International Journal of Cloud Computing (17 papers in press)
MSA: A Task Scheduling Algorithm for Cloud Computing
by Subhashree Mohapatra, Chhabi Rani Panigrahi, Bibudhendu Pati, Manohar Mishra
Abstract: Cloud computing is an effective technology to perform huge-scale and complex computing. Achieving a minimum makespan is the prime motto of any task scheduling algorithm for a cloud computing environment. This paper aims to propose a new task scheduling algorithm named as Min-sufferage scheduling. While selecting a task for execution, the Min-sufferage picks up the task with minimum sufferage value. The proposed method has been tested on numerous set of tasks and resources. The experimental results indicate that the proposed Min-sufferage algorithm results in less makespan as compared to Sufferage algorithm.
Keywords: Sufferage; Makespan; Cloud Computing.
Flow-based Dynamic Load balancing algorithm for the Cloud networks using Software Defined Networks
by Wilson Prakash, Deepa Lakshmi
Abstract: In recent days, cloud computing has become an outstanding technique in all areas that provide various computing resources. The specialty of cloud computing is that it provides excellent services in monitoring, communication, software platform,and infrastructure. Basically, cloud network limits its services in a specific location on the particular zone. When the targeted area is near, the specific server completes the request generates from the user. Few servers are busy while some servers are idle when it comes to the services. In this case, the provided resources are not used efficiently. To overcome this problem, this paper proposes a concept known as flow-based dynamic load balancing algorithm in cloud networking with Software Defined Networks (SDN). The fundamental principle of this proposed algorithm is distributing client request equally to all the available servers in a specific cloud network. The main aim of load balancing algorithm is optimizing the resource utilization by increasing the throughput, increase flow completion time, decrease the response time and eliminating the overloading of any individual resource. SDN is mainly used in applying various software functionalities into the hardware devices such as routers, switches etc.
Keywords: Load balancing; Data center; SDN; Traffic Engineering.
Medical Knowledge Extraction scheme for Cloudlet Based Healthcare System to avoid Malicious Attacks
by Anjali Chandavale, Anuja Gade
Abstract: Along with growth in technology, it has become the need of time that the response of doctor should be within few seconds along with his 24x7 availability. To perform this task, it is essential to share medical information that contains patients sensitive information. The medical information sharing involves information collection, information storage and sharing of this information. Security protection to this medical information is one of the concerns. As this medical information is finally stored at remote cloud, protection to the whole healthcare scheme against intrusions or malicious attacks is another important concern. Hence the current researchers are focusing on cloudlet based healthcare systems. Along with growth in technology, young generation always choose to examine health associated information and doctors recommendation for any health associated problem on web. In current question answering system, it remains challenging to extract medical knowledge from the clamorous question- answers pair and remove unwanted information. To overcome these challenges, in this paper, we propose Medical Knowledge Extraction scheme for Cloudlet based Healthcare System to avoid Malicious Attacks. In this proposed system, medical information sharing is done in energy efficient fashion using proposed modified Number Theory Research Unit (NTRU) algorithm. The proposed modified Number Theory Research Unit algorithm is used to perform the encryption of users physiological conditions i.e. body information. Collaborative Intrusion Detection System (CIDS) is used to detect and avoid malicious attacks. Medical Knowledge Extraction (MKE) method finds valid remedial triplicates from clamorous Question-Answer (Q-A) pairs and evaluate the reliability along with doctors proficiency using truth discovery method. rn Proposed modified NTRU algorithm gives 20% to 30% better delivery ratio as compared with the existing RSA algorithm. The response time of proposed system is 8 seconds which results in substantially reduction in time and cost for end user.rn
Keywords: Healthcare; Number Theory Research Unit (NTRU); Decision Making System; Proficiency score; reliable.
Dynamic Energy-saving Approaches in Mobile Cloud Computing: Issues, Challenges and Approaches
by RAJALAKSHMI KRISHNAMURTHI, Mukta Goyal
Abstract: The use of mobile devices is ubiquitous in the current information era. However, there is a critical need for the technical integration of mobile computing with cloud computing to enhance the performance of mobile devices. Mobile cloud computing promises several efficient ways of handling various constraints such as energy consumption, data transmission, bandwidth utilization, weak network connectivity, and user mobility. This paper addresses these issues and discusses the benefits of integrating mobile communications with cloud computing. and the main focus is on computation offloading into the cloud as an effective techniquefor overcoming the energy constraints of mobile devices. The characteristics and implications of various computation offloading techniques are explored.
Keywords: Mobile Cloud Computing; Dynamic Energy Saving; Content Offloading.
