A Decentralized Strategy for Genetic Scheduling in Heterogeneous Environments

Iordache, George; Boboila, Marcela S.; Pop, Florin; Stratan, Corina; Cristea, Valentin

doi:10.1007/11914952_13

Cited by 16 publications

(8 citation statements)

References 10 publications

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“…Genetic algorithms, a new evolutionary approach, have been used for resource allocation and task scheduling to increase user satisfaction that is linked to the SLA, and also cloud provider profits [19,[21][22][23]. A specific example of this approach is presented in [24] where the authors categorize incoming jobs by user priority, which is linked to deadline and cost.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Resource Allocation in Cloud Computing Based on Agreement Protocols

Pop

Tutueanu

Barbieru

et al. 2015

Studies in Big Data

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In the last years there has been considerable interest in using distributed systems, especially Cloud Systems, in any domains. Resource management contributes to ensure quality of services for any type of application, especially when a system involves elements of heterogeneity characterized by a variety of resources that may or may not be coupled with specific platforms or environments. A problem very close to the industry is the capability to allocate resources in an efficient way and estimate costs, especially when switching from one provider to another. In this chapter we present an extended work oriented on agreement-based resource allocation and a scheduling algorithm, aimed to bring an adaptive fault tolerant distributed system. For the agreement protocol we describe and analyze a 3-Tier structure of resources (hosts and virtual machines). Then an adaptive mechanism for agreement establishment is described. The allocation method considers workload distribution, resources heterogeneity, transparency, adaptability and also the ease to extend by combining with other scheduling algorithms.

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Section: Related Workmentioning

confidence: 99%

Adaptive Resource Allocation in Cloud Computing Based on Agreement Protocols

Pop

Tutueanu

Barbieru

et al. 2015

Studies in Big Data

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“…Increased dependability means the system has to be able to detect, recover, and tolerate every possible deviation from its normal operation, and a wide area of Autonomic Computing research is today dedicated to this subject. The models used in the development of systems with such capabilities combine monitoring, scheduling, data management, security, and fault tolerance (Iordache et al 2006). To this end, the special issue is oriented on computer and information advances aiming to develop and optimize system software, networking, and data management components to cope with Big Data processing and the introduction of Autonomic Computing capabilities for the supporting large-scale platforms.…”

Section: This Special Issuementioning

confidence: 99%

AutoCompBD: Autonomic Computing and Big Data platforms

2017

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The amount of data collected or generated by ICT systems is growing exponentially (today we reached a Petabyte Era and will soon enter the ExaScale one). Processing and storing ever-larger volumes of data introduces new challenges, and consequently, we need to constantly develop new technological means to face them. Massive parallel processing platforms are the answer and are already being developed over distributed systems (i.e., over cloud or fog computing). However, the problem is that such platforms need to support a wide variety of applications, coming with different processing requirements. Thus, self-* behavior is a must in this context, referring to self-managing characteristics of distributed computing resources, their capability to adapt to unpredictable changes while hiding intrinsic complexity to operators and users. This special issue is dedicated to dissemination and evaluation of advances in Autonomic Computing and Big Data platforms, supported by largescale distributed systems (LSDS). Autonomic Computing is facilitated by self-management capabilities that modern LSDS introduce, such as self-configuration, self-healing, self-optimization, and self-protection properties. In LSDS, an important characteristic is dependability (defined in terms B Florin Pop

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“…One approach similar to ours is [7], which uses genetic algorithms to build efficient schedules in an open environment. Although this approach has been shown to have very good performance in small-scale environments, it relies on global knowledge about the Grid condition.…”

Section: Related Workmentioning

confidence: 99%

“…However, in an environment composed of large numbers of small clusters or individual machines, scheduling jobs onto resources spanning multiple organizations becomes indispensable as no single cluster may have sufficient resources or the willingness to run a large job. This has lead a number of authors to propose peerto-peer solutions to the Grid scheduling problem [1,4,7]. However, such systems focus on discovery of available resources rather than on resource usage scheduling, and the limited level of control they give to users makes their practical applicability debatable.…”

Section: Introductionmentioning

confidence: 99%

On the Feasibility of Decentralized Grid Scheduling

Fiscato

Costa

Pierre

2008

2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops

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Many authors recognize the limitations of hierarchical Grid scheduling in scalable environments, and proposed peer-to-peer solutions to this problem. However, most peerto-peer grid resource management systems allow only to discover available resources at the time of the request. We claim that peer-to-peer techniques have the potential for actual Grid scheduling, where each resource maintains a schedule of its future allocation to jobs. We present such a protocol, which additionally allows users to specify desired properties about the requested schedules.

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A Decentralized Strategy for Genetic Scheduling in Heterogeneous Environments

Cited by 16 publications

References 10 publications

Adaptive Resource Allocation in Cloud Computing Based on Agreement Protocols

Adaptive Resource Allocation in Cloud Computing Based on Agreement Protocols

AutoCompBD: Autonomic Computing and Big Data platforms

On the Feasibility of Decentralized Grid Scheduling

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