794 research outputs found
Checkpointing as a Service in Heterogeneous Cloud Environments
A non-invasive, cloud-agnostic approach is demonstrated for extending
existing cloud platforms to include checkpoint-restart capability. Most cloud
platforms currently rely on each application to provide its own fault
tolerance. A uniform mechanism within the cloud itself serves two purposes: (a)
direct support for long-running jobs, which would otherwise require a custom
fault-tolerant mechanism for each application; and (b) the administrative
capability to manage an over-subscribed cloud by temporarily swapping out jobs
when higher priority jobs arrive. An advantage of this uniform approach is that
it also supports parallel and distributed computations, over both TCP and
InfiniBand, thus allowing traditional HPC applications to take advantage of an
existing cloud infrastructure. Additionally, an integrated health-monitoring
mechanism detects when long-running jobs either fail or incur exceptionally low
performance, perhaps due to resource starvation, and proactively suspends the
job. The cloud-agnostic feature is demonstrated by applying the implementation
to two very different cloud platforms: Snooze and OpenStack. The use of a
cloud-agnostic architecture also enables, for the first time, migration of
applications from one cloud platform to another.Comment: 20 pages, 11 figures, appears in CCGrid, 201
Application-centric Resource Provisioning for Amazon EC2 Spot Instances
In late 2009, Amazon introduced spot instances to offer their unused
resources at lower cost with reduced reliability. Amazon's spot instances allow
customers to bid on unused Amazon EC2 capacity and run those instances for as
long as their bid exceeds the current spot price. The spot price changes
periodically based on supply and demand, and customers whose bids exceed it
gain access to the available spot instances. Customers may expect their
services at lower cost with spot instances compared to on-demand or reserved.
However the reliability is compromised since the instances(IaaS) providing the
service(SaaS) may become unavailable at any time without any notice to the
customer. Checkpointing and migration schemes are of great use to cope with
such situation. In this paper we study various checkpointing schemes that can
be used with spot instances. Also we device some algorithms for checkpointing
scheme on top of application-centric resource provisioning framework that
increase the reliability while reducing the cost significantly
A Reliable and Cost-Efficient Auto-Scaling System for Web Applications Using Heterogeneous Spot Instances
Cloud providers sell their idle capacity on markets through an auction-like
mechanism to increase their return on investment. The instances sold in this
way are called spot instances. In spite that spot instances are usually 90%
cheaper than on-demand instances, they can be terminated by provider when their
bidding prices are lower than market prices. Thus, they are largely used to
provision fault-tolerant applications only. In this paper, we explore how to
utilize spot instances to provision web applications, which are usually
considered availability-critical. The idea is to take advantage of differences
in price among various types of spot instances to reach both high availability
and significant cost saving. We first propose a fault-tolerant model for web
applications provisioned by spot instances. Based on that, we devise novel
auto-scaling polices for hourly billed cloud markets. We implemented the
proposed model and policies both on a simulation testbed for repeatable
validation and Amazon EC2. The experiments on the simulation testbed and the
real platform against the benchmarks show that the proposed approach can
greatly reduce resource cost and still achieve satisfactory Quality of Service
(QoS) in terms of response time and availability
Reliable and energy efficient resource provisioning in cloud computing systems
Cloud Computing has revolutionized the Information Technology sector by giving computing a perspective of service. The services of cloud computing can be accessed by users not knowing about the underlying system with easy-to-use portals. To provide such an abstract view, cloud computing systems have to perform many complex operations besides managing a large underlying infrastructure. Such complex operations confront service providers with many challenges such as security, sustainability, reliability, energy consumption and resource management. Among all the challenges, reliability and energy consumption are two key challenges focused on in this thesis because of their conflicting nature. Current solutions either focused on reliability techniques or energy efficiency methods. But it has been observed that mechanisms providing reliability in cloud computing systems can deteriorate the energy consumption. Adding backup resources and running replicated systems provide strong fault tolerance but also increase energy consumption. Reducing energy consumption by running resources on low power scaling levels or by reducing the number of active but idle sitting resources such as backup resources reduces the system reliability. This creates a critical trade-off between these two metrics that are investigated in this thesis. To address this problem, this thesis presents novel resource management policies which target the provisioning of best resources in terms of reliability and energy efficiency and allocate them to suitable virtual machines. A mathematical framework showing interplay between reliability and energy consumption is also proposed in this thesis. A formal method to calculate the finishing time of tasks running in a cloud computing environment impacted with independent and correlated failures is also provided. The proposed policies adopted various fault tolerance mechanisms