| 1. |
- Deng, Shuiguang, et al.
(författare)
-
Composition-Driven IoT Service Provisioning in Distributed Edges
- 2018
-
Ingår i: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 6, s. 54258-54269
-
Tidskriftsartikel (refereegranskat)abstract
- The increasing number of Internet of Thing (IoT) devices and services makes it convenient for people to sense the real world and makes optimal decisions or complete complex tasks with them. However, the latency brought by unstable wireless networks and computation failures caused by constrained resources limit the development of IoT. A popular approach to solve this problem is to establish an IoT service provision system based on a mobile edge computing (MEC) model. In the MEC model, plenty of edge servers are placed with access points via wireless networks. With the help of cached services on edge servers, the latency can be reduced, and the computation can be offloaded. The cache services must be carefully selected so that many requests can by satisfied without overloading resources in edge servers. This paper proposes an optimized service cache policy by taking advantage of the composability of services to improve the performance of service provision systems. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of these IoT services.
|
|
| 2. |
- Deng, Shuiguang, et al.
(författare)
-
Dynamical Resource Allocation in Edge for Trustable Internet-of-Things Systems : A Reinforcement Learning Method
- 2020
-
Ingår i: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 16:9, s. 6103-6113
-
Tidskriftsartikel (refereegranskat)abstract
- Edge computing (EC) is now emerging as a key paradigm to handle the increasing Internet-of-Things (IoT) devices connected to the edge of the network. By using the services deployed on the service provisioning system which is made up of edge servers nearby, these IoT devices are enabled to fulfill complex tasks effectively. Nevertheless, it also brings challenges in trustworthiness management. The volatile environment will make it difficult to comply with the service-level agreement (SLA), which is an important index of trustworthiness declared by these IoT services. In this article, by denoting the trustworthiness gain with how well the SLA can comply, we first encode the state of the service provisioning system and the resource allocation scheme and model the adjustment of allocated resources for services as a Markov decision process (MDP). Based on these, we get a trained resource allocating policy with the help of the reinforcement learning (RL) method. The trained policy can always maximize the services' trustworthiness gain by generating appropriate resource allocation schemes dynamically according to the system states. By conducting a series of experiments on the YouTube request dataset, we show that the edge service provisioning system using our approach has 21.72% better performance at least compared to baselines.
|
|
| 3. |
- Deng, Shuiguang, et al.
(författare)
-
Optimal Application Deployment in Resource Constrained Distributed Edges
- 2021
-
Ingår i: IEEE Transactions on Mobile Computing. - : IEEE Computer Society. - 1536-1233 .- 1558-0660. ; 20:5, s. 1907-1923
-
Tidskriftsartikel (refereegranskat)abstract
- The dramatically increasing of mobile applications make it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on a mobile edge computing (MEC) paradigm. In the MEC paradigm, plenty of machines are placed at the edge of the network so that the performance of applications can be optimized by using the involved microservice instances deployed on them. In this paper, we explore the deployment problem of microserivce-based applications in the MEC environment and propose an approach to help to optimize the cost of application deployment with the constraints of resources and the requirement of performance. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of mobile services.
|
|
| 4. |
- Khoshkholghi, Mohammad Ali, et al.
(författare)
-
Service Function Chain Placement for Joint Cost and Latency Optimization
- 2020
-
Ingår i: Mobile Networks and Applications. - : Springer. - 1383-469X .- 1572-8153. ; 25:6, s. 2191-2205
-
Tidskriftsartikel (refereegranskat)abstract
- Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).
|
|
| 5. |
- Xiang, Z., et al.
(författare)
-
Computing Power Allocation and Traffic Scheduling for Edge Service Provisioning
- 2020
-
Ingår i: Proceedings - 2020 IEEE 13th International Conference on Web Services, ICWS 2020. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728187860 ; , s. 394-403
-
Konferensbidrag (refereegranskat)abstract
- The increasing number of mobile web services makes it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on the mobile edge computing (MEC) paradigm, in which the latency can be reduced and the computation can be offloaded with the help of services deployed on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to services, as well as designing the traffic scheduling strategy. In this paper, we investigate the edge-cloud cooperation mechanism in service provisioning as well as the billing model of it. To minimize the average service response time and make the expense acceptable, we model and formulate the performance-cost service provisioning problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. Then we propose an efficient online algorithm, called PCA- CATS, to decompose this problem into two individual subproblems. We conduct a series of experiments to evaluate the performance of our approach. The results show that PCA- CATS can easily balance the performance and expense with a factor V, and can reduce up to 53.3 % service response time as compared with the baselines.
|
|
| 6. |
- Xiang, Zhengzhe, et al.
(författare)
-
Dynamical Service Deployment and Replacement in Resource-Constrained Edges
- 2019
-
Ingår i: Mobile Networks and Applications. - New York, USA : Springer. - 1383-469X .- 1572-8153. ; 25, s. 674-689
-
Tidskriftsartikel (refereegranskat)abstract
- With the rapid development of mobile computing technology, more and more complex tasks are now able to be fulfilled on users’ mobile devices with an increasing number of novel services. However, the development of mobile computing is limited by the latency brought by unstable wireless network and the computation failure caused by the constrained resources of mobile devices. Therefore, people turn to establish a service provisioning system based on mobile edge computing (MEC) model to solve this problem. With the help of services deployed on edge servers, the latency can be reduced and the computation can be offloaded. Though the edge servers have more available resources than mobile devices, they are still resource-constrained, so they must carefully choose the services for deployment. In this paper, we focus on improving performance of the service provisioning system by deploying and replacing services on edge servers. Firstly, we design and implement a prototype of service provisioning system that simulates the behaviors between users and servers. Secondly, we propose an approach to deploy services on edge servers before the launching of these servers, and propose an approach to replace services on edge servers dynamically. Finally, we conduct a series of experiments to evaluate the performance of our approaches. The result shows that our approach can improve the performance of service provisioning systems.
|
|
| 7. |
- Xiang, Zhengzhe, et al.
(författare)
-
Energy-effective IoT Services in Balanced Edge-Cloud Collaboration Systems
- 2021
-
Ingår i: <em>2021 IEEE International Conference on Web Services (ICWS)</em>. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665416818 ; , s. 219-229
-
Bokkapitel (refereegranskat)abstract
- The rapid development of the Internet-of-Things (IoT) makes it convenient to sense and collect real-world information with different kinds of widely distributed sensors. With plenty of web services providing diverse functions on the cloud, the collected information can be sufficiently used to complete complex tasks after being uploaded. However, the latency brought by long-distance communication and network congestion limits the development of IoT platforms. A feasible approach to solve this problem is to establish an edge-cloud collaboration (ECC) system based on the multi-access edge computing (MEC) paradigm where the collected information can be refined with the services deployed on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to services, as well as designing the traffic scheduling strategy. In this paper, we investigated the edge-cloud cooperation mechanism of service provisioning in ECC systems, and to that end, proposed an energy-consumption model for it; we also proposed a performance model and balancing model to quantify the running state of ECC systems. Based on these, we further formulated the energy-effective ECC system optimization problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. With the convexity of this problem proved, we proposed an algorithm to solve it and conducted a series of experiments to evaluate its performance. The results showed that our approach can improve at least 4.3 % of the performance compared with representative baselines.
|
|
