vin-load balancing on grid

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Dynamic Load Balancing Strategy for Grid Computing

Dept of ISE, KLECET, Chikodi Page 1

CHAPTER 1

INTROUCTION

The term grid computing originated in the early 1990s as a metaphor for making

computer power as easy to access as an electric power grid in Ian Foster's and Carl Kesselman'sseminal work, "The Grid: Blueprint for a new computing infrastructure".

The development of computational grids and the associated middleware has been actively

pursued in recent years to deal with the emergence of greedy applications of large computing

tasks and amounts of data. Managing such applications leads to some complex problems for

which traditional architectures are insufficient. There are many potential advantages of use grid

architectures, including the ability to solve large scale advanced scientific and engineering

applications whose computational requirements exceed local resources, and the reduction of job

turnaround time through workload balancing across multiple computing facilities. The main

contributions of this are twofold. First we propose a tree-based model to represent gridarchitecture with the perspective of managing workload. This model is characterized by three

main features:

(1) Its hierarchical in order to minimize the load balancing computation overhead.

(2) The model is based on a structure using a univocal transformation of any grid architecture

into a tree, with at most four levels.

(3) Its totally independent from any physical grid architecture.

The second contribution, the load balancing algorithm which is layered in

accordance with the strategy based on the above model. We seek to achieve load

balancing that privilege workload neighborhood, to reduce amount of messages. Ourstrategy deals with a three layers algorithms (intra-site, intra-cluster and intra-grid).
http://en.wikipedia.org/wiki/Metaphorhttp://en.wikipedia.org/wiki/Power_gridhttp://en.wikipedia.org/wiki/Ian_Fosterhttp://en.wikipedia.org/wiki/Carl_Kesselmanhttp://en.wikipedia.org/wiki/Carl_Kesselmanhttp://en.wikipedia.org/wiki/Ian_Fosterhttp://en.wikipedia.org/wiki/Power_gridhttp://en.wikipedia.org/wiki/Metaphor


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CHAPTER 2

Literature Survey

Workload and resource management are two essential functions provided at the service

level of the grid software infrastructure. To improve the global throughput of these softwareenvironments, workloads have to be evenly scheduled among the available resources. To realize

this goal several load balancing strategies and algorithms have been. Most strategies were

developed in mind, assuming homogeneous set of sites linked with homogeneous and fast

networks. However for computational grids main new issues must address, namely:

heterogeneity, scalability and adaptability. Here it proposes a layered algorithm which achieves

dynamic load balancing in grid computing

Efficient job scheduling in grids is challenging due to the large number of distributed

autonomous resources. Here we study various resource allocation policies in a 2-level grid

system. A simulation model is used to evaluate performance of these policies at the grid leveland at the local level. Grid level policies include cases where the grid scheduler uses site

information (deferred policy), a random policy, and a combination of the two (hybrid).

Simulation results indicate that the hybrid performs better regardless of the local policy.

Resource Co-allocation is one of the crucial problems affecting the performance of the

grid. In addition to this if the system load in each of nodes is nearly equal; it indicates good

resource allocation and utilization. It is well known that load balancing is a key factor in

developing parallel and distributed applications. Instead of balancing the load in grid by process

migration, or by moving an entire process to a less loaded node, we make an attempt to balance

load by splitting up processes into separated jobs and then balance them to nodes. To address the

problem of load balancing, many centralized approaches have been in the literature but

centralization has proved to raise scalability tribulations. So in order to get the target, we use

mobile agents (MAs) to distribute load among nodes in the grid. Because a quick response time

is necessary for need of users in real grid environment so a real time resource co-allocation is

needed for such type of applications. So a parallel resource co-allocation using MA is in this

seminar which not only balances the load on grid using architecture but also allocates the

resources. It is concluded with the results of the experiments that parallel method not only

reduces total execution time but also reduces overall response time small.

Propose an optimal data migration algorithm in diffusive dynamic load balancing through

the calculation of Lagrange multiplier of the Euclidean form of transferred weight. This work

can effectively minimize the data movement in homogenous environments, but it does not

consider the network heterogeneity.

A large number of load balancing techniques and heuristics, presented in literature, target

only homogeneous resources. However, modern computing systems, such as the computational

Grid, are most likely to be widely distributed and strongly heterogeneous. Therefore, it is


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essential to consider the impact of these characteristics on the design of load balancing

techniques

A novel load balancing strategy to address the new challenges in Grid computing.

