development Theory ◽  
Life Stages ◽  
Socioemotional Selectivity Theory ◽  
Socioemotional Selectivity ◽  
Time Horizons

Abstract. We investigated the effects of time horizon and age on the socioemotional motives underlying individual’s bucket-list goals. Participants were randomly assigned to one of three time-horizon conditions to make a bucket list: (1) an open-ended time horizon (Study 1 & 2), (2) a 6-month horizon (i.e., “Imagine you have 6 months to live”; Study 1 & 2), and (3) a 1-week horizon (Study 2). Goal motives were coded based on socioemotional selectivity theory and psychosocial development theory. Results indicated that time horizon and age produced unique effects on bucket-list goal motives. Extending past findings on people’s motives considering the end of life, the findings suggest that different time horizons and life stages trigger different motives.

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