An Infrastructure-as-a-Service Cloud

2013 ◽  
pp. 302-324 ◽  
Author(s):  
Weijia Song ◽  
Zhen Xiao
Keyword(s):  
Cloud Model ◽  
Scale Up ◽  
Resource Provisioning ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Accounting Model ◽  
Cloud Elasticity ◽  
Business Customers ◽  
Cloud Users ◽  
Service Cloud

Cloud computing allows business customers to elastically scale up and down their resource usage based on needs. This feature eliminates the dilemma of planning IT infrastructures for Cloud users, where under-provisioning compromises service quality while over-provisioning wastes investment as well as electricity. It offers virtually infinite resource. It also made the desirable “pay as you go” accounting model possible. The above touted gains in the Cloud model come from on-demand resource provisioning technology. In this chapter, the authors elaborate on such technologies incorporated in a real IaaS system to exemplify how Cloud elasticity is implemented. It involves the resource provisioning technologies in hypervisor, Virtual Machine (VM) migration scheduler and VM replication. The authors also investigate the load prediction algorithm for its significant impacts on resource allocation.

Ranking Model for SLA Resource Provisioning Management

2014 ◽  
Vol 4 (3) ◽  
pp. 68-80 ◽  
Author(s):  
C. S. Rajarajeswari ◽  
M. Aramudhan
Keyword(s):  
Service Providers ◽  
Cloud Model ◽  
Cloud Service ◽  
Resource Provisioning ◽  
Differentiated Service ◽  
Cloud Service Provider ◽  
Cloud Broker ◽  
Cloud Users ◽  
Standard Framework

Cloud computing is an amazing technology, which provides services to users on-demand. Since there are many providers in the cloud, users get confused in selecting the optimal cloud service provider. To overcome this limitation, federated cloud management architecture was proposed. There is no standard framework for ranking the cloud service providers in the existing federated cloud model. The proposed work provides a new federated cloud mechanism, in which Cloud Broker Manager (CBM) takes up the responsibility of resource provisioning and ranking. Differentiated Service Module (DSM) is projected in CBM that classifies the incoming users as SLA or non-SLA members. Dynamic Loose Priority Scheduling (DLPS) is proposed in CBM to manage the number of services. To reduce the overload of the CBM, a secondary CBM (sCBM) is proposed. Experimental results show that the proposed DLPS technique improves the resource provisioning and Quality of Service (QoS) to SLA members and improves the performance of federated cloud by 18% than the existing model.

scholarly journals CacheInspector

2021 ◽  
Vol 18 (3) ◽  
pp. 1-25
Author(s):  
Weijia Song ◽  
Christina Delimitrou ◽  
Zhiming Shen ◽  
Robbert Van Renesse ◽  
Hakim Weatherspoon ◽  
...  
Keyword(s):  
Virtual Machines ◽  
Controlled Environment ◽  
Shared Resources ◽  
Throughput Performance ◽  
Shared Caches ◽  
Output Quality ◽  
Cloud Providers ◽  
Cloud Users ◽  
Service Cloud ◽  
Critical Aspects

Infrastructure-as-a-Service cloud providers sell virtual machines that are only specified in terms of number of CPU cores, amount of memory, and I/O throughput. Performance-critical aspects such as cache sizes and memory latency are missing or reported in ways that make them hard to compare across cloud providers. It is difficult for users to adapt their application’s behavior to the available resources. In this work, we aim to increase the visibility that cloud users have into shared resources on public clouds. Specifically, we present CacheInspector , a lightweight runtime that determines the performance and allocated capacity of shared caches on multi-tenant public clouds. We validate CacheInspector ’s accuracy in a controlled environment, and use it to study the characteristics and variability of cache resources in the cloud, across time, instances, availability regions, and cloud providers. We show that CacheInspector ’s output allows cloud users to tailor their application’s behavior, including their output quality, to avoid suboptimal performance when resources are scarce.

