36,297 research outputs found
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
Energy Saving Techniques for Phase Change Memory (PCM)
In recent years, the energy consumption of computing systems has increased
and a large fraction of this energy is consumed in main memory. Towards this,
researchers have proposed use of non-volatile memory, such as phase change
memory (PCM), which has low read latency and power; and nearly zero leakage
power. However, the write latency and power of PCM are very high and this,
along with limited write endurance of PCM present significant challenges in
enabling wide-spread adoption of PCM. To address this, several
architecture-level techniques have been proposed. In this report, we review
several techniques to manage power consumption of PCM. We also classify these
techniques based on their characteristics to provide insights into them. The
aim of this work is encourage researchers to propose even better techniques for
improving energy efficiency of PCM based main memory.Comment: Survey, phase change RAM (PCRAM
Inferring Energy Bounds via Static Program Analysis and Evolutionary Modeling of Basic Blocks
The ever increasing number and complexity of energy-bound devices (such as
the ones used in Internet of Things applications, smart phones, and mission
critical systems) pose an important challenge on techniques to optimize their
energy consumption and to verify that they will perform their function within
the available energy budget. In this work we address this challenge from the
software point of view and propose a novel parametric approach to estimating
tight bounds on the energy consumed by program executions that are practical
for their application to energy verification and optimization. Our approach
divides a program into basic (branchless) blocks and estimates the maximal and
minimal energy consumption for each block using an evolutionary algorithm. Then
it combines the obtained values according to the program control flow, using
static analysis, to infer functions that give both upper and lower bounds on
the energy consumption of the whole program and its procedures as functions on
input data sizes. We have tested our approach on (C-like) embedded programs
running on the XMOS hardware platform. However, our method is general enough to
be applied to other microprocessor architectures and programming languages. The
bounds obtained by our prototype implementation can be tight while remaining on
the safe side of budgets in practice, as shown by our experimental evaluation.Comment: Pre-proceedings paper presented at the 27th International Symposium
on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur,
Belgium, 10-12 October 2017 (arXiv:1708.07854). Improved version of the one
presented at the HIP3ES 2016 workshop (v1): more experimental results (added
benchmark to Table 1, added figure for new benchmark, added Table 3),
improved Fig. 1, added Fig.
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