Improving the Reliability of Software-Defined Networks with Distributed Controllers Through Leader Election Algorithm and Colored Petri-Net

Wireless Personal Communications - Software-Defined Networks (SDNs) are developed to compensate the complicated function of the controlling parts of the given network elements and making the...     

Improving the reliability of Byzantine fault‐tolerant distributed software‐defined networks

This paper presents a method to improve the reliability and fault tolerance of distributed software‐defined networks. This method is called “BIRDSDN (Byzantine‐Resilient Improved Reliable Distributed Software‐Defined Networks).” In BIRDSDN, a group communication is implemented among the controllers of the whole clusters. This method can detect the crash failure and Byzantine failure of any controller and undertakes a fast detection and recovery scheme to select the controllers to take over the orphan switches. BIRDSDN takes into account the reliability of the nodes considering the failure probability of intracluster and intercluster links, topology, load, and latency. The numerical results show that this approach performs better than the other approaches regarding failure detection, recovery, latency, throughput, reliability, and packet loss.     

A system for providing load‐aware virtual network service in a software‐defined data center network

Virtual networks may be used in a cloud data center to provide personalized networking services for the applications running in the cloud. As the virtual networks come to the data center and leave it, the data center network load may become unbalanced where some parts of the data center has accommodated many virtual networks while a few virtual networks are mapped to other parts of the data center. This situation may lead to packet loss and service level agreement violations in an oversubscribed data center. This unbalanced state of the load in data center can be resolved by migration of virtual networks from overloaded parts of the data center to places where the load is in a lower level. This paper presents implementation details of the prototype of a system that provides virtual networking service in a cloud data center and focuses primarily on virtual network migration as a means for controlling the state of load in data center. Experimental results show that the system has an acceptable performance in reducing the packet loss ratio and keeping the load in a balanced state.     

Improving energy efficiency in information‐centric mobile ad‐hoc networks using places of interest while respecting privacy

The fact that network users mostly look for content regardless of its location has led to the creation of information‐centric networks with NDN (named data networking project) as the most famous instance. NDN can be implemented in any type of network including MANET (mobile ad‐hoc network) which can be easily created among a collection of smartphones. The first step for content retrieval in NDN is propagating interest packets which has a dramatic effect on energy consumption because of wireless communications. Methods have been devised for limiting the amount of packet propagation. But they are not appropriate for smartphones either because they require multiple WiFi interfaces which is not available in usual smartphones or because they require the exact position of nodes which conflicts privacy. In our proposed approach, a single WiFi interface is used, and mobile nodes will share only an imprecise version of their current and their predicted next location, which complies much better with privacy. Using this information, the amount of interest and data packets will decrease of more than 15%. When location information is spread through the network, this reduction will be almost doubled.     

A novel collaborative approach for location prediction in mobile networks

Human location prediction has been a matter of concern for several years due to its many applications. It has become more important nowadays because of prevalence of mobile devices which have adequate tools for inferring location. Different approaches for making this prediction could be divided into three categories, based on the movement history they use. These include history of mobile user himself, history of all mobile users in a place, and history of only related mobile users. Besides the problem of limiting shared data to only required data, preserving privacy is the matter of concern for persuading mobile users to share their data. In this paper we have proposed a new method in which the amount of the shared data is decreased to a minimum, and only the data which will improve the partner’s prediction will be shared. Our method preserves privacy by blurring the shared data up to different degrees. The experimental results show that regardless of amount of blurring, as long as the user movement is not lost because of blurring, the accuracy of prediction will be improved about 7 %.     

OASM: An overload‐aware workload scheduling method for cloud computing based on biogeographical optimization

With the daily increase in the number of cloud users and the volume of submitted workloads, load balancing (LB) over clouds followed by a reduction in users' response time is emerging as a vital issue. To successfully address the LB problem, we have optimized workload distribution among virtual machines (VMs). This approach consists of two parts: Firstly, a meta‐heuristic method based on biogeographical optimization for workload dispatching among VMs is introduced; secondly, we propose an innovative heuristic algorithm inspired by the “Banker algorithm” that runs in core scheduler to control and avoid VM overloads. The combination of these two (meta‐)heuristic algorithms constitutes an LB approach through which we have been able to reduce the value of the makespan to a reasonable time frame. Moreover, an information base repository (IBR) is introduced to maintain the online processing status of physical machines (PMs) and VMs. In our approach, data stored in IBR are retrieved when needed. This approach is compared with well‐known (non‐)evolutionary approaches, such as round‐robin, max‐min, MGGS, and TBSLB‐PSO. Experimental results reveal that our proposed approach outperforms its counterparts in a heterogeneous environment when the resources are smaller than the workloads. Moreover, the utilization of physical resources gradually increases. Therefore, optimal workload scheduling, as well as the lack of overload occurrence, results in a reduction in makespan.