Optimal Scheduling of Proactive Service with Customer Deterioration and Improvement

2021 ◽  
Author(s):  
Yue Hu ◽  
Carri W. Chan ◽  
Jing Dong

Service systems are typically limited resource environments where scarce capacity is reserved for the most urgent customers. However, there has been a growing interest in the use of proactive service when a less urgent customer may become urgent while waiting. On one hand, providing service for customers when they are less urgent could mean that fewer resources are needed to fulfill their service requirement. On the other hand, using limited capacity for customers who may never need the service in the future takes the capacity away from other more urgent customers who need it now. To understand this tension, we propose a multiserver queueing model with two customer classes: moderate and urgent. We allow customers to transition classes while waiting. In this setting, we characterize how moderate and urgent customers should be prioritized for service when proactive service for moderate customers is an option. We identify an index, the modified [Formula: see text]-index, which plays an important role in determining the optimal scheduling policy. This index lends itself to an intuitive interpretation of how to balance holding costs, service times, abandonments, and transitions between customer classes. This paper was accepted by David Simchi-Levi, stochastic models and simulation.

1995 ◽  
Vol 22 (10-12) ◽  
pp. 247-259 ◽  
Author(s):  
M. Ohnishi ◽  
H. Maeda ◽  
T. Ibaraki

2017 ◽  
Author(s):  
Mohammad Noormohammadpour ◽  
Cauligi S. Raghavendra

Large cloud companies manage dozens of datacenters across the globe connected using dedicated inter-datacenter networks. An important application of these networks is data replication which is done for purposes such as increased resiliency via making backup copies, getting data closer to users for reduced delay and WAN bandwidth usage, and global load balancing. These replications usually lead to network transfers with deadlines that determine the time prior to which all datacenters should have a copy of the data. Inter-datacenter networks have limited capacity and need be utilized efficiently to maximize performance. In this report, we focus on applications that transfer multiple copies of objects from one datacenter to several datacenters given deadline constraints. Existing solutions are either deadline agnostic, or only consider point-to-point transfers. We propose DDCCast, a simple yet effective deadline aware point to multipoint technique based on DCCast and using ALAP traffic allocation. DDCCast performs careful admission control using temporal planning, uses rate-allocation and rate-limiting to avoid congestion and sends traffic over forwarding trees that are carefully selected to reduce bandwidth usage and maximize deadline meet rate. We perform experiments confirming DDCCast's potential to reduce total bandwidth usage by up to 45% while admitting up to 25% more traffic into the network compared to existing solutions that guarantee deadlines.


2019 ◽  
Vol 51 (3) ◽  
pp. 278-293
Author(s):  
Adarsh Anand ◽  
◽  
Subhrata Das ◽  
Mohini Agarwal ◽  
V.S.S. Yadavalli ◽  
...  

2018 ◽  
Vol 45 (3) ◽  
pp. 217-223
Author(s):  
Y. Lu ◽  
S.T. Maguluri ◽  
M.S. Squillante ◽  
T. Suk ◽  
X. Wu

Author(s):  
Erhun Özkan

A fork-join processing network is a queueing network in which tasks associated with a job can be processed simultaneously. Fork-join processing networks are prevalent in computer systems, healthcare, manufacturing, project management, justice systems, and so on. Unlike the conventional queueing networks, fork-join processing networks have synchronization constraints that arise because of the parallel processing of tasks and can cause significant job delays. We study scheduling in fork-join processing networks with multiple job types and parallel shared resources. Jobs arriving in the system fork into arbitrary number of tasks, then those tasks are processed in parallel, and then they join and leave the network. There are shared resources processing multiple job types. We study the scheduling problem for those shared resources (i.e., which type of job to prioritize at any given time) and propose an asymptotically optimal scheduling policy in diffusion scale.


2018 ◽  
Vol 22 (1) ◽  
pp. 121-133 ◽  
Author(s):  
Geunchul Park ◽  
Seungwoo Rho ◽  
Jik-Soo Kim ◽  
Dukyun Nam

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