An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers

Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.Comment: Submitted for publication consideration for the Journal of Network and Computer Applications (JNCA). Total page: 28. Number of figures: 15 figure

Energy-aware virtual machine consolidation in IaaS cloud computing

With immense success and rapid growth within the past few years, cloud computing has been established as the dominant paradigm of IT industry. To meet the increasing demand of computing and storage resources, infrastructure cloud providers are deploying planet-scale data centers across the world, consisting of hundreds of thousands, even millions of servers. These data centers incur very high investment and operating costs for the compute and network devices as well as for the energy consumption. Moreover, because of the huge energy usage, such data centers leave large carbon footprints and thus have adverse effects on the environment. As a result, efficient computing resource utilization and energy consumption reduction are becoming crucial issues to make cloud computing successful. Intelligent workload placement and relocation is one of the primary means to address these issues. This chapter presents an overview of the infrastructure resource management systems and technologies and detailed description of the proposed solution approaches for efficient cloud resource utilization and minimization of power consumption and resource wastages. Different types of server consolidation mechanisms are presented along with the solution approaches proposed by the researchers of both academia and industry. Various aspects of workload reconfiguration mechanisms and existing works on workload relocation techniques are described

Virtual Machine Consolidation in Cloud Data Centers using ACO Metaheuristic

Abstract. In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource utilization of servers across different computing resources (CPU, memory, and network I/O) with the goal of minimizing power consumption and resource wastage. Since the VM consolidation problem is strictly NP-hard and computationally infeasible for large data centers, we propose adaptation and integration of the Ant Colony Optimization (ACO) metaheuristic with balanced usage of computing resources based on vector algebra. Our simulation results show that AVVMC outperforms existing methods and achieves improvement in both energy consumption and resource wastage reduction.

Network-aware virtual machine placement and migration in cloud data centres

With the pragmatic realization of computing as a utility, Cloud Computing has recently emerged as a highly successful alternative IT paradigm. Cloud providers are deploying large-scale data centers across the globe to meet the Cloud customers’ compute, storage, and network resource demands. Efficiency and scalability of these data centers, as well as the performance of the hosted applications’ highly depend on the allocations of the data center resources. Very recently, network-aware Virtual Machine (VM) placement and migration is developing as a very promising technique for the optimization of compute-network resource utilization, energy consumption, and network traffic minimization. This chapter presents the relevant background information and a detailed taxonomy that characterizes and classifies the various components of VM placement and migration techniques, as well as an elaborate survey and comparative analysis of the state of the art techniques. Besides highlighting the various aspects and insights of the network-aware VM placement and migration strategies and algorithms proposed by the research community, the survey further identifies the benefits and limitations of the existing techniques and discusses on the future research directions

Profile-based ant colony optimization for energy-efficient virtual machine placement for energy-efficient data centers

Cloud computing data centers contain a large number of physical machines (PMs) and virtual machine (VMs). This number can increase the energy consumption of the data centers especially when the VMs placed inappropriately on the PMs. This paper presents a new VM placement approach with the objective of minimizing the total energy consumption of a data center. VM placement problem is formulated as a combinatorial optimization problem. Since this problem has been proven to be an NP hard problem, Ant Colony Optimization (ACO) algorithm is adopted to solve the formulated problem. Information heuristic of ACO is used differently based on PM energy efficiency. Experimental results show that the proposed approach scales well on large data centers and significantly outperforms selected benchmark (ACOVMP) in terms of energy consumption

Profile-based ant colony optimization for energy-efficient virtual machine placement

Cloud computing data centers contain a large number of physical machines (PMs) and virtual machine (VMs). This number can increase the energy consumption of the data centers especially when the VMs placed inappropriately on the PMs. This paper presents a new VM placement approach with the objective of minimizing the total energy consumption of a data center. VM placement problem is formulated as a combinatorial optimization problem. Since this problem has been proven to be an NP hard problem, Ant Colony Optimization (ACO) algorithm is adopted to solve the formulated problem. Information heuristic of ACO is used differently based on PM energy efficiency. Experimental results show that the proposed approach scales well on large data centers and significantly outperforms selected benchmark (ACOVMP) in terms of energy consumption

Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enterprise data centers

Enterprise cloud data centers consume a tremendous amount of energy due to the large number of physical machines (PMs). These PMs host a huge number of virtual machines (VMs), on which a vast number of applications are deployed. Existing research uses two separate layers to manage data center resources: application assignment to VMs, and VM placement to PMs, each of which is a bin packing problem. While this consecutive two-layer bin packing (Consec2LBP) makes the problems easier to solve, it also limits further improvement in the quality of solution. To address this issue, an integrated any colony optimization approach is proposed in this paper to deal with both layers simultaneously. It formulates the two-layer resource management into an integrated two-layer bin packing (Int2LBP) optimization problem. Then, an integrated first fit-decreasing (FFD) algorithm Int2LBP_FFD is proposed to solve this optimization problem. Using the result of Int2LBP_FFD as an initial solution, an integrated ant colony system (ACS) algorithm Int2LBP_ACS is further developed to improve the quality of solution. Simulation experiments are conducted to demonstrate the effectiveness of our integrated approach.</p