In a cloud computing environment, effective scheduling policies and load balancing have always been the aim. An efficient task scheduler must be proficient in a dynamically distributed environment and to the policy of efficient scheduling of jobs based upon the workload. In this research, a novel hybrid heuristic algorithm is developed for balancing the load among cloud nodes. This is achieved by hybridizing the existing ant colony optimization (ACO), artificial bee colony algorithm (ABC), and AHP (analytical hierarchy process) algorithm. The AHP algorithm and the artificial bee colony (ABC) algorithm is used for figuring out the best servers suitable for a particular job, and the ant colony algorithm is used to find the most efficient path to that particular server. The proposed algorithm is better in resource utilization. It also performs better load balancing, which keeps on improving with time. The result analysis shows better average response time and better average makespan time compared to other two existing algorithms.
{"title":"A Bio-Inspired and Heuristic-Based Hybrid Algorithm for Effective Performance With Load Balancing in Cloud Environment","authors":"Soumen Swarnakar, Souvik Bhattacharya, Chandan Banerjee","doi":"10.4018/IJCAC.2021100104","DOIUrl":"https://doi.org/10.4018/IJCAC.2021100104","url":null,"abstract":"In a cloud computing environment, effective scheduling policies and load balancing have always been the aim. An efficient task scheduler must be proficient in a dynamically distributed environment and to the policy of efficient scheduling of jobs based upon the workload. In this research, a novel hybrid heuristic algorithm is developed for balancing the load among cloud nodes. This is achieved by hybridizing the existing ant colony optimization (ACO), artificial bee colony algorithm (ABC), and AHP (analytical hierarchy process) algorithm. The AHP algorithm and the artificial bee colony (ABC) algorithm is used for figuring out the best servers suitable for a particular job, and the ant colony algorithm is used to find the most efficient path to that particular server. The proposed algorithm is better in resource utilization. It also performs better load balancing, which keeps on improving with time. The result analysis shows better average response time and better average makespan time compared to other two existing algorithms.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131489784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.4018/IJCAC.2021070101
Rajendra Kumar Dwivedi, Rakesh Kumar, R. Buyya
Sensor networks are integrated with cloud in many internet of things (IoT) applications for various benefits. Healthcare monitoring sensor cloud is one of the application that allows storing the patients' health data generated by their wearable sensors at cloud and facilitates the authorized doctors to monitor and advise them remotely. Patients' data at cloud must be secure. Existing security schemes (e.g., key policy attribute-based encryption [KP-ABE] and ciphertext policy attribute-based encryption [CP-ABE]) have higher computational overheads. In this paper, a security mechanism called attribute-based elliptical curve cryptography (ABECC) is proposed that guarantees data integrity, data confidentiality, and fine-grained access control. It also reduces the computational overheads. ABECC is implemented in .NET framework. Use of elliptical curve cryptography (ECC) in ABECC reduces the key length, thereby improving the encryption, decryption, and key generation time. It is observed that ABECC is 1.7 and 1.4 times faster than the existing approaches of KP-ABE and CP-ABE, respectively.
{"title":"Secure Healthcare Monitoring Sensor Cloud With Attribute-Based Elliptical Curve Cryptography","authors":"Rajendra Kumar Dwivedi, Rakesh Kumar, R. Buyya","doi":"10.4018/IJCAC.2021070101","DOIUrl":"https://doi.org/10.4018/IJCAC.2021070101","url":null,"abstract":"Sensor networks are integrated with cloud in many internet of things (IoT) applications for various benefits. Healthcare monitoring sensor cloud is one of the application that allows storing the patients' health data generated by their wearable sensors at cloud and facilitates the authorized doctors to monitor and advise them remotely. Patients' data at cloud must be secure. Existing security schemes (e.g., key policy attribute-based encryption [KP-ABE] and ciphertext policy attribute-based encryption [CP-ABE]) have higher computational overheads. In this paper, a security mechanism called attribute-based elliptical curve cryptography (ABECC) is proposed that guarantees data integrity, data confidentiality, and fine-grained access control. It also reduces the computational overheads. ABECC is implemented in .NET framework. Use of elliptical curve cryptography (ECC) in ABECC reduces the key length, thereby improving the encryption, decryption, and key generation time. It is observed that ABECC is 1.7 and 1.4 times faster than the existing approaches of KP-ABE and CP-ABE, respectively.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117234068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-01DOI: 10.4018/IJCAC.2021040106
Steven Yen, M. Moh, Teng-Sheng Moh
Social networks allow people to connect to one another. Over time, these accounts become an essential part of one's online identity. The account stores various personal data and contains one's network of acquaintances. Attackers seek to compromise user accounts for various malicious purposes, such as distributing spam, phishing, and much more. Timely detection of compromises becomes crucial for protecting users and social networks. This article proposes a novel system for detecting compromises of a social network account by considering both post behavior and textual content. A deep multi-layer perceptron-based autoencoder is leveraged to consolidate diverse features and extract underlying relationships. Experiments show that the proposed system outperforms previous techniques that considered only behavioral information. The authors believe that this work is well-timed, significant especially in the world that has been largely locked down by the COVID-19 pandemic and thus depends much more on reliable social networks to stay connected.
