Enhanced Cuckoo Search Algorithm for Virtual Machine Placement in Cloud Data Centers

In order to enhance resource utilisation and power efficiency in cloud data centres it is important to perform Virtual Machine (VM) placement in an optimal manner. VM placement uses the method of mapping virtual machines to physical machines (PM). Cloud computing researchers have recently introduced various meta-heuristic algorithms for VM placement considering the optimised energy consumption. However, these algorithms do not meet the optimal energy consumption requirements. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to address the issues with VM placement focusing on the energy consumption. The performance of the proposed algorithm is evaluated using three different workloads in CloudSim tool. The evaluation process includes comparison of the proposed algorithm against the existing Genetic Algorithm (GA), Optimised Firefly Search (OFS) algorithm, and Ant Colony (AC) algorithm. The comparision results illustrate that the proposed ECS algorithm consumes less energy than the participant algorithms while maintaining a steady performance for SLA and VM migration. The ECS algorithm consumes around 25% less energy than GA, 27% less than OFS, and 26% less than AC

Investigation of Cloud Scheduling Algorithms for Resource Utilization Using CloudSim

Compute Cloud comprises a distributed set of High-Performance Computing (HPC) machines to stipulate on-demand computing services to remote users over the internet. Clouds are capable enough to provide an optimal solution to address the ever-increasing computation and storage demands of large scientific HPC applications. To attain good computing performances, mapping of Cloud jobs to the compute resources is a very crucial process. Currently we can say that several efficient Cloud scheduling heuristics are available, however, selecting an appropriate scheduler for the given environment (i.e., jobs and machines heterogeneity) and scheduling objectives (such as minimized makespan, higher throughput, increased resource utilization, load balanced mapping, etc.) is still a difficult task. In this paper, we consider ten important scheduling heuristics (i.e., opportunistic load balancing algorithm, proactive simulation-based scheduling and load balancing, proactive simulation-based scheduling and enhanced load balancing, minimum completion time, Min-Min, load balance improved Min-Min, Max-Min, resource-aware scheduling algorithm, task-aware scheduling algorithm, and Sufferage) to perform an extensive empirical study to insight the scheduling mechanisms and the attainment of the major scheduling objectives. This study assumes that the Cloud job pool consists of a collection of independent and compute-intensive tasks that are statically scheduled to minimize the total execution time of a workload. The experiments are performed using two synthetic and one benchmark GoCJ workloads on a renowned Cloud simulator CloudSim. This empirical study presents a detailed analysis and insights into the circumstances requiring a load balanced scheduling mechanism to improve overall execution performance in terms of makespan, throughput, and resource utilization. The outcomes have revealed that the Sufferage and task-aware scheduling algorithm produce minimum makespan for the Cloud jobs. However, these two scheduling heuristics are not efficient enough to exploit the full computing capabilities of Cloud virtual machines

Journal of Telecommunications and Information Technology, 2017, nr 1

In this paper, the Stackelberg game models are used for supporting the decisions on task scheduling and resource utilization in computational clouds. Stackelberg games are asymmetric games, where a specific group of players’ acts first as leaders, and the rest of the players follow the leaders’ decisions and make their decisions based on the leader’s actions. In the proposed model, the optimal schedules are generated under the security criteria along with the generation of the optimal virtual machines set for the scheduled batch of tasks. The security criteria are defined as security requirements for mapping tasks onto virtual machines with specified trust level. The effectiveness of the proposed method has been verified in the realistic use cases with in the cloud environment with OpenStack and Amazon Cloud standards

Scalable algorithms for QoS-aware virtual network mapping for cloud services

Both business and consumer applications increasingly depend on cloud solutions. Yet, many are still reluctant to move to cloud-based solutions, mainly due to concerns of service quality and reliability. Since cloud platforms depend both on IT resources (located in data centers, DCs) and network infrastructure connecting to it, both QoS and resilience should be offered with end-to-end guarantees up to and including the server resources. The latter currently is largely impeded by the fact that the network and cloud DC domains are typically operated by disjoint entities. Network virtualization, together with combined control of network and IT resources can solve that problem. Here, we formally state the combined network and IT provisioning problem for a set of virtual networks, incorporating resilience as well as QoS in physical and virtual layers. We provide a scalable column generation model, to address real world network sizes. We analyze the latter in extensive case studies, to answer the question at which layer to provision QoS and resilience in virtual networks for cloud services

EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud

Cloud computing has become more popular in provision of computing resources under virtual machine (VM) abstraction for high performance computing (HPC) users to run their applications. A HPC cloud is such cloud computing environment. One of challenges of energy efficient resource allocation for VMs in HPC cloud is tradeoff between minimizing total energy consumption of physical machines (PMs) and satisfying Quality of Service (e.g. performance). On one hand, cloud providers want to maximize their profit by reducing the power cost (e.g. using the smallest number of running PMs). On the other hand, cloud customers (users) want highest performance for their applications. In this paper, we focus on the scenario that scheduler does not know global information about user jobs and user applications in the future. Users will request shortterm resources at fixed start times and non interrupted durations. We then propose a new allocation heuristic (named Energy-aware and Performance per watt oriented Bestfit (EPOBF)) that uses metric of performance per watt to choose which most energy-efficient PM for mapping each VM (e.g. maximum of MIPS per Watt). Using information from Feitelson's Parallel Workload Archive to model HPC jobs, we compare the proposed EPOBF to state of the art heuristics on heterogeneous PMs (each PM has multicore CPU). Simulations show that the EPOBF can reduce significant total energy consumption in comparison with state of the art allocation heuristics.Comment: 10 pages, in Procedings of International Conference on Advanced Computing and Applications, Journal of Science and Technology, Vietnamese Academy of Science and Technology, ISSN 0866-708X, Vol. 51, No. 4B, 201