Comparatively to the existing works, the main characteristics of our strategy are:

(i) It is a fully distributedstrategy, which allows solving bottleneck of the model.(ii) It uses a system-levelload balancing.

(iii) It privileges local tasks transfer than global ones, for the purpose of reducing communication

costsinduced by the task migration.

A Grid load balancer receives applications from Grid users, selects feasible resources for

these applications according to acquired information, and finally generates application-to-

resource mapping, on the basis of objective functions and predicted resource performance.

Basically, a load balancing system can be generalized into four basic steps:

(1) Monitoring resource load and state.

(2) Exchanging load and state information between resources.

(3) Calculating the new load distribution.

(4) Updating data movement.


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CHAPTER 3

DYNAMIC LOAD BALANCING

3.1 Load Balancing Problem

This problem has been discussed in traditional distributed systems literature for more

than two decades. Various strategies and algorithms have been, implemented and classified in a

number of studies. Load balancing algorithms can be classified into two categories: static or

dynamic. In static algorithms, the decisions related to load balance are made at compile time

when resource requirements are estimated. Multicomputers with dynamic load balancing

allocate/reallocate resources at runtime based on no a priori task information, which may

determine when and whose tasks can be migrated. A good description of customized load

balancing strategies for a network of workstations can be found in. More recently, Houle and al.

Consider algorithms for static load balancing on trees, assuming that the total load is fixed.

Contrary to the traditional distributed systems for which a of algorithms, few of which were

focused on grid computing. This is due to the innovation and the specific characteristics of this

infrastructure.

Load balancing algorithms can be defined by their implementation of the following policies:

Informationpolicy:

Specifies what workload information to be collected, when it is to be collected and from

where. Information policy covers most issues related to the load information necessary for

making load-balancing decisions. Information policy decides what information is collected, and

when information about the states of other nodes is to be collected, and from which nodes. It isalso responsible for the dissemination of each node load information.

Load measurement ruleMeasuring the load of the various nodes in the system accurately is very important for the

success of a load-balancing algorithm. Measuring the load of the nodes in a distributed system is

an extremely difficult task. Usually, load is measured by a metric, the load index. A number of

possible metrics have been studied in the past. These can be broadly divided into two main

categories: simple and complex.

Simple indices.They consider the load on only a single resource. This approach usually focuses on the

load on the CPU. A simple load index includes processor queue length, average processor queue

length over a given duration, the amount 19 of memory available, the context switch rate, the

system call rate, and CPU utilization.

Complex load indices.They consist of a number of metrics, each relating to a single resource, such as CPU,

disk, memory and network. The metrics that make up the load index may be combined to give a


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single load value or may be represented as a tuple consisting of a number of elements, one per

metric. The load index used in and MOSIX comprises the CPU load and the amount of free

memory. San Luis uses a load index based on the performance-weighted CPU run-queue length,

the amount of free memory, disk traffic level, and network traffic level. The memory

requirements and the desired response-time of tasks are taken into account in scheduling

decisions. Utopia uses a load index that incorporates: CPU run-queue length, available memory,

disk transfer rate, the amount of swap disk-space available, and the number of concurrent users.

LSF uses the same metrics as Utopia, with three additions: CPU utilization, paging rate, and the

amount of idle time at processing nodes. Triggering policy:

Determines the appropriate period to start a load balancing operation.

Resource type policy:

Classifies a resource as server or receiverof tasks according to its availability status.

Location policy:

Uses the results of the resource type policy to find a suitable partner for a server or

receiver. The responsibility of location policy is to find a suitable transfer partner. Location

policies can be distributed, each node selecting a transfer partner on the basis of locally held

information. Location policy, corresponding to information policy, specifies the balancing

domain for load-balancing actions; this could be global, nearest-neighbors, a group of random

polled nodes, or a set or cluster of nodes based on specified criteria. Alternatively, policies can

be devised using a central information source. Busy nodes attempt to locate transfer partners that

have low load levels in sender-initiated schemes. In receiver-initiated schemes, low-loaded nodes

attempt to locate a busy node from which to transfer work. Five typical policies are listed below.

Random policies:A transfer partner is selected at random, and its load-state is ignored. This can result in

useless task transfers when an already-busy node receives extra work, but has been shown to

provide performance improvements over no-load-distribution.

Lowest policies:Like threshold policies, lowest policies employ a threshold Lp. However, lowest policies

differ from threshold policies in that it probes a group of nodes until a node with a zero load

index is found, or until exactly Lp nodes have been probed. The lowest location policy is to

select the probed node with the lowest load index as the execution site for the incoming job,provided that the load index at that node is less than a preset threshold value.

