Joint dimensioning of server and network infrastructure for resilient optical grids/clouds

We address the dimensioning of infrastructure, comprising both network and server resources, for large-scale decentralized distributed systems such as grids or clouds. We design the resulting grid/cloud to be resilient against network link or server failures. To this end, we exploit relocation: Under failure conditions, a grid job or cloud virtual machine may be served at an alternate destination (i.e., different from the one under failure-free conditions). We thus consider grid/cloud requests to have a known origin, but assume a degree of freedom as to where they end up being served, which is the case for grid applications of the bag-of-tasks (BoT) type or hosted virtual machines in the cloud case. We present a generic methodology based on integer linear programming (ILP) that: 1) chooses a given number of sites in a given network topology where to install server infrastructure; and 2) determines the amount of both network and server capacity to cater for both the failure-free scenario and failures of links or nodes. For the latter, we consider either failure-independent (FID) or failure-dependent (FD) recovery. Case studies on European-scale networks show that relocation allows considerable reduction of the total amount of network and server resources, especially in sparse topologies and for higher numbers of server sites. Adopting a failure-dependent backup routing strategy does lead to lower resource dimensions, but only when we adopt relocation (especially for a high number of server sites): Without exploiting relocation, potential savings of FD versus FID are not meaningful

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

Simplified cloud-oriented virtual machine management with MLN

System administrators are faced with the challenge of making their existing systems power-efficient and scalable. Although Cloud Computing is offered as a solution to this challenge by many, we argue that having multiple interfaces and cloud providers can result in more complexity than before. This paper addresses cloud computing from a user perspective. We show how complex scenarios, such as an on-demand render farm and scaling web-service, can be achieved utilizing clouds but at the same time keeping the same management interface as for local virtual machines. Further, we demonstrate that by enabling the virtual machine to have its policy locally instead of in the underlying framework, it can move between otherwise incompatible cloud providers and sites in order to achieve its goals more efficiently

Joint Power-Efficient Traffic Shaping and Service Provisioning for Metro Elastic Optical Networks

Considering the time-averaged behavior of a metro elastic optical network, we develop a joint procedure for resource allocation and traffic shaping to exploit the inherent service diversity among the requests for power-efficient network operation. To support the quality of service diversity, we consider minimum transmission rate, average transmission rate, maximum burst size, and average transmission delay as the adjustable parameters of a general service profile. The work evolves from a stochastic optimization problem, which minimizes the power consumption subject to stability, physical, and service constraints. The optimal solution of the problem is obtained using a complex dynamic programming method. To provide a near-optimal fast-achievable solution, we propose a sequential heuristic with a scalable and causal software implementation, according to the basic Lyapunov iterations of an integer linear program. The heuristic method has a negligible optimality gap and a considerably shorter runtime compared to the optimal dynamic programming, and reduces the consumed power by 72% for an offered traffic with a unit variation coefficient. The adjustable trade-offs of the proposed scheme offer a typical 10% power saving for an acceptable amount of excess transmission delay or drop rate

Auto-scaling techniques for cloud-based Complex Event Processing

One key topic in cloud computing is elasticity, which is the ability of the cloud environment to timely adapt the resource assignment along with the workload demand. According to cloud on-demand model, the infrastructure should be able to scale up and down to unpredictable workloads, in order to achieve both a guaranteed service level and cost efficiency. This work addresses the cloud elasticity problem, with particular reference to the Complex Event Processing (CEP) systems. CEP systems are designed to process large volumes of event-driven data streams and continuously provide results with a low latency and in real-time. CEP systems need to adapt to changing query and events loads. Because of the high computational requirements and varying loads, CEP are distributed system and running on cloud infrastructures. In this work we review the cloud computing auto-scaling solutions, and study their suit- ability in the CEP model. We implement some solutions in a CEP prototype and evaluate the experimental results