In elastic optical inter-datacenter networks, multiple software-defined networking (SDN) controllers have been used to improve scalability and reliability. The static controller deployment faces the problem of controller load imbalance under dynamic changes in network traffic. In response to this problem, existing research works proposed to use dynamic switch migration to achieve the controller load balancing. However, the efficiency of the existing switch migration is low, because the load balancing performance of controllers does not improve significantly after the switch migration and the migration activities can also cause additional migration costs. Thus the efficiency of switch migration has not yet been well resolved. In this work, we propose a highly-efficient switch migration (HESM) for controller load balancing. The proposed HESM method defines multiple load metrics to measure the load of controllers, and selects the optimal target controller with the largest remaining resource, which improves the load balancing performance. HESM selects switches based on minimizing migration costs, reducing additional migration costs. In addition, HESM can handle the load of multiple controllers in parallel during a switch migration activity, which improves the efficiency of migration. The simulation results show that HESM significantly improves load balancing performance of controllers and reduces the migration cost compared to existing solutions.

Data center networks demand high-performance, robust, and scalable load balancing protocols. Despite progress, existing work still cannot meet these requirements well. Software defined networking (SDN) can bring considerable flexibility to the management of data center networks. In the software-defined data center network, we design, analyze, and evaluate RSLB, a robust and scalable load balancing protocol that overcomes these challenges. RSLB uses fine-grained flowcell as the transmission unit, and uses link delay as the congestion metric. It uses a three-step routing strategy to route flowcells to the path with the least congestion. Through global congestion awareness, RSLB reduces flow completion time (FCT), and is more robust to topological asymmetries compared to existing congestion-agnostic schemes. To collect and store congestion information, RSLB adopts a distributed control structure that monitors the congestion of the entire network through multiple controllers, which makes it much more scalable for implementation in large-scale networks compare to existing congestion-aware schemes. The simulation results show that RSLB can achieve lower FCT for mice flows and higher throughput for elephant flows than existing schemes, no matter in failure-free topology or asymmetric topology.

In data center networks, traffic needs to be distributed among different paths using traffic optimization strategies for mixed flows. Most of the existing strategies consider either distributed or centralized mechanisms to optimize the latency of mice flows or the throughput of elephant flows. However, low network performance and scalability issues are intrinsic limitations of both strategies. In addition, the current elephant flow detection methods are inefficient. In this paper, we propose a high-performance and scalable traffic optimization strategy (HPSTOS) based on a hybrid approach that leverages the advantages of both centralized and distributed mechanisms. HPSTOS improves the efficiency of elephant flow detection through sampling and flow-table identification. HPSTOS guarantees preferential transmission of mice flows using priority scheduling and adjusts their transmission rate by coding-based congestion control on the end-host, reducing their latency. Additionally, HPSTOS schedules elephant flows by cost-aware dynamic flow scheduling on a centralized controller to improve their throughput. The controller handles only elephant flows, which constitutes the minority of the flows, allowing effective scalability. Evaluations show that HPSTOS outperforms existing schemes by realizing efficient elephant flow detection and improving network performance and scalability.

By integrating Software-Defined Networking (SDN), Software-Defined Internet of Things (SD-IoT) simplifies network configuration while enhancing controllability. The expansion of the IoT scale has led to the emergence of the multiple controller architecture. However, it introduces the challenge of controller load imbalances. Existing schemes primarily focus on dynamic switch migration. Nonetheless, conventional strategies use real-time network information for load measurement and selection of candidate switches, which reduces load balancing performance due to inaccurate load measurement. Moreover, existing approaches struggle to balance load balancing rate and migration cost when selecting the target controllers. Therefore, we propose the controller load balancing based on load prediction (CLB-LP) scheme, which uses historical load data to predict future load, thereby avoiding unnecessary switch migrations. Additionally, we introduce a switch selection algorithm that combines load prediction and migration probability to select candidate switches, effectively improving load balancing performance. Furthermore, we present a target controller selection algorithm based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which improves the load balancing rate while reducing migration cost. Finally, we evaluate the effectiveness of CLB-LP, and compared to existing schemes, its load balancing rate and response time are 29.4% higher and 28.5% lower, respectively.

The proliferation of cloud computing technology has led to a significant number of users opting to store image data on the cloud. To ensure the confidentiality of image data, it is recommended to apply encryption techniques prior to uploading them to cloud servers. However, the traditional encrypted image retrieval schemes prove inadequate for practical application scenarios due to limitations in supporting multi-owner and multi-user settings, inefficient access control, and a lack of dynamic verifiability. To address the challenges presented by practical application scenarios, in this paper, we propose a Dynamic and Controllable Image Retrieval scheme in the Multi-owner multi-user settings (DCIRM). First of all, we realize the usability in multi-owner and multi-user scenarios through re-encryption settings. Then, we realize the verifiability of dynamic data by applying the dynamic authentication constructed by the chameleon hash function. Finally, we realize lightweight access control through the polynomial-based access strategy. The DCIRM scheme has been demonstrated to be privacy-preserving, efficient, and feasible through rigorous security analysis and experimentation with authentic datasets.