Markov Normal Algorithms Research Articles

On Parsing Programming Languages with Turing-Complete Parser

A new parsing method based on the semi-Thue system is described. Similar to, but with more efficient implementation than Markov normal algorithms, it can be used for parsing any recursively enumerable language. Despite its computational power, it is meant to be used primarily for parsing programming and domain-specific languages. It enables a straightforward simulation of a number of existing parsing algorithms based on context-free grammars. The list includes both top-down shift-produce methods (such as SLL and LL) and bottom-up shift-reduce methods (such as LALR and LR), as well as mixed top-down-and-bottom-up methods such as LLLR. To justify the use of the new parsing method, the paper provides numerous examples of how a parser can actually be made in practice. It is advised that the main part of the parser is based on some simple well-established approach, e.g., SLL(1), while syntactically more complicated phrases can be parsed by exploiting the full power of the new parser. These phrases may either be extensions to the original language or some embedded domain-specific language. In all such and similar cases, no part of the language is restricted to be context-free. In fact, context-sensitive languages can be handled quite efficiently.

K-mMA VM selection in dynamic VM consolidation for improving energy efficiency at cloud data centre

Dynamic virtual machine (VM) consolidation is an alternative solution for managing and optimising energy efficiency in a cloud data centre. This research proposed VM selection method in dynamic VM consolidation based on K-means clustering technique and computational model Markov normal algorithm (K-mMA). The objective of VM selection is to select proper VM, which should be migrated away from the overloaded physical machine and to avoid oversubscribe host. The VM selection method has been tested in simulation condition using CloudSim and PlanetLabs datasets with various conditions of VM instances (homogeneous and heterogeneous). The performance metrics in this research are energy consumption (EC), SLA time per active host (SLATAH), performance degradation due migration (PDM) and SLA violation (SLAV). Results experiment has shown that K-mMA could improve energy efficiency and quality of service (QOS) at cloud data centre significantly. Compared with existing method such as CFS, MMT, RC, and MC the proposed K-mMA could improve efficiency energy in cloud data centre by optimising VM selection problem up to 3.9%, 6.8%, 5.5%, and 5.3% respectively.

K-mMA VM selection in dynamic VM consolidation for improving energy efficiency at cloud data centre

Dynamic virtual machine (VM) consolidation is an alternative solution for managing and optimising energy efficiency in a cloud data centre. This research proposed VM selection method in dynamic VM consolidation based on K-means clustering technique and computational model Markov normal algorithm (K-mMA). The objective of VM selection is to select proper VM, which should be migrated away from the overloaded physical machine and to avoid oversubscribe host. The VM selection method has been tested in simulation condition using CloudSim and PlanetLabs datasets with various conditions of VM instances (homogeneous and heterogeneous). The performance metrics in this research are energy consumption (EC), SLA time per active host (SLATAH), performance degradation due migration (PDM) and SLA violation (SLAV). Results experiment has shown that K-mMA could improve energy efficiency and quality of service (QOS) at cloud data centre significantly. Compared with existing method such as CFS, MMT, RC, and MC the proposed K-mMA could improve efficiency energy in cloud data centre by optimising VM selection problem up to 3.9%, 6.8%, 5.5%, and 5.3% respectively.

FuMAEVMS Novel VM Selection in Dynamic Consolidation of Virtual Machine

Background/Objective: Dynamic VM consolidation has been known as one from several ways to reduce energy consumption in cloud data center. The problem in reducing energy consumption with dynamic VM Consolidation has recognize as NP-Hard problem that divided into four subs, where VM selection is one of sub-problem. The objective VM selection is to choose the best VM that suitable to move from overload host and avoid oversubscription host. Methods: This research proposed novel VM selection model based on Fuzzy Logic, Markov Normal Algorithm (MA) and Existing VM Selection (FuMAEVMS) in dynamic VM consolidation. Moreover, RAM VM and max MIPS VM are consider as attribute to be used. The proposed VM selection model have been evaluated using Cloudsim with datasets from PlanetLabs in various condition of VM instance. The research measured the performance using several parameters such as Energy Consumption (EC), SLA Violation (SLAV), SLA Time per active host (SLATAH) and Performance Degradation Due Migration (PDM). FuMAEVMS were compared with existing VM selection such asconstant first selection (CFS), minimum migration time (MMT), random choice (RC) and maximum correlation (MC). Findings: FuMAEVMS as Novel VM selection capable to decide which VM that should be migrate from overload host in various condition VM instances that gives improvement of energy efficiency in cloud data center. We analyze the used of fuzzy logic in categorizing attribute VM capable to help in minimize rules production in Markov Normal Algorithm. This condition gives benefit to MA in deciding suitable existing VM selection such as CFS, MMT or MC to be used in migrating VM. Improvement: Results experiment has showed FuMAEVMS VM selection capable to reduced energy consumption in cloud data center significantly up to 4.45% when applied in dynamic VM consolidation compared with RC, MMT and MC.

