1,715 research outputs found
The lockmaster's problem.
Inland waterways form a natural network that is an existing, congestion free infrastructure with capacity for more traffic.Transportation of goods by ship is widely promoted as it is a reliable, efficient and environmental friendly way of transport. A bottleneck for transportation over water are the locks that manage the water level. The lockmaster's problem concerns the optimal strategy for operating such a lock. In the lockmaster's problem we are given a lock, a set of ships coming from downstream that want to go upstream, and another set of ships coming from upstream that want to go downstream. We are given the arrival times of the ships and a constant lockage time; the goal is to minimize total waiting time of the ships. In this paper a dynamic programming algorithm (DP) is proposed that solves the lockmaster's problem in polynomial time. We extend this DP to different generalizations that consider weights, water usage, capacity, and (a fixed number of) multiple chambers. Finally, we prove that the problem becomes strongly NP-hard when the number of chambers is part of the input.Lock scheduling; Batch scheduling; Dynamic programming; Complexity;
A survey of scheduling problems with setup times or costs
Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Datacenter Traffic Control: Understanding Techniques and Trade-offs
Datacenters provide cost-effective and flexible access to scalable compute
and storage resources necessary for today's cloud computing needs. A typical
datacenter is made up of thousands of servers connected with a large network
and usually managed by one operator. To provide quality access to the variety
of applications and services hosted on datacenters and maximize performance, it
deems necessary to use datacenter networks effectively and efficiently.
Datacenter traffic is often a mix of several classes with different priorities
and requirements. This includes user-generated interactive traffic, traffic
with deadlines, and long-running traffic. To this end, custom transport
protocols and traffic management techniques have been developed to improve
datacenter network performance.
In this tutorial paper, we review the general architecture of datacenter
networks, various topologies proposed for them, their traffic properties,
general traffic control challenges in datacenters and general traffic control
objectives. The purpose of this paper is to bring out the important
characteristics of traffic control in datacenters and not to survey all
existing solutions (as it is virtually impossible due to massive body of
existing research). We hope to provide readers with a wide range of options and
factors while considering a variety of traffic control mechanisms. We discuss
various characteristics of datacenter traffic control including management
schemes, transmission control, traffic shaping, prioritization, load balancing,
multipathing, and traffic scheduling. Next, we point to several open challenges
as well as new and interesting networking paradigms. At the end of this paper,
we briefly review inter-datacenter networks that connect geographically
dispersed datacenters which have been receiving increasing attention recently
and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial
A survey of variants and extensions of the resource-constrained project scheduling problem
The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts. --project scheduling,modeling,resource constraints,temporal constraints,networks
Project portfolio management: capacity allocation, downsizing decisions and sequencing rules.
This paper aims to gain insight into capacity allocation, downsizing decisions and sequencing rules when managing a portfolio of projects. By downsizing, we mean reducing the scale or size of a project and thereby changing the project's content. In previous work, we have determined the amount of critical capacity that is optimally allocated to concurrently executed projects with deterministic or stochastic workloads when the impact of downsizing is known. In this paper, we extend this view with the possibility of sequential processing, which implies that a complete order is imposed on the projects. When projects are sequenced instead of executed in parallel, two effects come into play: firstly, unused capacity can be shifted to later projects in the same period; and secondly, reinvestment revenues gain importance because of the differences in realization time of the sequenced projects. When project workloads are known, only the second effect counts; when project workloads are stochastic, however, the project's capacity usage is uncertain so that unused capacity can be shifted to later projects in the same period. In this case, both effects need to be taken into account. In this paper, we determine optimal sequencing rules when the selection and capacity-allocation decisions for a set of projects have already been made. We also consider a combination of parallel and sequential planning and we perform simulation experiments that confirm the appropriateness of our capacity-allocation methods.Project portfolio management; Downsizing; Sequencing;
Resource Management and Scheduling for Big Data Applications in Cloud Computing Environments
This chapter presents software architectures of the big data processing
platforms. It will provide an in-depth knowledge on resource management
techniques involved while deploying big data processing systems on cloud
environment. It starts from the very basics and gradually introduce the core
components of resource management which we have divided in multiple layers. It
covers the state-of-art practices and researches done in SLA-based resource
management with a specific focus on the job scheduling mechanisms.Comment: 27 pages, 9 figure
Weighted tardiness minimization for unrelated machines with sequence-dependent and resource-constrained setups
Motivated by the need of quick job (re-)scheduling, we examine an elaborate
scheduling environment under the objective of total weighted tardiness
minimization. The examined problem variant moves well beyond existing
literature, as it considers unrelated machines, sequence-dependent and
machine-dependent setup times and a renewable resource constraint on the number
of simultaneous setups. For this variant, we provide a relaxed MILP to
calculate lower bounds, thus estimating a worst-case optimality gap. As a fast
exact approach appears not plausible for instances of practical importance, we
extend known (meta-)heuristics to deal with the problem at hand, coupling them
with a Constraint Programming (CP) component - vital to guarantee the
non-violation of the problem's constraints - which optimally allocates
resources with respect to tardiness minimization. The validity and versatility
of employing different (meta-)heuristics exploiting a relaxed MILP as a quality
measure is revealed by our extensive experimental study, which shows that the
methods deployed have complementary strengths depending on the instance
parameters. Since the problem description has been obtained from a textile
manufacturer where jobs of diverse size arrive continuously under tight
deadlines, we also discuss the practical impact of our approach in terms of
both tardiness decrease and broader managerial insights
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