Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power Function

Singh, Pawan; Khan, Baseem; Vidyarthi, Ankit; Alhelou, Hassan Haes; Siano, Pierluigi

doi:10.3390/app9071467

Cited by 29 publications

(8 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experimental results show the good performance of the proposed model. Furthermore, in [100], the online non-clairvoyant scheduling algorithm Highest Scaled Importance First (HSIF) method was proposed, in which HSIF chooses an active job with the highest scaled importance to minimize the sum of scaled importance-based flow time and energy consumed. The use of HSIF in data centers and battery-based devices reduces power consumption and improves computing capability.…”

Section: Solutions At Host Levelmentioning

confidence: 99%

“…The techniques to solve the virtual machines and hypervisor issues are intrusion detection and VM monitoring These techniques were limited their techniques to specific scenarios, known patterns of attacks or even specific software Techniques need more focus on other types of attacks such as distributed side-channel attacks while designing their detecting or preventing techniques [8,35,[90][91][92][93][94][95][96][97][98][99][100][101][102]…”

Section: Host Levelmentioning

confidence: 99%

See 1 more Smart Citation

Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges

et al. 2021

View full text Add to dashboard Cite

Cloud computing is currently becoming a well-known buzzword in which business titans, such as Microsoft, Amazon, and Google, among others, are at the forefront in developing and providing sophisticated cloud computing systems to their users in a cost-effective manner. Security is the biggest concern for cloud computing and is a major obstacle to users adopting cloud computing systems. Maintaining the security of cloud computing is important, especially for the infrastructure. Several research works have been conducted in the cloud infrastructure security area; however, some gaps have not been completely addressed, while new challenges continue to arise. This paper presents a comprehensive survey of the security issues at different cloud infrastructure levels (e.g., application, network, host, and data). It investigates the most prominent issues that may affect the cloud computing business model with regard to infrastructure. It further discusses the current solutions proposed in the literature to mitigate the different security issues at each level. To assist in solving the issues, the challenges that are still unsolved are summarized. Based on the exploration of the current challenges, some cloud features such as flexibility, elasticity and the multi-tenancy are found to pose new challenges at each infrastructure level. More specifically, the multi-tenancy is found to have the most impact at all infrastructure levels, as it can lead to several security problems such as unavailability, abuse, data loss and privacy breach. This survey concludes by giving some recommendations for future research.

show abstract

Section: Solutions At Host Levelmentioning

confidence: 99%

Section: Host Levelmentioning

confidence: 99%

Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Singh et al [13] considered task scheduling for data centers by considering process time flow in order to minimize the overall energy consumed by the data center. The work presented an interesting power model, but the focus of the work was to minimize overall energy, not constrain peak current.…”

Section: Introductionmentioning

confidence: 99%

Task Scheduling to Constrain Peak Current Consumption in Wearable Healthcare Sensors

Sherratt

Janko

Hui³

et al. 2019

Electronics

View full text Add to dashboard Cite

Small embedded systems, in our case wearable healthcare devices, have significant engineering challenges to reduce their power consumption for longer battery life, while at the same time supporting ever-increasing processing requirements for more intelligent applications. Research has primarily focused on achieving lower power operation through hardware designs and intelligent methods of scheduling software tasks, all with the objective of minimizing the overall consumed electrical power. However, such an approach inevitably creates points in time where software tasks and peripherals coincide to draw large peaks of electrical current, creating short-term electrical stress for the battery and power regulators, and adding to electromagnetic interference emissions. This position paper proposes that the power profile of an embedded device using a real-time operating system (RTOS) will significantly benefit if the task scheduler is modified to be informed of the electrical current profile required for each task. This enables the task scheduler to schedule tasks that require large amounts of current to be spread over time, thus constraining the peak current that the system will draw. We propose a solution to inform the task scheduler of a tasks' power profile, and we discuss our application scenario, which clearly benefited from the proposal.

show abstract

“…The competitiveness of MBS is the least to date.critical circumstances, the current issue in the scheduling is to attain the good quality of service by generating an optimal schedule of jobs and to save the energy consumption, which is a conflicting and complicated problem [5].The power P consumed by a processor running at speed s is sV 2 , where V is a voltage [6]. The traditional power function is P = s α (α ≥ 2 for CMOS based chips [7,8]). There are two types of speed models: the first unbounded speed model, in which the processor's speed range is, i.e., [0, ∞); the second bounded speed model, in which the speed of a processor can range from zero to some maximum speed, i.e., [0, η].…”

mentioning

confidence: 99%

“…The power P consumed by a processor running at speed s is sV 2 , where V is a voltage [6]. The traditional power function is P = s α (α ≥ 2 for CMOS based chips [7,8]). There are two types of speed models: the first unbounded speed model, in which the processor's speed range is, i.e., [0, ∞); the second bounded speed model, in which the speed of a processor can range from zero to some maximum speed, i.e., [0, η].…”

mentioning

confidence: 99%

Managing Energy Plus Performance in Data Centers and Battery-Based Devices Using an Online Non-Clairvoyant Speed-Bounded Multiprocessor Scheduling

Singh

Khan

Mahela³

et al. 2020

Applied Sciences

Self Cite

View full text Add to dashboard Cite

An efficient scheduling reduces the time required to process the jobs, and energy management decreases the service cost as well as increases the lifetime of a battery. A balanced trade-off between the energy consumed and processing time gives an ideal objective for scheduling jobs in data centers and battery based devices. An online multiprocessor scheduling multiprocessor with bounded speed (MBS) is proposed in this paper. The objective of MBS is to minimize the importance-based flow time plus energy (IbFt+E), wherein the jobs arrive over time and the job’s sizes are known only at completion time. Every processor can execute at a different speed, to reduce the energy consumption. MBS is using the tradition power function and bounded speed model. The functioning of MBS is evaluated by utilizing potential function analysis against an offline adversary. For processors m ≥ 2, MBS is O(1)-competitive. The working of a set of jobs is simulated to compare MBS with the best known non-clairvoyant scheduling. The comparative analysis shows that the MBS outperforms other algorithms. The competitiveness of MBS is the least to date.

show abstract

Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power Function

Cited by 29 publications

References 31 publications

Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges

Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges

Task Scheduling to Constrain Peak Current Consumption in Wearable Healthcare Sensors

Managing Energy Plus Performance in Data Centers and Battery-Based Devices Using an Online Non-Clairvoyant Speed-Bounded Multiprocessor Scheduling

Contact Info

Product

Resources

About