This page has only limited features, please log in for full access.
Due to the highly dynamic nature of Wireless Sensor Networks (WSN), Software-Defined Network (SDN) is a promising technology to ease network management by providing a logically centralized control plane. It is a common approach to employ multiple SDN controllers to form a physically distributed SDN to achieve better fault tolerance, boost scalability, and enhance performance. Despite physical distribution, since the notion behind SDN is to logically centralize network management, it is essential to provide a consistent view of the network’s state for all controllers. Deploying multiple controllers result in higher synchronization and deployment cost. Since network performance and inter-controller synchronization cost seem to be contradicting objectives, it is a research challenge to choose the best placement of SDN controllers to optimize both the performance and synchronization cost of an SDN-enabled WSN simultaneously. In this paper, we first formulate the controller placement problem as a multi-objective optimization problem. In this regard, multiple constraints are considered, including reliability, fault tolerance, performance in terms of latency, synchronization overhead, and deployment cost. Moreover, we leverage the Cuckoo optimization algorithm, a nature-inspired population-based meta-heuristic algorithm to solve the optimization problem. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. Finally, to evaluate our proposed method, we compare it against several existing methods in the literature. The experiments reveal that our proposed method considerably outperforms existing methods, namely Simulated Annealing (SA) and Quantum Annealing (QA), in terms of both performance and synchronization cost. Additionally, our proposed algorithm, in contrast to Integer Linear Programming (ILP), is considerably more scalable, which makes it applicable for large-scale WSNs.
Shirin Tahmasebi; Nayereh Rasouli; Amir Hosein Kashefi; Elmira Rezabeyk; Hamid Reza Faragardi. SYNCOP: An evolutionary multi-objective placement of SDN controllers for optimizing cost and network performance in WSNs. Computer Networks 2020, 185, 107727 .
AMA StyleShirin Tahmasebi, Nayereh Rasouli, Amir Hosein Kashefi, Elmira Rezabeyk, Hamid Reza Faragardi. SYNCOP: An evolutionary multi-objective placement of SDN controllers for optimizing cost and network performance in WSNs. Computer Networks. 2020; 185 ():107727.
Chicago/Turabian StyleShirin Tahmasebi; Nayereh Rasouli; Amir Hosein Kashefi; Elmira Rezabeyk; Hamid Reza Faragardi. 2020. "SYNCOP: An evolutionary multi-objective placement of SDN controllers for optimizing cost and network performance in WSNs." Computer Networks 185, no. : 107727.
Due to reliability and performance considerations, employing multiple software-defined networking (SDN) controllers is known as a promising technique in Wireless Sensor Networks (WSNs). Nevertheless, employing multiple controllers increases the inter-controller synchronization overhead. Therefore, optimal placement of SDN controllers to optimize the performance of a WSN, subject to the maximum number of controllers, determined based on the synchronization overhead, is a challenging research problem. In this paper, we first formulate this research problem as an optimization problem, then to address the optimization problem, we propose the Cuckoo Placement of Controllers (Cuckoo-PC) algorithm. Cuckoo-PC works based on the Cuckoo optimization algorithm which is a meta-heuristic algorithm inspired by nature. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. To evaluate the performance of Cuckoo-PC, we compare it against a couple of state-of-the-art methods, namely Simulated Annealing (SA) and Quantum Annealing (QA). The experiments demonstrate that Cuckoo-PC outperforms both SA and QA in terms of the network performance by lowering the average distance between sensors and controllers up to 13% and 9%, respectively. Comparing our method against Integer Linear Programming (ILP) reveals that Cuckoo-PC achieves approximately similar results (less than 1% deviation) in a noticeably shorter time.
Shirin Tahmasebi; Mohadeseh Safi; Somayeh Zolfi; Mohammad Reza Maghsoudi; Hamid Reza Faragardi; Hossein Fotouhi. Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs. Sensors 2020, 20, 3231 .
AMA StyleShirin Tahmasebi, Mohadeseh Safi, Somayeh Zolfi, Mohammad Reza Maghsoudi, Hamid Reza Faragardi, Hossein Fotouhi. Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs. Sensors. 2020; 20 (11):3231.
Chicago/Turabian StyleShirin Tahmasebi; Mohadeseh Safi; Somayeh Zolfi; Mohammad Reza Maghsoudi; Hamid Reza Faragardi; Hossein Fotouhi. 2020. "Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs." Sensors 20, no. 11: 3231.
