Semi-online scheduling: A survey

Dwibedy, Debasis; Mohanty, Rakesh

doi:10.1016/j.cor.2021.105646

Cited by 17 publications

(5 citation statements)

References 124 publications

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“…This problem is also closely related to the online l p -norm loadbalancing problem on two hierarchical machines [6,29] and the online machine covering problems on two hierarchical machines [30][31][32]. Furthermore, more related results can be found in the recent surveys [33][34][35].…”

Section: The Contributions Of Previous Studiesmentioning

confidence: 93%

Semi-Online Algorithms for the Hierarchical Extensible Bin-Packing Problem and Early Work Problem

Yang,

Xiao,

2024

Computation

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In this paper, we consider two types of semi-online problems with hierarchies. In the extensible bin-packing problem with two hierarchical bins, one bin can pack all items, while the other bin can only pack some items. The initial size of the bin can be expanded, and the goal is to minimize the total size of the two bins. When the largest item size is given in advance, we provide some lower bounds and propose online algorithms. When the total item size is given in advance, we provide some lower bounds and propose online algorithms. In addition, we also consider the relevant early-work-maximization problem on two hierarchical machines; one machine can process any job, while the other machine can only process some jobs. Each job shares a common due date, and the goal is to maximize the total early work. When the largest job size is known, we provide some lower bounds and propose two online algorithms whose competitive ratios are close to the lower bounds.

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Section: The Contributions Of Previous Studiesmentioning

confidence: 93%

Semi-Online Algorithms for the Hierarchical Extensible Bin-Packing Problem and Early Work Problem

Yang,

Xiao,

2024

Computation

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“…In different jobs this known extra information can be the maximum job size, the order in which jobs arrive, or the total execution time. This additional information in Dwibedy and Mohanty (2022) refers to the fact that the total time of task execution is known. In this paper, this additional information means that the task length is known, and that the processing speed of only some of the computing nodes and the routing delay when the task is assigned to that node is known.…”

Section: Core Scheduling Algorithmmentioning

confidence: 99%

“…The offline task scheduling algorithm usually means that all parameters are known in advance by the system, and it is a static scheduling algorithm (Zorbas et al, 2019). The online task scheduling algorithm is more complicated, and all parameters and information cannot be known before the task is executed, and the processing time of the task needs to be obtained according to the actual operation situation (Even et al, 2009). However, the actual situation is often more complicated, and there are situations where neither online nor offline scheduling algorithms can play a role.…”

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confidence: 99%

Edge computing driven sustainable development: A case study on professional farmer cultivation mechanism

Yuan

Nie

2023

Expert Systems

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New‐type professional farmers are new‐type rural talents with scientific and cultural literacy and professional knowledge of agriculture under the background of rural revitalization. The new type of professional farmers is of great significance for accelerating agricultural modernization and promoting the construction of a powerful modern socialist country. In the process of cultivating new types of professional farmers in rural areas, it is necessary to break through the previous informatization constraints of poor information flow, and give full play to the role of advanced information technology to promote the sustainable economic development. This paper proposes a new type of professional farmer cultivation platform based on edge computing architecture to improve the cultivation efficiency of new type of professional farmers, so as to better promote the development of rural informatization. Aiming at the challenges of energy consumption under the edge platform architecture, this paper proposes a dynamic scheduling method for semi‐online tasks for edge computing platforms. In order to evaluate the effectiveness of the algorithm, the method in this paper is compared with the classic scheduling algorithm, and simulated and verified on the CloudSim platform. Experimental results show that the proposed method outperforms other algorithms in the task completion time metric. With the expansion of task scale, more energy consumption can be saved by using the algorithm proposed in this paper.

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“…Note that this is different from the well-known online setting: There, an algorithm does not see the whole input instance but is presented with the jobs to be scheduled one at a time and has to make an immediate scheduling decision (Albers 2009). This limitation is also present in both the semi-online setting and in its generalization, scheduling with advice, where the algorithm has access to additional information on the input sequence or output properties (Dwibedy and Mohanty 2022;Boyar et al 2016). In contrast, our enumeration algorithms do have access to the complete input and are free to decide which jobs to schedule next.…”

Section: Introductionmentioning

confidence: 99%

“…One would, however, expect inferior results, as online algorithms are usually unable to give optimal solutions to input instances (cp. Dwibedy and Mohanty 2022). In this work we mostly look into enumeration algorithms that solve instances exactly.…”

Section: Introductionmentioning

confidence: 99%

Incremental Ordering for Scheduling Problems

Neubert,

Casel

2024

ICAPS

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Given an instance of a scheduling problem where we want to start executing jobs as soon as possible, it is advantageous if a scheduling algorithm emits the first parts of its solution early, in particular before the algorithm completes its work. Therefore, in this position paper, we analyze core scheduling problems in regards to their enumeration complexity, i.e. the computation time to the first emitted schedule entry (preprocessing time) and the worst case time between two consecutive parts of the solution (delay). Specifically, we look at scheduling instances that reduce to ordering problems. We apply a known incremental sorting algorithm for scheduling strategies that are at their core comparison-based sorting algorithms and translate corresponding upper and lower complexity bounds to the scheduling setting. For instances with n jobs and a precedence DAG with maximum degree Δ, we incrementally build a topological ordering with O(n) preprocessing and O(Δ) delay. We prove a matching lower bound and show with an adversary argument that the delay lower bound holds even in case the DAG has constant average degree and the ordering is emitted out-of-order in the form of insert operations. We complement our theoretical results with experiments that highlight the improved time-to-first-output and discuss research opportunities for similar incremental approaches for other scheduling problems.

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Semi-online scheduling: A survey

Cited by 17 publications

References 124 publications

Semi-Online Algorithms for the Hierarchical Extensible Bin-Packing Problem and Early Work Problem

Semi-Online Algorithms for the Hierarchical Extensible Bin-Packing Problem and Early Work Problem

Edge computing driven sustainable development: A case study on professional farmer cultivation mechanism

Incremental Ordering for Scheduling Problems

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