With the growth of cloud-based services, cloud data centers are experiencing large growth. A key component in a cloud data center is the network technology deployed. In particular, Ethernet technology, commonly deployed in cloud data centers, is already envisioned for 10 Tbps Ethernet. In this paper, we study and analyze the makespan, workload execution times, and virtual machine migrations as the network speed increases. In particular, we consider homogeneous and heterogeneous data centers, virtual machine scheduling algorithms, and workload scheduling algorithms. Results obtained from our study indicate that the increase in a network's speed reduces makespan and workloads execution times, while aiding in the increase of the number of virtual machine migrations. We further observed that the number of migrations' behaviors in relation to the speed of the networks also depends on the employed virtual machines scheduling algorithm.

Point spread function (PSF) estimation plays a paramount role in image deblurring processing, and traditionally it is solved by parameter estimation of a certain preassumed PSF shape model. In real life, the PSF shape is generally arbitrary and complicated, and thus it is assumed in this manuscript that a PSF may be decomposed as a weighted sum of a certain number of Gaussian kernels, with weight coefficients estimated in an alternating manner, and an l1 norm-based total variation (TVl1) algorithm is adopted to recover the latent image. Experiments show that the proposed method can achieve satisfactory performance on synthetic and realistic blurred images.

Considering diversified HTTP types, the performance bottleneck of signature-based classification must be resolved. We define a signature model classifying the traffic in multiple dimensions and suggest a hierarchical signature structure to remove signature redundancy and minimize search space. Our experiments on campus traffic demonstrated 1.8 times faster processing speed than the Aho-Corasick matching algorithm in Snort.

Middle-level parts have attracted great attention in the computer vision community, acting as discriminative elements for objects. In this paper we propose an unsupervised approach to mine discriminative parts for object detection. This work features three aspects. First, we introduce an unsupervised, exemplar-based training process for part detection. We generate initial parts by selective search and then train part detectors by exemplar SVM. Second, a part selection model based on consistency and distinctiveness is constructed to select effective parts from the candidate pool. Third, we combine discriminative part mining with the deformable part model (DPM) for object detection. The proposed method is evaluated on the PASCAL VOC2007 and VOC2010 datasets. The experimental results demons-trate the effectiveness of our method for object detection.

This paper proposes a new method that combines signal modulation and FDTD (Finite-Difference Time-Domain) simulations to reduce the computation time in multiple-antenna analysis. In this method, signals are modulated so as to maintain orthogonality among the excited signals; multiple antennas are excited at the same time. This means just one FDTD simulation is needed whereas the conventional method demands as many simulations as there are transmitting antennas. The simulation of a 2×2 multi-antenna system shows that the proposed method matches the performance of the conventional method even though its computation time is much shorter.

New payment technologies are coming that will raise user convenience. To support automatic hands-free payment, merchant devices will collect customer's information from the cloud of payment service providers or customer's smart phones, which should be removed after the transaction. Using Jaccard containment, we propose a proactive security approach of cleaning personal data at merchant-side point-of-sale terminals. We also propose a sampling method to reduce communication overhead by several orders of magnitude.

High gain extinction ratio and stable control of the phase in phase sensitive amplification are fundamental to realize either phase regeneration or quadrature squeezing of phase modulated signals in an efficient and robust manner. In this paper, we show that a combination of our previously demonstrated “sideband-assisted” dual-pump phase sensitive amplifier with a gain extinction ratio of more than 25dB, and a phase-locked loop based stabilization technique, enable efficient QPSK quadrature squeezing. Its stable operation is exploited to realize phase de-multiplexing of QPSK signals into BPSK tributaries. The phase de-multiplexed signals are evaluated through measurement of constellation diagrams, eye diagrams and more importantly, BER curves. The de-multiplexed BPSK signals exhibited an OSNR penalty of less than 1dB compared to the back-to-back BPSK signals.

