Towards tenant demand-aware bandwidth allocation strategy in cloud datacenter

bwSlicer: A bandwidth slicing framework for cloud data centers

Various applications in a multi-tenant architecture based cloud computing comprises various requirements and priorities. Severe failure of a critical application may result if the network is congested in a data center. Hence, it is an important to use an efficient resource provisioning technique for ensuring transfer of high priority data to the other traffics still when the network is congested. Therefore, a new framework of Home Cloud architecture is proposed in this paper to provide an effective and flexible delivery of new applications in an edge-cloud architecture which integrates the two complementary technologies of Software-Defined Networking (SDN) and Network Function Virtualization (NFV). In SDN infrastructure, this presents a priority aware resource placement algorithms by taking into consideration of both host and network resources. The Virtual Machines (VMs) of high priority applications are placed at the strongly connected hosts by introducing the priority aware VM allocation (PAVA) algorithm for reducing the possibility of causing network congestion by other tenants. It also guarantees the critical applications for a required bandwidth by means of allocating bandwidth through the setting of priority queues for each network connection within the network of data center handled by SDN controller. The simulation result of the proposed system shows that the combination of proposed approaches will allocate sufficient resources for high-priority applications within a Home Cloud data centre to meet the QoS requirements.

Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in computation resources enables the appropriate utilization of resources in cloud computing. One of the most significant challenging issues in virtualization technology is the placement of optimal virtual machines on physical machines in cloud data centers. The placement of virtual machines comprises a process wherein virtual machines are mapped onto physical machines in cloud data centers. Optimal deployment leads to the reduction in power consumption, optimal use of resources, traffic reduction in data centers, costs reduction, and efficiency enhancement of the data center in the cloud. The present article proposed a new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement. The first goal of the proposed algorithm was to reduce the power consumption in cloud data centers by condensing the number of active physical machines. The second goal was to reduce the waste of resources and manage it by optimally virtual machine placement on physical machines in cloud data centers. The third objective was to minimize and reduce Service Level Agreement among the active physical machines in cloud data centers. The proposed method prevent the increase in the migration of virtual machines onto physical machines. Ultimately, the results obtained from the proposed algorithm were compared with those of previous akin algorithms in the literature, including First Fit, Virtual Machine Placement Ant Colony System, and Modified Best Fit Decreasing. The proposed scheme is tested using Amazon EC2 Instances and the result indicated that the proposed algorithm performs better than the existing algorithms for various performance metrics.

A balanced virtual machine scheduling method for energy-performance trade-offs in cyber-physical cloud systems

Virtual Machine (VM) allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant's VMs may considerably impact the tenant's Quality of Service (QoS). In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs' network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem (LP-MKP). The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the QoS among tenants, and improves tenants' overall QoS in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.

Software-Defined Networking (SDN) is an emerging paradigm to logically centralize the network control plane and automate the configuration of individual network elements. At the same time, in Cloud Data Centers (DCs), even though network and server resources converge over the same infrastructure and typically under a single administrative entity, disjoint control mechanisms are used for their respective management. In this paper, we propose a unified server-network control mechanism for converged ICT environments. We present a SDN-based orchestration framework for live Virtual Machine (VM) management where server hypervisors exploit temporal network information to migrate VMs and minimize the network-wide communication cost of the resulting traffic dynamics. A prototype implementation is presented and Mininet is used to evaluate the impact of diverse orchestration algorithms.

Bandwidth is extremely valuable resource in wireless networks, in which the occurrence of handoff increases. Therefore, an effective bandwidth allocation is urgent. Recently, the dynamic method using stochastic control is found to be preferred. But the dynamic control for multi-services is still a problem. The main challenges come from variety of requirements of bandwidth, traffic characteristics, QoS guarantee and handoff rate. Moreover, the computational complexity is another challenge. In this paper, we set up a novel bandwidth allocation model. We consider optimal revenue instead of maximal bandwidth utility, the boundary conditions and the variety of channel occupation. In addition, we use a fast numerical method to reduce complexity. As a result, we get a precise multi-services dynamic bandwidth allocation scheme called MDBA to adapt for multiple types of services in broadband wireless networks. Numerical results of simulation show that our scheme steadily satisfies the hard constraint on call dropping probability of multi-services while maintaining a high channel utility.