{"title":"Detecting Compromised Social Network Accounts Using Deep Learning for Behavior and Text Analyses","authors":"Steven Yen, M. Moh, Teng-Sheng Moh","doi":"10.4018/IJCAC.2021040106","DOIUrl":"https://doi.org/10.4018/IJCAC.2021040106","url":null,"abstract":"Social networks allow people to connect to one another. Over time, these accounts become an essential part of one's online identity. The account stores various personal data and contains one's network of acquaintances. Attackers seek to compromise user accounts for various malicious purposes, such as distributing spam, phishing, and much more. Timely detection of compromises becomes crucial for protecting users and social networks. This article proposes a novel system for detecting compromises of a social network account by considering both post behavior and textual content. A deep multi-layer perceptron-based autoencoder is leveraged to consolidate diverse features and extract underlying relationships. Experiments show that the proposed system outperforms previous techniques that considered only behavioral information. The authors believe that this work is well-timed, significant especially in the world that has been largely locked down by the COVID-19 pandemic and thus depends much more on reliable social networks to stay connected.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129584848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.4018/ijcac.2021010103
Rajendra Kumar Dwivedi, Rakesh Kumar, R. Buyya
Smart information systems are based on sensors that generate a huge amount of data. This data can be stored in cloud for further processing and efficient utilization. Anomalous data might be present within the sensor data due to various reasons (e.g., malicious activities by intruders, low quality sensors, and node deployment in harsh environments). Anomaly detection is crucial in some applications such as healthcare monitoring systems, forest fire information systems, and other internet of things (IoT) systems. This paper proposes a Gaussian distribution-based supervised machine learning scheme of anomaly detection (GDA) for healthcare monitoring sensor cloud, which is an integration of various body sensors of different patients and cloud. This work is implemented in Python. Use of Gaussian statistical model in the proposed scheme improves precision, throughput, and efficiency. GDA provides 98% efficiency with 3% and 4% improvements as compared to the other supervised learning-based anomaly detection schemes (e.g., support vector machine [SVM] and self-organizing map [SOM], respectively).
{"title":"Gaussian Distribution-Based Machine Learning Scheme for Anomaly Detection in Healthcare Sensor Cloud","authors":"Rajendra Kumar Dwivedi, Rakesh Kumar, R. Buyya","doi":"10.4018/ijcac.2021010103","DOIUrl":"https://doi.org/10.4018/ijcac.2021010103","url":null,"abstract":"Smart information systems are based on sensors that generate a huge amount of data. This data can be stored in cloud for further processing and efficient utilization. Anomalous data might be present within the sensor data due to various reasons (e.g., malicious activities by intruders, low quality sensors, and node deployment in harsh environments). Anomaly detection is crucial in some applications such as healthcare monitoring systems, forest fire information systems, and other internet of things (IoT) systems. This paper proposes a Gaussian distribution-based supervised machine learning scheme of anomaly detection (GDA) for healthcare monitoring sensor cloud, which is an integration of various body sensors of different patients and cloud. This work is implemented in Python. Use of Gaussian statistical model in the proposed scheme improves precision, throughput, and efficiency. GDA provides 98% efficiency with 3% and 4% improvements as compared to the other supervised learning-based anomaly detection schemes (e.g., support vector machine [SVM] and self-organizing map [SOM], respectively).","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130778240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.4018/ijcac.2021010102
R. Tiwari, R. Kumar
Cloud computing has become a business model and organizations like Google, Amazon, etc. are investing huge capital on it. The availability of many organizations in the cloud has posed a challenge for cloud users to choose a best cloud service. To assist the cloud users, we have proposed a MCDM-based cloud service selection framework to choose a best service provider based on QoS requirement. The cloud service selection methods based on TOPSIS suffers from rank reversal problem as it ranks optimal service provider to non-optimal on addition or removal of a service provider and deludes the cloud user. Therefore, a robust and efficient TOPSIS (RE-TOPSIS)-based novel framework has been proposed to rank the cloud service providers using QoS provided by them and cloud user's priority for each QoS. The proposed framework is robust to rank reversal problem and its effectiveness has been demonstrated through a case study performed on a real dataset. Sensitivity analysis has also been performed to show the robustness against the rank reversal phenomenon.
