A Review of Energy-aware Cloud Computing Surveys

The increasing demands on the usage of data centers especially in provisioning cloud applications (i.e. data-intensive applications) have drastically increased the energy consumption and becoming a critical issue. Failing to handle the increasing in energy consumption leads to the negative impact on the environment, and also negatively affecting the cloud providers’ profits due to increasing costs. Various surveys have been carried out to address and classify energy-aware approaches and solutions. As an active research area with increasing number of proposals, more surveys are needed to support researchers in the research area. Thus, in this paper, we intend to provide the current state of existing related surveys that serve as a guideline for the researchers as well as the potential reviewers to embark into a new concern and dimension to compliment existing related surveys. Our review highlights four main topics and concludes to some recommendations for the future survey

Impact of Link Failures on the Performance of MapReduce in Data Center Networks

In this paper, we utilize Mixed Integer Linear Programming (MILP) models to determine the impact of link failures on the performance of shuffling operations in MapReduce when different data center network (DCN) topologies are used. For a set of non-fatal single and multi-links failures, the results indicate that different DCNs experience different completion time degradations ranging between 5% and 40%. The best performance under links failures is achieved by a server-centric PON-based DCN

Proactive Fault Tolerance Through Cloud Failure Prediction Using Machine Learning

One of the crucial aspects of cloud infrastructure is fault tolerance, and its primary responsibility is to address the situations that arise when different architectural parts fail. A sizeable cloud data center must deliver high service dependability and availability while minimizing failure incidence. However, modern large cloud data centers continue to have significant failure rates owing to a variety of factors, including hardware and software faults, which often lead to task and job failures. To reduce unexpected loss, it is critical to forecast task or job failures with high accuracy before they occur. This research examines the performance of four machine learning (ML) algorithms for forecasting failure in a real-time cloud environment to increase system availability using real-time data gathered from the Google Cluster Workload Traces 2019. We applied four distinct supervised machine learning algorithms are logistic regression, KNN, SVM, decision tree, and logistic regression classifiers. Confusion matrices as well as ROC curves were used to assess the reliability and robustness of each algorithm. This study will assist cloud service providers developing a robust fault tolerance design by optimizing device selection, consequently boosting system availability and eliminating unexpected system downtime

The minimum cost D-geodiverse anycast routing with optimal selection of anycast nodes

Consider a geographical network with associated link costs. In anycast routing, network nodes are partitioned into two sets - the source nodes and the anycast (destination) nodes - and the traffic of each source node is routed towards the anycast node providing the minimum routing cost path. By considering a given geographical distance parameter D, we define an anycast routing solution as D-geodiverse when for each source node there are two routing paths, each one towards a different anycast node, such that the geographical distance between the two paths is at least D. Such a solution has the property that any disaster with a coverage diameter below D affecting one routing path (but without involving neither the source node nor its entire set of outgoing links) cannot affect the other path, enhancing in this way the network robustness to natural disasters. The selection of the anycast nodes has an impact both on the feasibility and cost of a D- geodiverse anycast routing solution. Therefore, for a desired number of anycast nodes R, we define the minimum cost D- geodiverse anycast problem (MCD-GAP) aiming to identify a set of R anycast nodes that obtain a minimum cost routing solution. The problem is defined based on integer linear programming and is extended to consider the existence of vulnerability regions in the network, i.e., by imposing the geographical distance D only between network elements belonging to the same region. We present computational results showing the tradeoff between D and R in the optimal solutions obtained with and without vulnerability regions.This paper is based upon work from COST Action CA15127 ("Resilient communication services protecting end user applications from disaster-based failures ‒ RECODIS") supported by COST Association. The work was financially supported by FCT, Portugal, under the projects CENTRO- 01-0145-FEDER-029312 and UID/EEA/50008/2013 and through the postdoc grant SFRH/BPD/ 111503/2015.publishe