Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement

Virtualization is one of the main technologies used for improving resource efficiency in datacenters; it allows the deployment of co-existing computing environments over the same hardware infrastructure. However, the co-existing of environments — along with management inefficiencies — often creates scenarios of high-competition for resources between running workloads, leading to performance degradation. This phenomenon is known as Performance Interference, and introduces a non-negligible overhead that affects both a datacenter's Quality of Service and its energy-efficiency. This paper introduces a novel approach to workload allocation that improves energy-efficiency in Cloud datacenters by taking into account their workload heterogeneity. We analyze the impact of performance interference on energy-efficiency using workload characteristics identified from a real Cloud environment, and develop a model that implements various decision-making techniques intelligently to select the best workload host according to its internal interference level. Our experimental results show reductions in interference by 27.5% and increased energy-efficiency up to 15% in contrast to current mechanisms for workload allocation

A Review On Green Cloud Computing

The objective of green computing is to reap monetary growth and enhance the way the computing devices are used. In large data centers computational offloading is main problem due to increased demand for timely and response for real time application which lead to high energy consumption by data centers, so the aim of green computing is to find energy efficient solution which monopolize optimal utilization of the available resources. Green IT methods comprises of environmentally viable management, energy efficient computers and enhanced recycling procedures. By using different algorithm and energy efficient scheduling power consumption of virtual machine can be minimize, this paper provide an overview of different algorithms and techniques which are used to move towards the green computing

Intelligent Resource Scheduling at Scale: a Machine Learning Perspective

Resource scheduling in a computing system addresses the problem of packing tasks with multi-dimensional resource requirements and non-functional constraints. The exhibited heterogeneity of workload and server characteristics in Cloud-scale or Internet-scale systems is adding further complexity and new challenges to the problem. Compared with,,,, existing solutions based on ad-hoc heuristics, Machine Learning (ML) has the potential to improve further the efficiency of resource management in large-scale systems. In this paper we,,,, will describe and discuss how ML could be used to understand automatically both workloads and environments, and to help to cope with scheduling-related challenges such as consolidating co-located workloads, handling resource requests, guaranteeing application's QoSs, and mitigating tailed stragglers. We will introduce a generalized ML-based solution to large-scale resource scheduling and demonstrate its effectiveness through a case study that deals with performance-centric node classification and straggler mitigation. We believe that an MLbased method will help to achieve architectural optimization and efficiency improvement

Research Issues in Cloud Computing

Cloud computing moved away from personal computers and the individual enterprise application server to services provided by the cloud of computers The emergence of cloud computing has made a tremendous impact on the Information Technology IT industry over the past few years Currently IT industry needs Cloud computing services to provide best opportunities to real world Cloud computing is in initial stages with many issues still to be addressed The objective of this paper is to explore the different issues of cloud computing and identify important research opportunities in this increasingly important area We present different design challenges categorized under security challenges Data Challenges Performance challenges and other Design Challenge