Load balancing is used to distributing a larger processing load to smaller processing nodes for
enhancing the overall performance of system. In cloud computing environment load balancing is
required distribute the dynamic local workload evenly between all the nodes.
Load balancing helps in fair allocation of computing resource to achieve a high User satisfaction and proper Resource utilization .High resource utilization and Proper load balancing helps in minimizing resource consumption. It helps in implementing fail over, scalability, and avoiding bottlenecks.
Load balancing is a techniques that helped networks and resources by providing a Maximum throughput with minimum response time. Load balancing is dividing the traffic between all servers, so data can be sent and received without any delay with load balancing.
In cloud environment many algorithms are available that helps in proper traffic Loaded between all available servers .Most of them can be applied in the cloud environment with suitable verifications. In cloud computing environment load balancing algorithms can be divided into two main groups : first algorithm type is Batch mode heuristic scheduling algorithms (BMHA) and second is online mode heuristic algorithms. In BMHA Jobs are combined together when they are arriving in the system. The BMHA scheduling algorithm will start after a fixed time period.
The examples of BMHA based algorithms are: First Come First Served Scheduling algorithm (FCFS), Round Robin scheduling algorithm (RR), Min Min algorithm and Max Min algorithm. In On-line mode heuristic scheduling algorithm, all Jobs are scheduled when they are arriving in the system. The cloud environment is a heterogeneous system and in this speed of each processor varies quickly and easily. The online mode heuristic scheduling algorithms are more appropriate and better for a cloud environment.
It is very important to estimate proper load , need to do comparison of all load , stability of all different systems, performance of purposed system, interaction between all the nodes and nature of work to be transferred while developing a load balancing algorithm . The most important thing is selecting the nodes and its also include many other ones. CPU load, amount of memory required combine together to calculate the load of machine.
In our daily life example of load balancing is websites. Users could experience many Problems without Load balancing like delays, timeouts and long system responses.
1) Static approach: - This approach is mainly defined in the design or implementation of the system. Static load balancing algorithms divide the traffic equivalently between all servers.
2)Dynamic approach:- This approach considered only the current state of the system during load balancing decisions. Dynamic approach is more suitable for widely distributed systems such as cloud computing .
Dynamic load balancing approaches have two types .They are distributed approach and non-distributed (centralized) approach. It is defined as following:
a) Centralized approach: - In centralized approach, only a single node is responsible for managing and distribution within the whole system. Other all nodes are not responsible for this.
b) Distributed approach: - In distributed approach, each node independently builds its own load vector. Vector collecting the load information of other nodes. All decisions are made locally using local load vectors. Distributed approach is more suitable for widely distributed systems such as cloud computing.
B. Metrics for Load Balancing:
1. Throughput: - It is used to calculate the all tasks whose execution has been completed. The performance of any system is improved if throughput is high.
2. Fault Tolerance: -It means recovery from failure. The load balancing should be a good faulttolerant technique.
3. Migration time: -It is the time to migrate the jobs or resources from one node to other nodes. It should be minimized in order to enhance the performance of the system.
4. Response Time: - It is the amount of time that is taken by a particular load balancing algorithm to response a task in a system. This parameter should be minimized for better performance of a system.
5. Scalability: - It is the ability of an algorithm to perform Load balancing for any finite number of nodes of a system. This metric should be improved for a good system.
Goals of load balancing :
The main aim of load balancing is as follows:
To maintain the firmness of the system
To significantly improve the performance of the system
To have a backup plan in case of any failure
To encompass the future modification of the system
Load balancing helps in fair allocation of computing resource to achieve a high User satisfaction and proper Resource utilization .High resource utilization and Proper load balancing helps in minimizing resource consumption. It helps in implementing fail over, scalability, and avoiding bottlenecks.
Load balancing is a techniques that helped networks and resources by providing a Maximum throughput with minimum response time. Load balancing is dividing the traffic between all servers, so data can be sent and received without any delay with load balancing.
In cloud environment many algorithms are available that helps in proper traffic Loaded between all available servers .Most of them can be applied in the cloud environment with suitable verifications. In cloud computing environment load balancing algorithms can be divided into two main groups : first algorithm type is Batch mode heuristic scheduling algorithms (BMHA) and second is online mode heuristic algorithms. In BMHA Jobs are combined together when they are arriving in the system. The BMHA scheduling algorithm will start after a fixed time period.
The examples of BMHA based algorithms are: First Come First Served Scheduling algorithm (FCFS), Round Robin scheduling algorithm (RR), Min Min algorithm and Max Min algorithm. In On-line mode heuristic scheduling algorithm, all Jobs are scheduled when they are arriving in the system. The cloud environment is a heterogeneous system and in this speed of each processor varies quickly and easily. The online mode heuristic scheduling algorithms are more appropriate and better for a cloud environment.
It is very important to estimate proper load , need to do comparison of all load , stability of all different systems, performance of purposed system, interaction between all the nodes and nature of work to be transferred while developing a load balancing algorithm . The most important thing is selecting the nodes and its also include many other ones. CPU load, amount of memory required combine together to calculate the load of machine.
In our daily life example of load balancing is websites. Users could experience many Problems without Load balancing like delays, timeouts and long system responses.
1) Static approach: - This approach is mainly defined in the design or implementation of the system. Static load balancing algorithms divide the traffic equivalently between all servers.
2)Dynamic approach:- This approach considered only the current state of the system during load balancing decisions. Dynamic approach is more suitable for widely distributed systems such as cloud computing .
Dynamic load balancing approaches have two types .They are distributed approach and non-distributed (centralized) approach. It is defined as following:
a) Centralized approach: - In centralized approach, only a single node is responsible for managing and distribution within the whole system. Other all nodes are not responsible for this.
b) Distributed approach: - In distributed approach, each node independently builds its own load vector. Vector collecting the load information of other nodes. All decisions are made locally using local load vectors. Distributed approach is more suitable for widely distributed systems such as cloud computing.
B. Metrics for Load Balancing:
1. Throughput: - It is used to calculate the all tasks whose execution has been completed. The performance of any system is improved if throughput is high.
2. Fault Tolerance: -It means recovery from failure. The load balancing should be a good faulttolerant technique.
3. Migration time: -It is the time to migrate the jobs or resources from one node to other nodes. It should be minimized in order to enhance the performance of the system.
4. Response Time: - It is the amount of time that is taken by a particular load balancing algorithm to response a task in a system. This parameter should be minimized for better performance of a system.
5. Scalability: - It is the ability of an algorithm to perform Load balancing for any finite number of nodes of a system. This metric should be improved for a good system.
Goals of load balancing :
The main aim of load balancing is as follows:
To maintain the firmness of the system
To significantly improve the performance of the system
To have a backup plan in case of any failure
To encompass the future modification of the system
Tags:
cloud computing