Energy efficiency is a major issue in Cloud computing infrastructure. The large power consumption is mainly attributed to the large number of modern data centers operating within. Developing these data centers includes dynamically expanding their infrastructures to meet the ever-increasing demand for huge computation, large storage, and massive communication. Energy conservation through optimization of resources and management policies in the Cloud are a viable solution. Using virtualization to save power and employing such practices as using Virtual Machines (VMs), Server Consolidation, and VM Live Migration. This paper investigates the opportunities for Green Cloud Computing (GCC) to obtain a more comprehensive prospect towards achieving energy efficient Cloud Computing, and presents an energy efficient network resources management approach in an Infrastructure as a Service (IaaS) Cloud model. We focus on developing an energy efficient algorithm by proposing a practical multi-level Cloud Resource-Network Management (CRNM) algorithm, which is implemented in a virtual Cloud environment using Snooze framework as the Cloud energy efficiency manager. The optimization focuses on the utilization of network bandwidth as main resource under test, while also taking into account the other resources, such as CPU and memory, to get the desired performance. We choose a fat tree topology as a common three tier architecture for Cloud data canters. We conclude that our proposed algorithm will save up to 75% of power consumption in Cloud data centers, with an observed increase in efficiency compared to Non-Power Aware (NPA), Power aware(PA), and Greedy algorithms, where network elements consume about 30% of the total power of Cloud data centers.

With the proliferation of wireless multimedia applications, multicast/broadcast has been recognized as an efficient technique to transmit a large volume of data to multiple mobile stations at the same time. In most multicast systems, the transmitter (e.g. base station) adapts its data rate to the furthest located users, so as to guarantee service quality to as many users as possible. Predictably, the more users in a multicast group, the lower data rate the base station can transmit. On the other hand, grouping more users together leads to a more efficient utilization of spectrum bandwidth, as these users are served simultaneously. This bring the interesting problem that presses for solution: how to group users in a cell into multicast groups and how to allocate a fixed amount of bandwidth resource to the groups, to achieve a good balance between throughput and fairness in multicast systems. In this paper, we formulate the united user grouping and bandwidth allocation strategy into a utility-based optimization problem. One method of signomial programming is used to solve the non-convex optimization problem. Numerical results will show that this suboptimal algorithm performs well even compared to the optimal one. Moreover, through theoretical analysis, we prove that the best user grouping and bandwidth allocation scheme of throughput maximization is to allocate the entire bandwidth to the unique group containing the users located within a ring-shaped region with an optimal outer radius r*.

[Purpose] Server virtualization technology is the basic form for the cloud data center to serve customers. While the cloud data center reduces the SLA violations in the early stage, the increase in the utilization of resources such as virtual machine servers and network equipment and decrease in energy consumption have become an important issue for reduction of operating costs. Therefore, on the background of the cloud data center network based on SDN technology, this paper studies a cloud data center resource utilization and energy consumption optimization strategy through virtual machine migration and integration. [Method] The technology based on SDN is firstly introduced, the approximate optimal solution FFD algorithm for the multidimensional vector bin packing problem is then presented, the Partheno-Genetic Algorithm (PGA) is then recommended, finally a Hybrid Partheno genetic Algorithm (HPGA) based on the both is established and a mathematic model is created. [Results] By combining the advantages of FFD and PGA, this paper improves the utilization of the resources of the entire cloud data center, reduces energy consumption, and prevents the occurrence of computing “hot spots”. [Conclusion] Experiments in CloudSim prove the effectiveness of the algorithm. Compared with FFD and PGA algorithms, it is able to enhance the efficiency of the Data Center effectively, and obtain the optimal solution more quickly, thereby balancing the physical resources of the server.

