157 research outputs found
Performance-oriented Cloud Provisioning: Taxonomy and Survey
Cloud computing is being viewed as the technology of today and the future.
Through this paradigm, the customers gain access to shared computing resources
located in remote data centers that are hosted by cloud providers (CP). This
technology allows for provisioning of various resources such as virtual
machines (VM), physical machines, processors, memory, network, storage and
software as per the needs of customers. Application providers (AP), who are
customers of the CP, deploy applications on the cloud infrastructure and then
these applications are used by the end-users. To meet the fluctuating
application workload demands, dynamic provisioning is essential and this
article provides a detailed literature survey of dynamic provisioning within
cloud systems with focus on application performance. The well-known types of
provisioning and the associated problems are clearly and pictorially explained
and the provisioning terminology is clarified. A very detailed and general
cloud provisioning classification is presented, which views provisioning from
different perspectives, aiding in understanding the process inside-out. Cloud
dynamic provisioning is explained by considering resources, stakeholders,
techniques, technologies, algorithms, problems, goals and more.Comment: 14 pages, 3 figures, 3 table
Performance Modeling of Softwarized Network Services Based on Queuing Theory with Experimental Validation
Network Functions Virtualization facilitates the automation of the scaling of softwarized network services (SNSs).
However, the realization of such a scenario requires a way to
determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is
known as resource dimensioning, and it can be efficiently tackled
by performance modeling. In this vein, this paper describes an
analytical model based on an open queuing network of G/G/m
queues to evaluate the response time of SNSs. We validate our
model experimentally for a virtualized Mobility Management
Entity (vMME) with a three-tiered architecture running on
a testbed that resembles a typical data center virtualization
environment. We detail the description of our experimental
setup and procedures. We solve our resulting queueing network
by using the Queueing Networks Analyzer (QNA), Jackson’s
networks, and Mean Value Analysis methodologies, and compare
them in terms of estimation error. Results show that, for medium
and high workloads, the QNA method achieves less than half of
error compared to the standard techniques. For low workloads,
the three methods produce an error lower than 10%. Finally,
we show the usefulness of the model for performing the dynamic
provisioning of the vMME experimentally.This work has been partially funded by the H2020 research
and innovation project 5G-CLARITY (Grant No. 871428)National research
project 5G-City: TEC2016-76795-C6-4-RSpanish Ministry of
Education, Culture and Sport (FPU Grant 13/04833). We would also like to
thank the reviewers for their valuable feedback to enhance the quality
and contribution of this wor
Profile-based Resource Allocation for Virtualized Network Functions
Accepted in IEEE TNSM Journalhttps://ieeexplore.ieee.org/document/8848599International audienceThe virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurations with deterministic performance. The service operator must somehow map the performance specification in the Service Level Agreement (SLA) to an adequate resource allocation in the virtualized infrastructure. We propose the use of a VNF profile to alleviate this process. This is illustrated by profiling the performance of four example network functions (a virtual router, switch, firewall and cache server) under varying workloads and resource configurations. We then compare several methods to derive a model from the profiled datasets. We select the most accurate method to further train a model which predicts the services' performance, in function of incoming workload and allocated resources. Our presented method can offer the service operator a recommended resource allocation for the targeted service, in function of the targeted performance and maximum workload specified in the SLA. This helps to deploy the softwarized service with an optimal amount of resources to meet the SLA requirements, thereby avoiding unnecessary scaling steps
Demand-Side Management for Energy-efficient Data Center Operations with Renewable Energy and Demand Response
In recent years, we have noticed tremendous increase of energy consumption and carbon pollution in the industrial sector, and many energy-intensive industries are striving to reduce energy cost and to have a positive impact on the environment. In this context, this dissertation is motivated by opportunities to reduce energy cost from demand-side perspective. Specifically, industries have an opportunity to reduce energy consumption by improving energy-efficiency in their system operations. By improving utilization of their resources, they can reduce waste of energy, and thus, they are able to prevent paying unnecessary energy cost. In addition, because of today‘s high penetration of renewable generation (e.g. wind or solar), many industries consider renewable energy as a promising solution to reduce energy cost and carbon pollution, and they have tried to utilize renewable energy to meet their power demand by installing on-site generation facilities (e.g. PV panels on roof top) or making a contract with renewable generation farms. Moreover, it is becoming common for energy markets to allow industries to directly purchase electricity from them while providing the industries with day-ahead and real-time electricity price information. In this situation, industries have an opportunity to adjust purchase and consumption of energy in response to time-varying electricity price and intermittent renewable generation to reduce their energy procurement cost, which are called demand response.