High demand for computational power by business, science, and applications has led to the creation of large-scale data centers that consume enormous amounts of energy. This high energy consumption not only imposes a significant operating cost but also has a negative impact on the environment (greenhouse gas emissions). A promising solution to reduce the amount of energy used by data centers is the consolidation of virtual machines (VMs) that allows some hosts to enter low consuming sleep modes. Dynamic migration (replacement) of VMs between physical hosts is an effective strategy to achieve VM consolidation. Dynamic migration not only saves energy by migrating the VMs hosted by idle hosts but can also avoid hotspots by migrating VMs from over-utilized hosts. In this paper, we presented a new approach, called extended-placement by learning automata (EPBLA), based on learning automata for dynamic replacement of VMs in data centers to reduce power consumption. EPBLA consists of two parts (i) a linear reward penalty scheme which is a finite action-set learning automata that runs on each host to make a fully distributed VM placement considering CPU utilization as a metric to categorize the hosts, and (ii) a continuous action-set learning automata as a policy for selecting an underload host initiating the migration process. A real-world workload is used to evaluate the proposed method. Simulation results showed the efficiency of EPBLA in terms of reduction of energy consumption by 20% and 30% compared with PBLA and Firefly, respectively.
Nayereh Rasouli; Ramin Razavi; Hamid Reza Faragardi. EPBLA: energy-efficient consolidation of virtual machines using learning automata in cloud data centers. Cluster Computing 2020, 23, 3013 -3027.
AMA StyleNayereh Rasouli, Ramin Razavi, Hamid Reza Faragardi. EPBLA: energy-efficient consolidation of virtual machines using learning automata in cloud data centers. Cluster Computing. 2020; 23 (4):3013-3027.
Chicago/Turabian StyleNayereh Rasouli; Ramin Razavi; Hamid Reza Faragardi. 2020. "EPBLA: energy-efficient consolidation of virtual machines using learning automata in cloud data centers." Cluster Computing 23, no. 4: 3013-3027.
Software Defined Networking (SDN) provides network significant reconfiguration capability to Wireless Sensor Networks (WSNs). SDN is a promising technique for WSNs with high scalability and high reliability requirements. In SDN, a set of controller nodes are integrated into the network to advertise routing rules dynamically based on network and link changes. Determining the number and location of both sinks (are in charge of collecting the sensors data) and controller nodes in a WSN subject to both reliability and performance constraints is an important research challenge. In this paper, to address this research challenge, we propose a Quantum Annealing approach that improves the deployment cost of the system by minimizing the number of required sinks and SDN controller nodes. The experiments show that our approach improves the deployment cost of the network against the state-of-the-art by 10.7% on average.
Reihaneh Nikouei; Nayereh Rasouli; Shirin Tahmasebi; Somayeh Zolfi; Hamid Faragardi; Hossein Fotouhi. A Quantum-Annealing-Based Approach to Optimize the Deployment Cost of a Multi-Sink Multi-Controller WSN. Procedia Computer Science 2019, 155, 250 -257.
AMA StyleReihaneh Nikouei, Nayereh Rasouli, Shirin Tahmasebi, Somayeh Zolfi, Hamid Faragardi, Hossein Fotouhi. A Quantum-Annealing-Based Approach to Optimize the Deployment Cost of a Multi-Sink Multi-Controller WSN. Procedia Computer Science. 2019; 155 ():250-257.
Chicago/Turabian StyleReihaneh Nikouei; Nayereh Rasouli; Shirin Tahmasebi; Somayeh Zolfi; Hamid Faragardi; Hossein Fotouhi. 2019. "A Quantum-Annealing-Based Approach to Optimize the Deployment Cost of a Multi-Sink Multi-Controller WSN." Procedia Computer Science 155, no. : 250-257.
Hamid Reza Faragardi; Björn Lisper; Kristian Sandström; Thomas Nolte. A resource efficient framework to run automotive embedded software on multi-core ECUs. Journal of Systems and Software 2018, 139, 64 -83.
AMA StyleHamid Reza Faragardi, Björn Lisper, Kristian Sandström, Thomas Nolte. A resource efficient framework to run automotive embedded software on multi-core ECUs. Journal of Systems and Software. 2018; 139 ():64-83.
Chicago/Turabian StyleHamid Reza Faragardi; Björn Lisper; Kristian Sandström; Thomas Nolte. 2018. "A resource efficient framework to run automotive embedded software on multi-core ECUs." Journal of Systems and Software 139, no. : 64-83.