• KIPS Transactions on Computer and Communication Systems
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• v.3 no.9
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• pp.279-292
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• 2014

Energy aware server cluster aims to reduce power consumption at maximum while keeping QoS(quality of service) as much as energy non-aware server cluster. In the existing methods of energy aware server cluster, they calculate the minimum number of active servers needed to handle current user requests and control server power mode in a fixed time interval to make only the needed servers ON. When loads change rapidly, QoS of the existing methods become degraded because they cannot increase the number of active servers so quickly. To solve this QoS problem, we classify load change situations into five types of rapid growth, growth, normal, decline, and rapid decline, and apply five different thresholds respectively in calculating the number of active servers. Also, we use a flexible scheme to adjust the above classification criterion for multi threshold, considering not only load change but also the remaining capacity of servers to handle user requests. We performed experiments with a cluster of 15 servers. A special benchmarking tool called SPECweb was used to generate load patterns with rapid change. Experimental results showed that QoS of the proposed method is improved up to the level of energy non-aware server cluster and power consumption is reduced up to about 50 percent, depending on the load pattern.

• Journal of Internet Computing and Services
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• v.6 no.2
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• pp.69-84
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• 2005

As the Internet users are increasing rapidly, a cluster web server system is attracted by many researchers and Internet service providers. The cluster web server has been developed to efficiently support a larger number of users as well as to provide high scalable and available system. In order to provide the high performance in the cluster web server, efficient load distribution is important, and recently many content-aware request distribution techniques have been proposed. In this paper, we propose a new content-aware load balancing technique that can evenly distribute the workload to each node in the cluster web server. The proposed technique is based on the hash histogram transformation, in which each URL entry of the web log file is hashed, and the access frequency and file size are accumulated as a histogram. Each user request is assigned into a node by mapping of (hashed value-server node) in the histogram transformation. In the proposed technique, the histogram is updated periodically and then the even distribution of user requests can be maintained continuously. In addition to the load balancing, our technique can exploit the cache effect to improve the performance. The simulation results show that the performance of our technique is quite better than that of the traditional round-robin method and we can improve the performance more than $10\%$ compared with the existing workload-aware load balancing(WARD) method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.6
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• pp.109-116
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• 2007

The lack of load-balancing functionality in most existing ad hoc routing protocols often causes congestion resulting in bad performance and short lifetime of participating nodes. We present an auxiliary scheme for reactive routing protocols to discover an unloaded route, thereby decreasing possibility of congestion. The scheme allows an intermediate node to determine whether to be a relaying node for a newly constructed route autonomously based on the occupancy of its queue. Thus the scheme does not require network-wide load information. Simulation results show that the proposed scheme distributes traffic load well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.891-910
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• 2021

Previous QoS-aware service composition methods mainly focus on how to generate composite service with the optimal QoS efficiently for a single request. However, in the real application scenarios, there are multiple service requests and multiple service providers. It is more important to compose services with suboptimal QoS and maintain the load balance between services. To solve this problem, in this paper, we propose a service composition method, named as dynamically change and balancing composition method (DCBC). It assumes that the QoS of service is not static, and the services can adjust the value of QoS to gain more opportunities to be selected for composition. The method mainly includes two steps, which are the preprocessing step and the service selection step. In the preprocessing step, a backward global best QoS calculation is performed which regarding the static and dynamic QoS respectively; then guided by the global QoS, the feasible services can be selected efficiently in the service selection step. The experiments show that the DCBC method can not only improve the overall quality of composite services but also guarantee the fulfill ratio of requests and the load balance of services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.237-252
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• 2017

In recent years, data centers have become the core infrastructure to deal with big data processing. For these big data applications, network transmission has become one of the most important factors affecting the performance. In order to improve network utilization and reduce job completion time, in this paper, by real-time monitoring from the application layer, we propose job-aware priority scheduling. Our approach takes the correlations of flows in the same job into account, and flows in the same job are assigned the same priority. Therefore, we expect that flows in the same job finish their transmissions at about the same time, avoiding lagging flows. To achieve load balancing, two approaches (Flow-based and Spray) using ECMP (Equal-Cost multi-path routing) are presented. We implemented our scheme using NS-2 simulator. In our evaluations, we emulate real network environment by setting background traffic, scheduling delay and link failures. The experimental results show that our approach can enhance the Hadoop job execution efficiency of the shuffle stage, significantly reduce the network transmission time of the highest priority job.