{"title":"A Robust and Efficient MCDM-Based Framework for Cloud Service Selection Using Modified TOPSIS","authors":"R. Tiwari, R. Kumar","doi":"10.4018/ijcac.2021010102","DOIUrl":"https://doi.org/10.4018/ijcac.2021010102","url":null,"abstract":"Cloud computing has become a business model and organizations like Google, Amazon, etc. are investing huge capital on it. The availability of many organizations in the cloud has posed a challenge for cloud users to choose a best cloud service. To assist the cloud users, we have proposed a MCDM-based cloud service selection framework to choose a best service provider based on QoS requirement. The cloud service selection methods based on TOPSIS suffers from rank reversal problem as it ranks optimal service provider to non-optimal on addition or removal of a service provider and deludes the cloud user. Therefore, a robust and efficient TOPSIS (RE-TOPSIS)-based novel framework has been proposed to rank the cloud service providers using QoS provided by them and cloud user's priority for each QoS. The proposed framework is robust to rank reversal problem and its effectiveness has been demonstrated through a case study performed on a real dataset. Sensitivity analysis has also been performed to show the robustness against the rank reversal phenomenon.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125546951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.4018/IJCAC.2020100104
A. Owusu
This study explores the determinants of Cloud BI adoption among Ghanaian small-medium enterprises (SMEs). The study was guided by the technology-organization-environment (TOE) framework and information systems adoption model and employed the qualitative method through an in-depth interview with data collected from CEOs/key managers from 17 SMEs in Ghana. The results showed that technological characteristics (relative advantage, complexity, and compatibility), organizational characteristics (organization size and organizational readiness), environmental characteristics (competitive pressure and regulatory framework), and owner-manager characteristics (innovativeness and knowledge) influence the adoption of Cloud BI tools and services in Ghanaian SMEs. This study contributes to the body of knowledge by providing a Cloud BI adoption model from a developing country context. Practically, the study provides insights to vendors about the kind of Cloud BI Ghanaian SMEs require. Vendors can also use the findings to create awareness about the services they offer in terms of Cloud BI.
{"title":"Determinants of Cloud Business Intelligence Adoption Among Ghanaian SMEs","authors":"A. Owusu","doi":"10.4018/IJCAC.2020100104","DOIUrl":"https://doi.org/10.4018/IJCAC.2020100104","url":null,"abstract":"This study explores the determinants of Cloud BI adoption among Ghanaian small-medium enterprises (SMEs). The study was guided by the technology-organization-environment (TOE) framework and information systems adoption model and employed the qualitative method through an in-depth interview with data collected from CEOs/key managers from 17 SMEs in Ghana. The results showed that technological characteristics (relative advantage, complexity, and compatibility), organizational characteristics (organization size and organizational readiness), environmental characteristics (competitive pressure and regulatory framework), and owner-manager characteristics (innovativeness and knowledge) influence the adoption of Cloud BI tools and services in Ghanaian SMEs. This study contributes to the body of knowledge by providing a Cloud BI adoption model from a developing country context. Practically, the study provides insights to vendors about the kind of Cloud BI Ghanaian SMEs require. Vendors can also use the findings to create awareness about the services they offer in terms of Cloud BI.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116423832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.4018/IJCAC.2020100102
Ritu Bansal, V. Singh
This research work seeks to suggest the development of an efficient cloud computing system to show that the various forms of effective online learning for sustainable livelihoods for unemployed youth are accountable for and lead to a number of factors. In the scope of sustainable livelihoods for unemployed people, it also aims to recognize certain fields of data analysis and their interrelationship. One question seems to bubble to the surface more than any other in the authors' discussions with clients, friends, and peers: How does successful online learning for sustainable living for unemployed youth explain switching to the cloud? Cloud computing could allow more adequate performance of its own efficient distributed tools through the SaaS system; therefore, both design the cloud computing SaaS distribution framework for unemployed youth talent learning. This article proposes an efficient cloud computing system strategy for active online learning for unemployed youth sustainable livelihoods.
