| 1. |
- Sun, Gang, et al.
(författare)
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Energy-efficient and traffic-aware service function chaining orchestration in multi-domain networks
- 2019
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Ingår i: Future generations computer systems. - : Elsevier. - 0167-739X .- 1872-7115. ; 91, s. 347-360
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Tidskriftsartikel (refereegranskat)abstract
- Service function chaining (SFC) provisioning is helpful not only for saving the capital expenditure (CAPEX) and operational expenditure (OPEX) of a network provider but also for reducing energy consumption in the substrate network. However, to the best of our knowledge, there has been little research on the problem of energy consumption for orchestrating online SFC requests in multi-domain networks. In this paper, we first formulate the problem of an energy-efficient online SFC request that is orchestrated across multiple clouds as an integer linear programming (ILP) model to find an optimal solution. Then, we analyze the complexity of this ILP model and prove that the problem is NP-hard. Additionally, we propose a low-complexity heuristic algorithm named energy-efficient online SFC request orchestration across multiple domains (EE-SFCO-MD) for near-optimally solving the mentioned problem. Finally, we conduct simulation experiments to evaluate the performance of our algorithm. Simulation results show that EE-SFCO-MD consumes less energy than existing approaches while the online SFC’s requirements are met and the privacy of each cloud is effectively guaranteed. The low computational complexity of the heuristic approach makes it applicable for quickly responding to online SFC requests.
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| 2. |
- Sun, Gang, et al.
(författare)
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Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization
- 2020
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 7:7, s. 6116-6131
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Tidskriftsartikel (refereegranskat)abstract
- The efficient deployment of virtual network functions (VNFs) for network service provisioning is key for achieving network function virtualization (NFV); however, most existing studies address only offline or one-off deployments of service function chains (SFCs) while neglecting the dynamic (i.e., online) deployment and expansion requirements. In particular, many methods of energy/resource cost reduction are achieved by merging VNFs. However, the energy waste and device wear for large-scale collections of servers (e.g., cloud networks and data centers) caused by sporadic request updating are ignored. To solve these problems, we propose an energy-aware routing and adaptive delayed shutdown (EAR-ADS) algorithm for dynamic SFC deployment, which includes the following features. 1) Energy-aware routing (EAR): By considering a practical deployment environment, a flexible solution is developed based on reusing open servers and selecting paths with the aims of balancing energy and resources and minimizing the total cost. 2) Adaptive delayed shutdown (ADS): The delayed shutdown time of the servers can be flexibly adjusted in accordance with the usage of each device in each time slot, thus eliminating the no-load wait time of the servers and frequent on/off switching. Therefore, EAR-ADS can achieve dual energy savings by both decreasing the number of open servers and reducing the idle/switching energy consumption of these servers. Simulation results show that EAR-ADS not only minimizes the cost of energy and resources but also achieves an excellent success rate and stability. Moreover, EAR-ADS is efficient compared with an improved Markov algorithm (SAMA), reducing the average deployment time by more than a factor of 40.
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| 3. |
- Sun, Gang, et al.
(författare)
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Low-latency and Resource-efficient Service Function Chaining Orchestration in Network Function Virtualization
- 2020
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 7:7, s. 5760-5772
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Tidskriftsartikel (refereegranskat)abstract
- Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this paper, we propose a service function chain deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing.
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| 4. |
- Xu, Xiaoqiong, et al.
(författare)
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Latency performance modeling and analysis for hyperledger fabric blockchain network
- 2021
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Ingår i: Information Processing & Management. - : Elsevier. - 0306-4573 .- 1873-5371. ; 58:1
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Tidskriftsartikel (refereegranskat)abstract
- Blockchain has been one of the most attractive technologies for many modern and even future applications. Fabric, an open-source framework to implement the permissioned enterprise-grade blockchain, is getting increasing attention from innovators. The latency performance is crucial to the Fabric blockchain in assessing its effectiveness. Many empirical studies were conducted to analyze this performance based on different hardware platforms. These experimental results are not comparable as they are highly dependent on the underlying networks. Moreover, theoretical analysis on the latency of Fabric blockchain still receives much less attention. This paper provides a novel theoretical model to calculate the transaction latency under various network configurations such as block size, block interval, etc. Subsequently, we validate the proposed latency model with experiments, and the results show that the difference between analytical and experimental results is as low as 6.1%. We also identify some performance bottlenecks and give insights from the developer’s perspective.
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