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Energy is a considerable portion of the cost of a cloud data center. In addition to energy, carbon emission tax imposes another cost on data centers, which have different policies in different cities around the world. Most of the cloud data centers are geographically distributed, which can have a huge advantage in reducing such costs. In addition, in recent years, cloud federation, in which multiple cloud providers share their IT infrastructures voluntarily, could play a crucial role in minimizing the energy consumption of cloud data centers. On the other hand, improper VM placement results frequent VM migration, constant turning off/on physical machines, service quality degradation and energy consumption increase. To tackle these weaknesses, we propose a multi-objective algorithm, Called OUR-ACS, to minimize data centers' energy consumption, carbon emission, and the total expenses (energy cost + carbon tax) in a federated environment considering both initial VM placement and VM consolidation approaches. We evaluate the efficiency of our proposed algorithm by using the ClousSim Plus simulator toolkit using real-world datasets. Compared to the competing algorithms, our simulation results indicate that our proposed algorithm could reduce the total energy consumption, carbon emission, and the total costs of multiple cloud data centers by an average of 37.6%, 41%, and 25%, respectively, in a federated environment. • We propose a novel multi-objective cloud federation algorithm considering to minimize three objectives. • We have identified a new problem in VM migration called Migration lock-in status. • We employ new heuristics to make a tradeoff between servers' power consumption and their resource utilization simultaneously. • We conduct a comprehensive simulation under an IaaS cloud infrastructure using a real-world datasets. [ABSTRACT FROM AUTHOR]

The diverse needs of customers drive cloud providers to incorporate more GPU-enabled services. It is known that users barely utilize GPUs. Hence, GPU virtualization techniques are employed to enable GPU sharing and increase resource utilization. Among GPU virtualization techniques, API remoting leverages disaggregation for GPU provisioning, allowing GPUs to be accessed remotely by non-resident VMs. It enables a VM to access any available GPU in the data center which, in turn, provides more flexibility to reach a better placement. It also enables users to choose VM and vGPU instances separately based on computational needs as GPU-enabled VM instances with fixed configurations may not proportionally utilize allocated resources. However, an inefficient VM or vGPU placement may result in poor performance and increase in energy consumption and rejection ratio. There is also a network communication overhead for remotely allocated vGPUs. The main challenge is how to place VMs and vGPUs such that rejection ratio is decreased and the negative performance impacts caused by GPU sharing and remote access are kept at a minimum. In this work, we formally define the joint problem of VM and vGPU placement based on API remoting as a multi-objective ILP model and introduce gVMP as a heuristic to solve it. The proposed method is compared against state-of-the-art heuristics and commercial solutions that are based on API remoting and GRID technologies for the placement of GPU-enabled VMs. We study the effectiveness of the proposed gVMP algorithm for low and high arrival rates in slow and fast networks. Our results show that under different scenarios and in comparison to state-of-the-art policies, our heuristic improves average request duration, efficiency, and SLA by up to 40%, 25% and 29%, respectively. • The joint problem of VM and vGPU placement based on API remoting is formulated as a multi-objective ILP model. • The proposed heuristic reduces rejection ratio and negative performance impacts of GPU sharing and remote vGPU accesses. • The results are compared against state-of-the-art and commercial solutions for API remoting and GRID technologies. • The effectiveness is studied for low and high arrival rates in slow and fast networks under different scenarios. • The heuristic improves average request duration, efficiency, and SLA by up to 40%, 25% and 29%, respectively. [ABSTRACT FROM AUTHOR]

The workload of Cloud data centers is constantly fluctuating causing imbalances across physical hosts that may lead to violations of service-level agreements. To mitigate workload imbalances, this work proposes a concurrent agent-based problem-solving technique supported by cooperative game theory capable of balancing workloads by means of live migration of virtual machines (VMs). Nearby agents managing physical hosts are partitioned into coalitions in which agents play coalitional games to progressively balance separate sections of a data center while considering the coalition's benefit of migrating a VM as well as its associated network overhead. Simulation results show that, in general, the proposed coalition-based load balancing mechanism outperformed a load balancing mechanism based on a hill-climbing algorithm used by a top data center vendor when considering altogether (i) the standard deviation of resource usage, (ii) the number of migrations, and (iii) the number of switch hops per migration. • Agents in coalitions progressively balance data center sections. • Supported by a game-theoretical framework, agents cooperate to balance workloads. • Coalitions composed of nearby hosts are used as a basis to balance workloads. • Coalition-aware heuristics consider both the benefit of migrating a VM and its cost. [ABSTRACT FROM AUTHOR]

