scholarly journals Just-In-Time Execution Through On-Demand Resource Allocation in HPC Systems

2017 ◽  
Author(s):  
A. Portero ◽  
M. Podhoranyi ◽  
D. Hrbac ◽  
S. Libutti ◽  
G. Massari ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Just In Time ◽  
On Demand

On Demand Resource Allocation for LTE Uplink Transmission Based on Logical Channel Groups

2017 ◽  
Vol 22 (5) ◽  
pp. 868-879 ◽  
Author(s):  
Fang-Chang Kuo ◽  
Kuo-Chang Ting ◽  
Hwang-Cheng Wang ◽  
Chih-Cheng Tseng
Keyword(s):  
Resource Allocation ◽  
On Demand ◽  
Logical Channel ◽  
Lte Uplink

Resource Allocation Policies in Cloud Computing Environment

2017 ◽  
pp. 115-132 ◽  
Author(s):  
Suvendu Chandan Nayak ◽  
Sasmita Parida ◽  
Chitaranjan Tripathy ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Service Level Agreement ◽  
Service Level ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
On Demand ◽  
Different Types ◽  
Cloud Datacenter

The basic concept of cloud computing is based on “Pay per Use”. The user can use the remote resources on demand for computing on payment basis. The on-demand resources of the user are provided according to a Service Level Agreement (SLA). In real time, the tasks are associated with a time constraint for which they are called deadline based tasks. The huge number of deadline based task coming to a cloud datacenter should be scheduled. The scheduling of this task with an efficient algorithm provides better resource utilization without violating SLA. In this chapter, we discussed the backfilling algorithm and its different types. Moreover, the backfilling algorithm was proposed for scheduling tasks in parallel. Whenever the application environment is changed the performance of the backfilling algorithm is changed. The chapter aims implementation of different types of backfilling algorithms. Finally, the reader can be able to get some idea about the different backfilling scheduling algorithms that are used for scheduling deadline based task in cloud computing environment at the end.

Seamless Optical Path Restoration with Just-in-Time Resource Allocation Leveraging Machine Learning

2018 ◽  
Author(s):  
Fabien Boitier ◽  
Jelena Pesic ◽  
Vincent Lemaire ◽  
Eric Dutisseuil ◽  
Jose Manuel Estaran Tolosa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Optical Path ◽  
Just In Time ◽  
Path Restoration ◽  
Time Resource

scholarly journals Just-in-Time system in terms of real options

Competitio ◽  
2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Andrea Rozsa
Keyword(s):  
Resource Allocation ◽  
Real Options ◽  
Best Practice ◽  
Real Option ◽  
Corporate Strategy ◽  
Capital Budgeting ◽  
Just In Time ◽  
The Real ◽  
Practice Approach ◽  
Physical Assets

The value creation process in a company and the competitive position are critically influenced by corporate resource allocation and proper valuation of investment alternatives. After the Second World War, capital budgeting and strategic planning emerged as two complementary but different systems for resource allocation. The real options approach developed in the ’80s may provide a useful tool for making a connection between capital budgeting and strategic management. Real options are implicit managerial and operating flexibilities embedded in many non-financial assets and liabilities. In a wider sense: “A real option is the investment in physical assets, human competence, and organisational capabilities that provide the opportunity to respond to future contingent events” (Kogut-Kulatilaka, 2001). This paper shows that Just-in-Time (JIT) system as management philosophy can be regarded as a knowledge-based or capability-based implicit strategy rather than a simple, easy-toimitate best practice approach. Moreover, implementation of JIT can be considered as a strategic investment. The presentation focuses on how the relation among strategic investments, developed technological systems and corporate strategy can be expressed through the real options view.

A novel patient-centric approach between sites, CRO, and sponsor to accelerate FPI and drive more patients into oncology protocols.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 6573-6573
Author(s):  
Krystyna Kowalczyk ◽  
Rhonda U. Henry ◽  
Bellinda Conte
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Field Research ◽  
Just In Time ◽  
On Demand ◽  
Oncology Clinical Trial ◽  
Local Access ◽  
Oncology Clinical Trials ◽  
A Site ◽  
New Compounds

6573 Background: According to the Journal of Clinical Oncology, 50% of sites performing clinical trials never enroll a patient. And on top of that, it can take several months to activate a site for an oncology clinical trial; precious time that patients cannot afford when they’ve had a cancer diagnosis and precious time sponsors need when developing new compounds for market. While many patients are interested in participating in trials, they are limited in their opportunities because they do not live near a research site or work with a physician performing clinical trials. So with this crisis in the oncology field, research needs to be more efficient and inclusive. Methods: A combined partnership of sites, physicians, CRO and sponsor leveraging Just-in-Time enrollment methodology helped expedite clinical trial enrollment and diversify trial access driving faster first patient enrolled and expanding the potential patient denominator. Results: This on-demand methodology augmented existing sites that had access to oncology patients by providing broader access, faster, and with no quality loss. Strong partnerships between CRO and sponsor then facilitated two-week site activation allowing every identified patient to be converted to a study subject. This methodology was repeated across seven protocols driving patents on trials within six weeks of trial available. Conclusions: The benefits of this Just-in-Time methodology touch all areas of clinical trials: Patients have greater clinical trial access: A larger denominator of patients across broader geographies have local access to portfolios of clinical trials; Trials start to enroll faster: Patients can be randomized into oncology clinical trials within two weeks of study start up driving trial time to completion; Sites have more trial options to consider: Sites have a broader portfolio of trials to access on demand without added administrative burden; Trials complete faster: Sponsors accrue patients faster driving expedited timelines and accelerating drug development.

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