A Cooperative Coevolution Genetic Programming Hyper-Heuristic Approach for On-line resource Allocation in Container based Clouds

Abstract : Containers are lightweight and provide the potential to reduce more energy consumption of data centers than Virtual Machines (VMs) in container-based clouds. The on-line resource allocation is the most common operation in clouds. However, the on-line Resource Allocation in Container-based clouds (RAC) is new and challenging because of its two-level architecture, i.e. the allocations of containers to VMs and the allocation of VMs to physical machines. These two allocations interact with each other, and hence cannot be made separately. Since on-line container allocation requires a real-time response, most current allocation techniques rely on heuristics (e.g. First Fit and Best Fit), which do not consider the comprehensive information such as workload patterns and VM types. As a result, resources are not used efficiently and the energy consumption is not sufficiently optimized. We first propose a novel model of the on-line RAC problem with the consideration of VM overheads, VM types and an affinity constraint. Then, we design a Cooperative Coevolution Genetic Programming (CCGP) hyper-heuristic approach to solve the RAC problem. The CCGP can learn the workload patterns and VM types from historical workload traces and generate allocation rules. The experiments show significant improvement in energy consumption compared to the state-of-the-art algorithms.

 EXISTING SYSTEM :

 Container technology has become a new trend in both the software industry and cloud computing. Containers support the fast development of web applications and they have the potential to reduce energy consumption in data centers. Containers are usually first allocated to virtual machines (VMs) and VMs are allocated to physical machines. The container allocations a challenging task which involves a two-level allocation problem. Current research overly simplifies the container allocation into a one-level allocation problem and uses simple rule-based approaches to solve the problem. As a result, resource is not allocated efficiently which leads to high energy consumption. This paper provides a novel definition of the two-level container allocation problem.

 DISADVANTAGE :

 Online container allocation problem allocates containers to PMs immediately when containers arrive with the aim of achieving the lowest energy consumption. Using containers can reduce energy consumption from data centers . However, the problem of online container allocation (container allocation in short) is new and challenging to solve due to its NP-completeness. The major drawback of the simplification is that their allocation approaches can only be used in a narrow range of scenarios where all containers can be co-located.

 PROPOSED SYSTEM :

 We develop a hybrid approach using genetic programming hyper-heuristics combined with human-designed rules to solve the problem. The experiments show that our hybrid approach is able to significantly reduce energy consumption than solely using human-designed rules.

 ADVANTAGE :

 To introduce a novel problem definition for two-level container allocation. To develop a hybrid approach GPHH and human-designed rules. To evaluate our proposed approach on benchmark datasets.