56 research outputs found
Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population
Independent Job Scheduling is one of the most useful versions of scheduling in grid systems. It aims at computing efficient and optimal mapping of jobs and/or applications submitted by independent users to the grid resources. Besides traditional restrictions, mapping of jobs to resources should be computed under high degree of heterogeneity of resources, the large scale and the dynamics of the system. Because of the complexity of the problem, the heuristic and meta-heuristic approaches are the most feasible methods of scheduling in grids due to their ability to deliver high quality solutions in reasonable computing time. One class of such meta-heuristics is Hierarchic Genetic Strategy (HGS). It is defined as a variant of Genetic Algorithms (GAs) which differs from the other genetic methods by its capability of concurrent search of the solution space.
In this work, we present an implementation of HGS for Independent Job Scheduling in dynamic grid environments. We consider the bi-objective version of the problem in which makespan and flowtime are simultaneously optimized. Based on our previous work, we improve the HGS scheduling strategy by enhancing its main branching operations. The resulting HGS-based scheduler is evaluated under the heterogeneity, the large scale and dynamics conditions using a grid simulator. The experimental study showed that the HGS implementation outperforms existing GA-based schedulers proposed in the literature.Peer ReviewedPostprint (author's final draft
Modern approaches to modeling user requirements on resource and task allocation in hierarchical computational grids
Peer ReviewedPostprint (published version
Quality of cloud services determined by the dynamic management of scheduling models for complex heterogeneous workloads
The quality of services in Cloud Computing (CC)
depends on the scheduling strategies selected for processing of
the complex workloads in the physical cloud clusters. Using
the scheduler of the single type does not guarantee of the
optimal mapping of jobs onto cloud resources, especially in
the case of the processing of the big data workloads. In this
paper, we compare the performances of the cloud schedulers
for various combinations of the cloud workloads with different
characteristics. We define several scenarios where the proper
types of schedulers can be selected from a list of scheduling
models implemented in the system, and used to schedule the
concrete workloads based on the workloads’ parameters and
the feedback on the efficiency of the schedulers. The presented
work is the first step in the development and implementation
of an automatic intelligent scheduler selection system. In our
simple experimental analysis, we confirm the usefulness of such
a system in today’s data-intensive cloud computin
GAME-SCORE: Game-based energy-aware cloud scheduler and simulator for computational clouds
Energy-awareness remains one of the main concerns for today's cloud computing (CC) operators.
The optimisation of energy consumption in both cloud computational clusters and computing
servers is usually related to scheduling problems. The definition of an optimal scheduling policy
which does not negatively impact to system performance and task completion time is still
challenging. In this work, we present a new simulation tool for cloud computing, GAME-SCORE,
which implements a scheduling model based on the Stackelberg game. This game presents two
main players: a) the scheduler and b) the energy-efficiency agent. We used the GAME-SCORE
simulator to analyse the efficiency of the proposed game-based scheduling model. The obtained
results show that the Stackelberg cloud scheduler performs better than static energy-optimisation
strategies and can achieve a fair balance between low energy consumption and short makespan in
a very short tim
Stackelberg Game-based Models in Energy-aware Cloud Scheduling
Energy-awareness remians the important problem in
today’s cloud computing (CC). Optimization of the
energy consumed in cloud data centers and computing servers is usually related to the scheduling prob lems. It is very difficult to define an optimal schedul ing policy without negoative influence into the system
performance and task completion time. In this work,
we define a general cloud scheduling model based on
a Stackelberg game with the workload scheduler and
energy-efficiency agent as the main players. In this
game, the aim of the scheduler is the minimization of
the makespan of the workload, which is achieved by
the employ of a genetic scheduling algorithm that maps
the workload tasks into the computational nodes. The
energy-efficiency agent selects the energy-optimization
techniques based on the idea of switchin-off of the idle
machines, in response to the scheduler decisions. The
efficiency of the proposed model has been tested using
a SCORE cloud simmulator. Obtained results show
that the proposed model performs better than static
energy-optimization strategies, achieving a fair balance
between low energy consumption and short queue times
and makespan
Subcutaneous T-Cell Lymphoma. A Clinical and Histopathologic Study of An Additional Case
A case of a 62-year-old woman with recurrent subcutaneous nodules,
fever and pancytopenia diagnosed as subcutaneous T-cell lymphoma is presented. Incision biopsy revealed lobular panniculitis with an inflammatory infiltrate
of atypical T lymphocytes. She was treated with 7 courses of CHOP with transient
remission, and she died af ter 17 months of disease from fatal hemorrhagic com plications due to the hemophagocytic syndrome
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