• KIISE Transactions on Computing Practices
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• v.23 no.12
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• pp.677-683
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• 2017

Apache Storm is a representative real-time distributed processing system, which is able to process data streams quickly over distributed servers. Storm currently provides several stream grouping methods to distribute data traffic to multiple servers. Among them, the shuffle grouping may cause a processing delay problem and the local-or-shuffle grouping used to solve the problem may cause the problem of concentrating the traffic on a specific node. In this paper, we propose the locality-aware grouping to solve the problems that may arise in the existing Storm grouping methods. Experimental results show that the proposed locality-aware grouping is considerably superior to the existing shuffle grouping and the local-or-shuffle grouping. These results show that the new grouping is an excellent approach considering both the locality and load balancing which are limitations of the existing Storm.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06d
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• pp.235-237
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• 2006

최근 분산 해쉬 테이블(Distributed Hash Table)을 이용한 구조적 P2P(peer-to-peer) 시스템에서 로드 밸런싱에 관한 연구가 활발하게 진행되고 있다. 일반적으로 P2P 시스템에서의 피어들은 빈번하게 참여하는 동시에 탈퇴하기도 하기 때문에 로드의 불균형이 심화되는 문제점이 있다. 본 논문에서는 구조적 P2P 시스템에서 피어의 참여와 탈퇴 시 발생하는 가상서버의 이동을 효과적으로 관리하는 기법인 PALB(Peer Activity-aware Load Balancing)를 제안한다. 제안하는 PALB는 피어의 참여, 탈퇴 시에 발생하는 로드 불균형을 해결하기 위하여 적절한 수의 가상서버를 가장 적합한 피어에게 분산시킨다. 또한 이전 연구에서 제안한 OLAB(Object Lifetime-aware Load Balancing)와 함께 사용하여 로드 밸런싱 성능을 향상시킬 수 있다. PALB의 성능 평가를 위하여 동적 P2P 시스템 환경을 구성하였고, 피어와 오브젝트의 랜덤 데이터 셋을 이용하여 시뮬레이션을 수행하였다. 시뮬레이션 결과 PALB가 기존의 시스템에 비하여 더 적은 비용으로 더 나은 로드 밸런싱을 수행함을 확인하였다. 또한 OLAB와 동시에 사용하였을 경우, 매우 뛰어난 성능을 보임을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1238-1259
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• 2024

Previous studies on joint optimization of computation offloading and service caching policies in Mobile Edge Computing (MEC) have often neglected the impact of dependency-aware subtasks, edge server resource constraints, and multiple users on policy formulation. To remedy this deficiency, this paper proposes a many-objective joint optimization dependency-aware task offloading and service caching model (MaJDTOSC). MaJDTOSC considers the impact of dependencies between subtasks on the joint optimization problem of task offloading and service caching in multi-user, resource-constrained MEC scenarios, and takes the task completion time, energy consumption, subtask hit rate, load variability, and storage resource utilization as optimization objectives. Meanwhile, in order to better solve MaJDTOSC, a many-objective evolutionary algorithm TSMSNSGAIII based on a three-stage mating selection strategy is proposed. Simulation results show that TSMSNSGAIII exhibits an excellent and stable performance in solving MaJDTOSC with different number of users setting and can converge faster. Therefore, it is believed that TSMSNSGAIII can provide appropriate sub-task offloading and service caching strategies in multi-user and resource-constrained MEC scenarios, which can greatly improve the system offloading efficiency and enhance the user experience.

• Journal of information and communication convergence engineering
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• v.2 no.2
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• pp.106-109
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• 2004

This study designs a load balancer via direct routing that share a virtual service on a single IP address in the Linux system and suggests an efficient load balancing method to improve transmission speed in the web server cluster environment. It will increase performance and scalability with fast packet transfer and removing bottleneck problem by using TCP Splicing and Content-Aware Distributor method. This method is expected to be the noticeable technology that provides an important interface, which make application services for e-commerce effectively be applied to high-speed network infrastructure. At this time, it is required to study further on the optimum balancing method in the web server cluster environment so as to apply the hybrid (optimum load balancing method by software and hardware) method and improve the reuse of security cession based on high-speed TCP connections.