HostWatcher: Protecting hosts in cloud data centers through software-defined networking

To date, the mainstream ethernet passive optical network (EPON) bandwidth allocation schemes as well as the new IEEE 802.3ah Ethernet in the first mile Task Force specifications have been centralized, relying on a component in the central office [optical line termination (OLT)] to provision upstream traffic. Hence, the OLT is the only device that can arbitrate time-division access to the shared channel. Since the OLT has global knowledge of the state of the entire network, this is a centralized control plane in which the OLT has centralized intelligence. This paper proposes novel distributed EPON architectures, and in the process proves that these distributed networking architectures and the associated bandwidth allocation algorithms and protocols have characteristics that make them far better suited for provisioning quality of service (QoS) schemes necessary for properly handling data, voice, video, and other real-time streaming advanced multimedia services over a single line. Specifically, this paper proposes a novel ethernet over star coupler-based PON architecture that uses a fully distributed time-division multiple-access (TDMA) arbitration scheme. Supported by the decentralized scheme, this paper proposes several QoS-based dynamic bandwidth allocation (DBA) algorithms in which the OLT is excluded from the implementation of the time slot assignment. In contrast with the mainstream centralized EPON architectures that combine priority queuing [intra-optical network unit (ONU) scheduling] with DBA schemes (inter-ONU scheduling), the proposed distributed EPON architecture supports differentiated services through the integration of both scheduling mechanisms at the ONU (intra-ONU scheduling). The introduction of this integration feature that can only be supported by a decentralized architecture provides better QoS guarantees. Furthermore, in addition to the added flexibility and reliability, the overall performance of the proposed decentralized EPON architecture and the associated bandwidth allocation algorithms are shown to be at least as efficient as their centralized counterparts.

The rapid growth in cloud computing encourages the cloud provider to create more reliable, scalable and efficient cloud environment. For providing such a scalable environment, cloud provider develops large-scale Cloud Data Centers (CDC). Such CDCs consume a large amount of electrical energy for operational purpose and cooling purpose to keep the machines operating at a right temperature, but it also emits a large amount of carbon-dioxide. Thus to decrease the energy consumption, Virtual Machines (VMs) are consolidated to fewer numbers of active servers through VM migration techniques, this leads to Service Level Agreement (SLA) violation and performance degradation. Therefore to retain the energy-performance tradeoff, we should perform an optimum number of VM migrations. There are two cases when the VM migration performed: First when a hot spot is found and Second when a cold spot is found. But in both the cases we do not directly migrate the VM instead first we check whether it is required to migrate the VM. After that, the algorithm finds an appropriate destination host on which the VM will be migrated. In this paper, we implement three approaches to select destination host: First-fit, Best-fit, and Worst-fit. Finally compare the result of a number of VM migration performed, energy consumption and SLA violations with existing one.

Energy consumption is significant in cloud data centres even when some virtual machines (VMs) are idle. Taking into account both the energy efficiency and the response performance, we propose a VM allocation strategy with speed switch and VM consolidation in the cloud data centre. When the traffic is lighter, the processing speed of VMs in baseline module will be switched between a low speed and a high speed. When the traffic is much heavier, the VMs in reserved module will be consolidated with the VMs in baseline module, and operates at a high speed. We establish a two-dimensional Markov chain to mathematically estimate the energy-saving degree and the response performance. Numerical experiments with analysis and simulation show that our proposed VM allocation strategy can effectively reduce the energy consumption as well as guarantee the response performance.

Applications submitted to the cloud may have different priorities with specific requirements. These applications, when submitted to the overcrowded cloud data center network, data transfer on time could be delayed. The performance gets deteriorated for mission-critical applications. Hence, to obtain QoS, an effective resource provisioning technique is required to guarantee high-priority applications over the other applications in the cloud data center. By making use of software-interpreted networking, network flows can be reconfigured dynamically to adapt to the traffic of the data center network. In this work, a priority-aware service broker virtual machine allocation policy is proposed which takes into account host and network resources. Virtual machine with critical applications will be connected to closely connected hosts with the required amount of bandwidth. Censorious application is promised by configuring priority with TTL values on everyone networking instrument in the data center network. The investigation outcomes appear that the suggested service broker policy grants appropriate assets for critical applications to guarantee QoS in the cloud data center.