Paving the Way for Autonomic Clouds: State-of-the-Art and Future Directions

Cloud Computing is the core technology that helps in catering to the computing needs of the current generation. As the customers increase, data center providers are looking for efficient mechanisms to handle the management of the large reservoir of resources involved in the Cloud environment. In order to support efficient managing, it is the need of the day to adopt the concept of Autonomic Computing into Cloud. Several researchers have been attempted to improve the managing capability of the Cloud, by encorporating autonomic capabilities for resources in the Cloud. Most of the researches attempt to automate some aspects while the remaining portion of the Cloud does not have autonomic functionalities. An autonomic Cloud is one where all the operations can be handled without human intervention. There is a long way to go to achieve this vision. In our study, we first categorize the various existing approaches on the basis of the methodology employed and analyze the different self-*properties considered by the different approaches. It is observed that in each approach, researchers focus on one or at most two self-*properties. Based on our analysis, we suggest some of the future directions that can be paved on by researchers working in this domain.

[1]  Miguel Rio,et al.  Self-Tuning Service Provisioning for Decentralized Cloud Applications , 2016, IEEE Transactions on Network and Service Management.

[2]  Michela Meo,et al.  Probabilistic Consolidation of Virtual Machines in Self-Organizing Cloud Data Centers , 2013, IEEE Transactions on Cloud Computing.

[3]  Jordi Torres,et al.  Autonomic Placement of Mixed Batch and Transactional Workloads , 2012, IEEE Transactions on Parallel and Distributed Systems.

[4]  Dan Feng,et al.  SeDas: A Self-Destructing Data System Based on Active Storage Framework , 2013, IEEE Transactions on Magnetics.

[5]  D. Janaki Ram,et al.  Optimizing Ordered Throughput Using Autonomic Cloud Bursting Schedulers , 2013, IEEE Transactions on Software Engineering.

[6]  Rami Bahsoon,et al.  Toward a Smarter Cloud: Self-Aware Autoscaling of Cloud Configurations and Resources , 2015, Computer.

[7]  Jianfeng Ma,et al.  A Secure Data Self-Destructing Scheme in Cloud Computing , 2014, IEEE Transactions on Cloud Computing.

[8]  Brian J. Watson,et al.  Autonomic Virtual Machine Placement in the Data Center , 2008 .

[9]  Rami Bahsoon,et al.  Self-Adaptive and Online QoS Modeling for Cloud-Based Software Services , 2017, IEEE Transactions on Software Engineering.

[10]  Rami Bahsoon,et al.  A decentralized self-adaptation mechanism for service-based applications in the cloud , 2013, IEEE Transactions on Software Engineering.

[11]  Johan Tordsson,et al.  An Autonomic Approach to Risk-Aware Data Center Overbooking , 2014, IEEE Transactions on Cloud Computing.

[12]  Wei Tan,et al.  Self-Adaptive Learning PSO-Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud , 2014, IEEE Transactions on Automation Science and Engineering.

[13]  Liang Liu,et al.  GreenCloud: a new architecture for green data center , 2009, ICAC-INDST '09.

[14]  Claus Pahl,et al.  Managing Uncertainty in Autonomic Cloud Elasticity Controllers , 2016, IEEE Cloud Computing.

[15]  Abdul Hameed,et al.  Future Generation Computer Systems ( ) – Future Generation Computer Systems a Taxonomy and Survey on Green Data Center Networks Keywords: Data Center Data Center Networks Network Architectures Network Performance Network Management Network Experimentation , 2022 .

[16]  Cheng-Zhong Xu,et al.  Coordinated Self-Configuration of Virtual Machines and Appliances Using a Model-Free Learning Approach , 2013, IEEE Transactions on Parallel and Distributed Systems.

[17]  Philip Robinson,et al.  Using Mussel-Inspired Self-Organization and Account Proxies to Obfuscate Workload Ownership and Placement in Clouds , 2013, IEEE Transactions on Information Forensics and Security.

[18]  MengChu Zhou,et al.  Application-Aware Dynamic Fine-Grained Resource Provisioning in a Virtualized Cloud Data Center , 2017, IEEE Transactions on Automation Science and Engineering.

[19]  Inderveer Chana,et al.  Energy Efficiency Techniques in Cloud Computing , 2015, ACM Comput. Surv..

[20]  Fuyuki Ishikawa,et al.  SanGA: A Self-Adaptive Network-Aware Approach to Service Composition , 2014, IEEE Transactions on Services Computing.

[21]  Cho-Li Wang,et al.  Dynamic Optimization of Multiattribute Resource Allocation in Self-Organizing Clouds , 2013, IEEE Transactions on Parallel and Distributed Systems.

[22]  Changjun Jiang,et al.  Elastic Power-Aware Resource Provisioning of Heterogeneous Workloads in Self-Sustainable Datacenters , 2016, IEEE Transactions on Computers.

[23]  Sotirios A. Tsaftaris,et al.  Supporting Autonomic Management of Clouds: Service Clustering With Random Forest , 2016, IEEE Transactions on Network and Service Management.

[24]  Zibin Zheng,et al.  FTCloud: A Component Ranking Framework for Fault-Tolerant Cloud Applications , 2010, 2010 IEEE 21st International Symposium on Software Reliability Engineering.

[25]  Andrew Berns,et al.  Dissecting Self-* Properties , 2009, 2009 Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems.

[26]  Mohamed Mohamed,et al.  An autonomic approach to manage elasticity of business processes in the Cloud , 2015, Future Gener. Comput. Syst..

[27]  Giovanni Toffetti Carughi,et al.  Kriging-Based Self-Adaptive Cloud Controllers , 2016, IEEE Transactions on Services Computing.

[28]  Dario Pompili,et al.  Self-organizing sensing infrastructure for autonomic management of green datacenters , 2011, IEEE Network.