38,800 research outputs found
A WOA-based optimization approach for task scheduling in cloud Computing systems
Task scheduling in cloud computing can directly
affect the resource usage and operational cost of a system. To
improve the efficiency of task executions in a cloud, various
metaheuristic algorithms, as well as their variations, have been
proposed to optimize the scheduling. In this work, for the
first time, we apply the latest metaheuristics WOA (the whale
optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that
basis, we propose an advanced approach called IWC (Improved
WOA for Cloud task scheduling) to further improve the optimal
solution search capability of the WOA-based method. We present
the detailed implementation of IWC and our simulation-based
experiments show that the proposed IWC has better convergence
speed and accuracy in searching for the optimal task scheduling
plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource
utilization, in the presence of both small and large-scale tasks
Scheduling of data-intensive workloads in a brokered virtualized environment
Providing performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource management solutions that consider the brokered nature of these workloads, as well as the special demands of their intra-dependent components. In this paper, we present an offline mechanism for scheduling batches of brokered data-intensive workloads, which can be extended to an online setting. The objective of the mechanism is to decide on a packing of the workloads in a batch that minimizes the broker's incurred costs, Moreover, considering the brokered nature of such workloads, we define a payment model that provides incentives to these workloads to be scheduled as part of a batch, which we analyze theoretically. Finally, we evaluate the proposed scheduling algorithm, and exemplify the fairness of the payment model in practical settings via trace-based experiments
Learning Scheduling Algorithms for Data Processing Clusters
Efficiently scheduling data processing jobs on distributed compute clusters
requires complex algorithms. Current systems, however, use simple generalized
heuristics and ignore workload characteristics, since developing and tuning a
scheduling policy for each workload is infeasible. In this paper, we show that
modern machine learning techniques can generate highly-efficient policies
automatically. Decima uses reinforcement learning (RL) and neural networks to
learn workload-specific scheduling algorithms without any human instruction
beyond a high-level objective such as minimizing average job completion time.
Off-the-shelf RL techniques, however, cannot handle the complexity and scale of
the scheduling problem. To build Decima, we had to develop new representations
for jobs' dependency graphs, design scalable RL models, and invent RL training
methods for dealing with continuous stochastic job arrivals. Our prototype
integration with Spark on a 25-node cluster shows that Decima improves the
average job completion time over hand-tuned scheduling heuristics by at least
21%, achieving up to 2x improvement during periods of high cluster load
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Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art
Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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