Optimal allocation of cloud multi-tenant platform infrastructure resources
by Oleksiy Ignatyev
Abstract: Infrastructure resources optimisation is a significant challenge in multi-tenant cloud environment. A system and associated algorithm and processes to enable a multi-tenant platform operator or administrator to make more optimal decisions with regards to the allocation of platform infrastructure resources (such as computational capabilities, data storage, etc.) among one or more tenants or accounts are provided. In particular, we construct a data 'signature' for a set of identified users, accounts, or tenants, where the signature contains data regarding the user, account, or tenant's 'consumption' of platform infrastructure resources. Later tenants 'signatures' are being used to accomplish cloud infrastructure resources optimisation in multi-tenant environment. An innovative algorithm for cloud infrastructure resources optimisation in multi-tenant environment is introduced in current paper.
Keywords: cloud computing; multi-tenant architecture; cloud infrastructure; software-as-a-service; SaaS; data storage; computational processing power; resources optimisation.
An incremental and distributed inference method for large-scale ontologies over SPARK
by Mohamed Oubezza, Ali El Hore, Jamal El Kafi
Abstract: The study of the semantic interoperability and the reasoning over big data is today a major challenge for researchers, especially with the birth of semantic web and deep data. The existing solutions are not yet able to satisfy the requirements of the final user especially in terms of the consistency of the results and the request execution time. To do this we need an approach based on an ontology and a distributed and scalable system. Several studies have been done on the reasoning over large-scale ontologies, most are based on Hadoop and MapReduce or non-incremental, i.e., they recalculate the result at the arrival of new data. In this paper we propose an incremental and distributed method of reasoning over very large OWL ontologies based on SPARK, which offers a reduced execution time as it loads the RDF triplets in memory and not in disk. Our method allows creating transfer inference forest (TIF) and effective assertional triples (EAT) to reduce disk space and simplify and accelerate the reasoning process.
Keywords: semantic web; ontology reasoning; ontology web language; OWL; OWL Horst; semantic web rule language; SWRL; SPARQL; Hadoop; SPARK.
User support as moderator for cloud computing user satisfaction
by Tor Guimaraes, Ketan Paranjape
Abstract: Cloud computing (CC) promises to provide quicker, easier, and less expensive computing services supporting e-commerce. To ensure that CC is also able to maintain or enhance customer satisfaction, this study addresses the factors leading to higher user satisfaction with SaaS applications websites. The proposed model defines and empirically tests these relationships, including the moderating effect of user support. Data collected from 1,257 users/customers using the order entry systems of 176 client organisations available through four SaaS providers were analysed. Results confirm the importance of the proposed success factors for user satisfaction with the CC vendor websites. Management of CC application risks, knowledge about the service provider, and effective data management respectively explained significant percentages of the variance in user satisfaction with the CC vendor website. The positive impact of user support is also confirmed. The results provide the basis for several insights and recommendations for managers and future research.
Keywords: cloud computing; e-commerce; user satisfaction; SaaS success factors; user support.
A dynamic strategy-proof algorithm for allocation and pricing of cloud services
by Temidayo Oluwatosin Omotehinwa, Joseph Shuaibu Sadiku
Abstract: In this paper, we present a dynamic strategy-proof algorithm for allocation and pricing. The algorithm relies on market history to forecast a benchmark price to ensure truthful valuation from the market participants. The algorithm also ensures that the utility is evenly distributed between buyer and seller. The utility is the difference between the buyer's maximum offer and the seller's minimum acceptable price. The results of the experimental studies carried out shows that: 1) the budget limits of the buyers with successful allocations were not exceeded; 2) only market participants with truthful offers and bids were allocated services; 3) the utility is higher when there is no strategy-proof for preventing overbid and that there is at least a 50% increase in price per unit when there is no strategy-proof to prevent overbid; 4) in terms of computational efficiency, the algorithm converges in polynomial time with a worst-case running time of O(n2).
Keywords: cloud computing; resource allocation; resource pricing; cloud services; strategy-proof.
A scalable network-aware virtual machine allocation strategy in multi-datacentre cloud computing environments
by Marwa A. Abdelaal, Gamal A. Ebrahim, Wagdy R. Anis
Abstract: Virtual machine provisioning in multi-datacentre cloud computing environments is a challenging problem. However, most current virtual machine management strategies ignore their effect on cloud network. In this paper, a scalable network-aware resource allocation strategy is proposed that dynamically allocates virtual machines in cloud computing environments while minimising the overall cost. It is mainly utilised at the cloud service provider that has a large number of datacentres. Several important parameters have been taken into consideration that are mainly ignored in previous related studies. Additionally, software-defined network (SDN) has been adopted in the proposed strategy. Simulation studies have been conducted to evaluate the performance of the proposed strategy. Simulation results show a reduction in the utilisation in the valuable upper-layer links in datacentres that resulted mainly because of favouring lower-cost links during virtual machine allocation process. Additionally, the proposed strategy is able to cope with the dynamic nature of the allocation process.
