Processing resource scheduling in programmable networks

Sabrina, Fariza; Nguyen, Cindy; Jha, Sanjay; Platt, D.; Safaei, Farzad

doi:10.1016/j.comcom.2004.08.011

Cited by 11 publications

(4 citation statements)

References 15 publications

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“…One of the main functions of the operating system is the scheduling of the resources to serve all the running processes [115]. Scheduling CPU is the most important scheduling function of the OS as without it only a single program can use the CPU from start to finish and the rest of processes will starve [139].…”

Section: Resource Utilizationmentioning

confidence: 99%

Cloud Slicing a new architecture for cloud security monitoring

Aldribi

Traoré

Letourneau

2015

2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

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The adoption of cloud computing has increased dramatically in recent years due to attractive features such as flexibility, cost reductions, scalability, and pay per use. Shifting towards cloud computing is attracting not only industry but also government and academia. However, given their stringent privacy and security policies, this shift is still hindered by many security concerns related to the cloud computing features, namely shared resources, virtualization and multi-tenancy. These security concerns vary from privacy threats and lack of transparency to intrusions from within and outside the cloud infrastructure. Therefore, to overcome these concerns and establish a strong trust in cloud computing, there is a need to develop adequate security mechanisms for effectively handling the threats faced in the cloud. Intrusion Detection Systems (IDSs) represent an important part of such mechanisms. Developing cloud based IDS that can capture suspicious activity or threats, and prevent attacks and data leakage from both inside and outside the cloud environment is paramount. However, cloud computing is faced with a multidimensional and rapidly evolving threat landscape, which makes cloud based IDS more challenging. Moreover, one of the most significant hurdles for developing such cloud IDS is the lack of publicly available datasets collected from a real cloud computing environment. In this dissertation, we introduce the first public dataset of its kind, named ISOT Cloud Intrusion Dataset (ISOT-CID), for cloud intrusion detection. The dataset consists of several terabytes of data, involving normal activities and a wide variety of attack vectors, collected over multiple phases and periods of time in a real cloud environment. We also introduce a new hypervisor-based cloud intrusion detection system (HIDS) that uses online multivariate statistical change analysis to detect anomalous network behaviors. As a departure from the conventional monolithic network IDS feature model, we leverage the fact that a hypervisor consists of a collection of instances, to introduce an instance-oriented feature model that exploits individual as well as correlated behaviors of instances to improve the detection capability. The proposed approach is evaluated using ISOT-CID and the experiments along with results are presented.

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Section: Resource Utilizationmentioning

confidence: 99%

Cloud Slicing a new architecture for cloud security monitoring

Aldribi

Traoré

Letourneau

2015

2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

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“…When we have a process attempting to be executed, the long-term scheduler decides whether to admit or delay this process [1] [2].…”

Section: Amentioning

confidence: 99%

“…As a result, resource utilization and overall system performance will be affected by CPU scheduling, which is very important [2]. There are four types of scheduling involved in a multitasking system, with each solving a scheduling problem for each area of operating system functionality:thelong-term scheduler, mid-term scheduler, and short-term scheduler.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2015

International Journal of Advanced Research in Computer and Comm

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An operating system provides numerous functions, such as I/O management, memory management, process management, and file management. Since the operating system is a set of the programs that interacts with computer hardware during executing time, process management is the most important function provided by an operating system. CPU scheduling is extremely necessary, as it makes a multi-tasking environment that keeps the CPU and I/O devices busy at all times which results in increased CPU utilization [1]. However, numerous scheduling algorithms have already been designed to regulate the access of threads and processes to the CPU, such as FCFS-SJF-SRT-RR. We simulated these scheduling algorithms and evaluated their performance (throughput, latency, utilization, turnaround time, and waiting time) in a multi-processor environment.

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“…Sabrina et al [18], discuss issues in designing resource schedulers for processing engines in programmable networks. Authors developed two CPU scheduling algorithms that could schedule CPU resource adaptively among all the competing flows.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Scheduling for Work-Flow Applications in Distributed Computing Environments

Abraham

Liu

Zhao

2008

Metaheuristics for Scheduling in Industrial and Manufacturing Applications

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Recently, the scheduling problem in distributed data-intensive computing environments has been an active research topic. This Chapter models the scheduling problem for work-flow applications in distributed dataintensive computing environments (FDSP) and makes an attempt to formulate the problem. Several meta-heuristics inspired from particle swarm optimization algorithm are proposed to formulate efficient schedules. The proposed variable neighborhood particle particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experiment results illustrate the algorithm performance and its feasibility and effectiveness for scheduling work-flow applications.

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Processing resource scheduling in programmable networks

Cited by 11 publications

References 15 publications

Cloud Slicing a new architecture for cloud security monitoring

Cloud Slicing a new architecture for cloud security monitoring

Untitled

Particle Swarm Scheduling for Work-Flow Applications in Distributed Computing Environments

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