Joint Application Placement and Request Routing Optimization for Dynamic Edge Computing Service Management

Li, Rui; Zhou, Zhi; Zhang, Xiaoxi; Chen, Xu

doi:10.1109/tpds.2022.3195205

Cited by 14 publications

(6 citation statements)

References 48 publications

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“…Unfortunately, problem P3 and P4 both can be proven are NP-hard [34], to solve it, we will introduce an efficient approximated algorithm to get the near-optimal solution.…”

Section: B Two-timescale Online Algorithm For P2mentioning

confidence: 99%

Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

Zhou

Zhang

et al. 2023

IEEE Trans. on Mobile Comput.

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Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among accuracy loss, carbon emission, and unknown future costs. Besides, many governments are launching carbon emission rights (CER) for operators to reduce carbon emissions further to reverse climate change. Facing these challenges, to achieve carbon-aware ML task offloading under limited carbon emission rights thus to achieve green edge AI, we establish a joint ML task offloading and CER purchasing problem, intending to minimize the accuracy loss under the long-term time-averaged cost budget of purchasing the required CER. However, considering the uncertainty of the resource prices, the CER purchasing prices, the carbon intensity of sites, and ML tasks' arrivals, it is hard to decide the optimal policy online over a long-running period time. To overcome this difficulty, we leverage the two-timescale Lyapunov optimization technique, of which the T -slot drift-pluspenalty methodology inspires us to propose an online algorithm that purchases CER in multiple timescales (on-preserved in carbon future market and on-demanded in the carbon spot market) and makes decisions about where to offload ML tasks. Considering the NP-hardness of the T -slot problems, we further propose the resource-restricted randomized dependent rounding algorithm to help to gain the near-optimal solution with no help of any future information. Our theoretical analysis and extensive simulation results driven by the real carbon intensity trace show the superior performance of the proposed algorithms.

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“…Unfortunately, problem P3 and P4 both can be proven are NP-hard [34], to solve it, we will introduce an efficient approximated algorithm to get the near-optimal solution.…”

Section: B Two-timescale Online Algorithm For P2mentioning

confidence: 99%

Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

Zhou

Zhang

et al. 2023

IEEE Trans. on Mobile Comput.

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“…The work in Chen et al 32 studied the problem to minimize the expected energy consumption formulated as a mixed integer nonconvex problem, which was solved by a deep learning‐based scheme. The work in Li et al 33 addressed the problem to minimize delay while satisfying the long‐term budget constraints on service placement by using only current system information. These works considered that each mobile user belongs to only one BS coverage area.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, the work in Liu et al 30 studied the storage and bandwidth capacities without computation capacity to maximize the number of users assigned to edge nodes. The works in earlier studies 3,[31][32][33] studied joint service placement and user assignment with storage, computation, and bandwidth constraints in edge clouds. The work in He et al 3 studied the problem of service placement and request scheduling in MEC systems to maximize the number of users.…”

mentioning

confidence: 99%

Joint service placement and user assignment model in multi‐access edge computing networks against base‐station failure

Taka

Oki

2023

Int J Network Mgmt

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Multi‐access edge computing (MEC) enables users to exploit the resources of cloud computing at a base station (BS) in proximity to the users where an MEC server is hosted. While we have advantage of being able to communicate with low latency and small network load in MEC networks, the resources in BSs are limited. One challenge is where to provide users with services from to make efficient use of resources. Furthermore, to enhance the reliability of MEC system, the case that a BS fails needs to be considered. This paper proposes a service placement and user assignment model with preventive start‐time optimization against a single BS failure in MEC networks. The proposed model preventively determines the service placement and user assignment in each BS failure pattern to minimize the worst‐case penalty which is the largest penalty among all failure patterns. We formulate the proposed model as an integer linear programming problem. We prove that the considered problem is NP‐hard. When the problem size becomes large, it may not be solved in a practical computation time. To solve larger size problems, we introduce two algorithms: one is the greedy algorithm with allocation upgrade and the other is with allocation upgrade and preemption. The results show that the introduced algorithms obtain solutions with smaller worst‐case penalty than the benchmark in a practical time.

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“…Parameter Setting: The default settings of parameters in our simulations are collected in Table I. We note that the values chosen for the parameters are practical and are widely used in existing work [14], [26], [38] and the price of edge server resource refers to Alibaba Cloud servers 1 . Service Request Demand: We assume that the service request demands of each edge server follow Zipf distribution, witch are consistent with the other researches [14].…”

Section: A Experiments Setupmentioning

confidence: 99%

“…In general, service placement requires dynamically optimizing the placement of services on edge servers to better utilize edge server resources [10]- [13]. Resource provisioning requires the flexibility to adjust resource provisioning for each service to optimize overall system performance [14]- [16]. Additionally, to balance system workloads and improve system performance, workloads scheduling ensures that task requests are dynamically dispatched to appropriate edge nodes or remote cloud servers [17]- [19].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Resource Management in Cloud and Edge Computing

Zhang

2020

Network Management in Cloud and Edge Computing

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Due to the limited resource capacity of edge servers and the high purchase costs of edge resources, service providers are facing the new challenge of how to take full advantage of the constrained edge resources for Internet of Things (IoT) service hosting and task scheduling to maximize system performance. In this paper, we study the joint optimization problem on service placement, resource provisioning, and workloads scheduling under resource and budget constraints, which is formulated as a mixed integer non-linear programming problem. Given that the frequent service placement and resource provisioning will significantly increase system configuration costs and instability, we propose a two-timescale framework for resource management and workloads scheduling, named RMWS. RMWS consists of a Gibbs sampling algorithm and an alternating minimization algorithm to determine the service placement and resource provisioning on large timescales. And a sub-gradient descent method has been designed to solve the workload scheduling challenge on small timescales. We conduct comprehensive experiments under different parameter settings. The RMWS consistently ensures a minimum 10% performance enhancement compared to other algorithms, showcasing its superiority. Theoretical proofs are also provided accordingly.

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Joint Application Placement and Request Routing Optimization for Dynamic Edge Computing Service Management

Cited by 14 publications

References 48 publications

Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

Joint service placement and user assignment model in multi‐access edge computing networks against base‐station failure

A Survey of Resource Management in Cloud and Edge Computing

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