A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Wang, Bo; Wang, Changhai; Huang, Wanwei; Song, Ying; Qin, Xiaoyun

doi:10.1109/access.2020.3029649

“…To mitigate edge DDoS attacks, a hybrid computing paradigm known as an endedge-cloud (EEC) ecosystem [15,16,17,18] provides a possible solution, converging the designs of cloud [19,20] and edge [3,4] computing. As shown in Figure 2, such an ecosystem includes a super-capacity cloud center, numerous constrained-capacity ESs, and a large number of low-capacity end devices.…”

Section: Motivationmentioning

confidence: 99%

Optimal Cloud Assistance Policy of End-Edge-Cloud Ecosystem for Mitigating Edge Distributed Denial of Service Attacks

Li¹

2021

Preprint

0

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Edge computing has become a fundamental technology for Internet of Things (IoT) applications. To provide reliable services for latency-sensitive applications, edge servers must respond to end devices within the shortest amount of time possible. Edge distributed denial-of-service (DDoS) attacks, which render edge servers unusable by legitimate IoT applications by sending heavy requests from distributed attacking sources, is a threat that leads to severe latency. To protect edge servers from DDoS attacks, a hybrid computing paradigm known as an end-edge-cloud ecosystem provides a possible solution. Cloud assistance is allowed with this architecture. Edge servers can upload their pending tasks onto a cloud center for a workload reduction when encountering a DDoS attack, similar to borrowing resources from the cloud. Nevertheless, before using the ecosystem to mitigate edge DDoS attacks, we must address the core problem that edge servers must decide when and to what extent they should upload tasks to the cloud center. In this study, we focus on the design of optimal cloud assistance policies. First, we propose an edge workload evolution model that describes how the workload of the edge servers change over time with a given cloud assistance policy. On this basis, we quantify the effectiveness of the policy by using the resulting overall latency and formulate an optimal control problem for seeking optimal policies that can minimize such latency. We then provide solutions by deriving the optimality system and discuss some properties of the optimal solutions to accelerate the problem solving. Next, we introduce a numerical iterative algorithm to seek solutions that can satisfy the optimality system. Finally, we provide several illustrative numerical examples. The results show that the optimal policies obtained can effectively mitigate edge DDoS attacks.

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“…To mitigate edge DDoS attacks, a hybrid computing paradigm known as an end-edge-cloud (EEC) ecosystem [15][16][17][18] provides a possible solution, converging the designs of cloud [19,20] and edge [3,4] computing. As shown in Fig.…”

Section: Motivationmentioning

confidence: 99%

Optimal cloud assistance policy of end-edge-cloud ecosystem for mitigating edge distributed denial of service attacks

Li

¹

2021

J Cloud Comp

2

0

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Edge computing has become a fundamental technology for Internet of Things (IoT) applications. To provide reliable services for latency-sensitive applications, edge servers must respond to end devices within the shortest amount of time possible. Edge distributed denial-of-service (DDoS) attacks, which render edge servers unusable by legitimate IoT applications by sending heavy requests from distributed attacking sources, is a threat that leads to severe latency. To protect edge servers from DDoS attacks, a hybrid computing paradigm known as an end-edge-cloud ecosystem provides a possible solution. Cloud assistance is allowed with this architecture. Edge servers can upload their pending tasks onto a cloud center for a workload reduction when encountering a DDoS attack, similar to borrowing resources from the cloud. Nevertheless, before using the ecosystem to mitigate edge DDoS attacks, we must address the core problem that edge servers must decide when and to what extent they should upload tasks to the cloud center. In this study, we focus on the design of optimal cloud assistance policies. First, we propose an edge workload evolution model that describes how the workload of the edge servers change over time with a given cloud assistance policy. On this basis, we quantify the effectiveness of the policy by using the resulting overall latency and formulate an optimal control problem for seeking optimal policies that can minimize such latency. We then provide solutions by deriving the optimality system and discuss some properties of the optimal solutions to accelerate the problem solving. Next, we introduce a numerical iterative algorithm to seek solutions that can satisfy the optimality system. Finally, we provide several illustrative numerical examples. The results show that the optimal policies obtained can effectively mitigate edge DDoS attacks.

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“…2) Problem Formulation: Regarding to calculation of service total delay, the total delay for the whole system is equal to the summation of all IoT devices' total delay as shown in Eq. (9).…”

Section: Service Chainsmentioning

confidence: 99%

“…Task scheduling problems are investigated in several aspects such as architecture, independent tasks, offloading tasks completely or partially, executing tasks sequentially or in parallel, considering services composing some tasks, task as a unit or dividing to several sub-tasks [6] [7] [8] [9].…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of Recurrent Service Chains on Distributed Edge-Cloud Infrastructures

Mahjoubi

¹

,

Taheri

²

,

Grinnemo

³

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

4

0

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By increasing the number of IoT-devices, cloudcomputing faces challenges for some computation and timesensitive applications. Edge-computing has emerged to enable IoT-devices offload their computation tasks. Offloading tasks is a complex and challenging issue. We propose a comprehensive model including user, edge and cloud layers for scheduling continuous offering of services. Furthermore, we modeled the tasks of service as recurrent (repetitive) with a given frequency. The serviceplacement problem is formulated as a Mixed-Integer Linear Programming problem that aims to minimize the total delay of all services. We solve the problem with CPLEX, and proposed four fast heuristics to find near-optimal solutions. We compared the results of our proposed heuristics with the result obtained with CPLEX, in terms of problem-solving speed and accuracy, as well as resource utilization of all nodes. The results show that two of our proposed heuristics produce near-optimal solutions in a fraction of the time taken by CPLEX.Index Terms-edge computing, service placement, IoT devices, cloud computing• We propose a comprehensive model that includes all layers (user, edge, and cloud). It models three main node parameters (CPU, RAM, and network capacities), as well as the link

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A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Cited by 96 publications

References 149 publications

Optimal Cloud Assistance Policy of End-Edge-Cloud Ecosystem for Mitigating Edge Distributed Denial of Service Attacks

Optimal Cloud Assistance Policy of End-Edge-Cloud Ecosystem for Mitigating Edge Distributed Denial of Service Attacks

Optimal cloud assistance policy of end-edge-cloud ecosystem for mitigating edge distributed denial of service attacks

Optimal Placement of Recurrent Service Chains on Distributed Edge-Cloud Infrastructures

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