Heterogeneous Resource Allocation under Degree Constraints

Beaumont, Olivier; Eyraud-Dubois, Lionel; Thraves, Christopher; Rejeb, Hejer

doi:10.1109/tpds.2012.175

Cited by 36 publications

(36 citation statements)

References 23 publications

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“…Then, we compared our scheduling approach with extended versions of three other heuristic scheduling algorithms i.e., Min-Min, Max-Min and Max-Max. This is because their performance tends to produce competitive solutions with lower time complexity (Beaumont et al, 2013). Since our scheduling concerns deadline factor df i , we have revised Min-Min, Max-Min and Max-Max to fit into our model.…”

Section: Resultsmentioning

confidence: 99%

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Adaptive Resource Control Mechanism Through Reputation-Based Scheduling in Heterogeneous Distributed Systems

Hussin¹,

Latip²

2013

Journal of Computer Science

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The service-oriented distributed systems such as Grids and Clouds are unified computing platform that connect and share heterogeneous resources including computation resource, storage resource, information resource and knowledge resource. While these systems provide a vast amount of computing power their reliability are often hard to be guaranteed. It is due to the increased complexity of processing (e.g., overhead, latency) that can indirectly affect the system performance. In this study, we addressed the problem of dynamic control for resource management in distributed computing environment. Our dynamic resource control mechanism is designed based on reputation-based scheduling that aims for sustainable resource sharing. Particularly, each computational resource in the environment has its own reputation value that calculated online by considering the computing capacity and availability. The degree of resource reputation significantly helps in scheduling decisions in terms of successful execution while adaptively monitoring resource availability. Results demonstrate that our resource control mechanism significantly increases successful execution, while leading to robust resource management.

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Section: Resultsmentioning

confidence: 99%

“…One of the methods to solve the resource sharing in dynamic environment is through efficient resource management (Beaumont et al, 2013). In particular, the resource management addresses the monitoring and controlling abilities that able to take into accounts the users' different needs and performance requirements.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Resource Control Mechanism Through Reputation-Based Scheduling in Heterogeneous Distributed Systems

Hussin¹,

Latip²

2013

Journal of Computer Science

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“…In order to deal with resource allocation problems arising in the context of Clouds, several sophisticated techniques have been developed in order to optimally allocate user services onto PMs, either to achieve good load-balancing [8,5] or to minimize energy consumption [6]. Most of the approaches in this domain are based on offline [10] and online [11] variants of Bin-Packing strategies.…”

Section: Related Workmentioning

confidence: 99%

Column Generation Integer Programming for Allocating Jobs with Periodic Demand Variations

Belaïd

Eyraud-Dubois

2016

Algorithmic Aspects of Cloud Computing

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Abstract. In the context of service hosting in large-scale datacenters, we consider the problem faced by a provider for allocating services to machines. An analysis of a public Google trace corresponding to the use of a production cluster over a long period shows that long-running services experience demand variations with a periodic (daily) pattern, and that services with such a pattern account for most of the overall CPU demand. This leads to an allocation problem where the classical BinPacking issue is augmented with the possibility to co-locate jobs whose peaks occur at different times of the day, which is bound to be more efficient than the usual approach that consist in over-provisioning for the maximum demand. In this paper, we propose a column-generation approach to solving this problem, where the subproblem uses a sophisticated SOCP (Second Order Cone Program) formulation. This allows to explicitely select jobs which benefit from being co-allocated together. Experimental results comparing with theoretical lower bounds and with standard packing heuristics shows that this approach is able to provide very efficient assignments in reasonable time.

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“…First, we consider that the applications running on the Cloud platform can be seen as a set of independent services, and that the services themselves consist in a number of identical (in terms of requirements) and independent instances. Therefore, we do not consider the problems introduced by heterogeneity, that have already been considered (see for instance [6], [7]). Indeed, as soon as heterogeneity is considered, basic allocation problems are amenable to Bin Packing problem and are therefore intrinsically difficult.…”

Section: A Reliability and Energy Savings In Cloud Computingmentioning

confidence: 99%

Approximation algorithms for energy minimization in Cloud service allocation under reliability constraints

Beaumont

Duchon

Renaud-Goud

2013

20th Annual International Conference on High Performance Computing

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We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, we assume on the one part that each computing resource is associated with a capacity, that can be chosen using the Dynamic Voltage and Frequency Scaling (DVFS) method, and with a probability of failure. On the other hand, we assume that the services run as a set of independent instances of identical Virtual Machines (VMs). Moreover, there exists a Service Level Agreement (SLA) between the Cloud provider and the client that can be expressed as follows: the client comes with a minimal number of service instances that must be alive at anytime, and the Cloud provider offers a list of pairs (price, compensation), the compensation having to be paid by the Cloud provider if it fails to keep alive the required number of services. On the Cloud provider side, each pair actually corresponds to a guaranteed reliability of fulfilling the constraint on the minimal number of instances.In this context, given a minimal number of instances and a probability of success, the question for the Cloud provider is to find the number of necessary resources, their clock frequency and an allocation of the instances (possibly using replication) onto machines. This solution should satisfy all types of constraints (both capacity and reliability constraints). Moreover, it should remain valid during a time period (with a given reliability in presence of failures) while minimizing the energy consumption of used resources. We assume in this paper that this time period, that typically takes place between two redistributions, is fixed and known in advance. We prove deterministic approximation ratios on the consumed energy for algorithms that provide guaranteed reliability and we provide an extensive set of simulations that prove that homogeneous solutions are close to optimal.

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Heterogeneous Resource Allocation under Degree Constraints

Cited by 36 publications

References 23 publications

Adaptive Resource Control Mechanism Through Reputation-Based Scheduling in Heterogeneous Distributed Systems

Adaptive Resource Control Mechanism Through Reputation-Based Scheduling in Heterogeneous Distributed Systems

Column Generation Integer Programming for Allocating Jobs with Periodic Demand Variations

Approximation algorithms for energy minimization in Cloud service allocation under reliability constraints

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