Preferred list:Based on the topology of the system, each node orders all other nodes into a preferred

list.


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Selection policy:

Defines the tasks that should be migrated from overloaded resources (source) to most idle

resources (receiver). The role of selection policy is to select tasks for transfer. In sender-initiated

schemes, busy nodes choose tasks to transfer to another node, whereas in receiver-initiated

schemes, lightly loaded nodes inform potential senders of the types of task they are willing toaccept. The policy determines how much load, or how many tasks, to transfer. A task transfer

may be preemptive or non-preemptive. Preemptive transfers involve transferring a partially

executed task. This is generally expensive, as it involves collecting all of the tasks state. Non -

preemptive-task transfers involve only tasks that have not begun execution and hence do not

require a transfer of the task state. A node may be overloaded and have no tasks available for no

preemptive transfer if it is polled by a receiver. A selection policy should consider at least three

factors.

The overhead incurred in transferring the task should be minimized. No preemptivetransfers and small tasks (small amounts of information) carry less overhead.

The execution time of the transferred task should be sufficient to justify the cost of thetransfer. Even if task execution is unknown, it should be possible to classify the tasks as

short or long tasks, and to consider only the long tasks for migration. Some classification

errors might be tolerated as load-balancing algorithms are quite robust with regard to this

parameter

The number of location-dependent resources needed by the selected task should beminimal.

3.1.1 Performance parametersThe main objective of load balancing methods is to speed up the execution of applications on

resources whose workload varies at run time in unpredictable way. Hence, it is significant to

define metrics to measure the resource workload. Every dynamic load balancing method must

estimate the timely workload information of each resource. This is a key information in a load

balancing system where responses are given to following questions:

(i) How to measure resource workload?(ii)What criteria are retaining to define this workload?(iii) How to avoid the negative effects of resources dynamicity on the workload; and,

(iv)How to take into account the resources heterogeneity in order to obtain an instantaneousaverage operation. Workload representative of the system?

Several load indices have been in the literature, like CPU queue length, average CPU queuelength, CPU utilization, etc. The success of a load balancing algorithm depends from stability of

the number of messages (small overhead), support environment, low cost update of the

workload, and short mean response time which is a significant measurement for a user. It is also

essential to measure the communication cost induced by a load balancing.


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3.2 Problems Specific to grid computingThe most load balancing algorithms were developed in mind, assuming homogeneous set

of sites linked with homogeneous and fast networks. If this assumption is true in traditional

distributed systems, it is not realistic in grid architectures because following properties that

characterize them:

Heterogeneity:

A Grid involves multiple resources that are heterogeneous in nature and might span

numerous administrative domains across a potentially global expanse.

Scalability:

A Grid might grow from few resources to millions. This raises the problem of potential

performance degradation as the size of a Grid increases.

Adaptability:

In a Grid, a resource failure is the rule, not the exception. That means that the probability

of some resources fail is naturally high. Resource managers must tailor their behavior

dynamically so that they can extract he maximum performance from the available resources and

services.

These properties make the load balancing problem more complex than in traditional

parallel and distributed systems, which offer homogeneity and stability of their resources. Also

interconnected networks on grids have very disparate performances and tasks submitted to the

system can be very diversified and irregular. These various observations show that it is very

difficult to define a load balancing system which can integrate all these factors.

Load balancing is usually described in the literature as either load balancing or load

sharing. These terms are often used interchangeably, but can also attract quite distinct

definitions.


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CHAPTER 4

SYSTEM ARCHITECTURE

Dynamic Load Balancing

Dynamic load balancing algorithms make changes to the distribution of work among

nodes at run-time; they use current or load information when making distribution decisions.

Fig 4.1 Dynamic Load Balancing

Dynamic load balancing algorithms are advantageous over static algorithms. But to gain

this advantage, we need to consider the cost to collect and maintain the load information.

Based on the study of load balancing techniques in Grid environment, a technique which

dynamically balances the load. The rules generated by data mining techniques are used for

migrating jobs for load balancing. Load balancing should take place when some change occurs in

the load situation. There are some particular activities which change the load configuration inGrid environment which can be categorized as following:

Arrival of any new job. Completion of execution of any job. Arrival of any new resource. Withdrawal of any existing resource.


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Whenever any of above mentioned activities happens we have to retrieve the current load

information and the initial load information from the database. System will compare them and

generate specific rules to migrate the jobs, so jobs will be distributed optimally on all nodes.

