Pathfinder: Application-Aware Distributed Path Computation in Clouds

Optical mesh network infrastructure has emerged as the technology of choice for next-generation transport networks. At the same time, distributed, IP-based, control architecture has been proposed for intelligent optical networks, as a means to automate operations, enhance interoperability, and facilitate the deployment of new applications. While distributed control in general enhances scalability and flexibility, it has also been observed that the requisite network topology and link-state information summarization may result in suboptimal path computation, especially for shared mesh restored paths. This paper presents a distributed control plane for optical mesh networks, focusing on distributed path computation and provisioning mechanisms. It discusses the tradeoffs between the path computation efficiency for shared mesh restored paths and the amount of network topology and link-state information that is disseminated via routing protocols. We show that with appropriately aggregated link resource availability and sharing information, the proposed distributed path computation algorithms are able to determine the shareability of restoration links with remarkable accuracy. A local channel assignment scheme, which is used in conjunction with the distributed path computation algorithms to assign shared channels when provisioning restoration paths, is also proposed. The additional information that signaling messages are required to carry in order to perform the local channel assignment at each node along the restoration path is discussed. Furthermore, we specify the extensions to the open shortest-path first (OSPF) routing protocol in support of shared mesh restoration. We analyze the performance of the proposed distributed path computation algorithms and the local channel assignment scheme, as well as the overhead of OSPF extensions. In particular, we study the tradeoffs between network capacity utilization, restoration path computation complexity, OSPF extension overhead, and memory requirements for storing the modified link-state database.

Within ubiquitous computing environments, services and devices can be dynamically adapted to changing conditions, and thus adaptivity becomes a key requirement in providing possibly better system performance. Most existing ubiquitous computing systems either support only software adaptation or manage reconfigurable hardware functions as conventional hardware devices. As a result, system adaptation and performance improvement are still restricted. To provide more robust system adaptation, we propose a self-adaptive hardware/software system architecture (SAHA) that consists of service suppliers, a hardware adapter, a system manager, an observer, and a reconfigurable hardware architecture. Further, SAHA supports both hardware preemption and hardware virtualization within a complete self-aware system adaptation mechanism such that the utilization of system resources is enhanced and better performance is provided for ubiquitous computing applications. Experiments with a ubiquitous computing service for information encryption demonstrate that SAHA can reduce the turnaround time by at least 11.9% of that required by using the conventional method.

In the existing three layers multi-granularity OCDM switching system (TLMG-OCDMSS), F-LSP, L-LSP and OC-LSP can be bundled as switching granularity. For CPU-intensive network, the node not only needs to compute the path but also needs to bundle the switching granularity so that the load of single node is heavy. The node will paralyze when the traffic of the node is too heavy, which will impact the performance of the whole network seriously. The introduction of stateful PCE(S-PCE) will effectively solve these problems. PCE is composed of two parts, namely, the path computation element and the database (TED and LSPDB), and returns the result of path computation to PCC (path computation clients) after PCC sends the path computation request to it. In this way, the pressure of the distributed path computation in each node is reduced. In this paper, we propose the concept of Learning PCE (L-PCE), which uses the existing LSPDB as the data source of PCE’s learning. By this means, we can simplify the path computation and reduce the network delay, as a result, improving the performance of network.

As enterprises struggle to meet the demand for elastic cloud computing services in distributed data centers (DCs), a significant portion of the total expenditure is spent on the procurement and maintenance of server and network equipment. Network and server resource utilization efficiency is therefore crucial to minimize overall costs and increase return on investment. We present an adaptive and synergistic management of both network and IT resources with an autonomous control plane on a converged inter/ intra-DC network to address this issue. The control plane uses its awareness of both network and IT resource usage to provide dynamic resource allocations to meet quality of service guarantees, while also allowing for reduction in operational expenses by consolidating virtual machines to maintain high CPU usage on the active servers. We built a three-node inter-DC experimental testbed to demonstrate the feasibility of our concept and show that our algorithm successfully provisions both network resources and consolidates IT resources under various load conditions.