Keywords: cloud computing; multi-datacentre; software-defined network; SDN; virtual machine allocation; cloud service provider.
Special Issue on: ISCSA2017 Computer Sciences and Applications
A hybrid method for improving quality of service in constraint-based availability in the cloud for SMEs
by Alireza Nik Aein Koupaei
Abstract: Research in cloud computing has gone through rapid development during the last decade and has emerged as a key service of utility or on-demand computing. Small and medium-sized enterprises (SMEs) assure economic growth in the world. In the main, many SMEs are struggling to survive in an ongoing global recession and are often unwilling to use research results and new technologies for business and learning. Cloud computing provides many opportunities and could help companies expand and improve their business and use technology more resourcefully. The resource provision is an essential perspective of cloud computing technology to provide the quality of service (QoS) in cloud computing. It is used as an alternative to service level agreement (SLA). In this paper, we are going to present a new cloud hybrid method and architecture to improve, the QoS and availability under the cloud computing environment. Additionally, issues regarding the extent effect of cloud computing in SMEs have been highlighted. In conclusion, critical future challenges in the area are addressed.
Keywords: cloud computing; small and medium-sized enterprises; SMEs; availability; service level agreement; SLA.
Special Issue on: ICACB18 Advanced Computing and Communication Systems
Multi-Objective Optimization Techniques for Virtual Machine Migration-based Load Balancing in Cloud Data Center
by Meenakshi Priya, R.Kanniga Devi
Abstract: This paper aims to balance the load in Cloud Data Center (CDC) by migrating Virtual Machines (VM) across hosts using Multi-Objective Optimization techniques. The unpredictable rate of demand for the cloud services leads to load fluctuation and subsequently load imbalance in Cloud Data Center. Hence, to balance the load in Cloud Data Center, this work presents Multi-Objective Optimization Technique-based Load Balancing (MOOT-LB) method. This work proposes two Multi-Objective Optimization techniques namely, Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Differential Evolution (MODE) for load balancing the Cloud Data Center. These techniques identify an optimal set of hosts and set of VMs to be migrated from the source hosts and identify the target hosts for migration in an efficient way. The objectives are to minimize the frequency of VM migration and migration time. To evaluate the performance of the proposed techniques ClouSim 3.0.3 simulator is used. The performance of the proposed techniques is compared, and the results show that MOPSO based load balancing technique achieves better performance than MODE based load balancing technique.
Keywords: VM Migration; Load Balancing; Multi-Objective Optimization; MOPSO; MODE.
Optimized Handoff Mechanism using RFID tags for a Communication Based Train Control System
by Moahanasundaram Ranganathan, Kathirvel Brindhadevi, Arushi Sana, Ankit Malhotra
Abstract: In a Communication Based Train Control System, one of the most important aspects is ensuring that a Mobile Station is connected continuously to the Zone Controller to ensure continuous and precise transmission of the trains location, speed and other relevant information. Due to the continuous motion of the train handoffs between different APs is necessary to remain connected. This work aims to propose a location based handoff method which will minimize handoff latency and provide better connections. The location based handoff mechanism uses the concept of placing RFID tags at predetermined locations. Handoff is triggered at the location where the RFID tag is present and a connection is made to the new AP based on the channel information obtained. The architecture used in the scenario is a WiMAX architecture and connections are maintained with the help of routers contained within the mobile stations. The proposed handoff method provides better handoff latency and error free transmission in a system where precise information transmission is necessary.
Keywords: Communication Based Train Control System; WIMAX; Access Point; RFID; Mobile Station.
A survey: Comparative study on IoT and CoT (Internet of Things and Cloud of Things)
by Kathirvel Brindhadevi, Moahanasundaram Ranganathan, Navin Kumar, Rishikesh Y. Mule
Abstract: In todays era, an exponential rise in technology is changing our lives essay. Tens of millions of devices are getting connected to the internet every day. This extensive growth of the internet and increasing number of interconnected devices has given a rise to many new-age technologies. The Internet of Things (IoT) provides an innovative means of communication amongst these new-age technologies and the Web world. Cloud Computing enables a convenient, on demand and scalable network access to a shared pool of configurable computing resources. Integrating Cloud computing with Internet of Things brings the concept of Cloud of Things. The Cloud of Things framework is based on a combination of ubiquitous distributed sensing units, outcomes stored in the cloud for awareness. rnThis paper surveys a comparative study on internet of things and cloud of things demonstrating how cloud of things has prevalent benefits over internet of things. Also, this paper explains how introduction of fog computing concept in cloud and IoT improves efficiency of Cloud of Things.
