Jian-Jhih Kuo

Optimal Approximation Algorithm of Virtual Machine Placement for Data Latency Minimization in Cloud Systems

The MapReduce/Hadoop architecture has become very important and effective in cloud systems because many data-intensive applications are usually required to process big data. In such environments, big data is partitioned and stored over several data nodes; thus, the total completion time of a task would be delayed if the maximum access latency among all pairs of a data node and its assigned computation node is not bounded. Moreover, the computation nodes usually need to communicate with each other for aggregating the computation results; therefore, the maximum access latency among all pairs of assigned computation nodes also needs to be bounded. In the literature, it has been proved that the placement problem of computation nodes (virtual machines) to minimize the maximum access latency among all pairs of a data node and its assigned computation node and among all pairs of assigned computation nodes does not admit any approximation algorithm with a factor smaller than two, whereas no approximation algorithms have been proposed so far. In this paper, we first propose a 3-approximation algorithm for resolving the problem. Subsequently, we close the gap by proposing a 2-approximation algorithm, that is, an optimal approximation algorithm, for resolving the problem in the price of higher time complexity. Finally, we conduct simulations for evaluating the performance of our algorithms.

Reliable GPS-Free Double-Ruling-Based Information Brokerage in Wireless Sensor Networks

Because the global positioning system (GPS) consumes a large amount of power and does not work indoors, many GPS-free information brokerage schemes are proposed for wireless sensor networks. Each of them, however, either cannot guarantee successful data retrieval or demands a great deal of message overhead to replicate the data. In this paper, we propose a GPS-free information brokerage scheme, RDRIB, in which the double-ruling technique is used to replicate and retrieve the data. RDRIB guarantees successful data retrieval, and, in addition, simulations show that RDRIB has good performance in terms of the replication message overhead and the construction message overhead.

GPS-Free, Boundary-Recognition-Free, and Reliable Double-Ruling-Based Information Brokerage Scheme in Wireless Sensor Networks

We study the information brokerage schemes in wireless sensor networks, which allow consumers to obtain data from producers by replicating and retrieving data in a certain set of sensors, and propose a novel information brokerage scheme, termed RDRIB. Unlike existing information brokerage schemes, RDRIB guarantees successful data retrieval without using any boundary detection algorithm and the geographic location information acquired by the global positioning system (GPS). In RDRIB, the double-ruling technique is used to replicate and retrieve the data within a constructed virtual boundary, and simulations show that RDRIB has good performance in terms of the replication memory overhead, the replication message overhead, the retrieval message overhead, the retrieval latency, and the construction message overhead.

Service chain embedding with maximum flow in software defined network and application to the next-generation cellular network architecture

With software-defined network (SDN) and network function virtualization (NFV) techniques, we can embed the service chain consisting of a sequence of virtualized network functions (VNFs), i.e., we can determine the flow path and deploy the VNFs contained in the service chain at any place on the path. In the literature, the methods of service chain embedding bound the number of VNFs at a node, whereas the link capacities are disregarded and the amount of flows is not considered, which could cause serious congestion. In addition, according to our experiment, the process overhead on a computation node is linear to the total amount of flows processed. In this paper, we propose a method of service chain embedding to maximize the total amount of flows while bounding the process overhead of the flows on a node by its computation capability and the total amount of flows on an link by its bandwidth capacity. To our knowledge, our method is the first approximation algorithm of service chain embedding with considering flow in the literature. Simulations show our algorithm has good performance in terms of the total amount of flows.

Service Overlay Forest Embedding for Software-Defined Cloud Networks

Network Function Virtualization (NFV) on Software-Defined Networks (SDN) can effectively optimize the allocation of Virtual Network Functions (VNFs) and the routing of network flows simultaneously. Nevertheless, most previous studies on NFV focus on unicast service chains and thereby are not scalable to support a large number of destinations in multicast. On the other hand, the allocation of VNFs has not been supported in the current SDN multicast routing algorithms. In this paper, therefore, we make the first attempt to tackle a new challenging problem for finding a service forest with multiple service trees, where each tree contains multiple VNFs required by each destination. Specifically, we formulate a new optimization, named Service Overlay Forest (SOF), to minimize the total cost of all allocated VNFs and all multicast trees in the forest. We design a new 3ρST-approximation algorithm to solve the problem, where ρST denotes the best approximation ratio of the Steiner Tree problem, and the distributed implementation of the algorithm is also presented. Simulation results on real networks for data centers manifest that the proposed algorithm outperforms the existing ones by over 25%. Moreover, the implementation of an experimental SDN with HP OpenFlow switches indicates that SOF can significantly improve the QoE of the Youtube service.

A cost-effective shuffling-based defense against HTTP DDoS attacks with SDN/NFV

Software-Defined Networking and Network Function Virtualisation (SDN/NFV) can provide flexible resource allocation to support innovative security solutions in a central manner. To mitigate HTTP DDoS attacks, shuffling-based moving target defense has been regarded as one of the most effective ways by redirecting user traffic among a group of virtualized service functions. However, previous work did not notice that frequent changes of user traffic will significantly intensify the control overhead of SDN. In this paper, therefore, we first model the effectiveness and cost for shuffling in SDN/NFV networking with Multi-Objective Markov Decision Processes to find the optimal tradeoff between the effectiveness and cost. We then propose a cost-effective approximation algorithm with a guarantee performance bound to solve the problem. Simulation and implementation on an experimental SDN/NFV network manifest that, given 100 attackers among 1000 users and 50 virtualized functions of a web service, our algorithm achieves the approximation ratio of 0.68 and imposes only 2.4s rule modification latency for each shuffle.

Zero-knowledge GPS-free data replication and retrieval scheme in mobile ad hoc networks using double-ruling and landmark-labeling techniques

Using the double-ruling technique, many data replication and retrieval schemes achieve low data retrieval latency. However, these schemes require Global Positioning System (GPS) in mobile ad hoc networks (MANETs). In this paper, we propose a zero-knowledge double-ruling-based GPS-free data replication and retrieval scheme (MobiMark) in MANETs. Our primary idea is to label the landmarks with the grid-like structure in the network using the landmark-labeling, dynamically designate the node that is the nearest to a landmark as the landmark broker, and transmit the consumers interests (or producers data) to all horizontal (or vertical) landmark brokers using the double-ruling technique. Simulations show that MobiMark achieves good performance in terms of data retrieval rate and data retrieval latency. In addition, we also develop a theoretical analysis for MobiMark to obtain the upper bound of the hop distance between the consumer and the landmark broker with the consumers interest in a square region where the nodes are uniformly and densely distributed and have equal transmission ranges.