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This paper investigates the online scheduling problem on parallel and identical machines with a new feature that service requests from various customers are entitled to many different grade of service (GoS) levels. Hence each job and machine are labeled with the GoS levels, and each job can be processed by a particular machine only when the GoS level of the(More)
For most scheduling problems the set of machines is fixed initially and remains unchanged. Recently Imreh and Noga proposed adding the concept of machine cost to scheduling problems and considered the so-called List Model problem. For this problem, we are given a sequence of independent jobs with positive sizes, which must be processed non-preemptively on a(More)
This paper investigates two preemptive semi-online scheduling problems to minimize makespan on two uniform machines. In the first semi-online problem, we know in advance that all jobs have their processing times in between p and rp $(p > 0,r\geq 1)$ . In the second semi-online problem, we know the size of the largest job in advance. We design an optimal(More)
Effectiveness of MapReduce as a big data processing framework depends on efficiencies of scale for both map and reduce phases. While most map tasks are preemptive and parallelizable, the reduce tasks typically are not easily decomposed and often become a bottleneck due to constraints of data locality and task complexity. By assuming that reduce tasks are(More)
In this paper, we present a new method for classifying shot type in sports video based on visual attention. The problem is important for applications such as video structure analysis and content understanding. In particular, two-stage off-line learning processes perform knowledge extraction of semantic concepts and automatic shot classification,(More)
In semi-online scheduling problems, we always assume that some partial additional information is exactly known in advance. This may not be true in some application. This paper considers semi-online problems on identical machines with inexact partial information. Three problems are considered, where we know in advance that the optimal value, or the largest(More)