22,674 research outputs found
Load Balancing and Virtual Machine Allocation in Cloud-based Data Centers
As cloud services see an exponential increase in consumers, the demand for faster processing of data and a reliable delivery of services becomes a pressing concern. This puts a lot of pressure on the cloud-based data centers, where the consumers’ data is stored, processed and serviced. The rising demand for high quality services and the constrained environment, make load balancing within the cloud data centers a vital concern. This project aims to achieve load balancing within the data centers by means of implementing a Virtual Machine allocation policy, based on consensus algorithm technique. The cloud-based data center system, consisting of Virtual Machines has been simulated on CloudSim – a Java based cloud simulator
ERA: A Framework for Economic Resource Allocation for the Cloud
Cloud computing has reached significant maturity from a systems perspective,
but currently deployed solutions rely on rather basic economics mechanisms that
yield suboptimal allocation of the costly hardware resources. In this paper we
present Economic Resource Allocation (ERA), a complete framework for scheduling
and pricing cloud resources, aimed at increasing the efficiency of cloud
resources usage by allocating resources according to economic principles. The
ERA architecture carefully abstracts the underlying cloud infrastructure,
enabling the development of scheduling and pricing algorithms independently of
the concrete lower-level cloud infrastructure and independently of its
concerns. Specifically, ERA is designed as a flexible layer that can sit on top
of any cloud system and interfaces with both the cloud resource manager and
with the users who reserve resources to run their jobs. The jobs are scheduled
based on prices that are dynamically calculated according to the predicted
demand. Additionally, ERA provides a key internal API to pluggable algorithmic
modules that include scheduling, pricing and demand prediction. We provide a
proof-of-concept software and demonstrate the effectiveness of the architecture
by testing ERA over both public and private cloud systems -- Azure Batch of
Microsoft and Hadoop/YARN. A broader intent of our work is to foster
collaborations between economics and system communities. To that end, we have
developed a simulation platform via which economics and system experts can test
their algorithmic implementations
Predicting Scheduling Failures in the Cloud
Cloud Computing has emerged as a key technology to deliver and manage
computing, platform, and software services over the Internet. Task scheduling
algorithms play an important role in the efficiency of cloud computing services
as they aim to reduce the turnaround time of tasks and improve resource
utilization. Several task scheduling algorithms have been proposed in the
literature for cloud computing systems, the majority relying on the
computational complexity of tasks and the distribution of resources. However,
several tasks scheduled following these algorithms still fail because of
unforeseen changes in the cloud environments. In this paper, using tasks
execution and resource utilization data extracted from the execution traces of
real world applications at Google, we explore the possibility of predicting the
scheduling outcome of a task using statistical models. If we can successfully
predict tasks failures, we may be able to reduce the execution time of jobs by
rescheduling failed tasks earlier (i.e., before their actual failing time). Our
results show that statistical models can predict task failures with a precision
up to 97.4%, and a recall up to 96.2%. We simulate the potential benefits of
such predictions using the tool kit GloudSim and found that they can improve
the number of finished tasks by up to 40%. We also perform a case study using
the Hadoop framework of Amazon Elastic MapReduce (EMR) and the jobs of a gene
expression correlations analysis study from breast cancer research. We find
that when extending the scheduler of Hadoop with our predictive models, the
percentage of failed jobs can be reduced by up to 45%, with an overhead of less
than 5 minutes
Cloud engineering is search based software engineering too
Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE
Modeling cloud resources using machine learning
Cloud computing is a new Internet infrastructure paradigm where management optimization has become a challenge to be solved, as all current management systems are human-driven or ad-hoc automatic systems that must be tuned manually by experts. Management of cloud resources require accurate information about all the elements involved (host machines, resources, offered services, and clients), and some of this information can only be obtained a posteriori. Here we present the cloud and part of its architecture as a new scenario where data mining and machine learning can be applied to discover information and improve its management thanks to modeling and prediction. As a novel case of study we show in this work the modeling of basic cloud resources using machine learning, predicting resource requirements from context information like amount of load and clients, and also predicting the quality of service from resource planning, in order to feed cloud schedulers. Further, this work is an important part of our ongoing research program, where accurate models and predictors are essential to optimize cloud management autonomic systems.Postprint (published version
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