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APPLICATION OF THE HOT-SPOT EVALUATION METHOD FOR MONITORING OF DATA CENTER NETWORK

Babkin O.V.,

Varlamov A.А.,

Gorshunov R.А.,

Dos E.V.,

Kropachev A.V.,

Zuev D.О.

Nowadays requirements to data center services efficiency and stability have significantly grown. Hereby reliability of the network has become one of the most attention performances and server performance monitoring system becomes main tool of providing reliable network services. Organizing of data processing in real time regime is a serious task for developers while efficient monitoring system ought to be adaptive and scaled.

It should be noticed that due to virtualization of modern data center servers monitoring system functional nodes also should not be considered as physical elements. Up to the virtual machine (VM) life cycle phase the system should be divided into further main modules [1-4].

• VM monitor;

• monitoring data integration;

• cluster data integration;

• node information gathering.

VM monitor provides transfer of significant data (e.g., CPU or RAM usage) to the monitoring system by VM-scripts. Monitoring data integrator collects significant network data and stores it in the database, while cluster data integrator gathers other data blocks for the next VM layer. gathers different local information on a cloud node according to specific demands. The node information gathering module gathers local VMs information on the network nodes. There are also should be mentioned monitoring tool server, configuration generator, user interface and database module which used to retrieves information from database (e.g., configuration data), receive monitoring data and perform actions and stores information.

Due to virtualization paradigm it possible to develop universal data center monitoring technique based on mathematical model which could be implemented to different server platforms. Among the different methods hot-spot evaluation technique is proved to be most efficient one and should be properly considered.

Classification of index target type

According to the hot-spot evaluation of the network data indexes it has to be calculated the subjective weight and objective weight of each index to get the comprehensive evaluating index of hot-spot degree.

Subjective weight which represents each index influencing hot degree, could be determined by its triangular fuzzy number [5] up to FAHP technique (HAHP: Fuzzy Analytic Hierarchy Process) is deployed to determine the of each index influencing hot degree. Triangular Fuzzy Number is a triplet which includes smallest likely value, the most probable value and largest possible value of fuzzy event. Objective weight could be defined by the multi-objective decision-making method which includes determination of the index target type. Thereby basic algorithm includes further stages (Fig. 1):

• subjective weight determination (FAHP technique);

• multi-objective decision-making method;

• dimensionless processing (to gain optimal size of index and its matrix):

• maximizing deviation method; (determination the objective weight of index);

• combining and comparison of subjective weights and objective weights;

• receiving of the hierarchy of VM monitored spots.

Hot-spot evaluation is handled through optimal relative and classification of index target type, the comprehensive weight calculation, the hot-spot degree comprehensive evaluation value determination and hotspot sorting. Optimal relative value represents degree of relativeness to optimal, which is similar to the concept of membership degree. It should be determined up to the target type. Main types of attributes are:

fixed type attribute;

range type attribute;

cost type attribute;

efficiency type attribute.

Fixed type attribute represents stabilizing at a fixed value as a target of indexes analysis:

, (1)

where is the measured value of index of VM and is the optimal value of . To simplify equation

(1) it should be used absolute differences maximum among observed spot:

. (2)

Thereby equation (1) can be simplified as:

. (3)

Fig. 1. Hot-spot comprehensive evaluation algorithm

Range type attribute in other hand represents property values falling in a fixed interval as a target type of indexes.

, (4)

where is best lower bound, is best upper bound, is minimum of measured values of index and is maximum one. To simplify equation (4) it should be used absolute maximum of deviating the optimal range:

. (5)

Thereby equation (4) can be simplified as:

. (6)

In other hand cost type attribute represents smallest attribute value as the best index and efficiency type attribute represents biggest attribute value as the best index, contrary to the cost type attribute:

. (7)

Thereby to evaluate hot degree spots of monitored spots, it should be formed target decision matrix of the measured values and target decision matrix that converts to optimal relative matrix:

. (8)

At the same way range type attribute matrix, cost type attribute matrix, efficiency type attribute matrix could be determined:

. (9)

The judgment matrix thereby represents the relative importance of an upper layer element and other layer element. Importance scale is proved to be good digital measurement method in compassion to index importance.

The comprehensive weight determining algorithm

Triangular fuzzy numbers FAHP technique based on the index hierarchy (Fig. 2) is developed to build the fuzzy judgment matrix. Judgment matrix includes uncertainty of subjective judgment experts and can be used to form comparative judgment matrix [6] where weighted values are obtained by using the theory of fuzzy number comparison size. For hot-spots massive comprehensive evaluation index of the layer which is related to upper layer consists from set . Thus triangular fuzzy number could be obtained as importance fuzzy judgment of index relative to index (determined by experts): . Up to the triangular fuzzy numbers definition and represent the fuzzy extent of judgment, so value shows comparative fuzzy degree.

Fig. 2. The hot-spot evaluation indexes hierarchy structure

It demonstrates the technique of the fuzzy comparison judgment matrix determination method:

. (10)

Fuzzy comparison judgment matrix of layers as relative ones to this one could be formed at the same way. Thus, the fuzzy relative weight of index compared with other index in this layer shoul be determined as:

. (11)

It should be noticed that each triangle fuzzy number in the fuzzy relative weight vector is required to be clarified before stage of sorting the current layer index.

The corresponding subjective weight and objective weight [7-10] of can be determined as:

, (12)

where and indicate different monitoring spots, and thus corresponds to the absolute value of the membership degree. Finally the comprehensive weight is obtained by values the subjective weight and objective weight.

. (13)

The combination of the index evaluation value reflects the quality of comprehensive index and thus comprehensive evaluation value is not qualified when any of the indicators of evaluation value stays unqualified. Standard of being qualified is usually refers to actual situation.

Network monitoring strategy is a one of the key task of data center development due to requirements of services efficiency and stability. It should be noticed that organizing of adaptive and scaled algorithm of data processing in real time regime is a serious task for developers. Hot-spot evaluation of the network data indexes as a part of monitoring technique includes obtaining of subjective weight and objective weight of each index which could be used to get the comprehensive evaluating index. Optimal relative value represents degree of relativeness to optimal one up to the attributes of fixed type, range type, cost type and efficiency type.

Triangular fuzzy numbers based on the index hierarchy is used to form the fuzzy judgment matrix which includes uncertainty of subjective judgment experts. It has to be used to form comparative judgment matrix where weighted values are obtained by using the theory of fuzzy number comparison size. Based on the hot-spot comprehensive evaluation value, hot-spot degrees of all monitoring point can be ordered and hierarchy will be obtained during the sorting process. First stage includes getting hot-spot degree evaluation index system's original index grade by experts' judgment method. Next stage refers to determination of value for each level as monitoring original data. Hot-spot degree should be evaluated as the same ordinary monitoring points. Final step is achieving corresponding comprehensive evaluation value based on every level value. Hot-spot degree range of each monitoring point can be obtained as result of forming hot-spot comprehensive evaluation values' uniformly ranking of all levels. Hot-spot level of each monitoring point has to be correspondingly obtained and hot judgment of the monitoring will be received with the level of data center rules in advance.

network monitoring data matrix

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