Error Estimate and Fairness in Resource Allocation with Inaccurate Information Sharing

International audienceIn resource allocation systems, inaccurate information sharing situations are such that users can be aware, up to a small error, about the other users' demands and the available global resource (which can be insufficient to meet the overall demand). Consequently, given an allocation rule, users can predict an allocation that will not necessarily coincide with the actual one. In this work, we provide an estimation of the error for a number of allocation rules and compare their robustness in inaccurate information sharing settings

Time-varying resilient virtual network mapping for multi-location cloud data centers

Optical networks constitute a fundamental building block that has enabled the success of cloud computing. Virtualization, a cornerstone of cloud computing, today is applied in the networking field: physical network infrastructure is logically partitioned into separate virtual networks, thus providing isolation between distinct virtual network operators (VNOs). Hence, the problem of virtual network mapping has arisen: how to decide which physical resources to allocate for a particular virtual network? In a cloud context, not just network connectivity is required, but also data center (DC) resources located at multiple locations, for computation and/or storage. Given the underlying anycast routing principle, the network operator has some freedom to which specific DC to allocate these resources. In this paper, we solve a resilient virtual network mapping problem that optimally decides on the mapping of both network and multi-location data center resources resiliently using anycast routing, considering time-varying traffic conditions. In terms of resilience, we consider the so-called VNO-resilience scheme, where resilience is provided in the virtual network layer. To minimize physical resource capacity requirements, we allow reuse of both network and DC resources. The failures we protect against include both network and DC resource failures: we hence allocate backup DC resources, and also account for synchronization between primary and backup DC. As optimization criteria, we not only consider resource usage minimization, but also aim to limit virtual network reconfigurations from one time period to the next. We propose a scalable column generation approach to solve the dynamic resilient virtual network mapping problem, and demonstrate it in a case study on a nationwide US backbone network

Energy-Efficient dynamic virtual network traffic engineering for north-south traffic in multi-location data center networks

We consider the problem of allocating data center (DC) resources for cloud enterprise customers who require guaranteed services on demand. In particular, a request from an enterprise customer is mapped to a virtual network (VN) class that is allocated both bandwidth and compute resources by connecting it from an entry point of a data center to one or more hosts while there are multiple geographically distributed data centers to choose from. We take a dynamic traffic engineering approach over multiple time periods in which an energy-aware resource reservation model is solved at each review point. For the energy-aware resource reservation problem, we present a mixed-integer linear programming (MILP) formulation (for small-scale problems) and a heuristic approach (for large-scale problems). Our heuristic is fast for solving large-scale problems where the MILP problem becomes difficult to solve. Through a comprehensive set of studies, we found that a VN class with a low resource requirement has a low blocking even in heavy traffic, while the VN class with a high resource requirement faces a high service denial. Furthermore, the VN class having randomly distributed resource requirement has a high provisioning cost and blocking compared to the VN class having the same resource requirement for each request although the average resource requirement is same for both these VN classes. We also observe that our approach reduces the maximum energy consumption by about one-sixth at the low arrival rate to by about one-third at the highest arrival rate this also depends on how many different CPU frequency levels a server can run at. (C) 2017 Published by Elsevier B.V

Disaster Recovery Power and Communications for Smart Critical Infrastructures

In this paper, we propose a framework to leverage electrical microgrids and cellular networks to support post- disaster communications for the public, government and critical infrastructure operation. The framework involves both policy and technical components. The proposed approach is an integration of electrical microgrids to provide power together with self con- figuring wireless mesh communication networks and local edge computing infrastructure to support critical communications and smart infrastructure services/applications in a specific geographic area. Hence, geographic zones which are resilient safe havens are created in a city. We outline the basic components of our approach and discuss open challenges to realizing the visio

QoS Routing Computation with Path Caching: A Framework and Network Performance

In this paper, we present a framework for QoS routing computation with path caching. The framework has three phases to allow different levels of information to be processed at different time scales towards effectively meeting QoS requirement of a newly arrived flow. Path caching is introduced in the first phase to allow for selection and filtering in subsequent phases. We describe several routing schemes which can fit into this framework