3,445 research outputs found
Towards Autonomic Service Provisioning Systems
This paper discusses our experience in building SPIRE, an autonomic system
for service provision. The architecture consists of a set of hosted Web
Services subject to QoS constraints, and a certain number of servers used to
run session-based traffic. Customers pay for having their jobs run, but require
in turn certain quality guarantees: there are different SLAs specifying charges
for running jobs and penalties for failing to meet promised performance
metrics. The system is driven by an utility function, aiming at optimizing the
average earned revenue per unit time. Demand and performance statistics are
collected, while traffic parameters are estimated in order to make dynamic
decisions concerning server allocation and admission control. Different utility
functions are introduced and a number of experiments aiming at testing their
performance are discussed. Results show that revenues can be dramatically
improved by imposing suitable conditions for accepting incoming traffic; the
proposed system performs well under different traffic settings, and it
successfully adapts to changes in the operating environment.Comment: 11 pages, 9 Figures,
http://www.wipo.int/pctdb/en/wo.jsp?WO=201002636
Reliable Provisioning of Spot Instances for Compute-intensive Applications
Cloud computing providers are now offering their unused resources for leasing
in the spot market, which has been considered the first step towards a
full-fledged market economy for computational resources. Spot instances are
virtual machines (VMs) available at lower prices than their standard on-demand
counterparts. These VMs will run for as long as the current price is lower than
the maximum bid price users are willing to pay per hour. Spot instances have
been increasingly used for executing compute-intensive applications. In spite
of an apparent economical advantage, due to an intermittent nature of biddable
resources, application execution times may be prolonged or they may not finish
at all. This paper proposes a resource allocation strategy that addresses the
problem of running compute-intensive jobs on a pool of intermittent virtual
machines, while also aiming to run applications in a fast and economical way.
To mitigate potential unavailability periods, a multifaceted fault-aware
resource provisioning policy is proposed. Our solution employs price and
runtime estimation mechanisms, as well as three fault tolerance techniques,
namely checkpointing, task duplication and migration. We evaluate our
strategies using trace-driven simulations, which take as input real price
variation traces, as well as an application trace from the Parallel Workload
Archive. Our results demonstrate the effectiveness of executing applications on
spot instances, respecting QoS constraints, despite occasional failures.Comment: 8 pages, 4 figure
Resource and Application Models for Advanced Grid Schedulers
As Grid computing is becoming an inevitable future, managing, scheduling and monitoring dynamic, heterogeneous resources will present new challenges. Solutions will have to be agile and adaptive, support self-organization and autonomous management, while maintaining optimal resource utilisation. Presented in this paper are basic principles and architectural concepts for efficient resource allocation in heterogeneous Grid environment
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Scheduling, Characterization and Prediction of HPC Workloads for Distributed Computing Environments
As High Performance Computing (HPC) has grown considerably and is expected to grow even more, effective resource management for distributed computing sys- tems is motivated more than ever. As the computational workloads grow in quantity, it is becoming more crucial to apply efficient resource management and workload scheduling to use resources efficiently while keeping the computational performance reasonably good. The problem of efficiently scheduling workloads on resources while meeting performance standards is hard. Additionally, non-clairvoyance of job dimen- sions makes resource management even harder in real-world scenarios. Our research methodology investigates the scheduling problem compliant for HPC and researches the challenges for deploying the scheduling in real world-scenarios using state of the art machine learning and data science techniques.To this end, this Ph.D. dissertation makes the following core contributions: a) We perform a theoretical analysis of space-sharing, non-preemptive scheduling: we studied this scheduling problem and proposed scheduling algorithms with polyno- mial computation time. We also proved constant upper-bounds for the performance of these algorithms. b) We studied the sensitivity of scheduling algorithms to the accuracy of runtime and devised a meta-learning approach to estimate prediction accuracy for newly submitted jobs to the HPC system. c) We studied the runtime prediction problem for HPC applications. For this purpose, we studied the distri- bution of available public workloads and proposed two different solutions that can predict multi-modal distributions: switching state-space models and Mixture Density Networks. d) We studied the effectiveness of recent recurrent neural network models for CPU usage trace prediction for individual VM traces as well as aggregate CPU usage traces. In this dissertation, we explore solutions to improve the performance of scheduling workloads on distributed systems.We begin by looking at the problem from the theoretical perspective. Modeling the problem mathematically, we first propose a scheduling algorithm that finds a constant approximation of the optimal solution for the problem in polynomial time. We prove that the performance of the algorithm (average completion time is the constant approximation of the performance of the optimal scheduling. We