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The objectives of designing load balancing algorithms are to ensure that no server is overloaded
either in capacity or in performance requirements. These algorithms are divided into two
categories depending upon their knowledge base they use during taking the decision about
service requests as appeared from the clients.
If a load balancing algorithm, executing on the load balancer at front-end node make
decision about distributing the load without knowing any details about the requests, it is called
class-agnostic load balancing algorithm. They are named so, since they remains agnostic or
unsure to the nature of the request. This means that the algorithm acts without any information
about the type of service request or the type of client from where the request was raised.
Class-agnostic load balancing algorithms make decisions without considering the nature and
source of incoming requests.
The other category of load balancing algorithm is called class-aware load balancing algorithm.
This category of algorithms makes decisions with knowledge base about the nature and source
of the service requests. The load balancer at front-end node then distributes service requests to
appropriate back-end nodes for further processing.
Class-agnostic load balancing algorithm are simpler to design and implement. But class
aware algorithms have many advantages and are more suitable for balancing loads in critical
environment like cloud computing. Depending upon awareness about received content, the
class-aware algorithms can be classified in two categories. When it uses knowledge about the
content type of service requests, it is called content-aware load balancing algorithm. When
knowledge about content source (that is clients) is used in load distribution, those algorithms
are known as client-aware load balancing algorithmOften, some of the clients may request for service or content in common than others.
Assigning those similar types of requests received from different clients to the same (or same set
of) back-end-servers minimize the need of redundant processing at different server locations.
This not only decreases capacity requirements but also improves performance since content
can be accessed for a single storage location. In a constrained environment, this can make the
difference between meeting the capacity constraints and overflowing the system.
Content-aware load balancer can redirect similar kind of incoming requests to the same back
end-server to avoid duplicate processing.
When the class-aware load balancing algorithm uses knowledge base about clients before
distributing service requests to back-end servers to improve performance of system, they
are called as client-aware. Assigning requests from similar type of clients to some particular
server may often gain significant benefits in load balancing requirements due to the improved
processing performance. In practice, the applications need to maintain a balance between both
the client-aware and content-aware mechanisms.
Checking requests to find out its category is not simple and it needs going through some critical
processing. The potential improvement in system performance through the implementation of
client-aware load balancing technique largely depends on the degree of heterogeneity of the
environment. If the system is almost homogeneous, much could not be gained by implementing
client-aware algorithms, moreover, the overhead of dynamic client checking mechanism may
degrade the performance of the system.
Selection of right load balancer provides opportunity for future flexibility in cloud computing
The objectives of designing load balancing algorithms are to ensure that no server is overloaded
either in capacity or in performance requirements. These algorithms are divided into two
categories depending upon their knowledge base they use during taking the decision about
service requests as appeared from the clients.
If a load balancing algorithm, executing on the load balancer at front-end node make
decision about distributing the load without knowing any details about the requests, it is called
class-agnostic load balancing algorithm. They are named so, since they remains agnostic or
unsure to the nature of the request. This means that the algorithm acts without any information
about the type of service request or the type of client from where the request was raised.
Class-agnostic load balancing algorithms make decisions without considering the nature and
source of incoming requests.
The other category of load balancing algorithm is called class-aware load balancing algorithm.
This category of algorithms makes decisions with knowledge base about the nature and source
of the service requests. The load balancer at front-end node then distributes service requests to
appropriate back-end nodes for further processing.
Class-agnostic load balancing algorithm are simpler to design and implement. But class
aware algorithms have many advantages and are more suitable for balancing loads in critical
environment like cloud computing. Depending upon awareness about received content, the
class-aware algorithms can be classified in two categories. When it uses knowledge about the
content type of service requests, it is called content-aware load balancing algorithm. When
knowledge about content source (that is clients) is used in load distribution, those algorithms
are known as client-aware load balancing algorithmOften, some of the clients may request for service or content in common than others.
Assigning those similar types of requests received from different clients to the same (or same set
of) back-end-servers minimize the need of redundant processing at different server locations.
This not only decreases capacity requirements but also improves performance since content
can be accessed for a single storage location. In a constrained environment, this can make the
difference between meeting the capacity constraints and overflowing the system.
Content-aware load balancer can redirect similar kind of incoming requests to the same back
end-server to avoid duplicate processing.
When the class-aware load balancing algorithm uses knowledge base about clients before
distributing service requests to back-end servers to improve performance of system, they
are called as client-aware. Assigning requests from similar type of clients to some particular
server may often gain significant benefits in load balancing requirements due to the improved
processing performance. In practice, the applications need to maintain a balance between both
the client-aware and content-aware mechanisms.
Checking requests to find out its category is not simple and it needs going through some critical
processing. The potential improvement in system performance through the implementation of
client-aware load balancing technique largely depends on the degree of heterogeneity of the
environment. If the system is almost homogeneous, much could not be gained by implementing
client-aware algorithms, moreover, the overhead of dynamic client checking mechanism may
degrade the performance of the system.
Selection of right load balancer provides opportunity for future flexibility in cloud computing