A combined control of multimedia quality and network resource allocation is a core requirement for the deployment of communication services shared by multiple users (multi-user). The challenge resides in achieving a high number of satisfied users while saving the scarce network resources. This situation occurs due to the dynamic changes in resource demands, the existence of links with different capacities and the use of distinct QoS models along a session path. This paper proposes the Quality of Service Control for Multi-user Sessions (QUALITI) scheme to maintain multi-user sessions with acceptable QoS levels over heterogeneous networks, while optimizing the usage of network resources. QUALITI integrates QoS mapping and adaptation with network resource allocation along end-to-end session paths. QUALITI was evaluated through simulations that analyzed the convergence time, usage of network resources, throughput and one-way delay.

Docker is an open platform for developers and system administrators to build, ship, and run distributed applications using Docker Engine, a portable, lightweight runtime and packaging tool, and Docker Hub, a cloud service for sharing applications and automating workflows. The main advantage is that, Docker can get code tested and deployed into production as fast as possible. Different applications can be run over Docker containers with language independency. In this paper the performance of these Docker containers are evaluated based on their system performance. That is based on system resource utilization. Different benchmarking tools are used for this. Performance based on file system is evaluated using Bonnie++. Other system resources such as CPU utilization, memory utilization etc. are evaluated based on the benchmarking code (using psutil) developed using python. Detail results obtained from all these tests are also included in this paper. The results include CPU utilization, memory utilization, CPU count, CPU times, Disk partition, network I/O counter etc.

The existing peer-to-peer networks have several problems such as fake content distribution, free riding, white-washing and poor search scalability, lack of a robust trust model and absence of user privacy protection mechanism. Although, several trust management and semantic community-based mechanisms for combating free riding and distribution of malicious contents have been proposed by some researchers, most of these schemes lack scalability due to their high computational, communication and storage overhead. This paper presents a robust trust management scheme for P2P networks that utilizes topology adaptation by constructing an overlay of trusted peers where the neighbors are selected based on their trust ratings and content similarities. While increasing the search efficiency by intelligently exploiting the formation of semantic community structures by topology adaptation among the trustworthy peers, the scheme provides the users a very high level of privacy protection of their usage and consumption patterns of network resources. Simulation results demonstrate that the proposed scheme provides efficient searching to good peers while penalizing the malicious peers by increasing their search times as the network topology stabilizes.

Task scheduling is one of the very crucial facets of cloud computing. The task scheduling method must assign jobs to virtual machines. In cloud computing, task scheduling includes a frontal influence on a system's resource utilization and operational costs. Diverse meta-heuristic algorithms, in addition to their modifications, have been developed to improve the efficiency of task executions in the cloud. In this paper, a multiobjective optimization model is applied using the metaheuristics cuckoo search optimization algorithm (MCSO) to enhance the performance of a cloud system with limited computing resources while minimizing the time and cost. Finally, we analyze the performance of the proposed MCSO with the existing methods, such as Bee Life Algorithm (BLA), A Time–Cost aware Scheduling (TCaS) algorithm, Modified Particle Swarm Optimization (MPSO), and Round Robin (RR), for the evaluation metrics makespan and cost. Based on the outcomes of the experiments, it can be inferred that the proposed MCSO provides essential schedule jobs with the shortest makespan and average cost. Index Terms— Task Scheduling, Multiobjective, Cloud Computing, Cost, Makespan.

