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Sultan Alshamrani
Taif University
Computer Science & IT Electrical & Electronics Engineering Engineering

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High availability of data using Automatic Selection Algorithm (ASA) in distributed stream processing systems Sultan Alshamrani; Hesham Alhumyani; Quadri Waseem; Isbudeen Noor Mohamed
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (704.416 KB) | DOI: 10.11591/eei.v8i2.1414

Abstract

High Availability of data is one of the most critical requirements of a distributed stream processing systems (DSPS). We can achieve high availability using available recovering techniques, which include (active backup, passive backup and upstream backup). Each recovery technique has its own advantages and disadvantages. They are used for different type of failures based on the type and the nature of the failures. This paper presents an Automatic Selection Algorithm (ASA) which will help in selecting the best recovery techniques based on the type of failures. We intend to use together all different recovery approaches available (i.e., active standby, passive standby, and upstream standby) at nodes in a distributed stream-processing system (DSPS) based upon the system requirements and a failure type). By doing this, we will achieve all benefits of fastest recovery, precise recovery and a lower runtime overhead in a single solution. We evaluate our automatic selection algorithm (ASA) approach as an algorithm selector during the runtime of stream processing. Moreover, we also evaluated its efficiency in comparison with the time factor. The experimental results show that our approach is 95% efficient and fast than other conventional manual failure recovery approaches and is hence totally automatic in nature.
An efficient algorithm for monitoring virtual machines in clouds Sultan Alshamrani
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (688.566 KB) | DOI: 10.11591/eei.v8i2.1416

Abstract

Cloud computing systems consist of a pool of Virtual Machines (VMs), which are installed physically on the provider's set up. The main aim of the VMs is to offer the service to the end users. With the current increasing demand for the cloud VMs, there is always a huge requirement to secure the cloud systems. To keep these cloud systems secured, they need a continuous and a proper monitoring. For the purpose of monitoring, several algorithms are available with FVMs. FVM is a forensic virtual machine which monitors the threats among the VMs. Our formulated algorithm runs on FVM. In this paper, we formulate the Random-Start-Round-Robin algorithm for monitoring inside FVM.
High availability of data using Automatic Selection Algorithm (ASA) in distributed stream processing systems Sultan Alshamrani; Hesham Alhumyani; Quadri Waseem; Isbudeen Noor Mohamed
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (704.416 KB) | DOI: 10.11591/eei.v8i2.1414

Abstract

High Availability of data is one of the most critical requirements of a distributed stream processing systems (DSPS). We can achieve high availability using available recovering techniques, which include (active backup, passive backup and upstream backup). Each recovery technique has its own advantages and disadvantages. They are used for different type of failures based on the type and the nature of the failures. This paper presents an Automatic Selection Algorithm (ASA) which will help in selecting the best recovery techniques based on the type of failures. We intend to use together all different recovery approaches available (i.e., active standby, passive standby, and upstream standby) at nodes in a distributed stream-processing system (DSPS) based upon the system requirements and a failure type). By doing this, we will achieve all benefits of fastest recovery, precise recovery and a lower runtime overhead in a single solution. We evaluate our automatic selection algorithm (ASA) approach as an algorithm selector during the runtime of stream processing. Moreover, we also evaluated its efficiency in comparison with the time factor. The experimental results show that our approach is 95% efficient and fast than other conventional manual failure recovery approaches and is hence totally automatic in nature.
An efficient algorithm for monitoring virtual machines in clouds Sultan Alshamrani
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (688.566 KB) | DOI: 10.11591/eei.v8i2.1416

Abstract

Cloud computing systems consist of a pool of Virtual Machines (VMs), which are installed physically on the provider's set up. The main aim of the VMs is to offer the service to the end users. With the current increasing demand for the cloud VMs, there is always a huge requirement to secure the cloud systems. To keep these cloud systems secured, they need a continuous and a proper monitoring. For the purpose of monitoring, several algorithms are available with FVMs. FVM is a forensic virtual machine which monitors the threats among the VMs. Our formulated algorithm runs on FVM. In this paper, we formulate the Random-Start-Round-Robin algorithm for monitoring inside FVM.
High availability of data using Automatic Selection Algorithm (ASA) in distributed stream processing systems Sultan Alshamrani; Hesham Alhumyani; Quadri Waseem; Isbudeen Noor Mohamed
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (704.416 KB) | DOI: 10.11591/eei.v8i2.1414

Abstract

High Availability of data is one of the most critical requirements of a distributed stream processing systems (DSPS). We can achieve high availability using available recovering techniques, which include (active backup, passive backup and upstream backup). Each recovery technique has its own advantages and disadvantages. They are used for different type of failures based on the type and the nature of the failures. This paper presents an Automatic Selection Algorithm (ASA) which will help in selecting the best recovery techniques based on the type of failures. We intend to use together all different recovery approaches available (i.e., active standby, passive standby, and upstream standby) at nodes in a distributed stream-processing system (DSPS) based upon the system requirements and a failure type). By doing this, we will achieve all benefits of fastest recovery, precise recovery and a lower runtime overhead in a single solution. We evaluate our automatic selection algorithm (ASA) approach as an algorithm selector during the runtime of stream processing. Moreover, we also evaluated its efficiency in comparison with the time factor. The experimental results show that our approach is 95% efficient and fast than other conventional manual failure recovery approaches and is hence totally automatic in nature.
An efficient algorithm for monitoring virtual machines in clouds Sultan Alshamrani
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (688.566 KB) | DOI: 10.11591/eei.v8i2.1416

Abstract

Cloud computing systems consist of a pool of Virtual Machines (VMs), which are installed physically on the provider's set up. The main aim of the VMs is to offer the service to the end users. With the current increasing demand for the cloud VMs, there is always a huge requirement to secure the cloud systems. To keep these cloud systems secured, they need a continuous and a proper monitoring. For the purpose of monitoring, several algorithms are available with FVMs. FVM is a forensic virtual machine which monitors the threats among the VMs. Our formulated algorithm runs on FVM. In this paper, we formulate the Random-Start-Round-Robin algorithm for monitoring inside FVM.
Co-Authors Hesham Alhumyani Isbudeen Noor Mohamed Quadri Waseem