Keywords: Cloud of Things; Internet of Things; Cloud computing; Fog Computing.
Enhanced Classification of LISS-III satellite image Using Rough Set theory and ANN
by Anand Upadhyay
Abstract: Land use and land cover classification are one of the major aspects to detect land coverage in particular area. Same goes for water, forest, and mangroves. So by keeping these parameters in mind, our objective is to identify water, land, forest, and mangroves from a LISS-III satellite image by using rough set theory and artificial neural network. LISS-III is multi-spectral camera operating in four different bands. There are many problems related to the classification of the satellite image i.e. universal classifiers, parameter setting of classifiers and features. The classification accuracy is one of the major issues related to classification of satellite image therefor in this paper rough set based artificial neural network is used for classification of the satellite image. The rough set theory is used to reduce the number of the feature vector for improved classification of satellite image using the artificial neural network.
Keywords: LISS-III (Linear Imaging and Self Scanning Sensor); classification; satellite image; accuracy etc.
Analysis of Methane (CH4) and Nitrous Oxide (N2O) emission from paddy rice using IoT and Fuzzy logic
by Shriya A. Jadhav, Anisha Lal
Abstract: Most climate scientists accepted main cause of the "greenhouse effect" is human expansion. The study of rice paddy feilds revels the fact that they are the substantial sources of greenhouse gases such as methane (CH4) and nitrous oxide (N2O). So rapid increase in rice production will result in speedy rise in the level of emission of these gases. Therefore, the purpose of this research is to forecast the level of emission of methane (CH4) and nitrous oxide (N2O) from the paddy farm. Internet of Things can provide an integrated solution for data acquisition, monitoring and measuring methane (CH4) and nitrous oxide (N2O) concentration in air. Here we propose to define a fuzzy rule set considering the various environmental factors and conditions which are causing Greenhouse Gas emission, the fuzzy rule based system can provide a solution using linguistic variables based on which a decision support system can be designed. The combination of IoT and fuzzy logic can be used in the development of intelligent system for pattern recognition, event prediction and decision making.
Keywords: methane (CH4); nitrous oxide (N2O); Internet of things; IoT; Fuzzy ; rice paddy.
A Case study for an Incremental Classifier model in big data
by Blessy Trencia Lincy S S, Suresh Kumar Nagarajan
Abstract: Big data is a term that implies enormous voluminous of data which cannot be handled by the existing traditional systems. With the evolving standards and technologies this volume has reached to a rate, such that even if provided with the huge amount of data it is a challenging task to obtain useful insights or knowledge out of it. Thus, this is a foremost and most important challenge for the researchers and scientists to transform the data or manipulate the data for analysis and processing them with the significant purpose of gaining insights out of it. Prediction plays a vital role in various applications involving business decision making processes or in healthcare industries, and many other domains. This helps in determining the future events, or to understand and analyse the events to predict the future outcome. This in turn increases the performance of the system in terms of accuracy, reduction in cost, speed and many other aspects. In this paper, a incremental classifier model is applied for performing the classification with the evolving new instances of data and analysed as a case study. The experiment is carried out with the healthcare datasets to understand and analyse the suggested model and the proposed model is said to provide better performance that deals with large data.
Keywords: Big data; Incremental model; Classification; Predictive analytics.
A HYBRID ENCRYPTION METHOD HANDLING BIG DATA VULNERABILITIES
by Priyanka G, Anisha Lal
Abstract: As Big Data hits the maximum number of companies in all domains, secured data transfer can be done by cryptography. With increasing threats to Big Data, the security must focus on to avoid the attackers from the formation of any pattern to gain access to the information. Big data deals with the linguistic data which consists of low secured data and high secured data as well. Hence, the system should focus on providing multi-fold security and should avoid high-security common to all data categories. This paper presents a hybrid model for Big Data that ensures Data Confidentiality, Data Integrity, Access Control and Sequential Freshness by combining three symmetric key algorithms AES, DES and Blowfish for the encryption and decryption process in any desired order. Based on the level of security the combination of the algorithms can vary. This method of encryption and decryption process ensures safe data transformation between source and destination.
Keywords: Hybrid; Encryption;Big Data.