Profit-based scheduling and channel allocation for multi-item requests in real-time on-demand data broadcast systems

Lv, Jingsong; Lee, Victor C. S.; Li, Minming; Chen, Enhong

doi:10.1016/j.datak.2011.09.005

Cited by 30 publications

(17 citation statements)

References 23 publications

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“…In multi-channel wireless environment, Liu and Lee (2010b) extended a number of representative scheduling schemes to evaluate their performances in scheduling multi-item requests. Lv et al (2012) introduced the concepts of "profit" and "opportunity cost" to on-demand data broadcast for solving data scheduling problem on a single channel and multiple channels, and proved its NP-hard. Dewri et al (2012) scheduled real time requests with deadlines to reduce the miss ratio, and the proposed scheduler attempts to maximize the utility of requests and serves the requests in their deadlines as far as possible.…”

Section: Related Workmentioning

confidence: 99%

On-demand data broadcast with deadlines for avoiding conflicts in wireless networks

Shen

Tian

2015

Journal of Systems and Software

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Section: Related Workmentioning

confidence: 99%

On-demand data broadcast with deadlines for avoiding conflicts in wireless networks

Shen

Tian

2015

Journal of Systems and Software

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“…A much more comprehensive discussion on recent research results can be found in [22]. Lv et al introduced the concept of profit into the broadcast schedule and proposed an efficient scheduling algorithm in [23] which always choose the data item with highest profit to be broadcast at each time unit. It should be pointed out that, all the work mentioned above focused on one-shot broadcast schedule and the goals of them are fundamentally different from ours in providing timing predictability.…”

Section: Related Workmentioning

confidence: 99%

A Novel Scheduling Algorithm for Supporting Periodic Queries in Broadcast Environments

Zhou

2015

IEEE Trans. on Mobile Comput.

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Being a proven efficient approach to answering queries that have common data needs, data broadcast has received much attention in the past decade, especially for dynamic and large-scale data dissemination. An important class of emerging data broadcast applications must monitor multiple data items continuously in order to enable data-driven decision making. For such applications, an important problem that must be addressed is how to disseminate data to periodic continuous queries so that all the requests can be satisfied while the bandwidth utilization is minimized. To our best knowledge, the only known work on this topic is the RM-UO algorithm proposed in [27]. However, the RM-UO algorithm simply utilizes the Sr algorithm introduced in [13] to transform the original queries into 2-harmonic tasks, which would lead to a considerable waste of available bandwidth. In this paper, based on the observation that some queries can be merged to save bandwidth consumption, we propose two merging polices namely Multiple Query Merging (MQM) and Redundant Query Merging (RQM), and show that both can lead to notable bandwidth savings. Further, to disseminate data to periodic continuous queries, we implement a unified scheduling algorithm called UM, which combines both MQM and RQM. Extensive experiments have been conducted to compare our UM algorithm with RM-UO, and the results show that UM outperforms RM-UO considerably in terms of wireless bandwidth consumption and query service ratio.

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“…Profit [15] is a counterpart of item access frequency for multi-item requests. For a given multi-item request, the profit of a required item reflects the contribution of the item to the completion of the request.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…If the request is closer to completion, the profit of the remaining unserved items is higher. As profit is more effective than item access frequency for multi-item requests [15], a new algorithm called Slack time Inverse Profit (SIP), which is similar to SIN except that item access frequency is replaced by profit, is included for comparison.…”

Section: A Experimental Setupmentioning

confidence: 99%

Admission Control and Channel Allocation of Multi-item Requests for Real-Time Data Broadcast

Lee

et al. 2012

2012 IEEE International Conference on Embedded and Real-Time Computing Systems and Applications

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Owing to its potential to satisfy all outstanding requests for the same data item with a single response, ondemand data broadcast becomes a widely accepted approach to dynamic and scalable wireless information dissemination. In some emerging applications, such as road traffic navigation system, users prefer to request multiple dependent data items at a time. In addition, requests usually have deadline constraints for realtime applications. However, in existing works, clients will not know that their requests cannot be satisfied until the deadlines expire. In this paper, admission control is introduced to data broadcast systems such that clients can be informed in advance in the case that their requests have no hope to be satisfied so that earlier remedial actions can be taken. Furthermore, a matching based allocation scheme is proposed to maximize data sharing among requests in multi-channel architectures. Simulation results show that our proposed algorithms have better performance and quality of service (QoS) than traditional algorithms.

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Profit-based scheduling and channel allocation for multi-item requests in real-time on-demand data broadcast systems

Cited by 30 publications

References 23 publications

On-demand data broadcast with deadlines for avoiding conflicts in wireless networks

On-demand data broadcast with deadlines for avoiding conflicts in wireless networks

A Novel Scheduling Algorithm for Supporting Periodic Queries in Broadcast Environments

Admission Control and Channel Allocation of Multi-item Requests for Real-Time Data Broadcast

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