Joint scheduling of communication and computation resources in multiuser wireless application offloading

Molina, Marc; Muñoz, Olga; Pascual‐Iserte, Antonio; Vidal, Josep

doi:10.1109/pimrc.2014.7136330

Cited by 58 publications

(28 citation statements)

References 19 publications

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“…First, the arrival times of different users are in general asynchronous so that it is desirable for the edge server with finite computational resource to buffer and compute the tasks sequentially, which incurs the queuing delay. In [120], to cope with the bursty task arrivals, the server scheduling was integrated with uplinkdownlink transmission scheduling to minimize the average latency using queuing theory. Second, even for synchronized task arrivals, the latency requirements can differ significantly over users running different types of applications ranging from latency-sensitive to latency-tolerant applications.…”

Section: Stochastic Modelmentioning

confidence: 99%

A Survey on Mobile Edge Computing: The Communication Perspective

Mao

You

Zhang

et al. 2017

IEEE Commun. Surv. Tutorials

4,372

1,998

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Abstract-Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discusse a set of issues, challenges and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

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Section: Stochastic Modelmentioning

confidence: 99%

A Survey on Mobile Edge Computing: The Communication Perspective

Mao

You

Zhang

et al. 2017

IEEE Commun. Surv. Tutorials

4,372

1,998

View full text Add to dashboard Cite

show abstract

“…This would produce a coupled problem, consisting of both resource allocation and application offloading, that could be significantly more complex depending on the strategy adopted for the resource allocation. In this regard, some initial works with simplified assumptions have been done by the same authors in [40] for a single VM. However, the complete generalization is still to be done and is left for future work.…”

Section: Discussionmentioning

confidence: 99%

Energy Efficiency in Latency-Constrained Application Offloading From Mobile Clients to Multiple Virtual Machines

Lagén

Pascual‐Iserte

Muñoz

et al. 2018

IEEE Trans. Signal Process.

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Abstract-This paper addresses the energy-latency trade-off in distributed application offloading, in which an energy-limited handset offloads totally or partially an application to one or several virtual machines (VMs) located in remote locations or access points (APs) close to the mobile terminal (MT). One of the APs (the serving AP) provides radio access to the MT and is connected to the VMs through non-ideal backhaul (BH) links. In this setting, we optimize the offloading strategy (including the joint optimization of radio and computational resources) to minimize the energy consumption at the MT subject to a maximum latency constraint. In addition, we propose robust designs to cope with imperfect acquisition of the channel state information (CSI) and the BH parameters. Our findings show that, as far as the energy-latency trade-off is concerned, the optimal order of activation of the VMs does not depend on their processing capabilities but the delays of the BH links. However, once a VM is selected to participate in the processing, the optimal amount of processing allocated to such VM depends on its computational capabilities as well as on the features (capacity and delay) of the BH link. Additionally, offloading decisions become more conservative as the uncertainty in CSI and BH parameters increases.

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“…Remark 1 (Cascade Coupling of Local and Remote Queues). The local queue dynamics in (6) and the remote queue dynamics in (7) are coupled together by a cascade control, because the departure of the former is the arrival of the latter. This cascade coupling creates complex interdependence and makes the computation offloading problem an involved stochastic optimization problem.…”

Section: B Queue Dynamicsmentioning

confidence: 99%

“…Both the computation time and the waiting time at the offloading destination will influence the delay performance.In [5], a basic two-party communication complexity model is studied for the networked computation problems, with a particular emphasis on the communication aspect of computation. In [6], the communication and computation capabilities are jointly optimized to minimize the delay under the constraint of energy consumption. Since the cloud computing servers are usually computationally powerful, it is reasonable to neglect the executing time at the server.However, the remote cloud computing servers are always far away from the MTs and the large communication delay cannot be decreased, so the cloud computation offloading is not fit for the delay-sensitive applications.Mobile edge computing (MEC) [7] is emerged as a promising technology to handle the explosive computation demands and the everincreasing computation quality requirements.…”

mentioning

confidence: 99%

“…In [5], a basic two-party communication complexity model is studied for the networked computation problems, with a particular emphasis on the communication aspect of computation. In [6], the communication and computation capabilities are jointly optimized to minimize the delay under the constraint of energy consumption. Since the cloud computing servers are usually computationally powerful, it is reasonable to neglect the executing time at the server.…”

mentioning

confidence: 99%

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Closed-Form Delay-Optimal Computation Offloading in Mobile Edge Computing Systems

Meng

Wang

et al. 2019

IEEE Trans. Wireless Commun.

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Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we study the delay-optimal computation offloading in computation-constrained MEC systems. We consider the computation task queue at the MEC server due to its constrained computation capability.In this case, the task queue at the MT and that at the MEC server are strongly coupled in a cascade manner, which creates complex interdependencies and brings new technical challenges. We model the computation offloading problem as an infinite horizon average cost Markov decision process (MDP), and approximate it to a virtual continuous time system (VCTS) with reflections. Different to most of the existing works, we develop the dynamic instantaneous rate estimation for deriving the closedform approximate priority functions in different scenarios. Based on the approximate priority functions, we propose a closed-form multi-level water-filling computation offloading solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI). A extension is provided from single MT single MEC server scenarios to multiple MTs multiple MEC servers scenarios and several insights are derived. Finally, the simulation results show that the proposed scheme outperforms the conventional schemes.This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.Technology, Hong Kong. 2 I. INTRODUCTION Smart mobile terminals (MTs) with advanced communication and computation capabilities facilitate us with a pervasive and powerful platform to realize many emerging computationintensive mobile applications, e.g., interactive gaming, character recognition, and natural language processing [2], [3]. These pose exigent requirements on the quality of computation experience, especially for the delay-sensitive applications. Computation offloading [4], which offloads the computation tasks to the offloading destination, is one of the fundamental services to improve the computation performance, i.e., delay performance. In computation offloading services, both the communication capability of the MT and the computation capability of the offloading destination will influence the delay performance. Specifically, • The communication capability of the MT: The offloading rate varies according to the time-varying wireless channel quality between the MT and the offloading destination. Poor communication capabilities will result in the starvation of the computation of the offloading destination, which induces a large queuing delay at the MT.• The computation capability of the offloading destination: In practical scenarios, the offloaded tasks cannot be executed immediately because the computation capability of the offloading destination is not infinity. Both the computation time and the waiting time at ...

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Joint scheduling of communication and computation resources in multiuser wireless application offloading

Cited by 58 publications

References 19 publications

A Survey on Mobile Edge Computing: The Communication Perspective

A Survey on Mobile Edge Computing: The Communication Perspective

Energy Efficiency in Latency-Constrained Application Offloading From Mobile Clients to Multiple Virtual Machines

Closed-Form Delay-Optimal Computation Offloading in Mobile Edge Computing Systems

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