Energy-aware Load Balancing and Application Scaling for the Cloud Ecosystem
In this paper we introduce an energy-aware operation model used for load balancing and application scaling on a cloud. The basic philosophy of our approach is defining an energy-optimal operation regime and attempting to maximize the number of servers operating in this regime. Idle and lightly-loaded servers are switched to one of the sleep states to save energy. The load balancing and scaling algorithms also exploit some of the most desirable features of server consolidation mechanisms discussed in the literature.
An important strategy for energy reduction is concentrating the load on a subset of servers and, whenever possible, switching the rest of them to a state with low energy consumption. This observation implies that the traditional concept of load balancing in a large-scale system could be reformulated as follows: distribute evenly the workload to the smallest set of servers operating at optimal or near-optimal energy levels, while observing the Service Level Agreement (SLA) between the CSP and a cloud user. An optimal energy level is one when the performance per Watt of power is maximized.
In order to integrate business requirements and application level needs, in terms of Quality of Service (QoS), cloud service provisioning is regulated by Service Level Agreements (SLAs): contracts between clients and providers that express the price for a service, the QoS levels required during the service provisioning, and the penalties associated with the SLA violations. In such a context, performance evaluation plays a key role allowing system managers to evaluate the effects of different resource management strategies on the data center functioning and to predict the corresponding costs/benefits.
DISADVANTAGES OF EXISTING SYSTEM:
On-the-field experiments are mainly focused on the offered QoS, they are based on a black box approach that makes difficult to correlate obtained data to the internal resource management strategies implemented by the system provider.
Simulation does not allow to conduct comprehensive analyses of the system performance due to the great number of parameters that have to be investigated.
There are three primary contributions of this paper:
a new model of cloud servers that is based on different operating regimes with various degrees of energy efficiency” (processing power versus energy consumption);
a novel algorithm that performs load balancing and application scaling to maximize the number of servers operating in the energy-optimal regime; and
analysis and comparison of techniques for load balancing and application scaling using three differently-sized clusters and two different average load profiles.
The objective of the algorithms is to ensure that the largest possible number of active servers operate within the boundaries of their respective optimal operating regime. The actions implementing this policy are: (a) migrate VMs from a server operating in the undesirable-low regime and then switch the server to a sleep state; (b) switch an idle server to a sleep state and reactivate servers in a sleep state when the cluster load increases; (c) migrate the VMs from an overloaded server, a server operating in the undesirable-high regime with applications predicted to increase their demands for computing in the next reallocation cycles.
ADVANTAGES OF PROPOSED SYSTEM:
After load balancing, the number of servers in the optimal regime increases from 0 to about 60% and a fair number of servers are switched to the sleep state.
There is a balance between computational efficiency and SLA violations; the algorithm can be tuned to maximize computational efficiency or to minimize SLA violations according to the type of workload and the system management policies.
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Floppy Drive : 44 Mb.
Monitor : 15 VGA Colour.
Ram : 512 Mb.
Operating system : Windows XP/7.
Coding Language : net, C#.net
Tool : Visual Studio 2010
Database : SQL SERVER 2008
Ashkan Paya and Dan C. Marinescu, “Energy-aware Load Balancing and Application Scaling for the Cloud Ecosystem”, IEEE Transactions on Cloud Computing 2015.