General Perfectly Periodic Scheduling

Brakerski, Zvika; Nisgav, Aviv; Patt-Shamir, Boaz

doi:10.1007/s00453-005-1182-x

Cited by 5 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scheduling algorithms are used to determine the broadcasting priority of data items . Most of pull‐based data broadcasting and on‐demand data broadcasting focused on how to organise the broadcasting queue according to the data item size, waiting time and request distribution. Some other literatures studied scheduling algorithms for different real‐time data broadcasting , whereas a few literatures concentrated on how to calculate the tuning time of mobile terminals in on‐demand data broadcasting by index strategy .…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An on‐demand data broadcasting scheduling algorithm based on dynamic index strategy

Fan

Luo

et al. 2013

Wireless Communications

View full text Add to dashboard Cite

On-demand data broadcasting scheduling is an effective wireless data dissemination technique. Existing scheduling algorithms usually have two problems: (1) with the explosive growth of mobile users and real-time individual requirements, broadcasting systems present a shortage of scalability, dynamics and timeliness (request drop ratio); (2) with the growth of intelligent and entertained application, energy consumption of mobile client cannot be persistent (tuning time). This paper proposes an effective scheduling algorithm LxRxW. It takes into account the number of lost requests during next item broadcasting time, the number of requests and the waiting time. LxRxW can reduce the request drop ratio. At the same time, the algorithm employs a dynamic index strategy to put forward a dynamic adjusting method on the index cycle length (DAIL) to determine the proper index cycle. Extensive experimental results show that the LxRxW algorithm has better performance than other state-of-the-art scheduling algorithms and can significantly reduce the drop ratio of user requests by 40%-50%. The request drop ratio and accessing time of LxRxW with index increase by 1%-2% than LxRxW algorithm without index, but the tuning time decreases by 70%. The index strategy shows that when the index cycle length is less than 20 units, it can significantly reduce the average tuning time but when the index cycle length continues increasing, the average tuning time will increase contrarily. DAIL can dynamically determine the length of index cycle. Moreover, it can reach optimal integrated performance of the request drop ratio, the average accessing time and the average tuning time.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Depending on the users’ needs, they can select different data exchange methods. Push‐based broadcasting is one typical and effective way. On the basis of the distribution of the users’ requests, the server periodically broadcasts hot data.…”

Section: Introductionmentioning

confidence: 99%

An on‐demand data broadcasting scheduling algorithm based on dynamic index strategy

Fan

Luo

et al. 2013

Wireless Communications

View full text Add to dashboard Cite

show abstract

“…The Windows Scheduling Problem with exact periods has also been studied under the name Perfectly Periodic Schedules, which typically refers to the problem to minimize the deviation of the schedule from the demanded job periods. This optimization problem has been addressed in [2,3,4,9,10,22].…”

Section: Introductionmentioning

confidence: 99%

A New Perspective on the Windows Scheduling Problem

Jacobs¹,

Longo²

2014

Preprint

View full text Add to dashboard Cite

The Windows Scheduling Problem, also known as the Pinwheel Problem, is to schedule periodic jobs subject to their processing frequency demands. Instances are given as a set of jobs that have to be processed infinitely often such that the time interval between two consecutive executions of the same job j is no longer than the job's given period pj .The key contribution of this work is a new interpretation of the problem variant with exact periods, where the time interval between consecutive executions must be strictly pj. We show that this version is equivalent to a natural combinatorial problem we call Partial Coding. Reductions in both directions can be realized in polynomial time, so that both hardness proofs and algorithms for Partial Coding transfer to Windows Scheduling.Applying this new perspective, we obtain a number of new results regarding the computational complexity of various Windows Scheduling Problem variants. We prove that even the case of one processor and unitlength jobs does not admit a pseudo-polynomial time algorithm unless SAT can be solved by a randomized method in expected quasi-polynomial time. This result also extends to the case of inexact periods, which answers a question that has remained open for more than two decades. Furthermore, we report an error found in a hardness proof previously given for the multi-machine case without machine migration, and we show that this variant reduces to the single-machine case. Finally, we prove that even with unit-length jobs the problem is co-NP-hard when jobs are allowed to migrate between machines.

show abstract

Perfect periodic scheduling for three basic cycles

Kim

Glass

2013

J Sched

View full text Add to dashboard Cite

Periodic scheduling has many attractions for wireless telecommunications. It offers energy saving where equipment can be turned off between transmissions, and high-quality reception through the elimination of jitter, caused by irregularity of reception. However, perfect periodic schedules, in which each (of n) client is serviced at regular, prespecified intervals, are notoriously difficult to construct. The problem is known to be NP-hard even when service times are identical. This paper focuses on cases of up to three distinct periodicities, with unit service times. Our contribution is to derive a O(n 4 ) test for the existence of a feasible schedule, and a method of constructing a feasible schedule if one exists, for the given combination of client periodicities. We also indicate why schedules with a higher number of periodicities are unlikely to be useful in practice. This methodology can be used to support perfect periodic scheduling in a wide range in real world settings, including machine maintenance service, wireless mesh networks and various other telecommunication networks transmitting packet size data.

show abstract

General Perfectly Periodic Scheduling

Cited by 5 publications

References 20 publications

An on‐demand data broadcasting scheduling algorithm based on dynamic index strategy

An on‐demand data broadcasting scheduling algorithm based on dynamic index strategy

A New Perspective on the Windows Scheduling Problem

Perfect periodic scheduling for three basic cycles

Contact Info

Product

Resources

About