• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1721-1723
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• 2002

In 3rd-generation mobile communication systems providing packet service, optimal scheduling algorithms we needed to increase service efficiency. In this paper, three scheduling algorithms (SP, EDF, and RPQ) which have been studied in the field of computer networks are applied with transmit power constraint to an interference-limited CDMA system, proving to be efficient.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2000년도 추계학술대회 및 정기총회
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• pp.149-152
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• 2000

The chartered bus scheduling is to determine the allocation of buses to customer service requests. For the scheduling, we first represent the scheduling problem using a graph and develop a mathematical model. Second, we develop an algorithm for the scheduling problem including heuristic methods. We tested the performance of the algorithm on instances with real data. As a result, the total empty load driving time is reduced over 10% comparing with that of current practice at the company

• International Journal of Quality Innovation
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• 제10권2호
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• pp.19-27
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• 2009

This paper considers the short term fleet scheduling problem as described by Keskinocak and Tayur (1998). Fleet scheduling may directly affect the service quality of fractional jet aircraft business. The contributions of this paper are two: (i) we show how their model is easily implemented in a standard modeling language, LINGO, and (ii) an alternate formulation is given which is expected to perform better on large, difficult problems.

• 대한수학회보
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• 제46권4호
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• pp.673-689
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• 2009

We analyze $MAP_1,\;MAP_2$/G/1 finite queues with service scheduling function dependent upon queue lengths. The customers are classified into two types. The arrivals of customers are assumed to be the Markovian Arrival Processes (MAPs). The service order of customers in each buffer is determined by a service scheduling function dependent upon queue lengths. Methods of embedded Markov chain and supplementary variable give us information for queue length of two buffers. Finally, the performance measures such as loss probability and mean waiting time are derived. Some numerical examples also are given with applications in telecommunication networks.

• Journal of Communications and Networks
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• 제4권3호
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• pp.209-220
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• 2002

In this paper, we propose a hierarchical packet scheduling technique to closely approximate a hierarchical extension of the generalized processor sharing model, Hierarchical Generalized Processor Sharing (H-GPS). Our approach is to undertake the tasks of service guarantee and hierarchical link sharing in an independent manner so that each task best serves its own objective. The H-GPS model is decomposed into two separate service components: the guaranteed service component to consistently provide performance guarantees over the entire system, and the excess service component to fairly distribute spare bandwidth according to the hierarchical scheduling rule. For tight and harmonized integration of the two service components into a single packet scheduling algorithm, we introduce two novel concepts of distributed virtual time and service credit, and develop a packet version of H-GPS called Hierarchical Fair Queueing (HFQ). We demonstrate the layerindependent performance of the HFQ algorithm through simulation results.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.1074-1077
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• 2002

This paper proposes an adaptive pricing system with scheduling to balance the demand for contents and to realize an effective use of resources in contents delivery networks. In the proposed adaptive pricing system, the table of the service levels and prices (tariff) is shown to each user at the start of service and each user chooses one of the service classes. These prices are decided adaptively reflecting the congestion state of the networks. Then, by the proposed scheduling algorithm, these requests are scheduled so as to keep the service level agreements completely.

• 한국통신학회논문지
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• 제23권5호
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• pp.1241-1248
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• 1998

전송 속도가 재생 속도보다 빠른 통신망 환경에서 저장-재생 방식을 이용하여 멀티캐스팅 기회를 동적으로 생성해 멀티미디어 리트리벌 서비스의 효율을 높일 수 있다. 본 논문에서는 멀티캐스트 전송에서의 상이한 수신자 환경을 고려한 멀티캐스트 그룹 스케줄링 방법인 MTS(Maximum Throughput Scheduling) 알고리즘을 제안하였다. 이 방법은 수신자까지의 대역폭을 최대로 이용하는 단대단 전송을 반복하는 방식과 멀티캐스트 전송을 이용하여 서버의 효율을 높이는 방법을 절충한 것으로 서버의 처리율을 높일 수 있도록 가입자들의 수신율에 따라 멀티캐스트 그룹을 부그룹으로 나누어 실제 처리율이 가장 높은 세그먼트를 선택한다. MTS의 성능을 분석하기 위해 시뮬레이션을 통해 대역폭의 상이함을 고려하지 않은 알고리즘인 EDS(Earliest Deadline Scheduling), MMS(Most Multicasting Scheduling)와 비교하였다. MTS 알고리즘은 가입자들의 수신율이 상이할 때 다른 스케줄링 알고리즘에 비해 우수한 성능을 나타내는 것을 발견하였다.

• Journal of Multimedia Information System
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• 제2권1호
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• pp.143-152
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• 2015

Multimedia services in the cloud have become a popular trend in the big data environment. However, how to efficiently schedule a large number of multimedia services in the cloud is still an open and challengeable problem. Current cloud-based scheduling algorithms exist the following problems: 1) the content of the multimedia is ignored, and 2) the cloud platform is a known parameter, which makes current solutions are difficult to utilize practically. To resolve the above issues completely, in this work, we propose a novel distributed multimedia scheduling to satisfy the objectives: 1) Develop a general cloud-based multimedia scheduling model which is able to apply to different multimedia applications and service platforms; 2) Design a distributed scheduling algorithm in which each user makes a decision based on its local information without knowing the others' information; 3) The computational complexity of the proposed scheduling algorithm is low and it is asymptotically optimal in any case. Numerous simulations have demonstrated that the proposed scheduling can work well in all the cloud service environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1258-1275
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• 2023

Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues.

• ETRI Journal
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• 제29권2호
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• pp.201-211
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• 2007

This paper focuses on the scheduling problem with the objective of maximizing system throughput, while guaranteeing long-term quality of service (QoS) constraints for non-realtime data users and short-term QoS constraints for realtime multimedia users in multiclass service high-speed uplink packet access (HSUPA) systems. After studying the feasible rate region for multiclass service HSUPA systems, we formulate this scheduling problem and propose a multi-constraints HSUPA opportunistic scheduling (MHOS) algorithm to solve this problem. The MHOS algorithm selects the optimal subset of users for transmission at each time slot to maximize system throughput, while guaranteeing the different constraints. The selection is made according to channel condition, feasible rate region, and user weights, which are adjusted by stochastic approximation algorithms to guarantee the different QoS constraints at different time scales. Simulation results show that the proposed MHOS algorithm guarantees QoS constraints, and achieves high system throughput.