Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Introduction <s> For the problem that the energy efficiency of the cloud computing data center is low, from the point of view of the energy efficiency of the servers, we propose a new energy-efficient multi-task scheduling model based on Google's massive data processing framework. To solve this model, we design a practical encoding and decoding method for the individuals, and construct an overall energy efficiency function of the servers as the fitness value of the individual. Meanwhile, in order to accelerate the convergent speed and enhance the searching ability of our algorithm, a local search operator is introduced. Finally, the experiments show that the proposed algorithm is effective and efficient. <s> BIB001 </s> Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Introduction <s> Network-based cloud computing is rapidly expanding as an alternative to conventional office-based computing. As cloud computing becomes more widespread, the energy consumption of the network and computing resources that underpin the cloud will grow. This is happening at a time when there is increasing attention being paid to the need to manage energy consumption across the entire information and communications technology (ICT) sector. While data center energy use has received much attention recently, there has been less attention paid to the energy consumption of the transmission and switching networks that are key to connecting users to the cloud. In this paper, we present an analysis of energy consumption in cloud computing. The analysis considers both public and private clouds, and includes energy consumption in switching and transmission as well as data processing and data storage. We show that energy consumption in transport and switching can be a significant percentage of total energy consumption in cloud computing. Cloud computing can enable more energy-efficient use of computing power, especially when the computing tasks are of low intensity or infrequent. However, under some circum- stances cloud computing can consume more energy than conventional computing where each user performs all com- puting on their own personal computer (PC). <s> BIB002 </s> Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Introduction <s> Although cloud computing has rapidly emerged as a widely accepted computing paradigm, the research on cloud computing is still at an early stage. Cloud computing suffers from different challenging issues related to security, software frameworks, quality of service, standardization, and power consumption. Efficient energy management is one of the most challenging research issues. The core services in cloud computing system are the SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service). In this paper, we study state-of-the-art techniques and research related to power saving in the IaaS of a cloud computing system, which consumes a huge part of total energy in a cloud computing system. At the end, some feasible solutions for building green cloud computing are proposed. Our aim is to provide a better understanding of the design challenges of energy management in the IaaS of a cloud computing system. <s> BIB003 </s> Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Introduction <s> Computation grids and computational clouds are becoming increasingly popular in the organizations which require massive computational capabilities. Building such infrastructures makes a lucrative business case, thanks to availability of cheap hardware components and affordable software. Maintaining computational grids or cloud, however, require careful planning as in such dedicated environments, round-the-clock availability of workstations is very crucial. Ensuring uninterrupted availability, not only demands mechanism for failover redundancy but also results in constant power drainage. The tradeoff between the cost and the performance is the constant dilemma that the operations of the data centers face today. In this paper, we propose various heuristics for power-aware scheduling algorithms for scheduling jobs with dependent tasks onto the computational grid. We formulate the problem as a multi-objective function which results in various cost-performance tradeoffs each lying within the solution boundary. <s> BIB004 </s> Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Introduction <s> Energy consumption in large-scale distributed systems, such as computational grids and clouds gains a lot of attention recently due to its significant performance, environmental and economic implications. These systems consume a massive amount of energy not only for powering them, but also cooling them. More importantly, the explosive increase in energy consumption is not linear to resource utilization as only a marginal percentage of energy is consumed for actual computational works. This energy problem becomes more challenging with uncertainty and variability of workloads and heterogeneous resources in those systems. This paper presents a dynamic scheduling algorithm incorporating reinforcement learning for good performance and energy efficiency. This incorporation helps the scheduler observe and adapt to various processing requirements (tasks) and different processing capacities (resources). The learning process of our scheduling algorithm develops an association between the best action (schedule) and the current state of the environment (parallel system). We have also devised a task-grouping technique to help the decision-making process of our algorithm. The grouping technique is adaptive in nature since it incorporates current workload and energy consumption for the best action. Results from our extensive simulations with varying processing capacities and a diverse set of tasks demonstrate the effectiveness of this learning approach. <s> BIB005 </s> Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Introduction <s> At the moment, cloud computing has an interesting progression in academic and industrial areas. Many well-known companies like Microsoft, Amazon, Google and IBM has built their own clouds in addition to many other open source cloud computing platforms and frameworks. There are many characteristics which a cloud should have. Many of the existing platforms and frameworks lack of some of these