Reducing energy consumption and optimizing resource usage in large cloud data centers is still an essential target for the current researchers and cloud providers. The state-of-the-art highlights the effectiveness of VM consolidation and live migrations in achieving reasonable solutions. However, most proposals consider only the real-time workload variations to decide whether a host is overloaded or underloaded, or to trigger migration actions. Such approaches may apply frequent and needless VM migrations leading to energy waste, performance degradation, and service-level agreement (SLA) violations. In this paper, we propose a consolidation approach based on the resource utilization prediction to determine the overloaded and underloaded hosts. The prediction method combines a Kalman filter and support vector regression (SVR) to forecast the host's future CPU utilization. Simulations are conducted on Cloudsim using real PlanetLab workloads to verify the performance of our proposal against existing benchmark algorithms. Experimental results demonstrate that our consolidation technique significantly reduces the SLA violation rate, number of VM migrations, and energy consumed in the datacenter. • A multi-step workload prediction model based on Kalman Filter and SVR is proposed. • A dynamic VM consolidation approach based on resource prediction is presented. • A trade-off between SLA violation and energy consumption is pursued. • Analysis study is carried out on the energy consumed by executing our simulation. • Several experiments are conducted on Cloudsim to evaluate our approach. [ABSTRACT FROM AUTHOR]

• An GAN-based method for time-dependent cloud workload generation is proposed. • The method does not rely on any prior knowledge to capture the data distribution. • Spectral normalization is used to stabilize the adversarial training. • Achieving better results than state-of-art models. • Enabled conditional workload generation with simple changes on models. To design repeatable and comparable resource management policies for data centers, researchers mainly conduct experiments in the simulation environment, which requires large-scale workload traces to simulate real scenes. However, issues related to data collection, security and privacy hinder the public availability of cloud workload datasets. Though workload generation is a promising solution, due to the unpredictable time dependency, cloud workloads are difficult to model. In light of this, we propose a novel end-to-end model for time-dependent cloud workload generation using Generative Adversarial Networks, which adopts improved Temporal Convolution Networks and Spectral Normalization to capture the time dependency and stabilize the adversarial training. Experimental results on real cloud datasets demonstrate that our model can efficiently generate realistic workloads that fulfill the diversity, fidelity and usefulness. Further, we also propose a conditional GAN which is trained with labeled data and can generate specific kind of workloads according to the input. [ABSTRACT FROM AUTHOR]

In the age of the information explosion, the energy demand for cloud data centers has increased markedly; hence, reducing the energy consumption of cloud data centers is essential. Dynamic virtual machine VM consolidation, as one of the effective methods for reducing energy energy consumption is extensively employed in large cloud data centers. It achieves the energy reductions by concentrating the workload of active hosts and switching idle hosts into low-power state; moreover, it improves the resource utilization of cloud data centers. However, the quality of service (QoS) guarantee is fundamental for maintaining dependable services between cloud providers and their customers in the cloud environment. Therefore, reducing the power costs while preserving the QoS guarantee are considered as the two main goals of this study. To efficiently address this problem, the proposed VM consolidation approach considers the current and future utilization of resources through the host overload detection (UP-POD) and host underload detection (UP-PUD). The future utilization of resources is accurately predicted using a Gray-Markov-based model. In the experiment, the proposed approach is applied for real-world workload traces in CloudSim and were compared with the existing benchmark algorithms. Simulation results show that the proposed approaches significantly reduce the number of VM migrations and energy consumption while maintaining the QoS guarantee. • The proposed Virtual Machine consolidation approach reduces the power costs while preserving the quality of service (QoS) guarantee. • Utilization of resources is accurately predicted using a new model. • In the experiment, the proposed approach is applied for real-world workload traces in CloudSim and were compared with the existing benchmark algorithms. • Simulation results show that the proposed approaches significantly reduce the number of VM migrations and energy consumption while maintaining the QoS guarantee. [ABSTRACT FROM AUTHOR]

