A review of performance and energy aware improvement methods for future green cloud computing

With the advent of increased use of computers and computing power, state of the art of cloud computing has become imperative in the present-day global scenario. It has managed to remove the constraints in many organizations in terms of physical internetworking devices and human resources, leaving room for better growth of many organizations. With all these benefits, cloud computing is still facing a number of impediments in terms of energy consumption within data centers and performance degradation to end users. This has led many industries and researchers to find feasible solutions to the current problems. In the context of realizing the problems faced by cloud data centers and end users, this paper presents a summary of the work done, experimentation setup and the need for a greener cloud computing technique/algorithm which satisfies minimum energy consumption, minimum carbon emission and maximum quality of service

A proposed energy and performance aware cloud framework for improving service level agreements (SLAs) in cloud datacenters

Physical computer hardware is being replaced with virtual hardware in cloud computing for cost efficient operations. It is expected that by 2020, most of medium and large organizations will migrate to cloud computing for enhanced and sustainable business. However, the trade-off between meeting Service Level Agreements (SLA) and minimizing energy consumption of physical machines in data centers is not fully optimized, thus leaving scope for further improvement. In spite of the fact that cloud providers are using state-of-the-art technologies, cloud clients are still demanding higher and ever increasing Quality of Service (QoS) to satisfy the need of customers. Based on the authors’ previous practical work on a high-end server on ESXi 5.5 hypervisor platform, a novel framework for improving computational efficiency, performance and reducing cloud energy consumption is proposed in this paper. The proposed framework integrates with hardware through a server classification process (idle server, under-loaded server, balance server and over-loaded server) and distributes computational loads with a built-in logic to reduce energy consumption. In addition, the framework promises an efficient solution for Virtual Machines (VMs) allocation and optimization that will satisfy SLAs for cloud consumers. In all four server categories, tracking and a recording system is considered for Physical Machine (PMs) and VMs. For effective utilization of the idle server state, a wake-up and sleep mode decider are proposed. The uniqueness of the framework can be validated with its implementation on CloudSim softwar

Hybrid heuristic algorithm for better energy optimization and resource utilization in cloud computing

Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a Hybrid Heuristic algorithm based Energy-efficient cloud Computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the Chaotic based Particle Swarm Optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the Hybrid Heuristic Workflow Scheduling algorithm (HHWS) and Distributed Dynamic VM Management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods