8,785 research outputs found
The effects of qos level degradation cost on provider selection and task allocation model in telecommunication networks
Firms acquire network capacity from multiple suppliers which offer different Quality of Service (QoS) levels. After acquisition, day-to-day operations such as video conferencing, voice over IP and data applications are allocated between these acquired capacities by considering QoS requirement of each operation. In optimal allocation scheme, it is generally assumed each operation has to be placed into resource that provides equal or higher QoS Level. Conversely, in this study it is showed that former allocation strategy may lead to suboptimal solutions depending upon penalty cost policy to charge degradation in QoS requirements. We model a cost minimization problem which includes three cost components namely capacity acquisition, opportunity and penalty due to loss in QoS
Parameterized complexity of machine scheduling: 15 open problems
Machine scheduling problems are a long-time key domain of algorithms and
complexity research. A novel approach to machine scheduling problems are
fixed-parameter algorithms. To stimulate this thriving research direction, we
propose 15 open questions in this area whose resolution we expect to lead to
the discovery of new approaches and techniques both in scheduling and
parameterized complexity theory.Comment: Version accepted to Computers & Operations Researc
Co-Scheduling Algorithms for High-Throughput Workload Execution
This paper investigates co-scheduling algorithms for processing a set of
parallel applications. Instead of executing each application one by one, using
a maximum degree of parallelism for each of them, we aim at scheduling several
applications concurrently. We partition the original application set into a
series of packs, which are executed one by one. A pack comprises several
applications, each of them with an assigned number of processors, with the
constraint that the total number of processors assigned within a pack does not
exceed the maximum number of available processors. The objective is to
determine a partition into packs, and an assignment of processors to
applications, that minimize the sum of the execution times of the packs. We
thoroughly study the complexity of this optimization problem, and propose
several heuristics that exhibit very good performance on a variety of
workloads, whose application execution times model profiles of parallel
scientific codes. We show that co-scheduling leads to to faster workload
completion time and to faster response times on average (hence increasing
system throughput and saving energy), for significant benefits over traditional
scheduling from both the user and system perspectives
Solution and quality robust project scheduling: a methodological framework.
The vast majority of the research efforts in project scheduling over the past several years has concentrated on the development of exact and suboptimal procedures for the generation of a baseline schedule assuming complete information and a deterministic environment. During execution, however, projects may be the subject of considerable uncertainty, which may lead to numerous schedule disruptions. Predictive-reactive scheduling refers to the process where a baseline schedule is developed prior to the start of the project and updated if necessary during project execution. It is the objective of this paper to review possible procedures for the generation of proactive (robust) schedules, which are as well as possible protected against schedule disruptions, and for the deployment of reactive scheduling procedures that may be used to revise or re-optimize the baseline schedule when unexpected events occur. We also offer a methodological framework that should allow project management to identify the proper scheduling methodology for different project scheduling environments. Finally, we survey the basics of Critical Chain scheduling and indicate in which environments it is useful.Framework; Information; Management; Processes; Project management; Project scheduling; Project scheduling under uncertainty; Stability; Robust scheduling; Quality; Scheduling; Stability; Uncertainty;
Adaptive Dispatching of Tasks in the Cloud
The increasingly wide application of Cloud Computing enables the
consolidation of tens of thousands of applications in shared infrastructures.
Thus, meeting the quality of service requirements of so many diverse
applications in such shared resource environments has become a real challenge,
especially since the characteristics and workload of applications differ widely
and may change over time. This paper presents an experimental system that can
exploit a variety of online quality of service aware adaptive task allocation
schemes, and three such schemes are designed and compared. These are a
measurement driven algorithm that uses reinforcement learning, secondly a
"sensible" allocation algorithm that assigns jobs to sub-systems that are
observed to provide a lower response time, and then an algorithm that splits
the job arrival stream into sub-streams at rates computed from the hosts'
processing capabilities. All of these schemes are compared via measurements
among themselves and with a simple round-robin scheduler, on two experimental
test-beds with homogeneous and heterogeneous hosts having different processing
capacities.Comment: 10 pages, 9 figure
D-SPACE4Cloud: A Design Tool for Big Data Applications
The last years have seen a steep rise in data generation worldwide, with the
development and widespread adoption of several software projects targeting the
Big Data paradigm. Many companies currently engage in Big Data analytics as
part of their core business activities, nonetheless there are no tools and
techniques to support the design of the underlying hardware configuration
backing such systems. In particular, the focus in this report is set on Cloud
deployed clusters, which represent a cost-effective alternative to on premises
installations. We propose a novel tool implementing a battery of optimization
and prediction techniques integrated so as to efficiently assess several
alternative resource configurations, in order to determine the minimum cost
cluster deployment satisfying QoS constraints. Further, the experimental
campaign conducted on real systems shows the validity and relevance of the
proposed method
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