Improvement of Energy Efficiency at Cloud Data Center Based on Fuzzy Markov Normal Algorithm VM Selection in Dynamic VM Consolidation

The problem of the energy consumption in cloud data center has become an important study in recent years. Dynamic VM consolidation has been known as a solution able to reduce energy consumption in cloud data center. However, there are many problems in Dynamic VM consolidation, in particular the VM selection. The objective of VM selection is to choose VM candidates suitable to move from overload host for avoiding oversubscribed host and to give an impact for the reduction of energy consumption. This research proposes a VM selection model in dynamic VM consolidation to improve the energy efficiency in cloud data center based on Fuzzy Markov Normal Algorithm. Fuzzy logic has been used for categorizing the attributes of VM candidates. After that, Markov Normal Algorithm has been used for deciding to which category VM should be migrated from overload host. The proposed VM selection model has been evaluated using various VM instance conditions (homogeneous or heterogeneous) with datasets from PlanetLabs in Cloudsim. Moreover, several parameters were used to measure the performance in this research such as Energy Consumption, SLA Violation, SLA Time per active host, and Performance Degradation Due Migration. The results experiments have shown the proposed VM selection model capable of improving energy efficiency in cloud data center up to 3.74%, 6.65%, 5.36%, and 5.11%, compared with the existing VM selections such as Constant First Selection, Minimum Migration Time, Random Choice, and Maximum Correlation.

Application of Markov's Normal Algorithm

Application of Markov's Normal Algorithm

Inhibitor Petri net executing an arbitrary given Markov normal algorithm

An inhibitor Petri net with a fixed structure that executes an arbitrary given Markov normal algorithm is constructed. The algorithm and its input string are encoded by nonnegative integer numbers and put into dedicated places of the Petri net, which implements the application of the algorithm productions over the string of symbols. Rules of encoding sequential, branching, and cyclic processes by Petri nets are used. Upon completion of the net operation, the output string is restored (decoded) from the integer form of the representation. Within the paradigm of computations on Petri nets, the constructed net provides the compatibility of systems.

A flow diagram composition of Markov's normal algorithms without concluding formulas

Four operations on normal algorithms without concluding formulas, branching, connecting, juxtaposing, and looping are introduced and corresponding theorems are proved. In this way it is shown how the main difficulties in the practical use of Markov's normal algorithms can be overcome.

The algorithmic nature of a reaction-diffusion development process

An example is given of how steady state, reaction-diffusion concentration patterns can be used to generate a developmental sequence of linear arrays of cells. The generative process shows a distinct local character which is regular enough to be described algorithmically and which is suggestive of the successive regionalization which occurs in actual development. Two algorithmic descriptions are provided: one as the composition of two Markov normal algorithms, and the other in terms of a new kind, called a segment algorithm.

Embedding Markov normal algorithms within the λ-calculus

Most attempts to explain some aspect of computation formally have relied on a single formalism. This paper presents examples of a blending of two well-known formalisms, the ,λ-calculus and Markov normal algorithms, and illustrates the additional expressive power of the combined formalism.

PANON-1B

PANON-IB is a Universal Programming Language for Symbol Manipulation based on a particular extension of Markov Normal Algorithms. It consists essentially of a sequence of Labelled Structural Transformation Rules to be applied to an arbitrary argument string, according to appropriate sequencing rules. PANON-1B includes also specifications for input/output operations. The basic principles are discussed. Some details of a particular complete hardware representation are given.

Monotone congruence algorithms

Given an associative system in which each word is assigned a cost and in which an equivalence relation obtains between elements, it is often of interest to ask the question: what is the least costly word equivalent to a given word? The case in which the equivalence relation is a two-sided congruence relation is studied here, one example being the problem of optimizing a type of computer program. Such optimizing processes may be formalized as Markov normal algorithms. A general results concerning order relations on finite alphabets is established first. Then, the properties of a class of Markov normal algorithms are investigated. It is shown that each such algorithm must always terminate, and that every class of mutually equivalent algorithms contains a unique minimal algorithm which can be obtained by applying any algorithm of the class to itself.

Panon-1B: A programming language for symbol manipulation

PANON-1B is a Universal Programming Langnage for Symbol Manipulation, based on a particnlar extension of Markov Normal Algorithms. It consists essentially of a sequence of Labelled Structural Transformation Rules to be applied to an arbitrary argument string, according to appropriate sequencing rules. PANON-1B includes also specifications for input-output operation. The basic priuciples are discussed. Some details of a particular complete hardware representation are given.