As an essential infrastructure to support cloud computing services, data center networks (DCNs) are primarily used for transmitting and storing data. BCube is a large-scale DCN architecture that can effectively handle the massive data generated by network end devices due to the explosive growth of the Internet. However, as server failures in DCNs become more frequent, ensuring reliable data communication in BCube is critical. In this paper, we first establish a conditional local safe model in BCube, which divides the fault-free nodes in sub-BCube by adding some restrictions, and effectively avoids communication obstacles that may arise from faulty nodes. Then, based on the proposed model, a fault-tolerant unicast path algorithm is designed, which can construct a reliable data transmission path in the local safe sub-BCube of BCube and can tolerate more faulty nodes than existing algorithms. Finally, we conduct simulation experiments to verify that our algorithm can construct the shortest path with a high probability and achieve almost complete success in data transmission when the number of faulty nodes does not exceed half of the total nodes in BCube. • We propose a conditional local safe model in the data center network BCube. • We prove the existence of fault-free paths with local safe information in BCube. • We construct a fault-tolerant unicast algorithm in BCube based on the above model. [ABSTRACT FROM AUTHOR]

Abstract Live Virtual Machine (VM) migration within a data center is an important technology for cloud management, and has brought many benefits to both cloud providers and users. With the development of cloud computing, across-data-center VM migration is also desired. Normally, there is no shared storage system between data centers, hence the storage data (disk image) of a VM will be migrated to the destination data center as well. However, the slow network speed of the Internet and the comparatively large size of VM disk image make VM storage data migration become a bottleneck for live VM migration across data centers. In this paper, based on a detailed analysis of VM deployment models and the nature of VM image data, we design and implement a new migration system, called CBase. The key concept of CBase is a newly introduced central base image repository for reliable and efficient data sharing between VMs and data centers. With this central repository, further performance optimizations to VM storage data migration are made possible. Two migration mechanisms (data deduplication and Peer-to-Peer (P2P) file sharing) are utilized to accelerate base image migration, and a strategy is designed to elevate the synchronization of newly-written disk blocks. The results from an extensive experiment show that CBase significantly outperforms existing migration mechanisms under different conditions regarding total migration time and total network traffic. In particular, CBase with data deduplication is better than P2P file sharing for base image migration in our experimental environment. Highlights • A central repository is introduced to deploy and store base images for data centers. • Data deduplication and Peer-to-Peer (P2P) file sharing are used to accelerate the migration of a base image. • A mechanism is proposed to lower the overhead of migrating the writable layer of VM storage data. • An orchestration strategy is designed to improve the robustness of the migration system. • Experimental results validate the efficiency of our new approaches. [ABSTRACT FROM AUTHOR]

Abstract Edge computing is an emerging research area to incorporate cloud computing into edge network devices. An Edge datacenter, also referred to as EDC, processes data streams and user requests in real-time and is therefore used to decrease the latency and congestion in the network. EDC is usually setup as a distributed system and is accordingly placed between the cloud datacenter and the data source. These EDCs work as an intermediate layer in the fog hierarchy between IoT and Cloud datacenter. EDC's are aided by load balancers, responsible for distributing the workload amongst multiple EDC, in order to optimize resource utilization and response time. The load balancers make sure that the workload is equally divided amongst the available EDCs to avoid over loading of some EDCs while other remain idle as this directly impacts the user response and real-time event detection. Given the fact that EDCs are deployed in remote environments, the need for secure authentication is of major importance. In this paper we propose a novel load balancing technique that enables EDC authentication as well as identification of idle EDCs for better load balancing. The proposed load balancing technique is also compared with existing approaches and proves to be more efficient in locating EDC's with less workload. In addition to the improved efficiency, the proposed scheme also strengthens the security of the network by incorporating destination EDC authentication. Highlights • An adaptive EDC authentication technique with the help of a centralized cloud datacenter. • Authentication is initiated by the cloud and then all EDCs authenticate with each other. • Sustainable load balancing technique by considering the load of the destination EDCs. • The proposed approach combines both the authentication and load balancing technique. [ABSTRACT FROM AUTHOR]