Router virtualization is becoming more common as a method that uses network (NW) equipment effectively and robustly similar to server virtualization. Edge routers, which are gateways of core NWs, should be virtualized because they have many functions and resources just as servers do. To virtualize edge routers, a metro NW, which is a wide area layer-2 NW connecting each user's residential gateway to edge routers, must trace dynamic edge router re-allocation by changing the route of each Ethernet flow. Therefore, we propose a scalable centralized control architecture of a virtual layer-2 switch on a metro NW to trace virtual router re-allocation and use metro NW equipment effectively. The proposed scalable control architecture improves the centralized route control performance by processing in parallel on a flow-by-flow basis taking into account route information even in the worst case where edge routers fail. In addition, the architecture can equalize the load among parallel processes dynamically by using two proposed load re-allocation methods to increase the route control performance stably while minimizing the amount of resources for the control. We evaluate the scalability of the proposed architecture through theoretical analysis and experiments on a prototype and show that the proposed architecture increases the number of flows accommodated in a metro NW. Moreover, we evaluate the load re-allocation methods through simulation and show that they can evenly distribute the load among parallel processes. Finally, we show that the proposed architecture can be applied to not only large-scale metro NWs but also to data center NWs, which have recently become an important type of large-scale layer-2 NW.

In a multi-tenant data center, nodes and links of tenants' virtual networks (VNs) share a single component of the physical substrate network (SN). The failure of a single SN component can thereby cause the simultaneous failures of multiple nodes and links in a single VN; this complex of failures must significantly disrupt the services offered on the VN. In the present paper, we clarify how the fault tolerance of each VN is affected by a single SN failure, especially from the perspective of VN allocation in the SN. We propose a VN allocation model for multi-tenant data centers and formulate a problem that deals with the bandwidth loss in a single VN due a single SN failure. We conduct numerical simulations (with the setting that has 1.7×108bit/s bandwidth demand on each VN, (denoted by Ci)). When each node in each VN is scattered and mapped to an individual physical server, each VN can have the minimum bandwidth loss (5.3×102bit/s (3.0×10-6×Ci)) but the maximum required bandwidth between physical servers (1.0×109bit/s (5.7×Ci)). The balance between the bandwidth loss and the required physical resources can be optimized by assigning every four nodes of each VN to an individual physical server, meaning that we minimize the bandwidth loss without over-provisioning of core switches.

When there are multiple component predictors, it is promising to integrate them into one predictor for advanced reasoning. If each component predictor is given as a stochastic model in the form of probability distribution, an exponential mixture of the component probability distributions provides a good way to integrate them. However, weight parameters used in the exponential mixture model are difficult to estimate if there is no training samples for performance evaluation. As a suboptimal way to solve this problem, weight parameters may be estimated so that the exponential mixture model should be a balance point that is defined as an equilibrium point with respect to the distance from/to all component probability distributions. In this paper, we propose a weight parameter estimation method that represents this concept using a symmetric Kullback-Leibler divergence and generalize this method.

The conical area evolutionary algorithm (CAEA) has a very high run-time efficiency for bi-objective optimization, but it can not tackle problems with more than two objectives. In this letter, a conical hypervolume evolutionary algorithm (CHEA) is proposed to extend the CAEA to a higher dimensional objective space. CHEA partitions objective spaces into a series of conical subregions and retains only one elitist individual for every subregion within a compact elitist archive. Additionally, each offspring needs to be compared only with the elitist individual in the same subregion in terms of the local hypervolume scalar indicator. Experimental results on 5-objective test problems have revealed that CHEA can obtain the satisfactory overall performance on both run-time efficiency and solution quality.

A field multiplication in the extended binary field is often expressed using Toeplitz matrix-vector products (TMVPs), whose matrices have special properties such as symmetric or triangular. We show that such TMVPs can be efficiently implemented by taking advantage of some properties of matrices. This yields an efficient multiplier when a field multiplication involves such TMVPs. For example, we propose an efficient multiplier based on the Dickson basis which requires the reduced number of XOR gates by an average of 34% compared with previously known results.

