Teck Chaw Ling

Location Determination Using Radio Frequency RSSI and Deterministic Algorithm

In recent years, the employment of IEEE 802.11 radio signals for location determination in an indoor environment has drawn great attention. The basis of this technique is that the location of an object is estimated using an estimation algorithm and the radio frequency (RF) fingerprint database as a reference. The main target of this paper is to find an optimum strategy for the construction of the RF map and a decent estimation algorithm. By obtaining statistical data from several signal strength measurements, we investigate and identify three influential factors on the RSSI behavior. With this knowledge, we propose improvement to the existing deterministic algorithm. An algorithm for detection of and dealing with the aliasing problem is also suggested. Test results show that our proposed combinational deterministic method and the anti aliasing technique improves the system performance by up to 33% compared to other deterministic method using single scalar values.

Thermal-Aware Scheduling in Green Data Centers

Data centers can go green by saving electricity in two major areas: computing and cooling. Servers in data centers require a constant supply of cold air from on-site cooling mechanisms for reliability. An increased computational load makes servers dissipate more power as heat and eventually amplifies the cooling load. In thermal-aware scheduling, computations are scheduled with the objective of reducing the data-center-wide thermal gradient, hotspots, and cooling magnitude. Complemented by heat modeling and thermal-aware monitoring and profiling, this scheduling is energy efficient and economical. A survey is presented henceforth of thermal-ware scheduling and associated techniques for green data centers.

Efficient software clustering technique using an adaptive and preventive dendrogram cutting approach

Context: Software clustering is a key technique that is used in reverse engineering to recover a high-level abstraction of the software in the case of limited resources. Very limited research has explicitly discussed the problem of finding the optimum set of clusters in the design and how to penalize for the formation of singleton clusters during clustering. Objective: This paper attempts to enhance the existing agglomerative clustering algorithms by introducing a complementary mechanism. To solve the architecture recovery problem, the proposed approach focuses on minimizing redundant effort and penalizing for the formation of singleton clusters during clustering while maintaining the integrity of the results. Method: An automated solution for cutting a dendrogram that is based on least-squares regression is presented in order to find the best cut level. A dendrogram is a tree diagram that shows the taxonomic relationships of clusters of software entities. Moreover, a factor to penalize clusters that will form singletons is introduced in this paper. Simulations were performed on two open-source projects. The proposed approach was compared against the exhaustive and highest gap dendrogram cutting methods, as well as two well-known cluster validity indices, namely, Dunns index and the Davies-Bouldin index. Results: When comparing our clustering results against the original package diagram, our approach achieved an average accuracy rate of 90.07% from two simulations after the utility classes were removed. The utility classes in the source code affect the accuracy of the software clustering, owing to its omnipresent behavior. The proposed approach also successfully penalized the formation of singleton clusters during clustering. Conclusion: The evaluation indicates that the proposed approach can enhance the quality of the clustering results by guiding software maintainers through the cutting point selection process. The proposed approach can be used as a complementary mechanism to improve the effectiveness of existing clustering algorithms.

ENHANCED SOFTWARE DEVELOPMENT EFFORT AND COST ESTIMATION USING FUZZY LOGIC MODEL

The development of software has always been characterized by parameters that possess certain level of fuzziness. This requires that some degree of uncertainty be introduced in the models, in order to make the models realistic. Fuzzy logic fares well in this area. Many of the problems of the existing effort estimation models can be solved by incorporating fuzzy logic. Besides, fuzzy logic had been combined with algorithmic, non-algorithmic effort estimation models as well as a combination of them to deal with the inherent uncertainty issues. This paper also described an enhanced fuzzy logic model for the estimation of software development effort. The model (FLECE) possesses similar capabilities as the previous fuzzy logic model. In addition to that, the enhancements done in FLECE improved the empirical accuracy of the previous model in terms of MMRE (Mean Magnitude of Relative Error) and threshold-oriented prediction measure or prediction quality (pred).

