Towards green computing in wireless sensor networks: controlled mobility-aided balanced tree approach

Virtualization technology has revolutionized the mobile network and widely used in 5G innovation. It is a way of computing that allows dynamic leasing of server capabilities in the form of services like SaaS, PaaS, and IaaS. The proliferation of these services among the users led to the establishment of large-scale cloud data centers that consume an enormous amount of electrical energy and results into high metered bill cost and carbon footprint. In this paper, we propose three heuristic models namely Median Migration Time (MeMT), Smallest Void Detection (SVD) and Maximum Fill (MF) that can reduce energy consumption with minimal variation in SLAs negotiated. Specifically, we derive the cost of running cloud data center, cost optimization problem and resource utilization optimization problem. Power consumption model is developed for cloud computing environment focusing on liner relationship between power consumption and resource utilization. A virtual machine migration technique is considered focusing on synchronization oriented shorter stop-and-copy phase. The complete operational steps as algorithms are developed for energy aware heuristic models including MeMT, SVD and MF. To evaluate proposed heuristic models, we conduct experimentations using PlanetLab server data often ten days and synthetic workload data collected randomly from the similar number of VMs employed in PlanetLab Servers. Through evaluation process, we deduce that proposed approaches can significantly reduce the energy consumption, total VM migration, and host shutdown while maintaining the high system performance

Efficient Energy Management in Cloud Data center using VM Consolidation

Cloud computing is a model which can fast provisioned and released the computing resources by using minimum number of management effort. This can be done by the user without doing any communication with the cloud service providers. Cloud provide the computing resources, on-demand network access which is pooled together and it can be provisioned dynamically according to the user needs. Due to the large application, more number of computing nodes are required. A large amount of electrical energy is consumed due to the establishment of the data center. There is a problem of carbon dioxide emissions and increasing cost of operation due to the formation of large data center. A consolidation of virtual machines technique is proposed in our thesis to reduce the energy consumption and to maximize the utilization of the computing resources in the data center. Several virtual machines are taken together into a single physical machine in the consolidation technique and it helps to decrease the consumption of energy by putting idle server into inactive mode. A number of active hosts is minimized by continuously reallocating VMs using live migration. In each migration, Service Level Agreement(SLA) violations may occur, hence it is required to reduce the number of migrations.In order to satisfy quality of services in cloud computing environment, our proposed techniques mainly performs the following functions:(i)reducing the consumption of energy, (ii) minimize the number of migrations and (iii) minimize the percentage of SLA violations. Initially we detect whether any host is overloaded or not. The Overloaded host is detected by considering CPU utilization as a threshold Value. If an overloaded host is detected then some virtual machines are migrated from it by using VM selection policy. After selection of the VMs, the next step is to place the new VMs. For VM placement, the greedy algorithms such as Best Fit Decreasing(BFD) and Modified First Fit Decreasing(MFFD) are used in this thesis. The proposed techniques are compared with the existing EEDVM and PALVM techniques. Using proposed AUTREC technique there is 8% improved in energy consumption, 3% in number of migrations, 10% in SLA violation and 12% in host shutdown as compared to EEDVM technique. Using proposed DUTREC technique there is 9% improved in energy consumption, 6% in number of migrations, 20% in SLA violation and 13% in host shutdown as compared to PALVM technique

