Value of Service Based Task Scheduling for Cloud Computing Systems

Tunc, Cihan; Kumbhare, Nirmal; Akoglu, Ali; Hariri, Salim; Machovec, Dylan; Siegel, Howard Jay

doi:10.1109/iccac.2016.22

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…Wang and Xia [29] apply MIP to VM placement in the cloud to minimize power subject to processing and memory constraints (but ignoring response time and network delays). Tunc et al [30] use a real-time control rule based on performance monitoring and a Value of Service metric that enforces the response time constraint while controlling for energy consumption. This is close to our stated problem, but they do not include network delays in the response time.…”

Section: 3mentioning

confidence: 99%

Low-power multi-cloud deployment of large distributed service applications with response-time constraints

Singh

Kaur²,

Woodside

et al. 2023

J Cloud Comp

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Distributed service applications make heavy use of clouds and multi-clouds, and must (i) meet service quality goals (e.g. response time) while (ii) satisfying cloud resource constraints and (iii) conserving power. Deployment algorithms must (iv) provide a solution meeting these requirements within a short time to be useful in practice. Very few existing deployment methods address the first three requirements, and those that do take too long to find a deployment. The Low-Power Multi-Cloud Application Deployment (LPD) algorithm fills this gap with a low-complexity heuristic combination of generalized graph partitioning between clouds, bin-packing within each cloud and queueing approximations to control the response time. LPD has no known competitor that quickly finds a solution that satisfies response time bounds. A host execution time approximation for contention is fundamental to achieving sufficient solution speed. LPD is intended for use by cloud managers who must simultaneously manage hosts and application deployments and plan capacity to offer services such as Serverless Computing.On 104 test scenarios deploying up to 200 processes with up to 240 replicas (for scaling), LPD always produced a feasible solution within 100 s (within 20 seconds in over three-quarters of cases). Compared to the Mixed Integer Program solution by CPLEX (which took a lot longer and was sometimes not found) LPD solutions gave power consumption equal to MIP in a third of cases and within 6% of MIP in 95% of cases. In 93% of all 104 cases the power consumption is within 20% of an (unachievable) lower bound.LPD is intended as a stand-alone heuristic to meet solution time restrictions, but could easily be adapted for use as a repair mechanism in a Genetic Algorithm.

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Section: 3mentioning

confidence: 99%

Low-power multi-cloud deployment of large distributed service applications with response-time constraints

Singh

Kaur²,

Woodside

et al. 2023

J Cloud Comp

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“…The idea of Value of Service (VoS) based task scheduling for a CC system was represented in Tunc et al [11]. The author presented another time-based value metric to empower scheduling algorithms considers the arrival task time and also task completion value and power utilization for a given period.…”

Section: Survey Of Existing Researchesmentioning

confidence: 99%

An Extensive Survey over Traffic Management/Load Balance in Cloud Computing

C¹,

Sharvani²

2017

ijacsa

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Abstract-Cloud Computing (CC) is all about carrying out processing in other's system. There are various vendors who provide CC services. The basic algorithm that should be met to access CC services is a need for steady internet connection. As everything is done online the traffic across the internet is to be managed efficiently so that the transmission delay can be minimized and better quality of service can be given to the customers. The network should not be too congested at any moment of time. Hence the traffic management becomes a crucial factor for the better performance of the CC network. This paper addressed the most valuable terms and topics concerning the load balance/traffic management in cloud computing. Also, the paper is meant to discuss the study analysis of the recent researches in load balance of CC. From the study analysis the current research gap is addressed with a future scope of research to overcome the research gap.

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“…Tunc et al, in [24] presented a new metric called Value of Service mainly consider completed tasks within deadline through balancing its value of completing within deadline with energy consumption. They defined the proposed metric as the sum of the values for all tasks that are executed during a given period of time including task arrival time.…”

Section: Related Workmentioning

confidence: 99%

A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique

2017

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Cloud computing is a ubiquitous network access model to a shared pool of configurable computing resources where available resources must be checked and scheduled using an efficient task scheduler to be assigned to clients. Most of the existing task schedulers, did not achieve the required standards and requirements as some of them only concentrated on waiting time or response time reduction or even both neglecting the starved processes at all. In this paper, we propose a novel hybrid task scheduling algorithm named (SRDQ) combining Shortest-JobFirst (SJF) and Round Robin (RR) schedulers considering a dynamic variable task quantum. The proposed algorithms mainly relies on two basic keys the first having a dynamic task quantum to balance waiting time between short and long tasks while the second involves splitting the ready queue into two sub-queues, Q1 for the short tasks and the other for the long ones. Assigning tasks to resources from Q 1 or Q 2 are done mutually two tasks from Q 1 and one task from Q 2 . For evaluation purpose, three different datasets were utilized during the algorithm simulation conducted using CloudSim environment toolkit 3.0.3 against three different scheduling algorithms SJF, RR and Time Slice Priority Based RR (TSPBRR) Experimentations results and tests indicated the superiority of the proposed algorithm over the state of art in reducing waiting time, response time and partially the starvation of long tasks.

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Value of Service Based Task Scheduling for Cloud Computing Systems

Cited by 10 publications

References 31 publications

Low-power multi-cloud deployment of large distributed service applications with response-time constraints

Low-power multi-cloud deployment of large distributed service applications with response-time constraints

An Extensive Survey over Traffic Management/Load Balance in Cloud Computing

A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique

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