7,682 research outputs found
A Highly Available Cluster of Web Servers with Increased Storage Capacity
Ponencias de las Decimoséptimas Jornadas de Paralelismo de la Universidad de Castilla-La Mancha celebradas el 18,19 y 20 de septiembre de 2006 en AlbaceteWeb servers scalability has been traditionally solved by improving software elements or increasing hardware resources of the server machine.
Another approach has been the usage of distributed
architectures. In such architectures, usually, file al-
location strategy has been either full replication or full distribution. In previous works we have showed that partial replication offers a good balance between storage capacity and reliability. It offers much higher
storage capacity while reliability may be kept at an equivalent level of that from fully replicated solutions.
In this paper we present the architectural details of Web cluster solutions adapted to partial replication.
We also show that partial replication does not imply a penalty in performance over classical fully replicated architectures. For evaluation purposes we have used a simulation model under the OMNeT++ framework and we use mean service time as a performance comparison metric.Publicad
Internet of Things Cloud: Architecture and Implementation
The Internet of Things (IoT), which enables common objects to be intelligent
and interactive, is considered the next evolution of the Internet. Its
pervasiveness and abilities to collect and analyze data which can be converted
into information have motivated a plethora of IoT applications. For the
successful deployment and management of these applications, cloud computing
techniques are indispensable since they provide high computational capabilities
as well as large storage capacity. This paper aims at providing insights about
the architecture, implementation and performance of the IoT cloud. Several
potential application scenarios of IoT cloud are studied, and an architecture
is discussed regarding the functionality of each component. Moreover, the
implementation details of the IoT cloud are presented along with the services
that it offers. The main contributions of this paper lie in the combination of
the Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport
(MQTT) servers to offer IoT services in the architecture of the IoT cloud with
various techniques to guarantee high performance. Finally, experimental results
are given in order to demonstrate the service capabilities of the IoT cloud
under certain conditions.Comment: 19pages, 4figures, IEEE Communications Magazin
Weighted Round Robin Load Balancer to Enhance Web Server Cluster in OpenFlow Networks
Web server clusters require a reliable network management for increasing the quality of service (QoS). A load balancer system installed in a software-defined network (SDN) is one method that can improve the performance and availability of web server services. SDN is a dynamic and a programmable network management approach, and one protocol that supports it is OpenFlow. This research aims to design and analyse a model of a load balancer on OpenFlow networks, implementing a Weighted Round Robin (WRR) algorithm. The analysis process is conducted by measuring the value of a QoS web server performance parameters, such as response time, throughput, HTTP success, and loss connection. The results showed the WRR algorithm can be implemented for balancing a network system with dynamic resource allocation. The weight workload of each service can be obtained from the needs and existing network resources. The performance of a load balancer on an OpenFlow network is 57% better than in a traditional one for testing of response time conducted in a high connection. However, the throughput and HTTP success connection decreased by 2% and 10%, respectively, while HTTP loss connection increased by 49%
DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling
The dynamic provisioning of virtualized resources offered by cloud computing
infrastructures allows applications deployed in a cloud environment to
automatically increase and decrease the amount of used resources. This
capability is called auto-scaling and its main purpose is to automatically
adjust the scale of the system that is running the application to satisfy the
varying workload with minimum resource utilization. The need for auto-scaling
is particularly important during workload peaks, in which applications may need
to scale up to extremely large-scale systems.
Both the research community and the main cloud providers have already
developed auto-scaling solutions. However, most research solutions are
centralized and not suitable for managing large-scale systems, moreover cloud
providers' solutions are bound to the limitations of a specific provider in
terms of resource prices, availability, reliability, and connectivity.
In this paper we propose DEPAS, a decentralized probabilistic auto-scaling
algorithm integrated into a P2P architecture that is cloud provider
independent, thus allowing the auto-scaling of services over multiple cloud
infrastructures at the same time. Our simulations, which are based on real
service traces, show that our approach is capable of: (i) keeping the overall
utilization of all the instantiated cloud resources in a target range, (ii)
maintaining service response times close to the ones obtained using optimal
centralized auto-scaling approaches.Comment: Submitted to Springer Computin
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