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

The “Hypertension Approaches in the Elderly: a Lifestyle study” multicenter, randomized trial (HAEL Study): rationale and methodological protocol

Background: Hypertension is a clinical condition highly prevalent in the elderly, imposing great risks to cardiovascular diseases and loss of quality of life. Current guidelines emphasize the importance of nonpharmacological strategies as a first-line approach to lower blood pressure. Exercise is an efficient lifestyle tool that can benefit a myriad of health-related outcomes, including blood pressure control, in older adults. We herein report the protocol of the HAEL Study, which aims to evaluate the efficacy of a pragmatic combined exercise training compared with a health education program on ambulatory blood pressure and other health-related outcomes in older individuals. Methods: Randomized, single-blinded, multicenter, two-arm, parallel, superiority trial. A total of 184 subjects (92/center), ≥60 years of age, with no recent history of cardiovascular events, will be randomized on a 1:1 ratio to 12-week interventions consisting either of a combined exercise (aerobic and strength) training, three times per week, or an active-control group receiving health education intervention, once a week. Ambulatory (primary outcome) and office blood pressures, cardiorespiratory fitness and endothelial function, together with quality of life, functional fitness and autonomic control will be measured in before and after intervention. Discussion: Our conceptual hypothesis is that combined training intervention will reduce ambulatory blood pressure in comparison with health education group. Using a superiority framework, analysis plan prespecifies an intention-to-treat approach, per protocol criteria, subgroups analysis, and handling of missing data. The trial is recruiting since September 2017. Finally, this study was designed to adhere to data sharing practices. Trial registration: NCT03264443. Registered on 29 August, 2017

Energy-Efficient Virtual Machine Placement using Enhanced Firefly Algorithm

The consolidation of the virtual machines (VMs) helps to optimise the usage of resources and hence reduces the energy consumption in a cloud data centre. VM placement plays an important part in the consolidation of the VMs. The researchers have developed various algorithms for VM placement considering the optimised energy consumption. However, these algorithms lack the use of exploitation mechanism efficiently. This paper addresses VM placement issues by proposing two meta-heuristic algorithms namely, the enhanced modified firefly algorithm (MFF) and the hierarchical cluster based modified firefly algorithm (HCMFF), presenting the comparative analysis relating to energy optimisation. The comparisons are made against the existing honeybee (HB) algorithm, honeybee cluster based technique (HCT) and the energy consumption results of all the participating algorithms confirm that the proposed HCMFF is more efficient than the other algorithms. The simulation study shows that HCMFF consumes 12% less energy than honeybee algorithm, 6% less than HCT algorithm and 2% less than original firefly. The usage of the appropriate algorithm can help in efficient usage of energy in cloud computing

A Firefly Colony and Its Fuzzy Approach for Server Consolidation and Virtual Machine Placement in Cloud Datacenters

Managing cloud datacenters is the most prevailing challenging task ahead for the IT industries. The data centers are considered to be the main source for resource provisioning to the cloud users. Managing these resources to handle large number of virtual machine requests has created the need for heuristic optimization algorithms to provide the optimal placement strategies satisfying the objectives and constraints formulated. In this paper, we propose to apply firefly colony and fuzzy firefly colony optimization algorithms to solve two key issues of datacenters, namely, server consolidation and multiobjective virtual machine placement problem. The server consolidation aims to minimize the count of physical machines used and the virtual machine placement problem is to obtain optimal placement strategy with both minimum power consumption and resource wastage. The proposed techniques exhibit better performance than the heuristics and metaheuristic approaches considered in terms of server consolidation and finding optimal placement strategy

Ant colony optimization algorithm for dynamic scheduling of jobs in computational grid

Computational grid is gaining more importance due to the needs for large-scale computing capacity. In computational grid, job scheduling is one of the main factors affecting grid computing performance. Job scheduling problem is classified as an NP-hard problem.Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms.Among different optimization algorithms for job scheduling, ant colony system algorithm is a popular meta-heuristic algorithm which has the ability to solve different types of NP-hard problems.However, ant colony system algorithm has a deficiency in its heuristic function which affects the algorithm behavior in terms of finding the shortest connection between edges.This research focuses on a new heuristic function where information about recent ants’ discoveries has been considered.The new heuristic function has been integrated into the classical ant colony system algorithm.Furthermore, the enhanced algorithm has been implemented to solve the travelling salesman problem as well as in scheduling of jobs in computational grid.A simulator with dynamic environment feature to mimic real life application has been development to validate the proposed enhanced ant colony system algorithm. Experimental results show that the proposed enhanced algorithm produced better output in term of utilization and makespan in both domains

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. However, maintaining the desired level of Quality-of-Service (QoS) between data centers and their users is critical for satisfying users’ expectations concerning 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 utilization under workload independent quality of service constraints. These approaches 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 performance 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 contribution 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 consumption, the number of VM migrations, and performance degradations. Our fifth contribution is a Hierarchical VM management (HiVM) architecture 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 servers 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 scalability, SARMS uses a hierarchical architecture that is partially inspired from HiVM. Moreover, SARMS provides self-adaptive ability for resource management by dynamically adjusting the utilization thresholds for each server in data centers.Siirretty Doriast

Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

Grid computing is a distributed system with heterogeneous infrastructures. Resource management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms will enhance ACS in terms of exploration mechanism and solution refinement by implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment, performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan

The "Hypertension approaches in the elderly: a lifestyle study" multicenter, randomized trial (HAEL Study) : rationale and methodological protocol

Background: Hypertension is a clinical condition highly prevalent in the elderly, imposing great risks to cardiovascular diseases and loss of quality of life. Current guidelines emphasize the importance of nonpharmacological strategies as a first-line approach to lower blood pressure. Exercise is an efficient lifestyle tool that can benefit a myriad of health-related outcomes, including blood pressure control, in older adults. We herein report the protocol of the HAEL Study, which aims to evaluate the efficacy of a pragmatic combined exercise training compared with a health education program on ambulatory blood pressure and other health-related outcomes in older individuals. Methods: Randomized, single-blinded, multicenter, two-arm, parallel, superiority trial. A total of 184 subjects (92/center), ≥60 years of age, with no recent history of cardiovascular events, will be randomized on a 1:1 ratio to 12-week interventions consisting either of a combined exercise (aerobic and strength) training, three times per week, or an active-control group receiving health education intervention, once a week. Ambulatory (primary outcome) and office blood pressures, cardiorespiratory fitness and endothelial function, together with quality of life, functional fitness and autonomic control will be measured in before and after intervention. Discussion: Our conceptual hypothesis is that combined training intervention will reduce ambulatory blood pressure in comparison with health education group. Using a superiority framework, analysis plan prespecifies an intention-to-treat approach, per protocol criteria, subgroups analysis, and handling of missing data. The trial is recruiting since September 2017. Finally, this study was designed to adhere to data sharing practices