Inside Dropbox: Understanding Personal Cloud Storage Services

Personal cloud storage services are gaining popularity. With a rush of providers to enter the market and an increasing of- fer of cheap storage space, it is to be expected that cloud storage will soon generate a high amount of Internet traffic. Very little is known about the architecture and the perfor- mance of such systems, and the workload they have to face. This understanding is essential for designing efficient cloud storage systems and predicting their impact on the network. This paper presents a characterization of Dropbox, the leading solution in personal cloud storage in our datasets. By means of passive measurements, we analyze data from four vantage points in Europe, collected during 42 consecu- tive days. Our contributions are threefold: Firstly, we are the first to study Dropbox, which we show to be the most widely-used cloud storage system, already accounting for a volume equivalent to around one third of the YouTube traffic at campus networks on some days. Secondly, we characterize the workload typical users in different environments gener- ate to the system, highlighting how this reflects on network traffic. Lastly, our results show possible performance bot- tlenecks caused by both the current system architecture and the storage protocol. This is exacerbated for users connected far from control and storage data-center

FPGA-Based Intelligent Traffic Controller with Remote Operation Mode

This paper covers the design and implementation of an intelligent traffic management system for an isolated intersection. The vehicular traffic management system employs Field Programmable Gate Array (FPGA) as the central decision-making unit and combines its speed and high number of I/O with other system components such as the ESP8266 Wi-Fi module, to record real-time traffic information and apportion green-time for each phase of traffic signal. The design and implementation incorporate three different operational modes namely, fixed time mode, sensor actuated mode and remote control mode, to alleviate vehicular traffic congestion and also reduce the risk of accidents at intersections. The incorporation of remote control mode, which enables an authorized personnel to remotely control the traffic, using a cloud-based application running on a portable device, makes this system different from previous works. Simulation using a prototype of the model on a model intersection, shows that the system can reduce waiting time and increase throughput at the intersections

Decentralized Control of Distributed Cloud Networks with Generalized Network Flows

Emerging distributed cloud architectures, e.g., fog and mobile edge computing, are playing an increasingly important role in the efficient delivery of real-time stream-processing applications such as augmented reality, multiplayer gaming, and industrial automation. While such applications require processed streams to be shared and simultaneously consumed by multiple users/devices, existing technologies lack efficient mechanisms to deal with their inherent multicast nature, leading to unnecessary traffic redundancy and network congestion. In this paper, we establish a unified framework for distributed cloud network control with generalized (mixed-cast) traffic flows that allows optimizing the distributed execution of the required packet processing, forwarding, and replication operations. We first characterize the enlarged multicast network stability region under the new control framework (with respect to its unicast counterpart). We then design a novel queuing system that allows scheduling data packets according to their current destination sets, and leverage Lyapunov drift-plus-penalty theory to develop the first fully decentralized, throughput- and cost-optimal algorithm for multicast cloud network flow control. Numerical experiments validate analytical results and demonstrate the performance gain of the proposed design over existing cloud network control techniques

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads

Hybrid Simulation and Test of Vessel Traffic Systems on the Cloud

This paper presents a cloud-based hybrid simulation platform to test large-scale distributed System-of-Systems (SoS) for the management and control of maritime traffic, the so-called Vessel Traffic Systems (VTS). A VTS consists of multiple, heterogeneous, distributed and interoperating systems, including radar, automatic identification systems, direction finders, electro-optical sensors, gateways to external VTSs, information systems; identifying, representing and analyzing interactions is a challenge to the evaluation of the real risks for safety and security of the marine environment. The need for reproducing in fabric the system behaviors that could occur in situ demands for the ability of integrating emulated and simulated environments to cope with the different testability requirements of involved systems and to keep testing cost sustainable. The platform exploits hybrid simulation and virtualization technologies, and it is deployable on a private cloud, reducing the cost of setting up realistic and effective testing scenarios

Enriched Model of Case Based Reasoning and Neutrosophic Intelligent System for DDoS Attack Defence in Software Defined Network based Cloud

Software Defined Networking in Cloud paradigm is most suitable for dynamic functionality and reduces the computation complexity. The routers and switches located at the network's boundaries are managed by software-defined netwrking (SDN) using open protocols and specialised open programmable interfaces. But the security threats often degrade the performance of SDN due to its constraints of resource usage. The most sensitive components which are vulnerable to DDoS attacks are controller and control plane bandwidth. The existing conventional classification algorithms lacks in detection of new or unknown traffic packets which are malicious and results in degradation of SDN performance in cloud resources. Hence, in this paper double filtering methodology is devised to detect both known and unknown pattern of malicious packets which affects the bandwidth of the control panel and the controller. The case-based reasoning is adapted for determining the known incoming traffic patterns before entering the SDN system. It classifies the packets are normal or abnormal based on the previous information gathered. The traffic patterns which is not matched from the previous patterns is treated as indeterministic packet and it is defined more precisely using the triplet representation of Neutrosophic intelligent system. The grade of belongingness, non-belongingness and indeterminacyis used as the main factors to detect the new pattern of attacking packets more effectively. From the experimental outcomes it is proved that DDoS attack detection in SDN based cloud environment is improved by adopting CBR-NIS compared to the existing classification model