Software-defined networking (SDN) is an emerging network architecture that promises to simplify network management, improve network resource utilization, and boost evolution and innovation in traditional networks. The SDN allows the abstraction and centralized management of the lower-level network functionalities by decoupling the network logic from the data forwarding devices into the logically centralized distributed controllers. However, this separation introduces new scalability and performance challenges in large-scale networks of dynamic traffic and topology conditions. Many research studies have represented that centralization and maintaining the global network visibility over the distributed SDN controller introduce scalability concern. This paper surveys the state-of-the-art proposed techniques toward minimizing the control to data planes communication overhead and controllers' consistency traffic to enhance the OpenFlow-SDN scalability in the context of logically centralized distributed SDN control plane architecture. The survey mainly focuses on four issues, including logically centralized visibility, link-state discovery, flow rules placement, and controllers' load balancing. In addition, this paper discusses each issue and presents an updated and detailed study of existing solutions and limitations in enhancing the OpenFlow-SDN scalability and performance. Moreover, it outlines the potential challenges that need to be addressed further in obtaining adaptive and scalable OpenFlow-SDN flow control.

Toward adaptive and scalable openflow-SDN flow control: A survey

In social networks, nodes should analyze communication area during data transmission and find suitable neighbors to perform effective data classification transmission. This is similar to finding certain transmission destinations during data transmission with mobile devices. However, cache space with node in social opportunistic networks is limited, and waiting for destination node could also cause end-to-end delay. To improve the transmission environment, this study established a node recognition method for assessment probability, to satisfy the priority adjustment for the high probability nodes of cache, and then cache space should be reconstructed. To avoid accidentally deleting cached data, the cache task of the node is shared through the neighbor node cooperation, and the effective data transmission is performed. Through experiments and the comparison of social network algorithms, the proposed scheme improves delivery ratio by 82% and reduces delay by 74% with the traditional algorithms on average.

Information cache management and data transmission algorithm in opportunistic social networks

Cache memory plays an important role in improving the performance of web servers, especially for big data transmission, which response time is constrained. It is necessary to use an effective method, such as web cache. Because an outstanding cache replacement algorithm can not only reduce the users access time but also improve the performance of the system. The traditional used weighting replacement policy does not consider the size parameter, hence, it may perform poorly while the datasets are larger. In this paper, we propose a novel, high-performance cache replacement algorithm for the web cache, named weighting size and cost replacement policy (WSCRP) bases on the weighting replacement policy. The algorithm recalculates the objects weight with adding the cost attribute in the cache, then orders the weight. Additionally the influence of various factors on the Web object as frequency, time, and cost value are considered. When the cache space cannot satisfy the new request object, the replacement policy WSCRP replaces the largest weighting and cost object. The experiments show that proposed algorithm has higher hit rate and byte rate for different datasets, and can effectively improve the performance of web cache.

An Improved Web Cache Replacement Algorithm Based on Weighting and Cost

In software-defined networking (SDN), network rules are installed in ternary content-addressable memory (TCAM). TCAM is a scarce and expensive resource which is a bottleneck for scaling SDN. Rule distribution is a strategy to solve the TCAM shortage problem, which decomposes a large table stored at network ingress into a number of smaller sub-tables and distributes them across network switches. Rule distribution includes two sub-problems, sub-table allocation, and table decomposition. Sub-table allocation is to guarantee that each flow path passes all the partitioned sub-tables and maximize the number of partitioned sub-tables (for reducing the size of sub-tables). Table decomposition problem is to partition a large rule table into a number of balanced sub-tables and reduce the rules overhead. In this paper, we propose a sub-table allocation algorithm and size-balancing sub-table partition algorithm. Simulation results show that the performance of both algorithms is better than previous works in terms of reducing the TCAM entries used in each switch.

Efficient TCAM Rules Distribution Algorithms in Software-Defined Networking

The performance-driven design of SDN architectures leaves many security vulnerabilities, a notable one being the communication bottleneck between the controller and the switches. Functioning as a cache between the controller and the switches, the flow table mitigates this bottleneck by caching flow rules received from the controller at each switch, but is very limited in size due to the high cost and power consumption of the underlying storage medium. It thus presents an easy target for attacks. Observing that many existing defenses are based on simplistic attack models, we develop a model of intelligent attacks that exploit specific cache-like behaviors of the flow table to infer its internal configuration and state, and then design attack parameters accordingly. Our evaluations show that such attacks can accurately expose the internal parameters of the target flow table and cause measurable damage with the minimum effort.

Flow Table Security in SDN: Adversarial Reconnaissance and Intelligent Attacks

In software-defined networking, flow tables of OpenFlow switches are implemented by ternary content addressable memory (TCAM). Although TCAM can process input packets in high speed, it is a scarce and expensive resource providing only a few thousands of rule entries on a network switch. Rules caching is a technique to solve the TCAM capacity problem. However, the rule dependency problem is a challenging issue for wildcard rules caching where packets can mismatch rules. In this paper, we use a cover-set approach to solve the rule dependency problem and cache important rules to TCAM. We also propose a rule cache replacement algorithm considering the temporal and spatial traffic localities. Simulation results show that our algorithms have better cache hit ratio than previous works.

Yen-Cheng Chuo

Papers

Wildcard Rules Caching and Cache Replacement Algorithms in Software-Defined Networking