Service Placement and Request Scheduling for Data-intensive Applications in Edge Clouds

Farhadi, Vajiheh; Mehmeti, Fidan; He, Ting; Porta, Thomas La; Khamfroush, Hana; Wang, Shiqiang; Chan, Kevin

doi:10.1109/infocom.2019.8737368

Cited by 153 publications

(100 citation statements)

References 30 publications

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“…processing parameters like aggregation processing interval and throughput. Farhadi et al [41] proposed a data-intensive application scheduler using a mixed-integer linear program (MILP) on an edge cloud system. The technique aims to maximize the system utilization in terms of the number of placed scheduled services.…”

Section: B the Contribution Of Edge Computingmentioning

confidence: 99%

Hybrid Workflow Scheduling on Edge Cloud Computing Systems

Alsurdeh¹,

Calheiros

Matawie

et al. 2021

IEEE Access

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Internet of Things applications can be represented as workflows in which stream and batch processing are combined to accomplish data analytics objectives in many application domains such as smart home, health care, bioinformatics, astronomy, and education. The main challenge of this combination is the differentiation of service quality constraints between batch and stream computations. Stream processing is highly latency-sensitive while batch processing is more likely resource-intensive. In this work, we propose an end-to-end hybrid workflow scheduling on an edge cloud system as a two-stage framework. In the first stage, we propose a resource estimation algorithm based on a linear optimization approach, gradient descent search (GDS), and in the second stage, we propose a cluster-based provisioning and scheduling technique for hybrid workflows on heterogeneous edge cloud resources. We provide a multi-objective optimization model for execution time and monetary cost under constraints of deadline and throughput. Results demonstrate the framework performance in controlling the execution of hybrid workflows by efficiently tuning several parameters including stream arrival rate, processing throughput, and workflow complexity. In comparison to a meta-heuristics technique using Particle Swarm Optimization (PSO), the proposed scheduler provides significant improvement for large-scale hybrid workflows in terms of execution time and cost with an average of 8% and 35%, respectively.

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Section: B the Contribution Of Edge Computingmentioning

confidence: 99%

Hybrid Workflow Scheduling on Edge Cloud Computing Systems

Alsurdeh¹,

Calheiros

Matawie

et al. 2021

IEEE Access

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“…In our previous work, we had considered the problem of joint service placement and task scheduling on a multitiered network [2] [4]. In both works, authors proposed a joint placement and scheduling algorithm under realistic hardware constraints.…”

Section: Related Workmentioning

confidence: 99%

“…In both works, authors proposed a joint placement and scheduling algorithm under realistic hardware constraints. In [2], the problem was introduced and proven to be NP-Hard; then, an algorithm to approximate the optimal placement and scheduling was provided which ran only under certain conditions. This work was improved upon in [4], where a more general algorithm was developed to place and schedule while maximizing the number of users served.…”

Section: Related Workmentioning

confidence: 99%

Meeting Users’ QoS in a Edge-to-Cloud Platform via Optimally Placing Services and Scheduling Tasks

Turner

Khamfroush

2020

2020 International Conference on Computing, Networking and Communications (ICNC)

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This paper considers the problem of service placement and task scheduling on a three-tiered edgeto-cloud platform when user requests must be met by a certain deadline. Time-sensitive applications (e.g., augmented reality, gaming, real-time video analysis) have tight constraints that must be met. With multiple possible computation centers, the "where" and "when" of solving these requests becomes paramount when meeting their deadlines. We formulate the problem of meeting users' deadlines while minimizing the total cost of the edge-tocloud service provider as an Integer Linear Programming (ILP) problem. We show the NP-hardness of this problem, and propose two heuristics based on making decisions on a local vs global scale. We vary the number of users, the QoS constraint, and the cost difference between remote cloud and cloudlets(edge clouds), and run multiple Monte-Carlo runs for each case. Our simulation results show that the proposed heuristics are performing close to optimal while reducing the complexity.

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“…The next searching targetk is set to k ∈ K s with the minimum S o k value (lines [15][16]. If S ô k 0, this means that the current computation configuration could not host all the traffic; hence, the algorithm will go back to the while loop and continue to the next searching.…”

Section: Neighbor Search For Computation Candidatesmentioning

confidence: 99%

“…This permits to transform equation (16) as t kn,i L = l∈L γ kn,i l v l . We then need to linearize the product of the binary variable γ kn,i l and the continuous variable v l , and to this aim we introduce an auxiliary variable g kn,i l = γ kn,i l v l ,…”

Section: A2 Link Latencymentioning

confidence: 99%

Joint Network Slicing and Mobile Edge Computing in 5G Networks

Xiang

Elias

Martignon

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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Multi-access Edge Computing (MEC) facilitates the deployment of critical applications with stringent QoS requirements, latency in particular. Our paper considers the problem of jointly planning the availability of computational resources at the edge, the slicing of mobile network and edge computation resources, and the routing of heterogeneous traffic types to the various slices. These aspects are intertwined and must be addressed together to provide the desired QoS to all mobile users and traffic types still keeping costs under control. We formulate our problem as a mixed-integer nonlinear program (MINLP) and we define a heuristic, named Neighbor Exploration and Sequential Fixing (NESF), to facilitate the solution of the problem. The approach allows network operators to fine tune the network operation cost and the total latency experienced by users. We evaluate the performance of the proposed model and heuristic against two natural greedy approaches. We show the impact of the variation of all the considered parameters (viz., different types of traffic, tolerable latency, network topology and bandwidth, computation and link capacity) on the defined model. Numerical results demonstrate that NESF is very effective, achieving near-optimal planning and resource allocation solutions in a very short computing time even for large-scale network scenarios.

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Service Placement and Request Scheduling for Data-intensive Applications in Edge Clouds

Cited by 153 publications

References 30 publications

Hybrid Workflow Scheduling on Edge Cloud Computing Systems

Hybrid Workflow Scheduling on Edge Cloud Computing Systems

Meeting Users’ QoS in a Edge-to-Cloud Platform via Optimally Placing Services and Scheduling Tasks

Joint Network Slicing and Mobile Edge Computing in 5G Networks

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