Considering these opportunities, it is anticipated that the industrial sector can save a significant amount of energy cost, however, time-varying behavior, uncertainty and stochasticity in system operations, power demand, renewable energy, and electricity price make it challenging to determine optimal operational decision. Motivated by the aforementioned opportunities as well as challenges, this dissertation focuses on developing decision-making methodologies tailored for demand-side energy system operations to improve energy-efficiency based on energy-aware system operations and reduce energy procurement cost by utilizing renewable energy and demand response in an integrated fashion to optimally reduce energy cost.
For practical application, this dissertation considers real-world practices in data centers including their operations management and power procurement for the following research tasks: (i) develop a server provisioning algorithm that dynamically adapts server operations in response to heterogeneous and time-varying workloads to reduce energy consumption while providing performance guarantees based on time-stability; (ii) propose stochastic optimization models for optimal energy procurement to determine purchase and consumption of energy based on day-ahead and real-time energy market operations considering utilization of renewable energy based on demand response; (iii) suggest a decision-making model that integrate the proposed server provisioning algorithm with energy procurement to achieve energy-efficiency in data center operations to reduce both energy consumption and energy cost against variability and uncertainty. In terms of methodologies, this study uses operations research techniques including deterministic and stochastic models, such as, queueing analysis, mixed-integer program, Markov decision process, two-stage stochastic program, and probabilistic constrained program.
In conclusion, this dissertation claims that renewable energy, demand response, and energy storage are worth to be considered for data center operations to reduce energy consumption and procurement cost. Although variability and uncertainty in system operations, renewable generation, and electricity price make it challenging to determine optimal operational decisions, numerical results show that the proposed optimization problems can be efficiently solved by the developed algorithm. The proposed decision-making methodologies can also be extended to other industries, and thus, this dissertation study would be a good starting point to study demand-side management in energy system operations
Optimization of energy efficiency in data and WEB hosting centers
Mención Internacional en el tÃtulo de doctorThis thesis tackles the optimization of energy efficiency in data centers in terms of network
and server utilization.
For what concerns networking utilization the work focuses on Energy Efficient Ethernet
(EEE) - IEEE 802.3az standard - which is the energy-aware alternative to legacy Ethernet, and an
important component of current and future green data centers. More specifically the first contribution
of this thesis consists in deriving and analytical model of gigabit EEE links with coalescing
using M/G/1 queues with sleep and wake-up periods. Packet coalescing has been proposed to save
energy by extending the sojourn in the Low Power Idle state of EEE. The model presented in this
thesis approximates with a good accuracy both the energy saving and the average packet delay by
using a few significant traffic descriptors. While coalescing improves by far the energy efficiency
of EEE, it is still far from achieving energy consumption proportional to traffic. Moreover, coalescing
can introduce high delays. To this extend, by using sensitivity analysis the thesis evaluates
the impact of coalescing timers and buffer sizes, and sheds light on the delay incurred by adopting
coalescing schemes. Accordingly, the design and study of a first family of dynamic algorithms,
namely measurement-based coalescing control (MBCC), is proposed. MBCC schemes tune the
coalescing parameters on-the-fly, according to the instantaneous load and the coalescing delay
experienced by the packets. The thesis also discusses a second family of dynamic algorithms,
namely NT-policy coalescing control (NTCC), that adjusts the coalescing parameters based on
the sole occurrence of timeouts and buffer fill-ups. Furthermore, the performance of static as well
as dynamic coalescing schemes is investigated using real traffic traces. The results reported in this
work show that, by relying on run-time delay measurements, simple and practical MBCC adaptive
coalescing schemes outperform traditional static and dynamic coalescing while the adoption
of NTCC coalescing schemes has practically no advantages with respect to static coalescing when
delay guarantees have to be provided. Notably, MBCC schemes double the energy saving benefit
of legacy EEE coalescing and allow to control the coalescing delay.