This paper presents a novel VM allocation policy using the Eagle Strategy of Hybrid Krill Herd (KH) Optimization technique to enhance the cloud service experience to internet users. The Cloud Service provides data centre connectivity to share the resources through Virtual Machines (VM). The performance issue in Virtual Machine (VM) allocation during eccentrics degrades data centres load and clogging degrades the Quality of Experience (QoE) with Cloud Data Centres. Proposed optimized virtual machine allocation policy eradicates eccentrics effect and clogging effect, as it provides uninterrupted access throughput for better QoE. An optimization flag is additionally added in the Service Level Agreement (SLA). During burst load time, VM expansion is done automatically using Hybrid Krill Herd (KH) re-optimization if the optimization flag is set as true. Change and Response Protocol and Agreement Protocol have been proposed to predict the optimal resources for the Virtual Machine to avoid eccentrics and clogging. The prime parameters like delay, latency, packet rate and throughput are monitored continuously to activate the re-optimization and for achieving QoE with minimized load. Experimental analysis proves the capability of Hybrid KH algorithm over Particle Swarm Optimization, Ant Colony Optimization, Genetic Algorithm and Simulated Annealing algorithms. [ABSTRACT FROM AUTHOR]

Energy management is becoming one of the major issues from last many years due to an exponential increase in the smart device users for accessing various services from the geo-distributed cloud data centers (DCs) using Internet. During this time, there is an emergence of new technology called as cloud computing which provides on-demand pay-per-use services such as storage, computation, and network to the end users. These services are provided to the end users by various geographically located DCs which are hosted by different service providers such as—Microsoft, Amazon, IBM etc. With an increase in dependence of end users, DCs have expanded both in size and number. With such an expansion, these DCs consume huge amount of energy to accomplish their routine tasks. Such an increase in energy consumption generates a lot of load on the power grid. Moreover, the carbon emissions from these DCs has a global impact on the environment. To mitigate these issues, the integration of DCs with renewable energy sources (RES) can be helpful to reduce the carbon emissions and to ease the load on the power grid. For this purpose, a SDN-based energy management scheme for sustainability of DCs using RES is proposed in this paper. 1 To achieve the aforementioned objectives, an energy-efficient flow scheduling algorithm is proposed using SDN. Moreover, a charging-discharging scheme for penetration of electric vehicles (EVs) is also presented to manage the intermittency of renewable energy. An energy trading and reward point scheme is designed to attract the EVs to participate in the proposed energy management scheme. The efficacy of the proposed scheme is proved using realistic weather traces. The results obtained clearly show the effectiveness of the proposed scheme for sustainability of DCs. [ABSTRACT FROM AUTHOR]

How to manage the applications under computing systems such as a cloud computing system in a more efficient way is a focus problem. The primary performance goal is to reduce the execution time (makespan) of the application. As the need to cloud computing grows, the environmental influence of data centers attracts much attention. This paper aims at the scheduling of the precedence-constrained parallel application to minimize time and energy consumption efficiently. A multi-model estimation of distribution (mEDA) algorithm is adopted to determine both task processing permutation and voltage supply levels (VSLs). Specific operators to decrease execution time and energy consumption are designed. An improvement operator is also designed to enhance the diversity of the non-dominated solutions. The proposed algorithm is compared with the standard heuristic methods and a parallel bi-objective genetic algorithm (bGA). The comparative results show the Pareto solution set by the proposed algorithm is able to dominate a large proportion of those solutions by both the heuristic methods and the bGA. [ABSTRACT FROM AUTHOR]

Resource management in cloud computing consists of allocating processing resources, storage, and network to a set of software applications. Resource providers focus on performance and utilization of resources considering the constraints of service level agreement. Resource performance is achieved by virtualization techniques, which share infrastructure of the resource provider between different virtual machines. This study proposes a novel algorithm based on learning automata, which improves resource utilization and reduces energy consumption. The proposed algorithm considers changes in the user demanded resources to predict the PM, which may suffer from overload. Due to preventing server overload, the proposed algorithm improves PMs’ utilization, reduces the number of migrations, and shuts down idle servers to reduce the energy consumption of the data center. The proposed algorithm is simulated in CloudSim simulator; the 10-day processor information of a real PlanetLab cloud infrastructure system are used for workload data. Performance of the proposed algorithm is compared with existing algorithms such as DVFS, NPA, and the threshold algorithm in terms of energy consumption and the number of shut down PMs. Simulation results indicate that the proposed algorithm outperforms other algorithms with 175.48 Kwh, 0.00326 in energy consumption, SLA violation respectively. [ABSTRACT FROM AUTHOR]