166 research outputs found
Algorithms for Scheduling Problems
This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more
Support for flexible and transparent distributed computing
Modern distributed computing developed from the traditional supercomputing community rooted firmly
in the culture of batch management. Therefore, the field has been dominated by queuing-based resource
managers and work flow based job submission environments where static resource demands needed be
determined and reserved prior to launching executions. This has made it difficult to support resource
environments (e.g. Grid, Cloud) where the available resources as well as the resource requirements
of applications may be both dynamic and unpredictable. This thesis introduces a flexible execution
model where the compute capacity can be adapted to fit the needs of applications as they change during
execution. Resource provision in this model is based on a fine-grained, self-service approach instead
of the traditional one-time, system-level model. The thesis introduces a middleware based Application
Agent (AA) that provides a platform for the applications to dynamically interact and negotiate resources
with the underlying resource infrastructure.
We also consider the issue of transparency, i.e., hiding the provision and management of the distributed
environment. This is the key to attracting public to use the technology. The AA not only replaces
user-controlled process of preparing and executing an application with a transparent software-controlled
process, it also hides the complexity of selecting right resources to ensure execution QoS. This service
is provided by an On-line Feedback-based Automatic Resource Configuration (OAC) mechanism cooperating
with the flexible execution model. The AA constantly monitors utility-based feedbacks from the
application during execution and thus is able to learn its behaviour and resource characteristics. This
allows it to automatically compose the most efficient execution environment on the fly and satisfy any
execution requirements defined by users. Two policies are introduced to supervise the information learning
and resource tuning in the OAC. The Utility Classification policy classifies hosts according to their
historical performance contributions to the application. According to this classification, the AA chooses
high utility hosts and withdraws low utility hosts to configure an optimum environment. The Desired
Processing Power Estimation (DPPE) policy dynamically configures the execution environment according
to the estimated desired total processing power needed to satisfy users’ execution requirements.
Through the introducing of flexibility and transparency, a user is able to run a dynamic/normal
distributed application anywhere with optimised execution performance, without managing distributed
resources. Based on the standalone model, the thesis further introduces a federated resource negotiation
framework as a step forward towards an autonomous multi-user distributed computing world
A Framework for Approximate Optimization of BoT Application Deployment in Hybrid Cloud Environment
We adopt a systematic approach to investigate the efficiency of near-optimal deployment of large-scale CPU-intensive Bag-of-Task applications running on cloud resources with the non-proportional cost to performance ratios. Our analytical solutions perform in both known and unknown running time of the given application. It tries to optimize users' utility by choosing the most desirable tradeoff between the make-span and the total incurred expense. We propose a schema to provide a near-optimal deployment of BoT application regarding users' preferences. Our approach is to provide user with a set of Pareto-optimal solutions, and then she may select one of the possible scheduling points based on her internal utility function. Our framework can cope with uncertainty in the tasks' execution time using two methods, too. First, an estimation method based on a Monte Carlo sampling called AA algorithm is presented. It uses the minimum possible number of sampling to predict the average task running time. Second, assuming that we have access to some code analyzer, code profiling or estimation tools, a hybrid method to evaluate the accuracy of each estimation tool in certain interval times for improving resource allocation decision has been presented. We propose approximate deployment strategies that run on hybrid cloud. In essence, proposed strategies first determine either an estimated or an exact optimal schema based on the information provided from users' side and environmental parameters. Then, we exploit dynamic methods to assign tasks to resources to reach an optimal schema as close as possible by using two methods. A fast yet simple method based on First Fit Decreasing algorithm, and a more complex approach based on the approximation solution of the transformed problem into a subset sum problem. Extensive experiment results conducted on a hybrid cloud platform confirm that our framework can deliver a near optimal solution respecting user's utility function
LIPIcs, Volume 244, ESA 2022, Complete Volume
LIPIcs, Volume 244, ESA 2022, Complete Volum
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