{"title":"Proposed Technique for Efficient Cloud Computing Model in Effective Digital Training Towards Sustainable Livelihoods for Unemployed Youths","authors":"Ritu Bansal, V. Singh","doi":"10.4018/IJCAC.2020100102","DOIUrl":"https://doi.org/10.4018/IJCAC.2020100102","url":null,"abstract":"This research work seeks to suggest the development of an efficient cloud computing system to show that the various forms of effective online learning for sustainable livelihoods for unemployed youth are accountable for and lead to a number of factors. In the scope of sustainable livelihoods for unemployed people, it also aims to recognize certain fields of data analysis and their interrelationship. One question seems to bubble to the surface more than any other in the authors' discussions with clients, friends, and peers: How does successful online learning for sustainable living for unemployed youth explain switching to the cloud? Cloud computing could allow more adequate performance of its own efficient distributed tools through the SaaS system; therefore, both design the cloud computing SaaS distribution framework for unemployed youth talent learning. This article proposes an efficient cloud computing system strategy for active online learning for unemployed youth sustainable livelihoods.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130956117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.4018/IJCAC.2020100105
B. Soltani, Afifa Ghenai, N. Zeghib
A relatively new paradigm for the Cloud-based software deployment is serverless computing. By adopting stateless loosely-coupled functions, the system can obtain many compositions for several purposes. Contrarily to monolithic approach, serverless computing facilitates the evolution of the applications, since the functions may be independently scheduled for reconstitution. Nevertheless, serverless computing dictates that function execution should be within a short duration (five minutes max in most Cloud platforms), after which the function is abruptly ended even if it has not completed its task. This leads to prevent functions requiring longer time from being adopted as Serverless functions. This paper deals with this drawback. It proposes a migration-based approach that promotes the execution of long-duration serverless functions: each running function that reaches the maximum time limit is repeatedly transferred to another cloud platform where it is carried on. At each migration step, the destination cloud is selected regarding the most relevant criteria.
{"title":"Execution of Long-Duration Multi-Cloud Serverless Functions Using Selective Migration-Based Approach","authors":"B. Soltani, Afifa Ghenai, N. Zeghib","doi":"10.4018/IJCAC.2020100105","DOIUrl":"https://doi.org/10.4018/IJCAC.2020100105","url":null,"abstract":"A relatively new paradigm for the Cloud-based software deployment is serverless computing. By adopting stateless loosely-coupled functions, the system can obtain many compositions for several purposes. Contrarily to monolithic approach, serverless computing facilitates the evolution of the applications, since the functions may be independently scheduled for reconstitution. Nevertheless, serverless computing dictates that function execution should be within a short duration (five minutes max in most Cloud platforms), after which the function is abruptly ended even if it has not completed its task. This leads to prevent functions requiring longer time from being adopted as Serverless functions. This paper deals with this drawback. It proposes a migration-based approach that promotes the execution of long-duration serverless functions: each running function that reaches the maximum time limit is repeatedly transferred to another cloud platform where it is carried on. At each migration step, the destination cloud is selected regarding the most relevant criteria.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130861103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.4018/ijcac.2020070101
S. Ahuja, Emily Czarnecki, Sean Willison
Cloud computing has rapidly become a viable competitor to on-premise infrastructure from both management and cost perspectives. This research provides insight into cluster computing performance and variability in cloud-provisioned infrastructure from two popular public cloud providers. A comparative examination of the two cloud platforms using synthetic benchmarks is provided. In this article, we compared the performance of Amazon Web Services Elastic Compute Cluster (EC2) to the Google Cloud Platform (GCP) Compute Engine using three benchmarks: STREAM, IOR, and NPB-EP. Experiments were conducted on clusters with increasing nodes from one to eight. We also performed experiments over the course of two weeks where benchmarks were run at similar times. The benchmarks provided performance metrics for bandwidth (STREAM), read and write performance (IOR), and operations per second (NPB-EP). We found that EC2 outperformed GCP for bandwidth. Both provided good scalability and reliability for bandwidth with GCP showing a slight deviation during the two-week trial. GCP outperformed EC2 in both the read and write tests (IOR) as well as the operations per second test. However, GCP was extremely variable during the read and write tests over the two-week trial. Overall, each platform excelled in different benchmarks and we found EC2 to be more reliable in general.