characteristics, for example most of them cannot really be integrated, all of them just give the user some tools and platforms (such as service bus and development kits), but there is no provided intelligence and automation. This paper proposes a framework that supports all of the mentioned characteristics and some others like low coupling and high integration. The proposed framework not only supports traditional cloud facilities, but using a service, adds intelligence to cloud to provide an automatic environment. To achieve the above mentioned goals, we have founded our framework on SOA (service oriented architecture) that manages data flow with an event driven approach. <s> BIB006 </s> Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Introduction <s> The implementation of cloud computing has attracted computing as a utility and enables penetrative applications from scientific, consumer and business domains. However, this implementation faces energy, power and associated cost concerns. In this paper, we present formulations and solutions for Green Cloud Environments (GCE) to minimize its energy consumption under new models by considering static and dynamic portions of cloud components, to reduce severe environmental impacts. To implement this objective, an in-depth knowledge of energy consumption patterns in cloud environment is necessary. We investigate energy consumption patterns. Specifically, we make use of measurable metrics based on runtime tasks to compare rationally the relation existing between energy consumption and cloud workload and computational tasks, as well as system performance. Our research results can be integrated into cloud computing systems to monitor energy consumption and support static and dynamic system level-optimization. <s> BIB007 </s> Energy Management for Cloud Computing: A Survey from Scheduling Perspective of Heuristic, Game Theory and Learning Strategy <s> Introduction <s> several sensors extract data from the environment concurrently, transmit these data to a processing unit and receive processed data accompanied by appropriate commands from the processing unit [1-5]. The sensors and their receiver/transmitter are generally powered by battery and/or solar cells.  <s> BIB008
Evolution of computing started when Grid computing hit the world in the beginning of 90's  . The evolution continued with commercialization of Cloud computing in the early 21st century with Amazon Web Services (2002), Google Docs (2006) and Azure by Microsoft (2009) BIB006 . Cloud computing is a state-of-the-art technology where companies or organizations are able to have processing and storage services without having to physically own them. It has changed the way of computing massively from isolated sequential computing to high performance parallel distributed computing. This practical technology eliminates the hassles of procuring, managing and maintaining data centers while saving the maintenance and operation costs. Furthermore, the Cloud provides high reliability, high scalability, high security and location independency services to its users BIB006 . According to Wang BIB001 the highest energy cost of data centers is used to maintain servers (Fig. 1) . It is basically to fulfill service level agreement (SLA) between the Cloud providers and users. In order to sustain business demands, more hardware are added up to facilitate the processing requirements. The issue of expanding and upgrading hardware to meet the SLA needs leads to excessive and ineffective energy consumption. The massive and rapid growth of the Cloud computing in order to fulfill the world's needs ironically impacts the world in a negative way. Large computing infrastructure needs more energy thus more fuel is needed to be burnt to generate sufficient electricity. As a result, more greenhouse gas (GHG) is released in the atmosphere that leads to global warming, acid rain and smog. The energy consumption issues have inspired many researchers to focus on green Cloud computing BIB002 BIB003 BIB007 . Among the basic aspects in the green Cloud computing are power management, cooling, recycling, electronic waste disposal and virtualization. Based on Rizvandi and Zomaya BIB008 the virtualization technology is the core enabler for green Cloud computing. It is a method of enhancing the energy efficiency of servers in the Cloud by sharing limited resources with multiple workloads BIB007 . Through virtualization, the number of physical machines (PMs) is reduced and the task execution is performed by virtual machines (VMs). The VMs are so flexible that they act as independent servers/nodes thus maximize resources utilization. Abundance of work had been done to manage energy consumption in green Cloud. Dynamic voltage and frequency scaling (DVFS) are among the techniques used to adjust voltage and frequency of processors that are based on current workload. A special hardware that is compatible with DVFS is needed to control the energy consumption while sustaining business's SLA. Energy model, on the other hand, is a holistic method where it captures data centers at large. It covers not only the components related to computation but also the components for cooling and other common equipment in the data centers that consume electricity. In particular, the effectiveness of VM, DVFS and energy model is utilized for energy efficiency through resource management or more specifically scheduling. Under the energy management in Cloud, the issue that needs to be concerned is minimizing the energy consumption of components in data centers especially processors. Based on Aziz and El-Rewini BIB004 , most processors on average consume approximately 32Watt even when they are in idle mode compared to storages that merely use 6Watt. In peak processing state the energy consumption of a processor is boosted up to more than 80Watt to 95Watt BIB005 . Task scheduling approach promotes for better energy consumption by scheduling jobs into the processor in an effective way. The task scheduling approach contributes to the green Cloud by providing the adaptive scheduling decision in order to handle dynamicity and heterogeneity of resources. In this paper we focus on energy management in Cloud by comparing scheduling approaches from the three (3) different perspectives; heuristic, economy and intelligent.