Many public cloud datacenters have adopted the Edge-Overlay model which supports virtual switch-based network virtualization using IP tunneling. However, software-implemented virtual switches can cause performance degradation because the packet processing load can concentrate on a particular CPU core. As a result, such load concentration decreases and destabilizes the performance of virtual networks. Although multi-queue functions like Receive Side Scaling (RSS) can distribute the load onto multiple CPU cores, they still have performance problems such as IRQ core collision between priority flows as well as competitive resource use between host and guest machines for received packet processing. In this paper, we propose Virtual Switch Extension (VSE) that adaptively determines CPU core assignment for SoftIRQ to prevent performance degradation. VSE supports two types of SoftIRQ core selection mechanisms, on-the-fly or predetermined. In the on-the-fly mode, VSE selects a SoftIRQ core based on current CPU load to exploit low-loaded CPU resources. In the predetermined mode, SoftIRQ cores are assigned in advance to differentiate the performance of priority flows. This paper describes a basic architecture and implementation of VSE and how VSE assigns a SoftIRQ cores. Moreover, we evaluate fundamental throughput of various CPU assignment models in the predetermined mode. Finally, we evaluate the performance of a priority VM in two VM usecases, the client-usecase which is receive-oriented and the router-usecase which performs bi-directional communications. In the client-usecase, the throughput of the priority VM was improved by 31% compared with RSS when the priority VM had one dedicated core. In the router-usecase, the throughput was improved by 29% when three dedicated cores were provided for the VM.

In a previous study, we proposed a technique to recommend candidate verbs for a method name so that developers can consistently use various verbs. In this study, we improve the rule extraction technique proposed in this previous study. Moreover, we confirm that the rank of each correct verb recommended by the new technique is higher than that by the previous technique.

Although sparse coding has emerged as an extremely powerful tool for texture and image classification, it neglects the relationship of coding coefficients from the same class in the training stage, which may cause a decline in the classification performance. In this paper, we propose a novel coding strategy named compact sparse coding for ground-based cloud classification. We add a constraint on coding coefficients into the objective function of traditional sparse coding. In this way, coding coefficients from the same class can be forced to their mean vector, making them more compact and discriminative. Experiments demonstrate that our method achieves better performance than the state-of-the-art methods.

In a food image acquired by a digital camera, its intensity and saturation components are sometimes decreased depending on the illumination environment. In this case, the food image does not look delicious. In general, RGB components are transformed into hue, saturation and intensity components, and then the saturation and intensity components are enhanced so that the food image looks delicious. However, these processes are complex and involve a gamut problem. In this paper, we propose an intensity and saturation enhancement method while preserving the hue in the RGB color space for the food image. In this method, at first, the intensity components are enhanced avoiding the saturation deterioration. Then the saturation components of the regions having the hue components frequently appeared in foods are enhanced. In order to illustrate the effectiveness of the proposed method, the enhancement experiments using several food images are done.

The dramatic growth in wireless data traffic has triggered the investigation of fifth generation (5G) wireless communication systems. Small cells will play a very important role in 5G to meet the 5G requirements in spectral efficiency, energy savings, etc. In this paper, we investigate low complexity millimeter-wave communication systems with uniform circular arrays (UCAs) in line-of-sight (LOS) multiple-input multiple-output (MIMO) channels, which are used in fixed wireless access such as small cell wireless backhaul for 5G. First, we demonstrate that the MIMO channel matrices for UCAs in LOS-MIMO channels are circulant matrices. Next, we provide a detailed derivation of the unified optimal antenna placement which makes MIMO channel matrices orthogonal for 3×3 and 4×4 UCAs in LOS channels. We also derive simple analytical expressions of eigenvalues and capacity as a function of array design (link range and array diameters) for the concerned systems. Finally, based on the properties of circulant matrices, we propose a high performance low complexity LOS-MIMO precoding system that combines forward error correction (FEC) codes and spatial interleaver with the fixed IDFT precoding matrix. The proposed precoding system for UCAs does not require the channel knowledge for estimating the precoding matrix at the transmitter under the LOS condition, since the channel matrices are circulant ones for UCAs. Simulation results show that the proposed low complexity system is robust to various link ranges and can attain excellent performance in strong LOS environments and channel estimation errors.

An L2-in-L3 tunneling technology plays an important role in network virtualization based on the concept of Software-Defined Networking (SDN). VXLAN (Virtual eXtensible LAN) and NVGRE (Network Virtualization using Generic Routing Encapsulation) protocols are being widely used in public cloud datacenters. These protocols resolve traditional VLAN problems such as a limitation of the number of virtual networks, however, their network performances are low without dedicated hardware acceleration. Although STT (Stateless Transport Tunneling) achieves far better performance, it has pragmatic problems in that STT packets can be dropped by network middleboxes like stateful firewalls because of modified TCP header semantics. In this paper, we propose yet another layer 4 protocol (Segment-oriented Connection-less Protocol, SCLP) for existing tunneling protocols. Our previous study revealed that the high-performance of STT mainly comes from 2-level software packet pre-reassembly before decapsulation. The SCLP header is designed to take advantage of such processing without modifying existing protocol semantics. We implement a VXLAN over SCLP tunneling and evaluate its performance by comparing with the original VXLAN (over UDP), NVGRE, Geneve, and STT. The results show that the throughput of the proposed method was comparable to STT and almost 70% higher than that of other protocols.

Multi-layer transport networks that utilize sub-lambda paths over a wavelength path have been shown to be effective in accommodating traffic with various levels of granularity. For different service requirements, a virtualized network was proposed where the infrastructure is virtually sliced to accommodate different levels of reliability. On the other hand, network reconfiguration is a promising candidate for quasi-dynamic and multi-granular traffic. Reconfiguration, however, incurs some risks such as service disruption and fluctuations in delay. There has not yet been any study on accommodating and reconfiguring paths according to different service classes in multi-layer transport networks. In this paper, we propose differentiated reconfiguration to address the trade-off relationship between accommodation efficiency and disruption risk in virtualized multi-layer transport networks that considers service classes defined as a combination of including or excluding a secondary path and allowing or not allowing reconfiguration. To implement the proposed network, we propose a multi-layer redundant path accommodation design and reconfiguration algorithm. A reliability evaluation algorithm is also introduced. Numerical evaluations show that when all classes are divided equally, equipment cost can be reduced approximately by 6%. The proposed reconfigurable networks are shown to be a cost effective solution that maintains reliability.

Multi-tenant datacenter networking, with which multiple customer networks (tenants) are virtualized and consolidated in a single shared physical infrastructure, has recently become a promising approach to reduce device cost, thanks to advances of virtualization technologies for various networking devices (e.g., switches, firewalls, load balancers). Since network devices are configured with low-level commands (no context of tenants), network engineers need to manually manage the context of tenants in different stores such as spreadsheet and/or configuration management database (CMDB). The use of CMDB is also effective in increasing the ‘visibility’ of tenant configurations (e.g., information sharing among various teams); However, different from the ideal use, only limited portion of network configuration are stored in CMDB in order to reduce the amount of ‘double configuration management’ between device settings (running information) and CMDB (stored information). In this present work, we aim to bridge the gap between CDMB and device status. Our basic approach is to automatically analyze per-device configuration settings to recover per-tenant network-wide configuration (running information) based on a graph-traversal technique applied over abstracted graph representation of device settings (to handle various types of vendor-specific devices); The recovered running information of per-tenant network configurations is automatically uploaded to CMDB. An implementation of this methodology is applied to a datacenter environment that management of about 100 tenants involves approximately 5,000 CMDB records, and our practical experiences are that this methodology enables to double the amount of CMDB records. We also discuss possible use cases enabled with this methodology.