2,546 research outputs found
An adaptive admission control and load balancing algorithm for a QoS-aware Web system
The main objective of this thesis focuses on the design of an adaptive algorithm for admission control and content-aware load balancing for Web traffic. In order to set the context of this work, several reviews are included to introduce the reader in the background concepts of Web load balancing, admission control and the Internet traffic characteristics that may affect the good performance of a Web site. The admission control and load balancing algorithm described in this thesis manages the distribution of traffic to a Web cluster based on QoS requirements. The goal of the proposed scheduling algorithm is to avoid situations in which the system provides a lower performance than desired due to servers' congestion. This is achieved through the implementation of forecasting calculations. Obviously, the increase of the computational cost of the algorithm results in some overhead. This is the reason for designing an adaptive time slot scheduling that sets the execution times of the algorithm depending on the burstiness that is arriving to the system. Therefore, the predictive scheduling algorithm proposed includes an adaptive overhead control. Once defined the scheduling of the algorithm, we design the admission control module based on throughput predictions. The results obtained by several throughput predictors are compared and one of them is selected to be included in our algorithm. The utilisation level that the Web servers will have in the near future is also forecasted and reserved for each service depending on the Service Level Agreement (SLA). Our load balancing strategy is based on a classical policy. Hence, a comparison of several classical load balancing policies is also included in order to know which of them better fits our algorithm. A simulation model has been designed to obtain the results presented in this thesis
Virtualization services: scalable methods for virtualizing multicore systems
Multi-core technology is bringing parallel processing capabilities
from servers to laptops and even handheld devices. At the same time,
platform support for system virtualization is making it easier to
consolidate server and client resources, when and as needed by
applications. This consolidation is achieved by dynamically mapping
the virtual machines on which applications run to underlying
physical machines and their processing cores. Low cost processor and
I/O virtualization methods efficiently scaled to different numbers of
processing cores and I/O devices are key enablers of such consolidation.
This dissertation develops and evaluates new methods for scaling
virtualization functionality to multi-core and future many-core systems.
Specifically, it re-architects virtualization functionality to improve
scalability and better exploit multi-core system resources. Results
from this work include a self-virtualized I/O abstraction, which
virtualizes I/O so as to flexibly use different platforms' processing
and I/O resources. Flexibility affords improved performance and resource
usage and most importantly, better scalability than that offered by
current I/O virtualization solutions. Further, by describing system virtualization as a
service provided to virtual machines and the underlying computing platform,
this service can be enhanced to provide new and innovative functionality.
For example, a virtual device may provide obfuscated data to guest operating
systems to maintain data privacy; it could mask differences in device
APIs or properties to deal with heterogeneous underlying resources; or it
could control access to data based on the ``trust' properties of the
guest VM.
This thesis demonstrates that extended virtualization services are
superior to existing operating system or user-level implementations
of such functionality, for multiple reasons. First, this solution
technique makes more efficient use of key performance-limiting resource in
multi-core systems, which are memory and I/O bandwidth. Second, this
solution technique better exploits the parallelism inherent in multi-core
architectures and exhibits good scalability properties, in
part because at the hypervisor level, there is greater control in precisely
which and how resources are used to realize extended virtualization services.
Improved control over resource usage makes it possible to provide
value-added functionalities for both guest VMs and the platform.
Specific instances of virtualization services described in this thesis are the
network virtualization service that exploits heterogeneous processing cores,
a storage virtualization service that provides location transparent access
to block devices by extending
the functionality provided by network virtualization service, a multimedia
virtualization service that allows efficient media device sharing based on semantic
information, and an object-based storage service with enhanced access
control.Ph.D.Committee Chair: Schwan, Karsten; Committee Member: Ahamad, Mustaq; Committee Member: Fujimoto, Richard; Committee Member: Gavrilovska, Ada; Committee Member: Owen, Henry; Committee Member: Xenidis, Jim
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
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
Recommended from our members
Performance modelling and analysis of e-commerce systems using class based priority scheduling. An investigation into the development of new class based priority scheduling mechanisms for e-commerce system combining different techniques.
Recently, technological developments have affected most lifestyles, especially with the growth in Internet usage. Internet applications highlight the E-commerce capabilities and applications which are now available everywhere; they receive a great number of users on a 24-7 basis because online services are easy to use, faster and cheaper to acquire. Thus E-commerce web sites have become crucial for companies to increase their revenues. This importance has identified certain effective requirements needed from the performance of these applications. In particular, if the web server is overloaded, poor performance can result, due to either a huge rate of requests being generated which are beyond the server¿s capacity, or due to saturation of the communication links capacity which connects the web server to the network.
Recent researches consider the overload issue and explore different mechanisms for managing the performance of E-commerce applications under overload condition.
This thesis proposes a formal approach in order to investigate the effects of the extreme load and the number of dropped requests on the performance of E-
III
commerce web servers. The proposed approach is based on the class-based priority scheme that classifies E-commerce requests into different classes. Because no single technique can solve all aspects of overload problems, this research combines several techniques including: admission control mechanism, session-based admission control, service differentiation, request scheduling and queuing model-based approach.
Request classification is based on the premise that some requests (e.g. buy) are generally considered more important than others (e.g. browse or search). Moreover, this research considers the extended models from Priority Scheduling Mechanism (PSM). These models add a new parameter, such as a review model or modify the basic PSM to low priority fair model, after the discovery of ineffectiveness with low priority customers or to add new features such as portal models.
