CPU load prediction using neuro-fuzzy and Bayesian inferences

This work presents a method for predicting resource availability in opportunistic grids by means of use pattern analysis (UPA), a technique based on non-supervised learning methods. This prediction method is based on the assumption of the existence of several classes of computational resource use patterns, which can be used to predict the resource availability. Trace-driven simulations validate this basic assumptions, which also provide the parameter settings for the accurate learning of resource use patterns. Experiments made with an implementation of the UPA method show the feasibility of its use in the scheduling of grid tasks with very little overhead. The experiments also demonstrate the method's superiority over other predictive and non-predictive methods. An adaptative prediction method is suggested to deal with the lack of training data at initialization. Further adaptative behaviour is motivated by experiments which show that, in some special environments, reliable resource use patterns may not always be detected. Copyright © 2009 John Wiley & Sons, Ltd.

Resource use pattern analysis for predicting resource availability in opportunistic grids

The state prediction of resources in large scale distributed systems represents an important aspect for resources allocations, systems evaluation, and autonomic control. The paper presents advanced techniques for resources state prediction in Large Scale Distributed Systems, which include techniques based on bio-inspired algorithms like neural network improved with genetic algorithms. The approach adopted in this paper consists of a new fitness function, having prediction error minimization as the main scope. The proposed prediction techniques are based on monitoring data, aggregated in a history database. The experimental scenarios consider the ALICE experiment, active at the CERN institute. Compared with classical predicted algorithms based on average or random methods, the authors obtain an improved prediction error of 73%. This improvement is important for functionalities and performance of resource management systems in large scale distributed systems in the case of remote control ore advance reservation and allocation.

Bio-Inspired Techniques for Resources State Prediction in Large Scale Distributed Systems

Divisible loads are those workloads that can be partitioned by a scheduler into any arbitrary chunks. The problem of scheduling divisible loads has been defined for a long time, however, a handful of solutions have been proposed. Furthermore, almost all proposed approaches attempt to perform scheduling in dedicated environments such as LANs, whereas scheduling in non-dedicated environments such as Grids remains an open problem. In Grids, the incessant variation of a worker's computing power is a chief difficulty of splitting and distributing workloads to Grid workers efficiently. In this paper, we first introduce a computation model that explains the impact of local (internal) tasks and Grid (external) tasks that arrive at a given worker. This model helps estimate the available computing power of a worker under the fluctuation of the number of local and Grid applications. Based on this model, we propose the CPU power prediction strategy. Additionally, we build a new dynamic scheduling algorithm by incorporating the prediction strategy into a static scheduling algorithm. Lastly we demonstrate that the proposed dynamic algorithm is superior to the existing dynamic and static algorithms by a comprehensive set of simulations.

/pdf/a-dynamic-scheduling-algorithm-for-divisible-loads-in-grid-44xj1fuolb.pdf

A Dynamic Scheduling Algorithm for Divisible Loads in Grid Environments

Mixture of ANFIS systems for CPU load prediction in metacomputing environment

Divisible workloads are that kind of workloads that can be partitioned by the scheduler into arbitrary 'chunks'. The problem of scheduling divisible loads has been defined for a long time, however, handful solutions have been proposed. Furthermore, almost all proposed approaches attempt to perform scheduling in a dedicated environment (i.e., for processing local tasks only) such as a LAN, whereas scheduling in non-dedicated environments (i.e., for processing local and external tasks) such as Grids remains an open problem. In Grids, the incessant variation of workstation's power is the chief difficulty in planning how to split and distribute workloads to these workstations. This paper presents a new strategy, called 2-Phase Prediction (2PP) for CPU power. By integrating this strategy and the UMR algorithm, a static scheduling algorithm that is designed for dedicated environments, we develop a new dynamic scheduling algorithm suitable for non-dedicated environment. Our experimental results show that our algorithm is superior to the UMR as the former is able to adapt to the dynamicity of Grid workers.

/pdf/grid-scheduling-using-2-phase-prediction-2pp-of-cpu-power-ljndftk2za.pdf

Grid Scheduling using 2-Phase Prediction (2PP) of CPU Power

Scheduling divisible workloads in distributed systems has been one of the interesting research problems over the last few years. Most of the scheduling algorithms previously introduced are based on the master-worker model. However, the majority of these algorithms assume that workers are dedicated machines, which is a wrong assumption in distributed environments such as Grids. In this work, we propose a dynamic scheduling methodology that takes into account the three prominent aspects of Grids: heterogeneity, dynamicity, and uncertainty. Our contribution is threefold. First, we present an analytical model for processing local and Grid tasks at each non-dedicated worker. Second, we present a simple prediction method to forecast the available CPU capacity and bandwidth at each worker. Third, we introduce a dynamic, multi-round scheduling algorithm.

/pdf/a-scheduling-method-for-divisible-workload-problem-in-grid-4k55r7973c.pdf

A Scheduling Method for Divisible Workload Problem in Grid Environments

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Grid Scheduling using 2-Phase Prediction (2PP) of CPU Power

A Scheduling Method for Divisible Workload Problem in Grid Environments