After the job migration database will be updated by this current load information. This

kind of approach is advent to balance the load in Grid environment. Also with this kind ofbehavior using concept of unsupervised learning, we are able to decide a threshold value for each

node, which will help to distribute the load at the initial level when this grid will be used in

future. Data analysis is a practice in which raw data is ordered and organized so that useful

information can be extracted from it.

The process of organizing and thinking about data is a key to understanding what the data

does and does not contain. There are a variety of ways in which people can approach data

analysis, and it is notoriously easy to manipulate data during the analysis phase to push certain

conclusions or agendas. For this reason, it is important to pay attention when data analysis is

presented, and to think critically about the data and the conclusions which were drawn.

In this algorithm, Data analysis module will compare previously stored parameters with

current parameters of different nodes and generates rules, used for job migration. Rules

generated by data analysis module contain heavily loaded and lightly loaded node information.

This information will be used to balance load accordingly. Job migration refers to the process of

moving a job from one node to another. This facilitates load balancing by moving jobs from a

heavily-loaded host to a lightly-loaded host.

Following is the algorithm for Dynamic Load Balancing:

1: BEGIN

2: Previous = initial status of all nodes on grid.

3: while Tasks > 0 do

4: if Change state = true then

5: Current = Get current state ();

6: New rule = generate rule (Current, Previous);

7: Threshold = generate threshold (Threshold, Newrule);

8: Migrate jobs (New rule);

9: Previous = Current;

10: end if

11: end while

12: END


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Flow Diagram of system Dynamic Load Balancing:


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Variables Used:Tasks: indicates no of jobs already running on grid.

Type - integer

Change state: indicates change in the state of load in grid.

Type - Boolean

Current: indicates current load status of all the nodes on grid.

Type - structure of different parameters of grid.

Previous: indicates load status of all nodes on grid before the change.

Type - structure of different parameters of grid.

New rule: indicates the rules decided for load distribution on grid.

Type - structure of different parameters of node to transfer load.

Threshold: indicates maximum limit of handling load for each node on a grid.

Type - structure of arrays on different node and its threshold values.

Methods used:Get current state (): Io get the current status of load on each node of grid.

Generate rule (Current, Previous): To generate rules to balance change occurred in the load as

per the current and previous status of the nodes.

Generate threshold(Threshold, New rule) : To find out new threshold values of each nodes of

grid for future using concept of data mining from old threshold and newly generated rules.

Migrate jobs (New rule): to migrate jobs as per the newly generated rules.

Algorithms:Notations: Notations used in the following algorithms are defined in as follows:

Ck = kth cluster; Sjk = jth site of kth cluster; CEijk = ith computing element of jth site of kth

cluster; Avrg= grid average load;

1) Intra-site load balancing algorithm:

This algorithm is considered as the kernel of our load balancing strategy. It is executed

when CEs managers find that there exists an imbalance between computer elements under their

control. To make this report, the managers receive periodically local workload information from

computing elements. Based on this information and on the Avrg of average grid workload, themanagers analyze the workload of sites periodically. Depending on the result of this analysis,

either they decide to start a local load balancing between CEs of the same site, or either they

inform their manager site (cluster) that are currently overloaded.


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2) Intra-cluster load balancing algorithm:

This algorithm is executed only when some CEs managers fail to balance locally the

overload of their CEs. Knowing the global state of each site, the sites manager can evenly

distribute the global overload between its sites.

3) Intra-grid load balancing algorithm:

This third algorithm performs a global load balancing among all clusters of a grid. It is

executed only if the other two levels are failed to achieve a complete load balance. It is

significant to remark that at this level, the algorithm always succeeds load balancing all these

clusters. It is thus useless to test if some clusters are still imbalanced.

4.1 Load Balancing Strategy

Levels:

In accordance with the structure of model, the load balancing strategy is also hierarchical.

Hence, we distinguish between three load balancing levels:

1) Intra-site load balancing:

In this first level, depending on its current load, each site decides to start a load balancing

operation. In this case, the site tries, in priority, to load balance its workload among its

computing elements.

2) Intra-cluster load balancing:

In this second level, load balance concerns only one cluster, Ck, among the clusters of a

grid. This kind of load balance is achieved only if some sites Sjkfail to load balance its workloadamong their respective computing elements. In this case, they need the assistance of its direct

parent, namely cluster Ck.