For what concerns server utilization, the thesis presents an exhaustive empirical characterization
of the power requirements of multiple components of data center servers. The characterization
is the second key contribution of this thesis, and is achieved by devising different experiments
to stress server components, taking into account the multiple available CPU frequencies and the
presence of multicore servers. The described experiments, allow to measure energy consumption of server components and identify their optimal operational points. The study proves that the
curve defining the minimal CPU power utilization, as a function of the load expressed in Active
Cycles Per Second, is neither concave nor purely convex. Instead, it definitively shows a superlinear
dependence on the load. The results illustrate how to improve the efficiency of network
cards and disks. Finally, the accuracy of the model derived from the server components consumption
characterization is validated by comparing the real energy consumed by two Hadoop
applications - PageRank and WordCount - with the estimation from the model, obtaining errors
below 4:1%, on average.This work has been partially supported by IMDEA Networks Institute and the Greek State Scholarships
FoundationPrograma Oficial de Doctorado en IngenierÃa TelemáticaPresidente: Marco Giuseppe Ajmone Marsan.- Secretario: Jose Luis Ayala Rodrigo.- Vocal: Gianluca Antonio Rizz
Revenue maximization problems in commercial data centers
As IT systems are becoming more important everyday, one of the main concerns is that users may face major problems and eventually incur major costs if computing systems do not meet the expected performance requirements: customers expect reliability and performance guarantees, while underperforming systems loose revenues. Even with the adoption of data centers as the hub of IT organizations and provider of business efficiencies the problems are not over because it is extremely difficult for service providers to meet the promised performance guarantees in the face of unpredictable demand. One possible approach is the adoption of Service Level Agreements (SLAs), contracts that specify a level of performance that must be met and compensations in case of failure. In this thesis I will address some of the performance problems arising when IT companies sell the service of running ‘jobs’ subject to Quality of Service (QoS) constraints. In particular, the aim is to improve the efficiency of service provisioning systems by allowing them to adapt to changing demand conditions. First, I will define the problem in terms of an utility function to maximize. Two different models are analyzed, one for single jobs and the other useful to deal with session-based traffic. Then, I will introduce an autonomic model for service provision. The architecture consists of a set of hosted applications that share a certain number of servers. The system collects demand and performance statistics and estimates traffic parameters. These estimates are used by management policies which implement dynamic resource allocation and admission algorithms. Results from a number of experiments show that the performance of these heuristics is close to optimal.EThOS - Electronic Theses Online ServiceQoSP (Quality of Service Provisioning) : British TelecomGBUnited Kingdo
Effective Resource and Workload Management in Data Centers
The increasing demand for storage, computation, and business continuity has driven the growth of data centers. Managing data centers efficiently is a difficult task because of the wide variety of datacenter applications, their ever-changing intensities, and the fact that application performance targets may differ widely. Server virtualization has been a game-changing technology for IT, providing the possibility to support multiple virtual machines (VMs) simultaneously. This dissertation focuses on how virtualization technologies can be utilized to develop new tools for maintaining high resource utilization, for achieving high application performance, and for reducing the cost of data center management.;For multi-tiered applications, bursty workload traffic can significantly deteriorate performance. This dissertation proposes an admission control algorithm AWAIT, for handling overloading conditions in multi-tier web services. AWAIT places on hold requests of accepted sessions and refuses to admit new sessions when the system is in a sudden workload surge. to meet the service-level objective, AWAIT serves the requests in the blocking queue with high priority. The size of the queue is dynamically determined according to the workload burstiness.;Many admission control policies are triggered by instantaneous measurements of system resource usage, e.g., CPU utilization. This dissertation first demonstrates that directly measuring virtual machine resource utilizations with standard tools cannot always lead to accurate estimates. A directed factor graph (DFG) model is defined to model the dependencies among multiple types of resources across physical and virtual layers.;Virtualized data centers always enable sharing of resources among hosted applications for achieving high resource utilization. However, it is difficult to satisfy application SLOs on a shared infrastructure, as application workloads patterns change over time. AppRM, an automated management system not only allocates right amount of resources to applications for their performance target but also adjusts to dynamic workloads using an adaptive model.;Server consolidation is one of the key applications of server virtualization. This dissertation proposes a VM consolidation mechanism, first by extending the fair load balancing scheme for multi-dimensional vector scheduling, and then by using a queueing network model to capture the service contentions for a particular virtual machine placement
Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks
Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams.
This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness
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