next look at the problem in real-world scenarios. Considering High-Performance Computing (HPC) workload computing environments as the most similar real-world equivalent of our mathematical model, we explore the problem of predicting application runtime. We propose an algorithm to handle the existing uncertainties in the real world and show-case our algorithm with demonstrative effectiveness in terms of response time and resource utilization. After looking at the uncertainty problem, we focus on trying to improve the accuracy of existing prediction approaches for HPC application runtime. We propose two solutions, one based on Kalman filters and one based on deep density mixture networks. We showcase the effectiveness of our prediction approaches by comparing with previous prediction approaches in terms of prediction accuracy and impact on improving scheduling performance. In the end, we focus on predicting resource usage for individual applications during their execution. We explore the application of recurrent neural networks for predicting resource usage of applications deployed on individual virtual machines. To validate our proposed models and solutions, we performed extensive trace-driven simulation and measured the effectiveness of our approaches
Evaluating the Robustness of Resource Allocations Obtained through Performance Modeling with Stochastic Process Algebra
Recent developments in the field of parallel and distributed computing has led to a proliferation of solving large and computationally intensive mathematical, science, or engineering problems, that consist of several parallelizable parts and several non-parallelizable (sequential) parts. In a parallel and distributed computing environment, the performance goal is to optimize the execution of parallelizable parts of an application on concurrent processors. This requires efficient application scheduling and resource allocation for mapping applications to a set of suitable parallel processors such that the overall performance goal is achieved. However, such computational environments are often prone to unpredictable variations in application (problem and algorithm) and system characteristics. Therefore, a robustness study is required to guarantee a desired level of performance. Given an initial workload, a mapping of applications to resources is considered to be robust if that mapping optimizes execution performance and guarantees a desired level of performance in the presence of unpredictable perturbations at runtime. In this research, a stochastic process algebra, Performance Evaluation Process Algebra (PEPA), is used for obtaining resource allocations via a numerical analysis of performance modeling of the parallel execution of applications on parallel computing resources. The PEPA performance model is translated into an underlying mathematical Markov chain model for obtaining performance measures. Further, a robustness analysis of the allocation techniques is performed for finding a robustmapping from a set of initial mapping schemes. The numerical analysis of the performance models have confirmed similarity with the simulation results of earlier research available in existing literature. When compared to direct experiments and simulations, numerical models and the corresponding analyses are easier to reproduce, do not incur any setup or installation costs, do not impose any prerequisites for learning a simulation framework, and are not limited by the complexity of the underlying infrastructure or simulation libraries
Towards a novel biologically-inspired cloud elasticity framework
With the widespread use of the Internet, the popularity of web applications has
significantly increased. Such applications are subject to unpredictable workload
conditions that vary from time to time. For example, an e-commerce website may
face higher workloads than normal during festivals or promotional schemes. Such
applications are critical and performance related issues, or service disruption can
result in financial losses. Cloud computing with its attractive feature of dynamic
resource provisioning (elasticity) is a perfect match to host such applications.
The rapid growth in the usage of cloud computing model, as well as the rise in
complexity of the web applications poses new challenges regarding the effective
monitoring and management of the underlying cloud computational resources.
This thesis investigates the state-of-the-art elastic methods including the models
and techniques for the dynamic management and provisioning of cloud resources
from a service provider perspective.
An elastic controller is responsible to determine the optimal number of cloud resources,
required at a particular time to achieve the desired performance demands.
Researchers and practitioners have proposed many elastic controllers using versatile
techniques ranging from simple if-then-else based rules to sophisticated
optimisation, control theory and machine learning based methods. However,
despite an extensive range of existing elasticity research, the aim of implementing
an efficient scaling technique that satisfies the actual demands is still a challenge
to achieve. There exist many issues that have not received much attention from
a holistic point of view. Some of these issues include: 1) the lack of adaptability
and static scaling behaviour whilst considering completely fixed approaches; 2)
the burden of additional computational overhead, the inability to cope with the
sudden changes in the workload behaviour and the preference of adaptability
over reliability at runtime whilst considering the fully dynamic approaches; and 3)
the lack of considering uncertainty aspects while designing auto-scaling solutions.
This thesis seeks solutions to address these issues altogether using an integrated
approach. Moreover, this thesis aims at the provision of qualitative elasticity rules.
