Abstract
Load balancing techniques in cloud computing can be applied at three different levels: Virtual machine load balancing, task load balancing, and resource load balancing. At all levels, load balancing should also be implemented in an efficient manner, to increase system performance. In this paper, we propose a fair, in terms of added workload per VM, task load balancing strategy, that aims to improve the average response time and the makespan of the system in the cloud environment. The problem is formulated as an irreducible finite state Markov process, which is known to have a balance equation for each state. From the balance state probabilities we derive the expected utilizations for the virtual machines (VM), which play a vital role in our task allocation approach. In our model, the Load Balancer (LBer) acts as a central server, which uses our proposed fair task allocation scheme to distribute the incoming tasks in a fair, balanced manner among the virtual machines, taking into account their current state as well as their processing capabilities. Our scheme has been compared to recent algorithms that use the particle swarm optimization and the Honey bee foraging scheme to achieve load balancing. Our experimental results show that our proposed scheme outperforms other state of the art schemes in terms of makespan, average response time, and resource utilization and provides lower degree of imbalance.
Highlights
C LOUD computing is a very popular internet-based technology, which provides resources and computer services on demand to customers with different needs [1], [2]
A narrower classification divides the Load balancing (LB) schemes in the following categories: Virtual machine load balancing (VMLB) schemes, which distribute the virtual machines (VM) from overloaded nodes to less loaded nodes [4], [5], task load balancing (TLB) schemes, which evenly distribute the tasks among the VMs [6], [40], and resource load balancing (RLB) schemes, which focus on the management of the available resources like servers, network links [8], CPU, memory and bandwidth
An agent based strategy is proposed in [48]; the agents assigned to resources learn to select the best sequence of the tasks that can optimize the total makespan of the workflow, enhance utilization of resources, and improve load balancing between resources
Summary
C LOUD computing is a very popular internet-based technology, which provides resources and computer services on demand to customers with different needs [1], [2]. We introduce a new dynamic task load balancing scheme, to efficiently distribute the tasks among the system’s VMs. The novel idea introduced in our scheme is that it considers fairness as the main criterion of task distribution among the available VMs. A fair task distribution scheme: (1) assigns the new workload to each VM proportionally to its current processing capacity and (2) this assignment is implemented in such a manner that all the VMs are expected to be utilized after the distribution of the new tasks.
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