9 research outputs found
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
A Survey on Energy Consumption and Environmental Impact of Video Streaming
Climate change challenges require a notable decrease in worldwide greenhouse
gas (GHG) emissions across technology sectors. Digital technologies, especially
video streaming, accounting for most Internet traffic, make no exception. Video
streaming demand increases with remote working, multimedia communication
services (e.g., WhatsApp, Skype), video streaming content (e.g., YouTube,
Netflix), video resolution (4K/8K, 50 fps/60 fps), and multi-view video, making
energy consumption and environmental footprint critical. This survey
contributes to a better understanding of sustainable and efficient video
streaming technologies by providing insights into the state-of-the-art and
potential future directions for researchers, developers, and engineers, service
providers, hosting platforms, and consumers. We widen this survey's focus on
content provisioning and content consumption based on the observation that
continuously active network equipment underneath video streaming consumes
substantial energy independent of the transmitted data type. We propose a
taxonomy of factors that affect the energy consumption in video streaming, such
as encoding schemes, resource requirements, storage, content retrieval,
decoding, and display. We identify notable weaknesses in video streaming that
require further research for improved energy efficiency: (1) fixed bitrate
ladders in HTTP live streaming; (2) inefficient hardware utilization of
existing video players; (3) lack of comprehensive open energy measurement
dataset covering various device types and coding parameters for reproducible
research