This thesis proposes a novel biologically-inspired switched feedback control
methodology to address the horizontal elasticity problem. The switched methodology
utilises multiple controllers simultaneously, whereas the selection of a
suitable controller is realised using an intelligent switching mechanism. Each
controller itself depicts a different elasticity policy that can be designed using the
principles of fixed gain feedback controller approach. The switching mechanism
is implemented using a fuzzy system that determines a suitable controller/-
policy at runtime based on the current behaviour of the system. Furthermore,
to improve the possibility of bumpless transitions and to avoid the oscillatory
behaviour, which is a problem commonly associated with switching based control
methodologies, this thesis proposes an alternative soft switching approach. This
soft switching approach incorporates a biologically-inspired Basal Ganglia based
computational model of action selection.
In addition, this thesis formulates the problem of designing the membership functions
of the switching mechanism as a multi-objective optimisation problem. The
key purpose behind this formulation is to obtain the near optimal (or to fine tune)
parameter settings for the membership functions of the fuzzy control system in
the absence of domain experts’ knowledge. This problem is addressed by using
two different techniques including the commonly used Genetic Algorithm and
an alternative less known economic approach called the Taguchi method. Lastly,
we identify seven different kinds of real workload patterns, each of which reflects
a different set of applications. Six real and one synthetic HTTP traces, one for
each pattern, are further identified and utilised to evaluate the performance of
the proposed methods against the state-of-the-art approaches
Towards a novel biologically-inspired cloud elasticity framework
With the widespread use of the Internet, the popularity of web applications has
significantly increased. Such applications are subject to unpredictable workload
conditions that vary from time to time. For example, an e-commerce website may
face higher workloads than normal during festivals or promotional schemes. Such
applications are critical and performance related issues, or service disruption can
result in financial losses. Cloud computing with its attractive feature of dynamic
resource provisioning (elasticity) is a perfect match to host such applications.
The rapid growth in the usage of cloud computing model, as well as the rise in
complexity of the web applications poses new challenges regarding the effective
monitoring and management of the underlying cloud computational resources.
This thesis investigates the state-of-the-art elastic methods including the models
and techniques for the dynamic management and provisioning of cloud resources
from a service provider perspective.
An elastic controller is responsible to determine the optimal number of cloud resources,
required at a particular time to achieve the desired performance demands.
Researchers and practitioners have proposed many elastic controllers using versatile
techniques ranging from simple if-then-else based rules to sophisticated
optimisation, control theory and machine learning based methods. However,
despite an extensive range of existing elasticity research, the aim of implementing
an efficient scaling technique that satisfies the actual demands is still a challenge
to achieve. There exist many issues that have not received much attention from
a holistic point of view. Some of these issues include: 1) the lack of adaptability
and static scaling behaviour whilst considering completely fixed approaches; 2)
the burden of additional computational overhead, the inability to cope with the
sudden changes in the workload behaviour and the preference of adaptability
over reliability at runtime whilst considering the fully dynamic approaches; and 3)
the lack of considering uncertainty aspects while designing auto-scaling solutions.
This thesis seeks solutions to address these issues altogether using an integrated
approach. Moreover, this thesis aims at the provision of qualitative elasticity rules.
This thesis proposes a novel biologically-inspired switched feedback control
methodology to address the horizontal elasticity problem. The switched methodology
utilises multiple controllers simultaneously, whereas the selection of a
suitable controller is realised using an intelligent switching mechanism. Each
controller itself depicts a different elasticity policy that can be designed using the
principles of fixed gain feedback controller approach. The switching mechanism
is implemented using a fuzzy system that determines a suitable controller/-
policy at runtime based on the current behaviour of the system. Furthermore,
to improve the possibility of bumpless transitions and to avoid the oscillatory
behaviour, which is a problem commonly associated with switching based control
methodologies, this thesis proposes an alternative soft switching approach. This
soft switching approach incorporates a biologically-inspired Basal Ganglia based
computational model of action selection.
In addition, this thesis formulates the problem of designing the membership functions
of the switching mechanism as a multi-objective optimisation problem. The
key purpose behind this formulation is to obtain the near optimal (or to fine tune)
parameter settings for the membership functions of the fuzzy control system in
the absence of domain experts’ knowledge. This problem is addressed by using
two different techniques including the commonly used Genetic Algorithm and
an alternative less known economic approach called the Taguchi method. Lastly,
we identify seven different kinds of real workload patterns, each of which reflects
a different set of applications. Six real and one synthetic HTTP traces, one for
each pattern, are further identified and utilised to evaluate the performance of
the proposed methods against the state-of-the-art approaches
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