25 research outputs found
A Survey of Virtual Machine Placement Techniques and VM Selection Policies in Cloud Datacenter
The large scale virtualized data centers have been established due to the requirement of rapid growth in computational power driven by cloud computing model . The high energy consumption of such data centers is becoming more and more serious problem .In order to reduce the energy consumption, server consolidation techniques are used .But aggressive consolidation of VMs can lead to performance degradation. Hence another problem arise that is, the Service Level Agreement(SLA) violation. The optimized consolidation is achieved through optimized VM placement and VM selection policies . A comparative study of virtual machine placement and VM selection policies are presented in this paper for improving the energy efficiency
Multi-Criteria Decision-Making Approach for Container-based Cloud Applications: The SWITCH and ENTICE Workbenches
Many emerging smart applications rely on the Internet of Things (IoT) to provide solutions to time-critical problems. When building such applications, a software engineer must address multiple Non-Functional Requirements (NFRs), including requirements for fast response time, low communication latency, high throughput, high energy efficiency, low operational cost and similar. Existing modern container-based software engineering approaches promise to improve the software lifecycle; however, they fail short of tools and mechanisms for NFRs management and optimisation. Our work addresses this problem with a new decision-making approach based on a Pareto Multi-Criteria optimisation. By using different instance configurations on various geo-locations, we demonstrate the suitability of our method, which narrows the search space to only optimal instances for the deployment of the containerised microservice.This solution is included in two advanced software engineering environments, the SWITCH workbench, which includes an Interactive Development Environment (IDE) and the ENTICE Virtual Machine and container images portal. The developed approach is particularly useful when building, deploying and orchestrating IoT applications across multiple computing tiers, from Edge-Cloudlet to Fog-Cloud data centres
SLO-aware Colocation of Data Center Tasks Based on Instantaneous Processor Requirements
In a cloud data center, a single physical machine simultaneously executes
dozens of highly heterogeneous tasks. Such colocation results in more efficient
utilization of machines, but, when tasks' requirements exceed available
resources, some of the tasks might be throttled down or preempted. We analyze
version 2.1 of the Google cluster trace that shows short-term (1 second) task
CPU usage. Contrary to the assumptions taken by many theoretical studies, we
demonstrate that the empirical distributions do not follow any single
distribution. However, high percentiles of the total processor usage (summed
over at least 10 tasks) can be reasonably estimated by the Gaussian
distribution. We use this result for a probabilistic fit test, called the
Gaussian Percentile Approximation (GPA), for standard bin-packing algorithms.
To check whether a new task will fit into a machine, GPA checks whether the
resulting distribution's percentile corresponding to the requested service
level objective, SLO is still below the machine's capacity. In our simulation
experiments, GPA resulted in colocations exceeding the machines' capacity with
a frequency similar to the requested SLO.Comment: Author's version of a paper published in ACM SoCC'1
A Measurement-based Analysis of the Energy Consumption of Data Center Servers
Energy consumption is a growing issue in data centers, impacting their
economic viability and their public image. In this work we empirically
characterize the power and energy consumed by different types of servers. In
particular, in order to understand the behavior of their energy and power
consumption, we perform measurements in different servers. In each of them, we
exhaustively measure the power consumed by the CPU, the disk, and the network
interface under different configurations, identifying the optimal operational
levels. One interesting conclusion of our study is that the curve that defines
the minimal CPU power as a function of the load is neither linear nor purely
convex as has been previously assumed. Moreover, we find that the efficiency of
the various server components can be maximized by tuning the CPU frequency and
the number of active cores as a function of the system and network load, while
the block size of I/O operations should be always maximized by applications. We
also show how to estimate the energy consumed by an application as a function
of some simple parameters, like the CPU load, and the disk and network
activity. We validate the proposed approach by accurately estimating the energy
of a map-reduce computation in a Hadoop platform
Evolutionary computing based QoS oriented energy efficient VM consolidation scheme for large scale cloud data centers using random work load bench
In order to assess the performance of an approach, it is unavoidable to
inspect the performance with distinct datasets with diverse characteristics. In
this paper we had assessed the system performance with random workbench
datasets. A-GA (Adaptive Genetic Algorithm) based consolidation technique
has been compared with other consolidation techniques including dynamic
CPU utilization techniques, VM (Virtual Machine) selection and placement
policies. The proposed consolidation system had exhibited better results
in terms of energy conservation, minimal Service Level Agreement (SLA)
violation and Quality of Service (QoS) assurance