scholarly journals Job Migration in a Rivalry Setting

2019 ◽  
Author(s):  
Robert Fletcher ◽  
Santiago Saavedra
Keyword(s):  
Job Migration

scholarly journals Online Makespan Scheduling with Job Migration on Uniform Machines

Algorithmica ◽  
2021 ◽  
Author(s):  
Matthias Englert ◽  
David Mezlaf ◽  
Matthias Westermann
Keyword(s):  
Competitive Ratio ◽  
Online Algorithm ◽  
Parallel Machines ◽  
Scheduling Algorithm ◽  
Input Sequence ◽  
Job Migration ◽  
Average Completion Time ◽  
Uniform Machine ◽  
Uniform Machines ◽  
Completion Times

AbstractIn the classic minimum makespan scheduling problem, we are given an input sequence of n jobs with sizes. A scheduling algorithm has to assign the jobs to m parallel machines. The objective is to minimize the makespan, which is the time it takes until all jobs are processed. In this paper, we consider online scheduling algorithms without preemption. However, we allow the online algorithm to change the assignment of up to k jobs at the end for some limited number k. For m identical machines, Albers and Hellwig (Algorithmica 79(2):598–623, 2017) give tight bounds on the competitive ratio in this model. The precise ratio depends on, and increases with, m. It lies between 4/3 and $$\approx 1.4659$$ ≈ 1.4659 . They show that $$k = O(m)$$ k = O ( m ) is sufficient to achieve this bound and no $$k = o(n)$$ k = o ( n ) can result in a better bound. We study m uniform machines, i.e., machines with different speeds, and show that this setting is strictly harder. For sufficiently large m, there is a $$\delta = \varTheta (1)$$ δ = Θ ( 1 ) such that, for m machines with only two different machine speeds, no online algorithm can achieve a competitive ratio of less than $$1.4659 + \delta $$ 1.4659 + δ with $$k = o(n)$$ k = o ( n ) . We present a new algorithm for the uniform machine setting. Depending on the speeds of the machines, our scheduling algorithm achieves a competitive ratio that lies between 4/3 and $$\approx 1.7992$$ ≈ 1.7992 with $$k = O(m)$$ k = O ( m ) . We also show that $$k = \varOmega (m)$$ k = Ω ( m ) is necessary to achieve a competitive ratio below 2. Our algorithm is based on maintaining a specific imbalance with respect to the completion times of the machines, complemented by a bicriteria approximation algorithm that minimizes the makespan and maximizes the average completion time for certain sets of machines.

Cooling aware job migration for reducing cost in cloud environment

2014 ◽  
Vol 71 (3) ◽  
pp. 1018-1037 ◽  
Author(s):  
Elahe Naserian ◽  
Seyed Mohammad Ghoreyshi ◽  
Hossein Shafiei ◽  
Payam Mousavi ◽  
Ahmad Khonsari
Keyword(s):  
Cloud Environment ◽  
Job Migration

Changes in the International Mobility of Labour: Job Migration of Polish Nationals to Ireland

2005 ◽  
Vol 14 (1) ◽  
pp. 27-44 ◽  
Author(s):  
Izabela Grabowska
Keyword(s):  
Demographic Characteristics ◽  
Personal Factors ◽  
International Mobility ◽  
Job Migration ◽  
Broad Term ◽  
Polish People ◽  
The Social

This paper, based on the case study of job migration from Poland to Ireland, is focused on changes in the international mobility of labour. The study is undertaken with a broad term of reference: (a) to consider factors of changes in the international mobility of labour in a macro-sphere; (b) to examine the social and personal factors which might motivate individuals to migrate; (c) to ascertain socio-demographic characteristics of those who migrate; (d) to document some experience of Polish people working in Ireland, to identify the extent of migration among young emigrants and the nature of their difficulties in Poland.

Design and Implementation of Job Migration Policies in P2P Grid Systems

2008 ◽  
Author(s):  
Shih-Jie Lin ◽  
Min-Chun Huang ◽  
Kuan-Chou Lai ◽  
Kuo-Chan Huang
Keyword(s):  
Migration Policies ◽  
Grid Systems ◽  
Job Migration ◽  
Design And Implementation
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