A Bio-inspired Algorithm for Energy Optimization in a Self-organizing Data Center

Barbagallo, Donato; Nitto, Elisabetta Di; Dubois, Daniel J.; Mirandola, Raffaela

doi:10.1007/978-3-642-14412-7_7

Cited by 51 publications

(36 citation statements)

References 19 publications

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“…Barbagallo et al (2010) put forward self-organizing architectures for dynamic workload distribution, using a decentralized approach built on top of SelfLet architecture. SelfLet architecture (Devescovi et al 2007) is a bioinspired system that possesses the capability to change and adapt its internal behavior dynamically, according to variations in the environment.…”

Section: Datacenter Optimizationmentioning

confidence: 99%

Bioinspired Computing: Swarm Intelligence

Awad¹,

Khanna²

2015

Efficient Learning Machines

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Brains exist because the distribution of resources necessary for survival and the hazards that threaten survival vary in space and time.-John M. Allman, Evolving Brains Natural systems solve multifaceted problems using simple rules, and exhibit organized, complex, and intelligent behavior. Natural process control systems are adaptive, evolutionary, distributed (decentralized), reactive, and aware of their environment. Bioinspired computing (or biologically inspired computing) is a field of study that draws its inspiration from the sophistication of the natural world in adapting to environmental changes through self-management, self-organization, and self-learning. Bioinspired computational methods produce informatics tools that are predicated on the profound conceptions of self-adaptive distributed architectures seen in natural systems. Heuristics that imitate these natural processes can be expressed as theoretical methods of constrained optimization. Such heuristics define a representation, in the form of a fitness function. This function describes the problem, evaluates the quality of its solution, and uses its operators (such as crossover, mutation, and splicing) to generate a new set of solutions. Ashby's (1952) book Design for a Brain discusses the mechanisms that shape the concept of adaptive behavior, as demonstrated in living organisms, and the adaptive behavior of the brain. The author defines adaptation as a form of behavior that promotes stability and that maintains the essential variables, within physiological limits. Additionally, stability is expressed as a combined function of multiple fields with changing dynamics. Therefore, stability is assumed to be associated with a coordination function between various fields. As the system and feedbacks become more complex, the achievement of stability becomes more difficult, and the likelihood of instability, greater.Biologically, an important factor in the survival of an organism is its ability to maintain its essential variables, within viable bounds. Otherwise, the organism faces the possibility of disintegration or loss of identity (dissolution, death), or both. Adaptation provides an organismic stability criterion that contributes to the maintenance of the essential variables, within viable limits; an adaptive system is a stable system (Harvey et al. 2005, the region of stability being that part of the state space where all essential variables are within physiological limits.In the natural world the brain exhibits the properties of a highly efficient informatics tool that gathers data (sensor function), infers and stores useful patterns in the data (knowledge base, memory), uses that data for planning and anticipating future actions (decision making), executes those actions (control functions), and learns from the consequences of those actions (learning). The brain acts as an information processing machine that enables a fast and adequate response to environmental perturbations by filtering disrupting triggers.

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Section: Datacenter Optimizationmentioning

confidence: 99%

Bioinspired Computing: Swarm Intelligence

Awad¹,

Khanna²

2015

Efficient Learning Machines

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“…Thus, power consumption limits the advances in computer technology and is becoming a relevant part on the budget of present data centers. According to [12] power is the second-highest operating cost in 70% of all data centers and data centers are responsible for the emission of tens of millions of metric tons of carbon dioxide annually, more than 2% of the total global emissions. As a result there has been as well a recent research interest in the development of energy efficient data centers.…”

Section: Related Workmentioning

confidence: 99%

A novel energy-driven computing paradigm for e-health scenarios

Zapater

Arroba

Ayala

et al. 2014

Future Generation Computer Systems

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A first-rate e-Health system saves lives, provides better patient care, allows complex but useful epidemiologic analysis and saves money. However, there may also be concerns about the costs and complexities associated with e-health implementation, and the need to solve issues about the energy footprint of the high-demanding computing facilities. This paper proposes a novel and evolved computing paradigm that: i) provides the required computing and sensing resources; ii) allows the population-wide diffusion; iii) exploits the storage, communication and computing services provided by the Cloud; iv) tackles the energy-optimization issue as a first-class requirement, taking it into account during the whole development cycle. The novel computing concept, and the multilayer top-down energy-optimization methodology, obtain promising results in a realistic scenario for cardiovascular tracking and analysis, making the Home Assisted Living a reality.

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“…Barbagallo et al [19] described a bio-inspired algorithm hinged on the scout-worker migration method where some of the scouts are professed to move from one physical node to another so that they can cooperatively find a suitable destination for the workers (i.e. the VMs) which are migrated.…”

Section: A Review On Resource Management In Cloud Computingmentioning

confidence: 99%

Dynamic Consolidation of Virtual Machines with Multi-Agent System

Barlaskar¹,

Singh²

2014

IJCA

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Cloud computing systems supply massive infrastructures for high-performance computing that are flexible as they are able to acclimate to user and application requirements. Cloud Computing offers on demand services which are used by virtue of a service-oriented interface that execute the anything-as-a-service archetype. The enlargement of Cloud computing has resulted in the establishment of large-scale data centers around the world containing thousands of compute nodes and these data centers consume excessive amounts of electrical energy resulting in high operating costs. Therefore, to cut down the cost of energy consumption the cloud providers must optimize resource usage by performing dynamic consolidation of virtual machines (VMs) in an effective way to improve energy efficiency in cloud data center. The problem of VM consolidation can be split into four sub-problems namely physical machine overload detection; physical machine under-load detection; VM selection and VM placement. Each of the afore-stated subproblems must operate in an optimized manner to maintain the tradeoff between energy and performance. In this research paper a new multi-agent system (MAS) for dynamic consolidation of VMs is proposed with the aim of making the cloud system smarter by incorporating the five traits of multiagent systems which are ubiquity, interconnection, intelligence, delegation and human orientation. The Cloud Computing systems require intelligent and perceptive based software with progressive, elastic, self-ruling style which can be provided by MASs. The proposed method has significantly reduced energy consumption and at the same time ensures a high level of constancy to the Service Level Agreements (SLA).

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A Bio-inspired Algorithm for Energy Optimization in a Self-organizing Data Center

Cited by 51 publications

References 19 publications

Bioinspired Computing: Swarm Intelligence

Bioinspired Computing: Swarm Intelligence

A novel energy-driven computing paradigm for e-health scenarios

Dynamic Consolidation of Virtual Machines with Multi-Agent System

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