The proposed model is formally specified using the ¿ -calculus in early stage of models design and a multi-actor simulation was developed to reflect the target models as accurately as possible and is implemented as a Java-based prototype system.
A formal specification that captures the essential PSM features while keeping the performance model sufficiently simple is presented. Furthermore, the simplicity of the UML bridges the gap between ¿-calculus and Java programming language.
IV
There are many metrics for measuring the performance of E-commerce web servers. This research focuses on the performance of E-commerce web servers that refer to the throughput, utilisation, average response time, dropped requests and arrival rate. A number of experiments are conducted in order to test the performance management of the proposed approaches
Multi-attribute demand characterization and layered service pricing
As cloud computing gains popularity, understanding the pattern and structure of its workload is increasingly important in order to drive effective resource allocation and pricing decisions. In the cloud model, virtual machines (VMs), each consisting of a bundle of computing resources, are presented to users for purchase. Thus, the cloud context requires multi-attribute models of demand. While most of the available studies have focused on one specific attribute of a virtual request such as CPU or memory, to the best of our knowledge there is no work on the joint distribution of resource usage. In the first part of this dissertation, we develop a joint distribution model that captures the relationship among multiple resources by fitting the marginal distribution of each resource type as well as the non-linear structure of their correlation via a copula distribution. We validate our models using a public data set of Google data center usage.
Constructing the demand model is essential for provisioning revenue-optimal configuration for VMs or quality of service (QoS) offered by a provider. In the second part of the dissertation, we turn to the service pricing problem in a multi-provider setting: given service configurations (qualities) offered by different providers, choose a proper price for each offered service to undercut competitors and attract customers. With the rise of layered service-oriented architectures there is a need for more advanced solutions that manage the interactions among service providers at multiple levels. Brokers, as the intermediaries between customers and lower-level providers, play a key role in improving the efficiency of service-oriented structures by matching the demands of customers to the services of providers. We analyze a layered market in which service brokers and service providers compete in a Bertrand game at different levels in an oligopoly market while they offer different QoS. We examine the interaction among players and the effect of price competition on their market shares. We also study the market with partial cooperation, where a subset of players optimizes their total revenue instead of maximizing their own profit independently. We analyze the impact of this cooperation on the market and customers' social welfare
Toward Open and Programmable Wireless Network Edge
Increasingly, the last hop connecting users to their enterprise and home networks is wireless. Wireless is becoming ubiquitous not only in homes and enterprises but in public venues such as coffee shops, hospitals, and airports. However, most of the publicly and privately available wireless networks are proprietary and closed in operation. Also, there is little effort from industries to move forward on a path to greater openness for the requirement of innovation. Therefore, we believe it is the domain of university researchers to enable innovation through openness. In this thesis work, we introduce and defines the importance of open framework in addressing the complexity of the wireless network. The Software Defined Network (SDN) framework has emerged as a popular solution for the data center network. However, the promise of the SDN framework is to make the network open, flexible and programmable. In order to deliver on the promise, SDN must work for all users and across all networks, both wired and wireless. Therefore, we proposed to create new modules and APIs to extend the standard SDN framework all the way to the end-devices (i.e., mobile devices, APs). Thus, we want to provide an extensible and programmable abstraction of the wireless network as part of the current SDN-based solution. In this thesis work, we design and develop a framework, weSDN (wireless extension of SDN), that extends the SDN control capability all the way to the end devices to support client-network interaction capabilities and new services. weSDN enables the control-plane of wireless networks to be extended to mobile devices and allows for top-level decisions to be made from an SDN controller with knowledge of the network as a whole, rather than device centric configurations. In addition, weSDN easily obtains user application information, as well as the ability to monitor and control application flows dynamically. Based on the weSDN framework, we demonstrate new services such as application-aware traffic management, WLAN virtualization, and security management
Towards effective dynamic resource allocation for enterprise applications
The growing use of online services requires substantial supporting infrastructure.
The efficient deployment of applications relies on the cost effectiveness of
commercial hosting providers who deliver an agreed quality of service as governed
by a service level agreement for a fee. The priorities of the commercial
hosting provider are to maximise revenue, by delivering agreed service levels,
and minimise costs, through high resource utilisation.
In order to deliver high service levels and resource utilisation, it may be
necessary to reorganise resources during periods of high demand. This reorganisation
process may be manual or alternatively controlled by an autonomous
process governed by a dynamic resource allocation algorithm. Dynamic resource
allocation has been shown to improve service levels and utilisation and
hence, profitability.
In this thesis several facets of dynamic resource allocation are examined
to asses its suitability for the modern data centre. Firstly, three theoretically
derived policies are implemented as a middleware for a modern multi-tier Web
application and their performance is examined under a range of workloads in a
real world test bed.
The scalability of state-of-the art resource allocation policies are explored in
two dimensions, namely the number of applications and the quantity of servers
under control of the resources allocation policy. The results demonstrate that current policies presented in the literature demonstrate poor scalability in one
or both of these dimensions. A new policy is proposed which has significantly
improved scalability characteristics and the new policy is demonstrated at scale
through simulation.
The placement of applications in across a datacenter makes them susceptible
to failures in shared infrastructure. To address this issue an application
placement mechanism is developed to augment any dynamic resource allocation
policy. The results of this placement mechanism demonstrate a significant improvement
in the worst case when compared to a random allocation mechanism.
A model for the reallocation of resources in a dynamic resource allocation
system is also devised. The model demonstrates that the assumption of a constant
resource reallocation cost is invalid under both physical reallocation and
migration of virtualised resources
- …