1,211 research outputs found
Datacenter Traffic Control: Understanding Techniques and Trade-offs
Datacenters provide cost-effective and flexible access to scalable compute
and storage resources necessary for today's cloud computing needs. A typical
datacenter is made up of thousands of servers connected with a large network
and usually managed by one operator. To provide quality access to the variety
of applications and services hosted on datacenters and maximize performance, it
deems necessary to use datacenter networks effectively and efficiently.
Datacenter traffic is often a mix of several classes with different priorities
and requirements. This includes user-generated interactive traffic, traffic
with deadlines, and long-running traffic. To this end, custom transport
protocols and traffic management techniques have been developed to improve
datacenter network performance.
In this tutorial paper, we review the general architecture of datacenter
networks, various topologies proposed for them, their traffic properties,
general traffic control challenges in datacenters and general traffic control
objectives. The purpose of this paper is to bring out the important
characteristics of traffic control in datacenters and not to survey all
existing solutions (as it is virtually impossible due to massive body of
existing research). We hope to provide readers with a wide range of options and
factors while considering a variety of traffic control mechanisms. We discuss
various characteristics of datacenter traffic control including management
schemes, transmission control, traffic shaping, prioritization, load balancing,
multipathing, and traffic scheduling. Next, we point to several open challenges
as well as new and interesting networking paradigms. At the end of this paper,
we briefly review inter-datacenter networks that connect geographically
dispersed datacenters which have been receiving increasing attention recently
and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial
dReDBox: Materializing a full-stack rack-scale system prototype of a next-generation disaggregated datacenter
Current datacenters are based on server machines, whose mainboard and hardware components form the baseline, monolithic building block that the rest of the system software, middleware and application stack are built upon. This leads to the following limitations: (a) resource proportionality of a multi-tray system is bounded by the basic building block (mainboard), (b) resource allocation to processes or virtual machines (VMs) is bounded by the available resources within the boundary of the mainboard, leading to spare resource fragmentation and inefficiencies, and (c) upgrades must be applied to each and every server even when only a specific component needs to be upgraded. The dRedBox project (Disaggregated Recursive Datacentre-in-a-Box) addresses the above limitations, and proposes the next generation, low-power, across form-factor datacenters, departing from the paradigm of the mainboard-as-a-unit and enabling the creation of function-block-as-a-unit. Hardware-level disaggregation and software-defined wiring of resources is supported by a full-fledged Type-1 hypervisor that can execute commodity virtual machines, which communicate over a low-latency and high-throughput software-defined optical network. To evaluate its novel approach, dRedBox will demonstrate application execution in the domains of network functions virtualization, infrastructure analytics, and real-time video surveillance.This work has been supported in part by EU H2020 ICTproject dRedBox, contract #687632.Peer ReviewedPostprint (author's final draft
Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy
Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE – a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3% - 43.6% less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2% with 0.17% SLA violation rate as the performance penalty
Power Management Techniques for Data Centers: A Survey
With growing use of internet and exponential growth in amount of data to be
stored and processed (known as 'big data'), the size of data centers has
greatly increased. This, however, has resulted in significant increase in the
power consumption of the data centers. For this reason, managing power
consumption of data centers has become essential. In this paper, we highlight
the need of achieving energy efficiency in data centers and survey several
recent architectural techniques designed for power management of data centers.
We also present a classification of these techniques based on their
characteristics. This paper aims to provide insights into the techniques for
improving energy efficiency of data centers and encourage the designers to
invent novel solutions for managing the large power dissipation of data
centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy
Efficiency, Green Computing, DVFS, Server Consolidatio
AutoAccel: Automated Accelerator Generation and Optimization with Composable, Parallel and Pipeline Architecture
CPU-FPGA heterogeneous architectures are attracting ever-increasing attention
in an attempt to advance computational capabilities and energy efficiency in
today's datacenters. These architectures provide programmers with the ability
to reprogram the FPGAs for flexible acceleration of many workloads.
Nonetheless, this advantage is often overshadowed by the poor programmability
of FPGAs whose programming is conventionally a RTL design practice. Although
recent advances in high-level synthesis (HLS) significantly improve the FPGA
programmability, it still leaves programmers facing the challenge of
identifying the optimal design configuration in a tremendous design space.
This paper aims to address this challenge and pave the path from software
programs towards high-quality FPGA accelerators. Specifically, we first propose
the composable, parallel and pipeline (CPP) microarchitecture as a template of
accelerator designs. Such a well-defined template is able to support efficient
accelerator designs for a broad class of computation kernels, and more
importantly, drastically reduce the design space. Also, we introduce an
analytical model to capture the performance and resource trade-offs among
different design configurations of the CPP microarchitecture, which lays the
foundation for fast design space exploration. On top of the CPP
microarchitecture and its analytical model, we develop the AutoAccel framework
to make the entire accelerator generation automated. AutoAccel accepts a
software program as an input and performs a series of code transformations
based on the result of the analytical-model-based design space exploration to
construct the desired CPP microarchitecture. Our experiments show that the
AutoAccel-generated accelerators outperform their corresponding software
implementations by an average of 72x for a broad class of computation kernels
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