Optimizing Virtual Resource Management in Cloud Datacenters

Datacenter clouds (e.g., Microsoft\u27s Azure, Google\u27s App Engine, and Amazon\u27s EC2) are emerging as a popular infrastructure for computing and storage due to their high scalability and elasticity. More and more companies and organizations shift their services (e.g., online social networks, Dropbox file hosting) to clouds to avoid large capital expenditures. Cloud systems employ virtualization technology to provide resources in physical machines (PMs) in the form of virtual machines (VMs). Users create VMs deployed on the cloud and each VM consumes resources (e.g., CPU, memory and bandwidth) from its host PM. Cloud providers supply services by signing Service Level Agreement (SLA) with cloud customers that serves as both the blueprint and the warranty for cloud computing. Under-provisioning of resources leads to SLA violations while over-provisioning of resources leads to resource underutilization and then revenue decrease for the cloud providers. Thus, a formidable challenge is effective management of virtual resource to maximize energy efficiency and resource utilization while satisfying the SLA. This proposal is devoted to tackle this challenge by addressing three fundamental and essential issues: i) initial VM allocation, ii) VM migration for load balance, and iii) proactive VM migration for long-term load balance. Accordingly, this proposal consists of three innovative components: (1) Initial Complementary VM Consolidation. Previous resource provisioning strategies either allocate physical resources to virtual machines (VMs) based on static VM resource demands or dynamically handle the variations in VM resource requirements through live VM migrations. However, the former fail to maximize energy efficiency and resource utilization while the latter produce high migration overhead. To handle these problems, we propose an initial VM allocation mechanism that consolidates complementary VMs with spatial/temporal-awareness. Complementary VMs are the VMs whose total demand of each resource dimension (in the spatial space) nearly reaches their host\u27s capacity during VM lifetime period (in the temporal space). Based on our observation of the existence of VM resource utilization patterns, the mechanism predicts the lifetime resource utilization patterns of short-term VMs or periodical resource utilization patterns of long-term VMs. Based on the predicted patterns, it coordinates the requirements of different resources and consolidates complementary VMs in the same physical machine (PM). This mechanism reduces the number of PMs needed to provide VM service hence increases energy efficiency and resource utilization and also reduces the number of VM migrations and SLA violations. (2) Resource Intensity Aware VM Migration for Load Balance. The unique features of clouds pose formidable challenges to achieving effective and efficient load balancing. First, VMs in clouds use different resources (e.g., CPU, bandwidth, memory) to serve a variety of services (e.g., high performance computing, web services, file services), resulting in different overutilized resources in different PMs. Also, the overutilized resources in a PM may vary over time due to the time-varying heterogenous service requests. Second, there is intensive network communication between VMs. However, previous load balancing methods statically assign equal or predefined weights to different resources, which leads to degraded performance in terms of speed and cost to achieve load balance. Also, they do not strive to minimize the VM communications between PMs. This proposed mechanism dynamically assigns different weights to different resources according to their usage intensity in the PM, which significantly reduces the time and cost to achieve load balance and avoids future load imbalance. It also tries to keep frequently communicating VMs in the same PM to reduce bandwidth cost, and migrate VMs to PMs with minimum VM performance degradation. (3) Proactive VM Migration for Long-Term Load Balance. Previous reactive load balancing algorithms migrate VMs upon the occurrence of load imbalance, while previous proactive load balancing algorithms predict PM overload to conduct VM migration. However, both methods cannot maintain long-term load balance and produce high overhead and delay due to migration VM selection and destination PM selection. To overcome these problems, we propose a proactive Markov Decision Process (MDP)-based load balancing algorithm. We handle the challenges of allying MDP in virtual resource management in cloud datacenters, which allows a PM to proactively find an optimal action to transit to a lightly loaded state that will maintain for a longer period of time. We also apply the MDP to determine destination PMs to achieve long-term PM load balance state. Our algorithm reduces the numbers of SLA violations by long-term load balance maintenance, and also reduces the load balancing overhead (e.g., CPU time, energy) and delay by quickly identifying VMs and destination PMs to migrate. Finally, we conducted extensive experiments to evaluate the proposed three mechanisms. i) We conducted simulation experiments based on two real traces and real-world testbed experiments to show that the initial complementary VM consolidation mechanism significantly reduces the number of PMs used, SLA violations and VM migrations of the previous resource provisioning strategies. ii) We conducted trace-driven simulation and real-world testbed experiments to show that RIAL outperforms other load balancing approaches in regards to the number of VM migrations, VM performance degradation and VM communication cost. iii) We conducted trace-driven experiments to show that the MDP-based load balancing algorithm outperforms previous reactive and proactive load balancing algorithms in terms of SLA violation, load balancing efficiency and long-term load balance maintenance

Enabling Green Computing in Cloud Environments: Network Virtualization Approach Towards 5G Support

Virtualization technology has revolutionized the mobile network and widely used in 5G innovation. It is a way of computing that allows dynamic leasing of server capabilities in the form of services like SaaS, PaaS, and IaaS. The proliferation of these services among the users led to the establishment of large-scale cloud data centers that consume an enormous amount of electrical energy and results into high metered bill cost and carbon footprint. In this paper, we propose three heuristic models namely Median Migration Time (MeMT), Smallest Void Detection (SVD) and Maximum Fill (MF) that can reduce energy consumption with minimal variation in SLAs negotiated. Specifically, we derive the cost of running cloud data center, cost optimization problem and resource utilization optimization problem. Power consumption model is developed for cloud computing environment focusing on liner relationship between power consumption and resource utilization. A virtual machine migration technique is considered focusing on synchronization oriented shorter stop-and-copy phase. The complete operational steps as algorithms are developed for energy aware heuristic models including MeMT, SVD and MF. To evaluate proposed heuristic models, we conduct experimentations using PlanetLab server data often ten days and synthetic workload data collected randomly from the similar number of VMs employed in PlanetLab Servers. Through evaluation process, we deduce that proposed approaches can significantly reduce the energy consumption, total VM migration, and host shutdown while maintaining the high system performance