while satisfying the constraints such as task deadlines and utility values. This thesis also provides a novel failure-aware VM consolidation method, which takes the failure characteristics of resources into consideration before performing VM consolidation. All the proposed resource management methods are evaluated by using real failure traces collected from various distributed computing sites. In order to perform the evaluation, a cloud computing framework, 'ReliableCloudSim' capable of simulating failure-prone cloud computing systems is developed. The key research findings and contributions of this thesis are: 1. If the emphasis is given only to energy optimization without considering reliability in a failure prone cloud computing environment, the results can be contrary to the intuitive expectations. Rather than reducing energy consumption, a system ends up consuming more energy due to the energy losses incurred because of failure overheads. 2. While performing VM consolidation in a failure prone cloud computing environment, a significant improvement in terms of energy efficiency and reliability can be achieved by considering failure characteristics of physical resources. 3. By considering correlated occurrence of failures during resource provisioning and VM allocation, the service downtime or interruption is reduced significantly by 34% in comparison to the environments with the assumption of independent occurrence of failures. Moreover, measured by our mathematical model, the ratio of reliability and energy consumption is improved by 14%
Fail Over Strategy for Fault Tolerance in Cloud Computing Environment
YesCloud fault tolerance is an important issue in cloud computing platforms and applications. In the event of an unexpected
system failure or malfunction, a robust fault-tolerant design may allow the cloud to continue functioning correctly
possibly at a reduced level instead of failing completely. To ensure high availability of critical cloud services, the
application execution and hardware performance, various fault tolerant techniques exist for building self-autonomous
cloud systems. In comparison to current approaches, this paper proposes a more robust and reliable architecture using
optimal checkpointing strategy to ensure high system availability and reduced system task service finish time. Using
pass rates and virtualised mechanisms, the proposed Smart Failover Strategy (SFS) scheme uses components such as
Cloud fault manager, Cloud controller, Cloud load balancer and a selection mechanism, providing fault tolerance via
redundancy, optimized selection and checkpointing. In our approach, the Cloud fault manager repairs faults generated
before the task time deadline is reached, blocking unrecoverable faulty nodes as well as their virtual nodes. This scheme
is also able to remove temporary software faults from recoverable faulty nodes, thereby making them available for future
request. We argue that the proposed SFS algorithm makes the system highly fault tolerant by considering forward and
backward recovery using diverse software tools. Compared to existing approaches, preliminary experiment of the SFS
algorithm indicate an increase in pass rates and a consequent decrease in failure rates, showing an overall good
performance in task allocations. We present these results using experimental validation tools with comparison to other
techniques, laying a foundation for a fully fault tolerant IaaS Cloud environment
A proactive fault tolerance framework for high performance computing (HPC) systems in the cloud
High Performance Computing (HPC) systems have been widely used by scientists and researchers in both industry and university laboratories to solve advanced computation problems. Most advanced computation problems are either data-intensive or computation-intensive. They may take hours, days or even weeks to complete execution. For example, some of the traditional HPC systems computations run on 100,000 processors for weeks. Consequently traditional HPC systems often require huge capital investments. As a result, scientists and researchers sometimes have to wait in long queues to access shared, expensive HPC systems. Cloud computing, on the other hand, offers new computing paradigms, capacity, and flexible solutions for both business and HPC applications. Some of the computation-intensive applications that are usually executed in traditional HPC systems can now be executed in the cloud. Cloud computing price model eliminates huge capital investments. However, even for cloud-based HPC systems, fault tolerance is still an issue of growing concern. The large number of virtual machines and electronic components, as well as software complexity and overall system reliability, availability and serviceability (RAS), are factors with which HPC systems in the cloud must contend. The reactive fault tolerance approach of checkpoint/restart, which is commonly used in HPC systems, does not scale well in the cloud due to resource sharing and distributed systems networks. Hence, the need for reliable fault tolerant HPC systems is even greater in a cloud environment. In this thesis we present a proactive fault tolerance approach to HPC systems in the cloud to reduce the wall-clock execution time, as well as dollar cost, in the presence of hardware failure. We have developed a generic fault tolerance algorithm for HPC systems in the cloud. We have further developed a cost model for executing computation-intensive applications on HPC systems in the cloud. Our experimental results obtained from a real cloud execution environment show that the wall-clock execution time and cost of running computation-intensive applications in the cloud can be considerably reduced compared to checkpoint and redundancy techniques used in traditional HPC systems
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