3) Intra-grid load balancing:

The load balance at this level is used only if some clusters Cks fail to load balance their

load among their associated sites. The main advantage of this strategy is to prioritize local load

balancing first (within a site, then within a cluster and finally on the whole grid). The goal of this

neighborhood strategy is to decrease the amount of messages between computing elements. As

consequence of this goal, the overhead induced by our strategy is reduced. In terms of

complexity, we can easily prove that the amount of messages is linear according to the number

of computing elements.


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4.2 Tree Based Balancing Model

Tree Based Balancing Model

In order to well explain our model, we first define the topological structure for a grid computing.

4.2.1 Grid Topology

We suppose that a grid computing is a finite set of Gclusters Ck, interconnected by gates

gtk, k {0, ...,G 1}, where each cluster contains one or more sites Sjk interconnected by

switches SWjk and every site contains some Computing Elements CEijk and some Storage

Elements SEijk, interconnected by a local area network.

Fig 4.2.1 Grid Topology


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4.2.2 Load balancing generic model

In this model is based on an incremental tree. First, for each site we create a two-level

sub tree. The leaves of this sub tree correspond to the computing elements of a site, and the root

is a virtual node associated to the site. These sub tree, that correspond to sites of a cluster, arethen aggregated to form a three-level sub-tree. Finally, these sub-trees are connected together to

generate a four-level tree called load balancing generic model. This model is denoted by G/S/M,

where G is the number of clusters that compose a grid, S the number of sites in the grid and M

the number of Computing Elements. This model can be transformed into three specific models:

G/S/M, 1/S/M and 1/1/M, depending on the values of G and S.

Fig 4.2.2 Load Balancing for Generic Model


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The generic model is a non cyclic connected graph where each level has specific

functions.

Level 0: In this first level (top level of the tree), we have a virtual node that corresponds to the

root of the tree. It is associated to the grid and performs two main functions: Manage the workload information of the grid; Decides, upon receiving tasks from users, where these tasks can be launched, based on

the user requirements and the current load of the grid.

Level 1: This level contains G virtual nodes, each one associated to a physical cluster of the

grid. In our load balancing strategy, this virtual node is responsible to manage workload of its

sites.

Level 2: In this third level, we find Snodes associated to physical sites of all clusters of the

grid. The main function of these nodes is to manage the workload of their physical computing

elements.

Level 3: At this last level (leaves of the tree), we find the MComputing Elements of the grid

linked to their respective sites and clusters.


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CHAPTER 5

CHARACTERISTICS

The model is characterized by the following:

It facilitates information flow through the Grid. We distinguish three types of information

flows:

1) Ascending flowThis flow is related to the workload information flow in order to get current workload

status of Grid resources.

2) Horizontal flowIt concerns the inputs used to perform load balancing operations.

3) Descending flowThis flow conveys the load balancing decisions made by cluster managers to their

corresponding resources.

It supports heterogeneity and scalability of Grids:

Connecting/disconnecting Grid resources correspond to adding/removing leaves or sub

trees from the tree model.

It is totally independent of any physical Grid architecture.


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CHAPTER 6

FEATURES

Features of the this model1) It is hierarchical:This characteristic facilitates the information flow through the tree and well defines the message

traffic in our strategy.

2) It supports heterogeneity and scalability of grids:Adding or removing entities (computing elements, sites or clusters) are very simple

operations in our model (adding or removing nodes, sub tree).

3) It is totally independent from any physical architecture of a grid:The transformation of a grid into a tree is a univocal transformation. Each grid

corresponds to one and only one tree.

Dynamic Load Strategy featuresThe strategy presents the following features:

1) It is an asynchronous strategy:Every worker node has its specificperiod during which it sends its information workload to

the other nodes.

2) It is a distributed load balancing strategy:Insofar as several operations of load balancing can be started by using the local load

information of Grid clusters.

3) It is a source initiative load balancing strategy:Inter-clusters load balancing is started by the overloaded clusters (source).

4) It reduces the communication cost:Since it gives the privilege, when it is possible, to a local load balancing to avoid the use

of large scale communication network. In addition, the strategy starts a load balancing operation

only if it is beneficial.

.


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CHAPTER 7

CONCLUSION

Here, we addressed the problem of load balancing in grid computing.In this a load

balancing strategy based on a tree model representation of grid architecture. The model allows

the transformation of any grid architecture into a unique tree with at most four levels. From this

generic tree, we can derive three sub-models depending on the elements that compose a grid: one

site, one cluster, or in the general case multiple clusters. Using this model, we defined a

hierarchical load balancing strategy that gives priority to local load balancing within sites. The

strategy leads to a layered algorithm which an prototype was implemented and evaluated on a

grid simulator developed for the circumstance.


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