从管理和成本的角度来看，云计算已经迅速成为内部部署基础设施的有力竞争者。本研究提供了对来自两个流行的公共云提供商的云配置基础设施的集群计算性能和可变性的见解。本文使用综合基准对这两个云平台进行了比较分析。在本文中，我们使用三个基准测试:STREAM、IOR和NPB-EP，比较了Amazon Web Services Elastic Compute Cluster (EC2)和Google Cloud Platform (GCP)计算引擎的性能。在节点从1个增加到8个的集群上进行实验。我们还进行了为期两周的实验，在相似的时间运行基准测试。基准测试提供了带宽(STREAM)、读写性能(IOR)和每秒操作数(NPB-EP)的性能指标。我们发现EC2在带宽方面优于GCP。两者都提供了良好的带宽可扩展性和可靠性，在为期两周的试用期间，GCP略有偏差。GCP在读写测试(IOR)以及每秒操作数测试中都优于EC2。然而，在为期两周的试验中，GCP在读写测试期间变化很大。总的来说，每个平台在不同的基准测试中都表现出色，我们发现EC2总体上更可靠。
{"title":"Multi-Factor Performance Comparison of Amazon Web Services Elastic Compute Cluster and Google Cloud Platform Compute Engine","authors":"S. Ahuja, Emily Czarnecki, Sean Willison","doi":"10.4018/ijcac.2020070101","DOIUrl":"https://doi.org/10.4018/ijcac.2020070101","url":null,"abstract":"Cloud computing has rapidly become a viable competitor to on-premise infrastructure from both management and cost perspectives. This research provides insight into cluster computing performance and variability in cloud-provisioned infrastructure from two popular public cloud providers. A comparative examination of the two cloud platforms using synthetic benchmarks is provided. In this article, we compared the performance of Amazon Web Services Elastic Compute Cluster (EC2) to the Google Cloud Platform (GCP) Compute Engine using three benchmarks: STREAM, IOR, and NPB-EP. Experiments were conducted on clusters with increasing nodes from one to eight. We also performed experiments over the course of two weeks where benchmarks were run at similar times. The benchmarks provided performance metrics for bandwidth (STREAM), read and write performance (IOR), and operations per second (NPB-EP). We found that EC2 outperformed GCP for bandwidth. Both provided good scalability and reliability for bandwidth with GCP showing a slight deviation during the two-week trial. GCP outperformed EC2 in both the read and write tests (IOR) as well as the operations per second test. However, GCP was extremely variable during the read and write tests over the two-week trial. Overall, each platform excelled in different benchmarks and we found EC2 to be more reliable in general.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126438119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.4018/ijcac.2020070102
Ahmad Al Nawasrah, Ammar Almomani, Samer H. Atawneh, Mohammad Alauthman
A botnet refers to a set of compromised machines controlled distantly by an attacker. Botnets are considered the basis of numerous security threats around the world. Command and control (C&C) servers are the backbone of botnet communications, in which bots send a report to the botmaster, and the latter sends attack orders to those bots. Botnets are also categorized according to their C&C protocols, such as internet relay chat (IRC) and peer-to-peer (P2P) botnets. A domain name system (DNS) method known as fast-flux is used by bot herders to cover malicious botnet activities and increase the lifetime of malicious servers by quickly changing the IP addresses of the domain names over time. Several methods have been suggested to detect fast-flux domains. However, these methods achieve low detection accuracy, especially for zero-day domains. They also entail a significantly long detection time and consume high memory storage. In this survey, we present an overview of the various techniques used to detect fast-flux domains according to solution scopes, namely, host-based, router-based, DNS-based, and cloud computing techniques. This survey provides an understanding of the problem, its current solution space, and the future research directions expected.
{"title":"A Survey of Fast Flux Botnet Detection With Fast Flux Cloud Computing","authors":"Ahmad Al Nawasrah, Ammar Almomani, Samer H. Atawneh, Mohammad Alauthman","doi":"10.4018/ijcac.2020070102","DOIUrl":"https://doi.org/10.4018/ijcac.2020070102","url":null,"abstract":"A botnet refers to a set of compromised machines controlled distantly by an attacker. Botnets are considered the basis of numerous security threats around the world. Command and control (C&C) servers are the backbone of botnet communications, in which bots send a report to the botmaster, and the latter sends attack orders to those bots. Botnets are also categorized according to their C&C protocols, such as internet relay chat (IRC) and peer-to-peer (P2P) botnets. A domain name system (DNS) method known as fast-flux is used by bot herders to cover malicious botnet activities and increase the lifetime of malicious servers by quickly changing the IP addresses of the domain names over time. Several methods have been suggested to detect fast-flux domains. However, these methods achieve low detection accuracy, especially for zero-day domains. They also entail a significantly long detection time and consume high memory storage. In this survey, we present an overview of the various techniques used to detect fast-flux domains according to solution scopes, namely, host-based, router-based, DNS-based, and cloud computing techniques. This survey provides an understanding of the problem, its current solution space, and the future research directions expected.","PeriodicalId":442336,"journal":{"name":"Int. J. Cloud Appl. Comput.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129505725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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