The impact of heterogeneous multi-core clusters on graph partitioning: an empirical study

The advent of multi-core architectures provides an opportunity for accelerating parallelism in mesh-based applications. This multi-core environment, however, imposes challenges not addressed by conventional graph-partitioning techniques that are originally designed for distributed-memory uniprocessors. As the first step to exploit the multi-core platform, this paper presents experimental evaluation to understand partitioning performance on small-scaled heterogeneous multi-core clusters. With results and analyses gathered, we propose a hierarchical framework for resource-aware graph partitioning on heterogeneous multi-core clusters. Preliminary evaluation demonstrates the potential of the framework and motivates directions for incorporating application requirements into graph partitioning.

Thermal-aware relocation of servers in green data centers

Rise in inlet air temperature increases the corresponding outlet air temperature from the server. As an added effect of rise in inlet air temperature, some active servers may start exhaling intensely hot air to form a hotspot. Increase in hot air temperature and occasional hotspots are an added burden on the cooling mechanism and result in energy wastage in data centers. The increase in inlet air temperature may also result in failure of server hardware. Identifying and comparing the thermal sensitivity to inlet air temperature for various servers helps in the thermal-aware arrangement and location switching of servers to minimize the cooling energy wastage. The peak outlet temperature among the relocated servers can be lowered and even be homogenized to reduce the cooling load and chances of hotspots. Based upon mutual comparison of inlet temperature sensitivity of heterogeneous servers, this paper presents a proactive approach for thermal-aware relocation of data center servers. The experimental results show that each relocation operation has a cooling energy saving of as much as 2.1 kW·h and lowers the chances of hotspots by over 77%. Thus, the thermal-aware relocation of servers helps in the establishment of green data centers.

Considering Thermal-aware Proactive and Reactive Scheduling and Cooling for Green Data-centers

Data-centers require huge amount of electricity to continue meeting the computing demands of consumers each year. Fossil fuel based electricity is utilized due to lack of abundant renewable energy resources, resulting in the emission of CO2 in atmosphere and causing global temperature hike. The world is in dire need of efficient utilization of electricity. At the same time, advent of cloud computing has brought the innovation of everything as a service. This has led to proliferation of cloud services in every computing field. It has increased the load on cloud hosting data-centers, resulting in excessive use of electricity. A cloud data-center can manage to save electricity by reducing resource exploitation through either or both of the efficient utilization based and thermal based scheduling and monitoring. In this paper we take a peek into recently proposed thermal aware scheduling and monitoring techniques to maintain a cost effective Green Cloud Computing environment.

Leveraging open-source software for federated multisite SDN-cloud playground

This paper shows a prototyping and operating effort for a federated multisite SDN-Cloud playground that leverages open-source software such as OpenStack cloud, ONOS SDN controller, and Quagga router. A SDN-based federation possibility for distributed multisite cloud is verified by providing an open playground over three international sites under separate administrative domains.

Minimizing thermal stress for data center servers through thermal-aware relocation.

A rise in inlet air temperature may lower the rate of heat dissipation from air cooled computing servers. This introduces a thermal stress to these servers. As a result, the poorly cooled active servers will start conducting heat to the neighboring servers and giving rise to hotspot regions of thermal stress, inside the data center. As a result, the physical hardware of these servers may fail, thus causing performance loss, monetary loss, and higher energy consumption for cooling mechanism. In order to minimize these situations, this paper performs the profiling of inlet temperature sensitivity (ITS) and defines the optimum location for each server to minimize the chances of creating a thermal hotspot and thermal stress. Based upon novel ITS analysis, a thermal state monitoring and server relocation algorithm for data centers is being proposed. The contribution of this paper is bringing the peak outlet temperatures of the relocated servers closer to average outlet temperature by over 5 times, lowering the average peak outlet temperature by 3.5% and minimizing the thermal stress.

Fuzzy database framework-relational versus object-oriented model

The paper describes: fuzzy data and linguistic qualifiers; fuzzy data representation and retrieval; fuzzy database aspects; relational and object-oriented models; and the advantages of using the object-oriented database framework in a fuzzy database. A prototype fuzzy object-oriented database system (FOODS) has been implemented to demonstrate its feasibility.