This paper presents a novel security approach called Anomalous Resource Consumption Detection (ARCD), which acts as an additional layer of protection to detect cyberattacks in embedded systems (ESs). The ARCD approach is based on the differentiation between the predefined standard resource consumption pattern and the anomalous consumption pattern of system resource utilization. The effectiveness of the proposed approach is tested in a rigorous manner by simulating four types of cyberattacks: a denial-of-service attack, a brute-force attack, a remote code execution attack, and a man-in-the-middle attack, which are executed on a Smart PiCar (used as the testbed). A septenary tuple model consisting of seven parameters, representing the embedded system's architecture, has been created as the core of the detection mechanism. The approach's efficiency and effectiveness has been validated in terms of range and pattern by analyzing the collected data statistically in terms of mean, median, mode, standard deviation, range, minimum, and maximum values. The results demonstrated the potential for defining a standard pattern of resource utilization and performance of the embedded system due to a significant similarity of the parameters' values at normal states. In contrast, the attacked cases showed a definite, observable, and detectable impact on resource consumption and performance of the embedded system, causing an anomalous pattern. Thus, by merging these two findings, the ARCD approach has been developed. ARCD facilitates building secure operating systems in line with the ES's capabilities. Furthermore, the ARCD approach can work along with existing countermeasures to augment the security of the operating system layer.

Scalability and resiliency are two significant requirements for upcoming beyond 5G networks. As network resource usage is expected to increase, scalability of the control plane handling the required amount of network resources is also necessary. Moreover, resiliency of cloudified control plane software mechanisms is also a requirement. This demonstration presents an Erlang-based Path Computation Element (PCE) able to perform traffic engineering for Segment Routing. Specific scalability and resiliency properties are provided in order to support transport connectivity in beyond 5G networks.

In recent era surveillance systems play a vital role in our daily life. The goal of surveillance systems is to collect the desired information about concerned targets in the sensing environment. Traditional surveillance systems are limited by a predefined monitoring pattern. It can render a reduced system reliability. Moreover, the system is always functional regardless of the target presence. It results in an increased and useless system activity, resources utilization and power consumption. In this context an event driven human surveillance system is devised. It overcomes these drawbacks up to a certain extent by smartly combining a sensors assembly along with a camera, embedded processor and classifiers. Except the low power object detector, detection of concerned object in the surveillance zone, the remaining system is kept in sleep mode. On the detection of an event the system modules like embedded processor, target location detectors, camera, etc. are activated. The event occurrence can also activate the lightning system depending on the sensing environment illumination condition. The target location, within the field of view, is provided by the location detectors. This information is employed to pilot the actuator in order to update the camera positioning. The captured images are analysed by the human detector module. Further actions like image storage, video recording, etc. are taken depending on the human detector output. The proposed system functionality is verified with an experimental setup. Results demonstrate the devised system self-organisation feature. It results into an improved performance of the proposed system, in terms of system resources utilization, reliability and power consumption compared to the counter traditional ones.

In trust-based network computing environment, in order to establish a harmonious network environment, and enhance the network resources sharing utilization, resources scheduling research faces more problems than ever before. A resource scheduling algorithm, using cooperative particle optimization and putting task completion price and overall system trust benefits as optimized object, is proposed based on the trust model proposed by Internet-based Virtual Computing Environment (iVCE). Simulation results indicate that the algorithm has obvious performance advantage in the aspects of whole system credibility and resource utilization rate than other classic scheduling algorithms. The algorithm can realize rational scheduling of network resources, and optimize the system performance in trusted computing environment.

Most of the existing solutions on task scheduling and resource management in grid computing are based on the traditional client/server model, enforcing a homogeneous policy on making decisions and limiting the flexibility, unpredictable reliability and scalability of the system. Thus, we need a well organised system architecture to provide high system availability with resource scheduling scheme for grid environment. In this paper, we integrate grid with P2P on to the hypercube topology with additional neighbourhood links for task scheduling and load balancing, which gives better makespan and balances the load. We propose the SPA-based task scheduling methodology in order to enhance system performance in hypercubic P2P grid (HPGRID). The simulation results show that the SPA-based min-mean scheduling minimises the makespan along with load balancing and guarantees the high system availability in system performance. At last, the min-mean algorithm is compared with min-min and max-min algorithm using SPA, by simulate experimentation, and it shows that the new algorithm has a better quality of system load balancing and the utilisation of system resources.

A multi-level collaborative framework for elastic stream computing systems

Chapter 10 - Constraint-Based Path Computation