Meeting Users’ QoS in a Edge-to-Cloud Platform via Optimally Placing Services and Scheduling Tasks

Turner, Martin; Khamfroush, Hana

doi:10.1109/icnc47757.2020.9049749

Cited by 9 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By emphasizing and maximizing quality and professionalism in KPO activities, the cost of service provider selection can be minimized (Ecer, 2018). Recognizing the recurring nature of client requests and optimizing service deployment accordingly allows the service provider to achieve significant efficiency gains, including reduced costs, faster response times, and better resource utilization (Turner & Khamfroush, 2020). Hence, a KPO organization must invest time in the proper selection of a service provider.…”

Section: Service Provider Selection Costmentioning

confidence: 99%

ISM approach to model financial risks in Indian KPO organization

Payal,

Chauhan

2023

IJERR

View full text Add to dashboard Cite

Globalization has entered a new phase due to significant advancements in technology. This progress has allowed organizations to reduce costs while enhancing their market responsiveness, thus gaining a competitive edge. Nowadays, organizations prioritize knowledge process outsourcing (KPO) as a crucial element in their strategies, particularly in the service sector. However, research indicates that several challenges are negatively impacting the performance of the supply chain network when it comes to KPO. One major challenge is the financial risk associated with this environment. Various factors, such as switching costs, adaptation costs, and layoff costs, influence this financial risk. The aim of this research is to comprehend and identify the interconnectedness of these financial risk factors, which can affect the operational performance of KPO. The authors have meticulously reviewed relevant literature to identify these financial risk factors. Based on this subject matter, an integrated model has been constructed using Interpretive Structural Modeling (ISM) to address the finance-related challenges impacting KPO. Additionally, the Cross-Impact Matrix analysis evaluates the dependency and driving power of financial challenges. This analysis assists decision-makers in classifying and identifying important factors, as well as understanding the direct and indirect effects of each factor on operations related to KPO. The results indicate that switching costs, disputes & litigation, and measurement problems have strong driving power and weak dependency, making them independent factors. By mitigating these independent factors, the overall performance of the model can be improved.

show abstract

Section: Service Provider Selection Costmentioning

confidence: 99%

ISM approach to model financial risks in Indian KPO organization

Payal,

Chauhan

2023

IJERR

View full text Add to dashboard Cite

show abstract

“…Many works study the problem with the goal to optimize resource utilization, energy consumption [5], [6], and Quality-of-Service (QoS) [7], [8]. Some works consider a static placement decision where the placement decision is made in a single shot [9], [10]. Other works focus on dynamic placement where placement decisions are made over some timespan [11], [12].…”

Section: Related Workmentioning

confidence: 99%

“…User delay thresholds, δ u , are set to a sampled value (clipped to range [0, δ max ] where δ max = 10 seconds) from an exponential distribution where λ = 1.5. Finally, we consider |E| = 10 edge clouds, |S| = 100 services with each service having a random number of implementations in the range of [1,10] (sampled uniformly). We increase the number of users for experiments.…”

Section: B Numerical Simulationsmentioning

confidence: 99%

QoS-Aware Placement of Deep Learning Services on the Edge with Multiple Service Implementations

Hudson¹,

Khamfroush²,

Lucani³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Mobile edge computing pushes computationallyintensive services closer to the user to provide reduced delay due to physical proximity. This has led many to consider deploying deep learning models on the edge -commonly known as edge intelligence (EI). EI services can have many model implementations that provide different QoS. For instance, one model can perform inference faster than another (thus reducing latency) while achieving less accuracy when evaluated. In this paper, we study joint service placement and model scheduling of EI services with the goal to maximize Quality-of-Servcice (QoS) for end users where EI services have multiple implementations to serve user requests, each with varying costs and QoS benefits. We cast the problem as an integer linear program and prove that it is NP-hard. We then prove the objective is equivalent to maximizing a monotone increasing, submodular set function and thus can be solved greedily while maintaining a (1 − 1/e)-approximation guarantee. We then propose two greedy algorithms: one that theoretically guarantees this approximation and another that empirically matches its performance with greater efficiency. Finally, we thoroughly evaluate the proposed algorithm for making placement and scheduling decisions in both synthetic and realworld scenarios against the optimal solution and some baselines. In the real-world case, we consider real machine learning models using the ImageNet 2012 data-set for requests. Our numerical experiments empirically show that our more efficient greedy algorithm is able to approximate the optimal solution with a 0.904 approximation on average, while the next closest baseline achieves a 0.607 approximation on average.

show abstract

Proactive Fault Tolerant Service Management in Edge Computing

Tambe,

2023

2023 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

View full text Add to dashboard Cite

Meeting Users’ QoS in a Edge-to-Cloud Platform via Optimally Placing Services and Scheduling Tasks

Cited by 9 publications

References 10 publications

ISM approach to model financial risks in Indian KPO organization

ISM approach to model financial risks in Indian KPO organization

QoS-Aware Placement of Deep Learning Services on the Edge with Multiple Service Implementations

Proactive Fault Tolerant Service Management in Edge Computing

Contact Info

Product

Resources

About