Energy Efficiency in Cloud Data Centers Using Load Balancing

ABSTRACT Cloud computing is an expanding area in research and industry today, which involves virtualization, distributed computing, internet, and software and web services. This paper presents an approach for scheduling algorithms that can maintain the load balancing. In this research work we have developed power optimization algorithm which over comes the limitations of the previous algorithms[Round Robin, Equally Spread Current Execution Algorithm, Throttled Load Balancing which are used for the over load management of the data leading to positive consequences in terms of overall power consumption of the data centre thus helping in green computing. As due to undue overload of traffic and then overhead due to mitigation and migration of the virtual machines to balance out the operations there is always an impact on the power consumption, if there is more overload, there is bound to be more power consumption, and if balancing works well, there is bound to be an optimized trade-off for energy consumption. Results have shown that overall impact of power consumption is reduced by using the proposed algorithm

Energy and Performance Management of Virtual Machines: Provisioning, Placement and Consolidation

Cloud computing is a new computing paradigm that offers scalable storage and compute resources to users on demand through Internet. Public cloud providers operate large-scale data centers around the world to handle a large number of users request. However, data centers consume an immense amount of electrical energy that can lead to high operating costs and carbon emissions. One of the most common and effective method in order to reduce energy consumption is Dynamic Virtual Machines Consolidation (DVMC) enabled by the virtualization technology. DVMC dynamically consolidates Virtual Machines (VMs) into the minimum number of active servers and then switches the idle servers into a power-saving mode to save energy. Ho- wever, maintaining the desired level of Quality-of-Service (QoS) between data centers and their users is critical for satisfying users’ expectations con- cerning performance. Therefore, the main challenge is to minimize the data center energy consumption while maintaining the required QoS. This thesis address this challenge by presenting novel DVMC approaches to reduce the energy consumption of data centers and improve resource utili- zation under workload independent quality of service constraints. These ap- proaches can be divided into three main categories: heuristic, meta-heuristic and machine learning. Our first contribution is a heuristic algorithm for solving the DVMC problem. The algorithm uses a linear regression-based prediction model to detect over-loaded servers based on the historical utilization data. Then it migrates some VMs from the over-loaded servers to avoid further performan- ce degradations. Moreover, our algorithm consolidates VMs on fewer number of server for energy saving. The second and third contributions are two novel DVMC algorithms based on the Reinforcement Learning (RL) approach. RL is interesting for highly adaptive and autonomous management in dynamic environments. For this reason, we use RL to solve two main sub-problems in VM consolidation. The first sub-problem is the server power mode detection (sleep or active). The second sub-problem is to find an effective solution for server status detection (overloaded or non-overloaded). The fourth con- tribution of this thesis is an online optimization meta-heuristic algorithm called Ant Colony System-based Placement Optimization (ACS-PO). ACS is a suitable approach for VM consolidation due to the ease of parallelization, that it is close to the optimal solution, and its polynomial worst-case time complexity. The simulation results show that ACS-PO provides substantial improvement over other heuristic algorithms in reducing energy consump- tion, the number of VM migrations, and performance degradations. Our fifth contribution is a Hierarchical VM management (HiVM) archi- tecture based on a three-tier data center topology which is very common use in data centers. HiVM has the ability to scale across many thousands of ser- vers with energy efficiency. Our sixth contribution is a Utilization Prediction- aware Best Fit Decreasing (UP-BFD) algorithm. UP-BFD can avoid SLA violations and needless migrations by taking into consideration the current and predicted future resource requirements for allocation, consolidation, and placement of VMs. Finally, the seventh and the last contribution is a novel Self-Adaptive Resource Management System (SARMS) in data centers. To achieve scala- bility, SARMS uses a hierarchical architecture that is partially inspired from HiVM. Moreover, SARMS provides self-adaptive ability for resource mana- gement by dynamically adjusting the utilization thresholds for each server in data centers.  </div

Time dependent virtual machine consolidation with SLA(Service Level Agreement) consideration

Cloud data center is becoming the most essential infrastructure for computing services. In effect, the operational cost of a data center is also increasing drastically. To decrease this cost, consolidation of VMs with less degradation of performance is so important. To guarantee the expected Quality of Service (QOS) the important factors to be controlled are performance of the service including timely leverage and overall resource utilization of the data center. In this paper, we tried to investigate how to efficiently utilize resources with reduced SLA violation in a data center. In order to optimize efficiency, VMs ought to be consolidated as tight as possible. To achieve this, an algorithm based on first fit decreasing (FFD) bin packing is designed and implemented. Hence, the algorithm is implemented on the following three approaches to pursue the goal: a)Deterministic Approach, which is mainly based on mean of the individual VMs;b)Stochastic Approach I, which is basically done by treating individual VMs based on their mean and variances and ;c) Stochastic Approach II, which depends on mean and covariance of individual VMs. The results obtained show that consolidating VMs based on mean and variance(stochastic approach I) performed better than the other two approaches for minimizing total percentage of SLA violation and stochastic approach II performed better than the two approaches for minimizing the number of PMs in consolidation