A server-side accelerator framework for multi-core CPUs and Intel Xeon Phi co-processor systems

You, Guohua; Wang, Xuejing

doi:10.1007/s10586-019-03030-z

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…In order to increase the computational speed of parallel tasks in a real-time simulation, a framework is proposed that is based on thread pool, where the size of pool depends upon particular GPU architecture running the application. Similarly, the work of [31] targeted those applications that are running in multicore CPU's. The authors presented a framework for web servers to increase the performance of multicore CPU's.…”

Section: Related Workmentioning

confidence: 99%

The Double Edge Sword Based Distributed Executor Service

Bahadur¹,

Umar²,

Ullah³

et al. 2022

Computer Systems Science and Engineering

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Scalability is one of the most important quality attribute of softwareintensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a clientserver based simulator and found that our system outperformed by sustaining smaller response times.

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Section: Related Workmentioning

confidence: 99%

The Double Edge Sword Based Distributed Executor Service

Bahadur¹,

Umar²,

Ullah³

et al. 2022

Computer Systems Science and Engineering

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“…1. Dynamic Scalability: Further, the dynamic scalability can be incorporated [14], which can provide operational capabilities to improve performance of cloud computing applications in a cost-effective way, yet to be fully exploited [33].In addition, the vertical scaling strategy can be designed to overcome the challenge of startup delay of VMs [70]. 2.…”

Section: Future Directions and Open Challengesmentioning

confidence: 99%

RHAS: robust hybrid auto-scaling for web applications in cloud computing

et al. 2020

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The elasticity characteristic of cloud services attracts application providers to deploy applications in a cloud environment. The scalability feature of cloud computing gives the facility to application providers to dynamically provision the computing power and storage capacity from cloud data centers. The consolidation of services to few active servers can enhance the service sustainability and reduce the operational cost. The state-of-art algorithms mostly focus either on reactive or proactive auto-scaling techniques. In this article, a Robust Hybrid Auto-Scaler (RHAS) is presented for web applications. The time series forecasting model has been used to predict the future incoming workload. The reactive approach is used to deal with the current

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