Resource Provisioning in the Cloud

2014 ◽  
pp. 589-612
Author(s):  
Ming Mao ◽  
Marty Humphrey
Keyword(s):  
Job Scheduling ◽  
Data Transfer ◽  
Resource Provisioning ◽  
Research Trends ◽  
Future Research ◽  
Application Performance ◽  
Grid Environments ◽  
Solution Methods ◽  
The One ◽  
Cloud Users

It is a challenge to provision and allocate resources in the Cloud so as to meet both the performance and cost goals of Cloud users. For a Cloud consumer, the ability to acquire and release resources dynamically and trivially in the Cloud, while being a powerful and useful aspect, complicates the resource provisioning and allocation task in the Cloud. While on the one hand, resource under-provisioning may hurt application performance and deteriorate service quality; on the other hand, resource over-provisioning could cost users more and offset Cloud advantages. Although resource management and job scheduling have been studied extensively in the Grid environments and the Cloud shares many common features with the Grid, the mapping from user objectives to resource provisioning and allocation in the Cloud has many challenges due to the seemingly unlimited resource pools, virtualization, and isolation features provided by the Cloud. This chapter focuses on surveying the research trends in resource provisioning in the Cloud based on several factors such as the type of the workload, the VM heterogeneity, data transfer requirements, solution methods, and optimization goals and constraints, and attempts to provide guidelines for future research.

scholarly journals District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2122 ◽  
Author(s):  
Guixiang Xue ◽  
Yu Pan ◽  
Tao Lin ◽  
Jiancai Song ◽  
Chengying Qi ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Feature Fusion ◽  
District Heating ◽  
Accurate Prediction ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Short Term ◽  
District Heating System ◽  
Heating Load ◽  
The Impact

The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data’s intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly.

A Collaborative Filtering Recommendation Algorithm Based on Cloud Model, Item Attribute and User Data

2014 ◽  
Vol 610 ◽  
pp. 747-751
Author(s):  
Jian Sun ◽  
Xiao Ying Chen
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Cloud Model ◽  
Prediction Algorithm ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
User Data ◽  
Item Attribute ◽  
Rating Prediction ◽  
User Ratings

Aiming at the problems of extremely sparse of user-item rating data and poor recommendation quality, we put forward a collaborative filtering recommendation algorithm based on cloud model, item attribute and user data which combined with the existing literatures. A rating prediction algorithm based on cloud model and item attribute is proposed, based on idea that the similar users rating for the same item are similar and the same user ratings for the similar items are similar and stable. Through compare and analysis this paper’s and other studies experimental results, we get the conclusion that the rating prediction accuracy is improved.

Enhancing performance of IoT applications with load prediction and cloud elasticity

2020 ◽  
Vol 109 ◽  
pp. 689-701 ◽  
Author(s):  
Rodrigo da Rosa Righi ◽  
Everton Correa ◽  
Márcio Miguel Gomes ◽  
Cristiano André da Costa
Keyword(s):  
Load Prediction ◽  
Iot Applications ◽  
Cloud Elasticity

scholarly journals VM Consolidation and Migration for IAAS In Cloud Storage

2020 ◽  
pp. 552-560
Author(s):  
Saravanan S
Keyword(s):  
Cloud Computing ◽  
Cloud Storage ◽  
Resource Provisioning ◽  
Live Migration ◽  
Enabling Technology ◽  
Huge Amount ◽  
Physical Infrastructure ◽  
And Migration ◽  
Business Customers ◽  
Vm Consolidation

Cloud computing is capable of handling a huge amount of growing work in a predestined manner for the usage of the business customers. The main enabling technology for cloud computing is virtualization which generalize the physical infrastructure and makes it easy to use and manage. In this project virtualization is used to allocate resources based on their needs and also supports green computing concept. “Skewness” concept is introduced here in which the same is minimized to combine various workloads to improve the utilization of the server. Managing the customer demand creates the challenges of on demand resource allocation. So can implement Virtual Machine (VM) technology has been employed for resource provisioning. It is expected that using virtualized environment will reduce the average job response time as well as executes the task according to the availability of resources. Hence VMs are allocated to the user based on characteristics of the job. The VM live migration technology makes the VM and PM (Physical machine) mapping possible when the execution is running.

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