On Reliability-Aware Server Consolidation in Cloud Datacenters

In the past few years, datacenter (DC) energy consumption has become an important issue in technology world. Server consolidation using virtualization and virtual machine (VM) live migration allows cloud DCs to improve resource utilization and hence energy efficiency. In order to save energy, consolidation techniques try to turn off the idle servers, while because of workload fluctuations, these offline servers should be turned on to support the increased resource demands. These repeated on-off cycles could affect the hardware reliability and wear-and-tear of servers and as a result, increase the maintenance and replacement costs. In this paper we propose a holistic mathematical model for reliability-aware server consolidation with the objective of minimizing total DC costs including energy and reliability costs. In fact, we try to minimize the number of active PMs and racks, in a reliability-aware manner. We formulate the problem as a Mixed Integer Linear Programming (MILP) model which is in form of NP-complete. Finally, we evaluate the performance of our approach in different scenarios using extensive numerical MATLAB simulations.Comment: International Symposium on Parallel and Distributed Computing (ISPDC), Innsbruck, Austria, 201

Distributed VNF Scaling in Large-scale Datacenters: An ADMM-based Approach

Network Functions Virtualization (NFV) is a promising network architecture where network functions are virtualized and decoupled from proprietary hardware. In modern datacenters, user network traffic requires a set of Virtual Network Functions (VNFs) as a service chain to process traffic demands. Traffic fluctuations in Large-scale DataCenters (LDCs) could result in overload and underload phenomena in service chains. In this paper, we propose a distributed approach based on Alternating Direction Method of Multipliers (ADMM) to jointly load balance the traffic and horizontally scale up and down VNFs in LDCs with minimum deployment and forwarding costs. Initially we formulate the targeted optimization problem as a Mixed Integer Linear Programming (MILP) model, which is NP-complete. Secondly, we relax it into two Linear Programming (LP) models to cope with over and underloaded service chains. In the case of small or medium size datacenters, LP models could be run in a central fashion with a low time complexity. However, in LDCs, increasing the number of LP variables results in additional time consumption in the central algorithm. To mitigate this, our study proposes a distributed approach based on ADMM. The effectiveness of the proposed mechanism is validated in different scenarios.Comment: IEEE International Conference on Communication Technology (ICCT), Chengdu, China, 201

Homa: Online In-Flight Service Provisioning With Dynamic Bipartite Matching

Airline companies are currently investigating means to improve in-flight services for passengers. Given emerging Air-to-Ground (A2G) communication technologies and the high desire of passengers for in-flight services, the servers providing in-flight services can be moved from the airplane to Data Centers (DCs) on the ground. In this scenario, network nodes (airplanes) demanding network services move over a ground core network. Therefore, the selection of DCs to connect to, as well as the underlying routing decisions are challenging. In particular, to keep a low-delay in-flight connection during the flight, airplanes connections can be reconfigured from a DC to another one, which comes at a delay cost. This paper presents a formal model for the in-flight service provisioning problem, also as an Integer Linear Program (ILP). We show that the problem is NP-hard and hence propose an efficient online heuristic, HOMA, which addresses the above challenges in polynomial time. HOMA models the problem as a dynamic matching with special properties, and then efficiently solves it by a transformation into the shortest-path routing problem. Our simulation results indicate that HOMA can achieve near-optimal performance and outperform the baseline and state-of-the-art algorithms by up to 15% while reducing the runtime from hours to seconds

Study of Abrasion of Rubber Materials by Experimental Design, Response Surface and Artificial Neural Network Modeling

Effect of different formulation ingredients on the abrasion behavior, crack growth and modulus of tire tread formulation was studied using two different case studies. In the first case study, the effect of the partial substitution of natural rubber by cis-butadiene and the content variation of oil and sulfur in the presence of modified clay was studied on the basis of central composite design experiment in a NR/SBR-based truck tire tread formulation. In the second case study, the effect of oil, sulfur and highly dispersible silica level was investigated via Box-Benken design experiment in a SBR/BR-based passenger tire tread formulation. In each study a suitable response surface model was developed on the basis of the data obtained using the experimental design. Artificial neural network models with forwarding multi-layers were also developed to investigate the potential of the current approach in modeling of fracture behavior of rubber materials. It was observed that the complex dependency of the fracture/abrasion behavior of rubbery materials on formulation variations could be modeled with high accuracy through response surface and artificial neural models. The response surface profiles were developed to explain the abrasion behavior better. The observed behaviors for the abrasion of rubber formulations were also investigated with the aid of the modulus statistical analysis, deMattia crack growth model and also the Fukahori and mechano-chemical abrasion theories. In the presence of high levels of cis-butadiene, the abrasion with the mechano-chemical mechanism is dominant. However, according to the Fukahori model, the mean amplitude strain has a key effect on the abrasion of rubbery materials

Olfactory dysfunction in allergic fungal rhinosinusitis

To correlate patient reports of olfactory dysfunction after surgical intervention for allergic fungal rhinosinusitis (AFRS) with endoscopic findings, psychophysical testing, and quality-of-life scores