Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Resource Orchestration in Softwarized Networks

Network softwarization is an emerging research area that is envisioned to revolutionize the way network infrastructure is designed, operated, and managed today. Contemporary telecommunication networks are going through a major transformation, and softwarization is recognized as a crucial enabler of this transformation by both academia and industry. Softwarization promises to overcome the current ossified state of Internet network architecture and evolve towards a more open, agile, flexible, and programmable networking paradigm that will reduce both capital and operational expenditures, cut-down time-to-market of new services, and create new revenue streams. Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are two complementary networking technologies that have established themselves as the cornerstones of network softwarization. SDN decouples the control and data planes to provide enhanced programmability and faster innovation of networking technologies. It facilitates simplified network control, scalability, availability, flexibility, security, cost-reduction, autonomic management, and fine-grained control of network traffic. NFV utilizes virtualization technology to reduce dependency on underlying hardware by moving packet processing activities from proprietary hardware middleboxes to virtualized entities that can run on commodity hardware. Together SDN and NFV simplify network infrastructure by utilizing standardized and commodity hardware for both compute and networking; bringing the benefits of agility, economies of scale, and flexibility of data centers to networks. Network softwarization provides the tools required to re-architect the current network infrastructure of the Internet. However, the effective application of these tools requires efficient utilization of networking resources in the softwarized environment. Innovative techniques and mechanisms are required for all aspects of network management and control. The overarching goal of this thesis is to address several key resource orchestration challenges in softwarized networks. The resource allocation and orchestration techniques presented in this thesis utilize the functionality provided by softwarization to reduce operational cost, improve resource utilization, ensure scalability, dynamically scale resource pools according to demand, and optimize energy utilization

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Traffic engineering in dynamic optical networks

Traffic Engineering (TE) refers to all the techniques a Service Provider employs to improve the efficiency and reliability of network operations. In IP over Optical (IPO) networks, traffic coming from upper layers is carried over the logical topology defined by the set of established lightpaths. Within this framework then, TE techniques allow to optimize the configuration of optical resources with respect to an highly dynamic traffic demand. TE can be performed with two main methods: if the demand is known only in terms of an aggregated traffic matrix, the problem of automatically updating the configuration of an optical network to accommodate traffic changes is called Virtual Topology Reconfiguration (VTR). If instead the traffic demand is known in terms of data-level connection requests with sub-wavelength granularity, arriving dynamically from some source node to any destination node, the problem is called Dynamic Traffic Grooming (DTG). In this dissertation new VTR algorithms for load balancing in optical networks based on Local Search (LS) techniques are presented. The main advantage of using LS is the minimization of network disruption, since the reconfiguration involves only a small part of the network. A comparison between the proposed schemes and the optimal solutions found via an ILP solver shows calculation time savings for comparable results of network congestion. A similar load balancing technique has been applied to alleviate congestion in an MPLS network, based on the efficient rerouting of Label-Switched Paths (LSP) from the most congested links to allow a better usage of network resources. Many algorithms have been developed to deal with DTG in IPO networks, where most of the attention is focused on optimizing the physical resources utilization by considering specific constraints on the optical node architecture, while very few attention has been put so far on the Quality of Service (QoS) guarantees for the carried traffic. In this thesis a novel Traffic Engineering scheme is proposed to guarantee QoS from both the viewpoint of service differentiation and transmission quality. Another contribution in this thesis is a formal framework for the definition of dynamic grooming policies in IPO networks. The framework is then specialized for an overlay architecture, where the control plane of the IP and optical level are separated, and no information is shared between the two. A family of grooming policies based on constraints on the number of hops and on the bandwidth sharing degree at the IP level is defined, and its performance analyzed in both regular and irregular topologies. While most of the literature on DTG problem implicitly considers the grooming of low-speed connections onto optical channels using a TDM approach, the proposed grooming policies are evaluated here by considering a realistic traffic model which consider a Dynamic Statistical Multiplexing (DSM) approach, i.e. a single wavelength channel is shared between multiple IP elastic traffic flows

A unified service-based capability exposure framework for closed-loop network automation

The ongoing quest for the tight integration of network operation and the network service provisioning initiated with the introduction of 5G often clashes with the capacity of current network architectures to provide means for such integration. Owing to the traditional design of mobile networks, which barely required a tight interaction, network elements offer capabilities for their continuous optimization just within their domain (eg, access, or core), allowing for a "silo-style" automation that falls short when aiming at closed-loop automation that embraces all the actors involved in the network, from network functions up to the service-provider network functions. To this end, in this article, we make the case for the network-wide capability exposure framework for closed-loop automation by (i) defining the different entities that shall expose capabilities, and (ii) discussing why the state of the art solutions are not enough to support this vision. Our proposed architecture, which relies on registration and discovery, and exposure functions, allows for enhanced use cases that are currently not possible with state of the art solution. We prove the feasibility of our solution by implementing it in a real-world testbed, employing Artificial Intelligence algorithms to close the loop for the management of the radio access network.Part of this work was performed in the context of the H2020 5G-MoNArch project (grant agreement no. 761445). The work of Marco Gramaglia has been partially funded by the H2020 5G-TOURS project (grant agreement no. 856950), and by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D projects 6G-CLARION-NFD, 6G-CLARION-OR, 6G-CLARION-SI, and 6G-CLARION-O