• Journal of the Korean Operations Research and Management Science Society
• /
• v.24 no.2
• /
• pp.31-39
• /
• 1999

A new approximation method for finding the steady-state probabilities of the number of customers present in queueing systems with Poisson arrivals and two servers with different deterministic service times with infinite waiting room capacity is developed. The major assumption made for the approximation is that the residual service times of the servers have mutually independent uniform distributions with densities equal to the reciprocals of the respective service times. The method reflects the heterogeneity of the servers only through the ratio of their service times, irrespective of the actual magnitudes and difference. The transition probability matrix is established and the steady-state probabilities are found for a variety of traffic intensities and ratios of the two service times; also the mean number of customers present in the system and in the queue, and server utilizations are found and tabulated. The method was validated by simulation and turned out to be very sharp.

• The KIPS Transactions:PartA
• /
• v.12A no.6 s.96
• /
• pp.507-514
• /
• 2005

This paper presents a partially decentralized passive replication algorithm for deterministic servers in message-passing distributed systems. The algorithm allows any backup server, not necessarily the primary server, to take responsibility for processing its received client request and coordinating with the other replica servers after obtaining the delivery sequence number of the request from the primary. Thanks to thus desirable feature, the algorithm with conventional load balancing techniques can efficiently avoid extreme load conditions on the primary. Therefore, it can provide better scalability of deterministic and replicated sewer systems than traditional passive replication algorithms. Simulation results indicate that the proposed algorithm can reduce $16.5\%{\~}52.3\%$ of the average response time of a client request compared with the traditional ones.

• Journal of Korea Society of Industrial Information Systems
• /
• v.4 no.4
• /
• pp.32-39
• /
• 1999

Video-On-Demand servers have to provide timely processing guarantees and reduce the storage and reduce the storage and bandwidth requirements for continuous media However, compression techniques used in Video-On-Demand servers make the bit rates of compressed video data significantly variable from frame to frame Consequently, most pervious Video-On-Demand servers which use constant bit rate retrieval to guarantee deterministic service under-utilize the system resources and restrict the number of clients. In this paper, I propose a buffer management scheme called CAP(Caching And Prefetching) for Video-On-Demand server using variable bit rate continuous media. By caching and prefetching the data CAP reduces the variation of the compressed data and increases the number of clients simultaneously served and maximizes the utilization of system resources. Results of trace-driven simulations show the effectiveness of the scheme.

• Journal of Information Processing Systems
• /
• v.20 no.2
• /
• pp.226-238
• /
• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.